The volume of data in circulation is growing on an exponential basis, with much of this data being fed into systems to help businesses gain insights they can act on. Humans still need to be able to read and process data, though, in their own right, which is where small data comes into play.

One might think of small data as the definition of data as we once knew it – data on a scale that’s easy for most people to comprehend, digest and act on. With the rise of big data and big data analytics, though, has come the tendency to forget about the importance of small data in a modern business context.

As trendy (and useful) as big data might be, only small data can actually allow us to make sense of why these trends machines help us see are happening. As such, while big data technologies can find patterns and correlations, small data helps us understand the context.

What is small data?

What makes small data ‘small’ isn’t the data itself, but the scope of the query. You can think about small data as corresponding to an infographic. This type of visualisation is designed to tell a strictly specific story with an absolute minimum of necessary context (or none).

You’re not expected to dive into the data or pivot it into different dimensions: nobody has so far come up with a standard for live, tappable infographics, and that’s probably a good thing. That’s not to say that such a presentation wouldn’t have its uses. It could be valuable to the mobile gaming development community, or the old-school telecoms businesses, who are having all sorts of fun with their proprietary phone-call-hacking API services. But once you start delving into the numbers, you’re leaving the realm of small data.

Meanwhile, you shouldn’t think of small data as merely a selective distillation of big data. Focusing on individual data points can – with a little human intuition and understanding – yield valuable information that’s overlooked by traditional big-data analysis.

That’s the thesis of Martin Lindstrom’s book Small Data: Tiny Clues that Uncover Huge Trends. Based on extensive consumer analysis, Lindstrom advises businesses not to focus on figures and trends, but to examine the behaviour of individual customers to understand why they act in the way they do.

In particular, it’s suggested that a customer’s decision-making process can be affected by intangible factors such as the climate, the governmental and religious context or cultural tradition. Ultimately, the goal is not only to predict what customers will do, but to identify the underlying drivers that motivate them. This is the sort of analysis that big data alone could never produce: it’s fair to say that few customer relationship management (CRM) systems are set up to record and track this type of data, and interpreting it would be problematic for even the most advanced AI.

Why are businesses turning to small data?

Engaging with big data is a lot more complicated than just pulling up an Excel spreadsheet. A query is likely to involve multiple systems and data sources, and often some sort of fuzzy artificial intelligence (AI) interpretation. It’s too big and woolly for most people’s daily tasks.

Even formulating the right questions for a big data system is a challenge. “How do our last five years of sales figures compare to those of our top three competitors?” is a human-grade question: you’re probably better off spending your time putting the graph together yourself and drawing your own conclusions, rather than trying to figure out how to get a meaningful answer out of the AI.

Although we humans can only process limited amounts of data, we have a natural talent for identifying which items are important, and making actionable decisions on that basis. We can also draw on our wider contextual understanding to interpret information: every big data event starts out as small data, so it certainly doesn’t hurt to have eyes on that end of things.

There isn’t so much of a backlash against big data, as there is a newfound recognition of its limitations. The point of the “big data” approach is to not get bogged down in details. For example, you don’t need to know exactly which numbers are feeding into the output, or how many petabytes they take up – it’s just big data. That’s great as far as it goes, but you can’t run a business entirely on not knowing things.

Big data is, indeed, as powerful as it ever was. The problem, however, is that anything that qualifies for the term is, by definition, too large and complex for a human to comprehend. It’s great to have machines poring over a huge aggregated mass of data to extrapolate emerging global trends, but staff need to focus on much smaller sets of facts and figures. These might be customer profiles, daily sales totals or server statistics – things we can understand and process for ourselves.

What are the limitations of small data?

There are reasons to be wary of embracing small data too wholeheartedly. When a vital part of your process is actively excluding data, it’s easy to be misled.

There are also risks associated with keeping human-readable records and spreadsheets that aren’t connected to the original data source – it’s all too easy for things to get accidentally

changed in ways that could lead to disconnected and misconceived decisions. Ideally, small data should be an application of your big data assets, and treated with at least as much caution and seriousness as the main repository.

Google Ad Manager experienced a prolonged outage on Thursday night, potentially costing prominent websites thousands of pounds in lost ad revenue.

A large percentage of online businesses rely on Ad Manager, the search giant’s advertising revenue engine, to automatically exchange and profit from third-party advertising, a process curtailed while the service was down.

During the roughly three hour outage, affected users were unable to list available ad inventory or sell advertising space to third-party buyers, although they still had access to their Google Ad Manager dashboards. Along with the outage of services, users reported unexpected errors and network issues.

Google Ad Manager accounts for 90% of the online advertising market in the United States, indicating that if widespread, the outage could have a large impact on the revenue of businesses in this region alone. Since the rise of AdTech and real-time bidding for publisher ad inventory, advertising services have increasingly relied on the service for insight into advertising campaigns as well as automated revenue optimisation.

Social media posts suggest that the outage affected users in Japan as well as the United States, but Google has not specified whether the outage was regional or international.

“We're investigating reports of an issue with Google Ad Manager. We will provide more information shortly. The affected users are able to access Google Ad Manager, but are seeing error messages, high latency, and/or other unexpected behaviour,” Google Ads said in a 01:06 UTC status update on its dashboard.

Reuters reported that Google had ranked the incident as a ‘P0’ event, an internal code for the most serious possible problem to fix. The relatively long turnaround between recognising the incident and restoring service raises questions around the complexity of underlying cause.

A follow-up post, put up a little over three and a half hours after the initial report, stated that the problem had been resolved without providing explanation for what first prompted the incident. How many businesses were significantly affected by the outage is still unclear, but many are likely to have missed out on key advertising analytics throughout the blackout, in addition to missed profit opportunities.

However, in a counterintuitive way, the outage could have been revealing for some of the affected publishers. In 2017, Adidas experienced an outage in Google Ads that lasted for two days, preventing it from investing in paid search for the period. Despite the outage, the firm discovered that it received a similar amount of traffic and revenue through search engine optimisation (SEO), revealing that it over-investing into its Google Ads strategy. A later outage that lasted for a full week resulted in another negligible drop in revenue, prompting the company to reevaluate its advertising campaigns.

Reports that customers experienced an ‘ad free internet’ during the outage exaggerated the issue, which did not affect adverts provided through other services, nor advertising on Google platforms such as YouTube.

IT Pro has approached Google for comment.

What is cloud analytics?

Cloud analytics refers to the use of cloud computing to analyse large amounts of data with the goal of identifying patterns and extracting insights, typically for business use.

How does cloud analytics work?

Cloud analytics enables businesses to leverage superior computing power from a third-party cloud computing provider without having to invest in expensive infrastructure. Many such providers offer built-in cloud analytics services, while others simply allow businesses to make use of powerful, scalable processing power and storage. In all scenarios, however, there are two main components involved:

• The data, stored either in the cloud, on-premises, or a combination of both: it may evolve over time, especially as new data points are added or deleted, and data can be stored in a variety of formats - usually, it’s stored in a database and organised according to a specific data model, which determines the relation of each data point to its neighbours
• The analytics software, stored and run in the cloud: this software reads the data and can be used to uncover trends or patterns within; different algorithms can be used for different purposes or insights, like understanding customer behavior, sequencing genomes, or identifying how diseases propagate

By bringing cloud analytics to bear on customer and other data, businesses can gain precious new insights to improve products, services, processes, and policies.

How departments can use cloud analytics

Sales and marketing

Understanding customer behaviour is one of the most common uses of cloud analytics for most SMBs. Customer interactions, whether digital (via a business’ website, mobile app, or desktop application) or physical (when visiting a store or service center), generate huge quantities of data. Businesses that are able to collect and then analyse that data can better understand their customers’ expectations, desires, and frustrations.

Salesforce is one example of a CRM platform that can easily be paired with cloud analytics software like Tableau, enabling sales and marketing teams to reveal hidden trends and insights in their existing and growing data.

Finance

Another business department typically flush with large quantities of data is the finance department. Finance departments are responsible for bookkeeping, managing cash flow, budgets and forecasting, sourcing long-term investments, and managing taxes. These are its day-to-day tasks, but it’s also responsible for analysing all the financial data generated by the business’ operations and providing reports to guide the decisions of managers and executives.

Financial cloud analytics solutions like Oracle Cloud include powerful, ready-made algorithms and analytical tools for businesses to use as needed. The raw processing is performed by powerful machines in data centers that have been custom-built to power through billions of data points in mere hours. Thus, even small businesses can gain greater insight from their financial activities.

Research and development (R&D)

For businesses in pharmaceuticals, life sciences, biotech, consumer technology, and more, research and development departments have the important role of discovering innovative new products for consumers. What bridges the gap between the research (data collection) and development (data application) is, of course, analytics.

Most businesses recognise the value in a dedicated R&D department, but historically the costs prohibited all but the largest from doing so. Cloud analytics platforms allow even small businesses to set up an R&D centre that can process huge amounts of data in record time and for little cost.

Information technology (IT)

IT departments are well placed to take advantage of cloud analytics. In addition to having the technical expertise, they have multiple means of collecting valuable information on how employees use the various applications and tools at their disposal.

Cloud analytics provide a way for IT departments to organise and understand that data, offering numerous benefits. Trends in application use data may point to potential security threats, for example, or highlight new ways to reduce operational costs.

Features and benefits of cloud analytics

Scalability

Cloud analytics, and cloud computing in general, provides greater scalability to businesses, enabling them to increase (or decrease) the level of processing power and storage as necessary. Historically, this couldn’t be done easily, as businesses had to purchase (and sell off) their own machines—a cost-ineffective process.

As a business’ data analytics needs grow, they need only request additional processing power.

Flexibility

Cloud analytics providers typically offer numerous ways for businesses to analyse their data, with specific algorithms designed to uncover trends in a wide variety of datasets. As a result, businesses can pick and choose the algorithms that matter to them most, without having to worry about the expense of developing them.

It also dramatically increases the number of questions businesses can ask about their data and thus the insights they can glean.

Cost

Cloud providers offer top-tier infrastructure at a dramatically-reduced cost, passing along economies of scale to businesses. Subscription-based models also enable businesses to move from the capital expenses of purchasing costly machines to operational expenses, bringing certain tax and cash flow benefits.

Security

Cloud analytics companies typically offer robust security to meet or exceed on-premises solutions. For example, Amazon Web Services (AWS), a popular cloud computing solution with powerful analytics software, provides a range of security measures including identity and data access management, threat detection, data protection, incidence response, and guaranteed compliance.

Collaboration and connectivity

Cloud platforms tend to allow for greater collaboration, as the data and software are decentralised, easily accessible from a desktop, tablet, or mobile app, and can be used by any number of individuals at once.

Additionally, modern cloud analytics solutions usually offer a variety of integrations—seamless connections to third-party software your business may already be using, including CRM like Salesforce and financial suites like Freshbooks or Quickbooks.

How much do cloud analytics solutions cost?

Like cloud computing more generally, cloud analytics solutions vary widely in cost, although cloud analytics giants like Google Cloud and Amazon Web Services offer very cost-effective packages for businesses of all sizes. Typically, you’ll only pay for processing power and operations as you use them.

For example, Google’s Big Query costs per TB of data read from your database, with the first 1TB per month free. Its Dataflow analytics software is priced per “dataflow processing unit,” which roughly translates to the number of operations run per hour. You can take advantage of Google’s pricing calculator for a better idea of your monthly costs.

Many small analytics companies offer simple subscriptions. SAP Analytics Cloud, for example, has a Business Intelligence plan for £27 a month. It includes a variety of data connections, pre-built data modeling, discovery and exploration, advanced analytics, live data connectivity, and more.

Small businesses with lighter analytics needs can expect to pay up to £80 a month. Enterprise companies requiring advanced, data-heavy, power-hungry analytics may pay thousands, even tens of thousands of pounds a month.

Cloud analytics FAQ

We've answered some of the most frequently-asked questions about cloud analytics below.

Why do analytics in the cloud?

There are many advantages to doing your business analytics in the cloud. It’s typically cheaper than acquiring on-premises infrastructure, with more flexible pricing. Security is superior and built-in, and you can rapidly scale up or down as necessary.

Cloud analytics providers also offer a wide range of analytics and algorithms, plus customer and technical service agents to help you make the most of your data.

Which cloud is best for data analytics?

There is no “best cloud” for data analytics, although there are some that stand out. Microsoft Azure, Amazon Web Services, and Google Cloud are all cloud computing heavy-hitters that offer cost-effective analytics solutions. The level of processing power, cloud storage, and diverse analytics and algorithms available to businesses is unmatched, making these an excellent choice for most companies.

What is “Big Data”?

Big data refers to the particularly large amounts of data collected by many businesses, typically to understand customer behavior. Like “Big Pharma,” it can also mean companies whose business model is based on collecting, analysing, and exploiting such data, like Facebook and Google.

What are the key objectives of data analytics?

The key objective of data analytics is to uncover patterns and trends in data, which can then be used to learn more about the world, inform decision-making processes, and drive policies.

It hinges on the assumption that data is structured, not random, and that with sufficient processing power, that structure can be understood and exploited.

Main takeaways

• Cloud analytics leverages cloud computing power to provide scalable, cost-effective data analytics solutions to businesses, governments, and others
• In cloud analytics, data stored in the cloud, on-premises, or both, typically in the form of a database, is read and passed through any number of algorithms designed to organise it and expose trends and patterns
• A variety of departments can benefit from cloud analytics, including sales and marketing (to understand customer behaviour), IT departments (to understand employee behaviour), and finance departments (to understand business processes)
• Cloud analytics solutions offer scalability, flexibility, lower costs, improved security, easier collaboration, and greater connectivity
• Cloud analytics solutions vary in cost depending on your needs, although many SMBs won’t need to spend more than a few dollars a month for even advanced analytics, and may spend as little as $20 or $30 for basic analytics

Further reading

Beyond cloud analytics, there are a number of reasons for businesses to switch to cloud computing. The best cloud computing services offer cost-effective ways to store, manipulate and analyze data, plus a variety of other tools such as IaaS, PaaS and SaaS.

Oracle NetSuite has revealed SuitePeople Workforce Management, a new solution for optimising work shifts and improving profitability.

Through heavy use of automation, SuitePeople Workforce Management aims to improve the overall system of shift scheduling, attendance monitoring, and wage calculations for employers. It integrates shift forecasts, payroll and financial systems, alongside employee attendance to provide each company with end-to-end oversight of its workforce.

With the solution, users can automatically produce visual shift schedules, as informed by information such as forecasts and staffing needs to provide detailed plans. This includes the option to generate recommendations for users, to optimise staff scheduling according to individual business needs.

SuitePeople Workforce Management also uses a ‘wage rule engine’ to improve the process of calculating time worked, utilising integration with SuitePeople Payroll to automatically use data on approved hours to inform accurate payrolls for employee hours, overtime and holiday pay.

The new tool, announced at NetSuite's annual SuiteWorld conference, is part of a larger package of announcements all designed to make it easier to deploy automation across a company, including accounting tools to make it easier to pay bills.

In addition to generating employee rotas, the solution is integrated to track and analyse employee attendance. SuitePeople Time Clock enables shifts to be logged using a mobile app as well as through the use of photo capture and biometric fingerprint scans. This has the stated benefit of ensuring that employees are themselves clocking into work, rather than having it been done on their behalf by coworkers, and thus provides a better idea of workforce engagement and punctuality.

Through the app, employees can also swap or pick up shifts and direct message managers. It also provides a unified channel through which managers can contact employees with attendance discrepancies, focusing engagement between managers and employees and simplifying professional channels.

“With SuitePeople Workforce Management, scheduling shifts and tracking employees’ time is more efficient than ever. I make all approvals directly in NetSuite and create a payroll batch with one click,” said Urban Legacy product and platform director, Kristi Miller.

“This helps put Urban Legacy in a position to plan intelligently and change operations quickly.”

Managers are additionally able to use SuitePeople Workforce Management’s real-time dashboard to view metrics such as wages, sales and operational forecasts, and compare these to live results. With this data in hand, firms are able to make more informed, up-to-date decisions regarding workforces, and leverage greater insight into the costs of their existing employees.

With the skills gap affecting the majority of firms, and hiring skilled IT workers harder than ever, many organisations are looking inwards to address the problem through reskilling. With the right insight, chief information officers (CIOs) can address the problem, and this is an area where the oversight into metrics and profit provided by SuitePeople Workforce Management could be instrumental.

Many companies are responding to the cost of living crisis and international economic headwinds by reducing employee numbers, such as Twilio’s recent 11% workforce cut, and SuitePeople Workforce Management can also assist companies to best identify the areas within their workforces where such cuts can be made.

Workforce Management forms part of the wider NetSuite SuitePeople, the company’s HR management system that works across departments to improve efficiency in company decision-making. This is key to the centralisation of human resources and financial operations for NetSuite customers, marrying oversight with business insight.

Evan Goldberg, founder and EVP, Oracle NetSuite, highlighted the important role that SuitePeople Workforce Management can play in optimising access to workers in the current economy.

“In a tight labour market, organisations need to elevate the management of their workforce to be competitive,” said Evan Goldberg, founder and EVP, Oracle NetSuite.

“With SuitePeople Workforce Management, organisations can quickly adjust staffing to align with changing business conditions. They can also ensure they accurately pay their employees and meet compliance requirements.

“This addition to SuitePeople reinforces our commitment to helping our customers increase employee engagement, which in turn positively impacts the bottom line.”

NetSuite SuitePeople Workforce Management will be available in early 2023, within the United States, Canada, Australia and New Zealand. Its release in the UK and Ireland is expected in the summer of 2023.

Across the globe, people love using messaging apps to stay in touch with friends and family. And they increasingly expect the same, effortless communication with businesses. Phoning a call centre no longer appeals.

WhatsApp connects businesses to more than 2 billion monthly users. With analytics, automation and more, it’s the platform that turns messaging into a powerful business tool.

Download this guide now to learn how to bring your customers closer with easy, responsive support.

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71% of CIOs say that the increasing amount of data resulting from cloud-native technology stacks exceeds human management capabilities, according to a new study.

According to the results, CIOs are increasingly concerned about the strain that managing technology stacks puts on their team members, and advise strategy change now before cloud complexity overwhelms teams entirely.

Software intelligence and digital transformation firm Dynatrace announced the results after a survey of 1,303 senior IT professionals, including CIOs and IT operations managers, across five continents. The survey indicates that complexity is already at unmanageable levels, with the average technology stack relying on seven separate platforms and systems, and each transaction involving an average of 35 different technologies from end to end.

Lack of overall insight into issues within the technology stack stood out among IT professionals as a problem arising from the scale of stacks. 97% of organisations state that obstacles are preventing them from achieving full-stack observability, or from adequately expanding security across their multi-cloud environments. On average, surveyed organisations had end-to-end observability of just 9% of their cloud environment.

Pressures of the coronavirus pandemic led to the widespread acceleration of digital services across the UK and the world, in both the private and public sectors. This has led to many benefits, particularly for small and medium-sized businesses (SMBs) whose transformation allowed for far greater predictive analytics.

But respondents also identified the rapidly changing nature of their environment as a source of strain, with 77% of CIOs indicating that their IT environment changes every minute or less. Amongst UK CIOs, this figure was even higher, at 93%.

The general lack of observability, the results make clear, is having a large knock-on effect on organisations, particularly over their ability to properly carry out and utilise big data analytics. Over half (57%) of the surveyed organisations state that data is frequently unavailable for analytics on demand, and just under two-thirds (63%) point to rehydration delays, in which data is located and decompressed for use, as preventing businesses from unlocking the value of their data.

Many respondents indicated that to improve efficiency and utilise team skills more effectively, a far greater level of back-end automation is required. 59% of CIOs state that without increased automation, their respective teams could become overwhelmed by the sheer scale and complexity of their stack, and the majority of CIOs (93%) argue that artificial intelligence in the form of AIOPs is becoming ever more integral to staving off burnout.

IT teams indicated that 40% of their time is currently spent simply “keeping the lights on”, and that automation will allow this time to be put to better use elsewhere. To use automation at its limitless potential, however, CIOs also indicate that an overhaul of data repositories is required. Almost half (47%) of surveyed CIOs want to see more adoption of the data lakehouse, a relatively new repository model that combines data warehousing and data lake techniques to allow for greater searchability through raw data that requires far less DevOp time to be spent on manual retrieval or rehydration.

“Multicloud and cloud-native architectures are critical to helping organizations achieve their digital transformation goals,” stated Bernd Greifeneder, Dynatrace founder and CTO.

“While organizations benefit from the flexibility and scale that these technologies bring, the explosion of observability and security data they produce is increasingly hard to manage and analyze.

“Existing tools – and there can be dozens of them – keep data in silos, making it difficult and expensive to unlock insights when organizations need them. As a result, they struggle to achieve the highest standards for security and performance across their digital services.”

Twitter has seen its largest ever number of legal demands to remove content, as well as the most government legal demands targeting journalists and news organisations, the company has revealed.

In its 20th transparency report, Twitter released information for the period running from July 1 2021 to December 31 2021. Across the period, a record 47,572 legal demands were made regarding 198,931 accounts, comprising a mixture of court orders and other such formal demands by government entities and the lawyers of individuals for content to be taken down.

The largest ever measurement of such reports, it represents a 10% increase compared to the previous six-month period. This is reflective of both the increased user base of the platform and the greater role that Twitter is asked to play in disputes over disinformation and illegal content.

Twitter has credited the proactive removal of tweets that their in-house technology detects as abusive or spam, with keeping the platform clear of content that violates the terms of use even as the user base increases. The company revealed that 71% of tweets removed in this fashion were removed before they reached 100 impressions.

Also revealed in the report was that only 2.6% of Twitter users had put in place any kind of two-factor authentication (2FA) for their account, a low figure in the face of the threat to user passwords posed by phishing and hackers on any social media. Businesses with Twitter could face particularly severe consequences for lacklustre security, with threat actors already targeting Facebook business accounts.

The company's advocacy for greater transparency, bringing in labels for misinformation in the past few years among the aforementioned technology, has pulled the focus of governments and other public bodies, and could set a precedent for other social media giants. Just last month, TikTok announced a new API aimed at greater insight into their platform, and user demand for transparency around data use has already cost companies like Facebook billions of dollars.

Legislation such as the UK government's proposed Online Safety Bill would require platforms to provide far more moderation of the content on their platforms, compelling them to use or develop technology to detect and remove child sexual exploitation and abuse (CSEA) content and take it down. It would also require 'Category 1' companies to transparently identify 'legal but harmful content' on their platforms, such as cyberbullying or disinformation and prevent users from seeing it.

The efficacy of Twitter’s automated system in dealing with spam, bots and fake accounts has been the subject of scrutiny after Elon Musk cited his reservations about the system as his reason to pull out of his $44 billion acquisition of the company. In response, Twitter has sued Musk, and a trial date has now been scheduled for October 17.

The company also revealed that 349 accounts of journalists and news sources were subject to legal demands by governments, representing a 103% increase compared to the previous period.

On the whole, however, government information requests were down 7% in the period, with 11,460 total. Of this, the United States government accounted for 20% of global traffic, closely followed by Japan (24%) and India (11%). Despite the lower number of individual reports, the aggregate of accounts specified by government entities rose by 9%, indicating each government report concerned a larger range of accounts.

Due to US legislation, Twitter does not report on information requests related to national security processes. It is contesting this in court to provide greater transparency in further reports.

When faced with requests, Twitter can ‘narrow’ information, meaning that it selectively discloses some but not all information demanded in the request. It did this in response to 60% of global government information requests.

“This update comes at a time when government requests for account information and content removal continually hit new records, including demands to reveal the identity of anonymous account owners,” said the company in a blog post.

“This is why we continue to advocate for greater transparency from governments themselves about how these powers are used.”

Rackspace has partnered with Digivante to offer artificial intelligence (AI)-based application testing services to its customers in Europe, the Middle East, and Africa.

The partnership will help significantly reduce testing cycle times for Rackspace customers, the companies have said.

Rackspace aims to capitalise on Digivante's automated continuous web testing tools, built using AI and machine learning (ML). Alongside usability testing, Digivante offers tools such as regression testing, accessibility testing, localisation testing, and cross-browser testing.

Furthermore, Digivante's testing portal generates automated reports that summarize actionable insights, allowing organizations to identify trends in quality, risk areas, and functional areas that are subject to change.

A REST API, available within the testing platform, also makes it possible to sync performance reports and test results with third-party project management tools such as JIRA and Azure DevOps.

“With little or no automation, traditional manual application testing can be extremely time-consuming, error-prone and difficult to scale, often resulting in either poor quality applications deployed to production or significantly delayed releases,” said Daniel Berry, Digivante CEO.

“Building automation into your testing and blending it with crowd testing in the right way reduces costs and increases velocity.”

According to Rackspace, Digivante will aid its customers in EMEA accelerate software delivery while simultaneously reducing testing cycle times.

“Application testing can be an unnecessary stress for businesses that already have a lot of challenges to overcome and are simply trying to deliver on customer expectations. Our joint offering with Digivante is a crucial step towards helping further augment the process for our customers and take the burden off their shoulders,” added Mahesh Desai, chief relationship officer, EMEA, at Rackspace Technology.

“App modernisation is a fundamental element of our customers’ ongoing cloud journeys, and this partnership enables us to help them to do so more easily.”

IBM has acquired Envizi, an Australian data and analytics software provider for environmental performance management, as it looks to help customers better measure their environmental impact.

The company said the acquisition adds to its investments in AI-powered software to help organisations create more resilient and sustainable operations and supply chains. It added that companies are under mounting pressure from regulators, investors, and consumers to progress towards more sustainable and socially responsible business operations while demonstrating these measures in a robust and verifiable way.

Financial terms of the deal were not announced. IT Pro contacted IBM for more information, but the company had not responded at the time of publication.

Envizi’s software automates the collection and consolidation of over 500 data types and supports major sustainability reporting frameworks. It helps companies analyse, manage, and report on environmental goals and identify efficiency opportunities while assessing sustainability risk.

IBM said by using Envizi with its broader AI-powered software, companies will be able to automate feedback generated between their corporate environmental initiatives and the operational endpoints being used in daily business operations. Envizi is set to be integrated with IBM Maximo, IBM Sterling, IBM Environmental Intelligence Suite, and IBM Turbonomic and Red Hat OpenShift.

"To drive real progress towards sustainability, companies need the ability to transform data into predictive insights that help them make more intelligent, actionable decisions every day," said Kareem Yusuf, general manager of IBM AI Applications.

"Envizi's software provides companies with a single source of truth for analyzing and understanding emissions data across the full landscape of their business operations and dramatically accelerates IBM's growing arsenal of AI technologies for helping businesses create more sustainable operations and supply chains."

Envizi is available as a SaaS product and runs in multi-cloud environments, serving companies like Microsoft, Qantas, and Uber.

In February last year, IBM vowed to become carbon-neutral by 2030. It planned to procure 75% of its electricity from renewable sources and cut its greenhouse gas emissions 65% from its 2010 emission levels by 2025. By 2030, it plans to reach its carbon-neutral goal by obtaining 90% of its electricity from renewable sources and implementing tech to neutralise residual emissions.

IBM isn’t the only tech company helping customers to monitor their emissions. In October, Microsoft launched a preview of Microsoft Cloud for Sustainability to help organisations more effectively record, report, and reduce their carbon emissions on a path to net-zero. The SaaS product connects to data sources and centralises and organisations data in a common format to provide a more accurate system of record that enables more comprehensive sustainability management.

WeSpire, a provider of employee experience solutions with an environmental, social, and governance (ESG) sustainability focus, has announced the launch of its Employee Carbon Management Solution.

Developed in partnership with Cox Enterprises and South Pole, the new solution provides enterprises with a comprehensive set of tools to measure, reduce, balance, and report on employee carbon emissions both on the job and at home.

“Until now, getting employee-based Scope 3 carbon emissions data quickly, accurately, and on-demand has been challenging due to a lack of workplace-specific tools to reliably capture and report the data,” explained WeSpire.

“The rapid move to hybrid and remote work environments and an increasing number of companies setting Scope 3 Net Zero goals has magnified the urgency of implementing a better approach.”

Employee Carbon Management Solution by WeSpire enables companies to establish a corporate ESG strategy across the workforce by providing employees with specific steps they can take to reduce their individual carbon footprints.

Through curated actions based on behavioral science, companies can promote continual, progressive reductions in carbon emissions without relying on offsets immediately.

WeSpire has also partnered with South Pole to provide high-quality offset opportunities as rewards to employees and employers.

WeSpire's Employee Carbon Management Solution will be available by Earth Day 2022 after it passes its testing phase.

“We’ve pioneered a data-first, behavior-change solution to drive much needed personal climate action in the workplace,” said Susan Hunt Stevens, founder and CEO of WeSpire.

“Our technology enables organizations to engage their employees to better understand their own impact, take targeted carbon reduction actions, and, when needed, the company or employees can balance what remains. This holistic solution will inspire further innovation as employees see their collective impact on their company’s ESG goals and will significantly simplify employee-related Scope 3 emissions reporting,” added Stevens.

HPE has announced a series of new GreenLake offerings which signal its entrance into the analytics and data protection markets.

HPE GreenLake for analytics is a set of open and unified analytics cloud services that aim to modernise data and applications stored on-premises, at the edge, and in the cloud. It will enable analytics and data science teams to scale up Apache Spark lakehouses and speed up artificial intelligence (AI) and machine learning (ML) workflows, according to HPE.

HPE GreenLake for data protection offers backup cloud services as well as disaster recovery sourced from HPE’s recent acquisition of Zerto. It provides restore times of as little as minutes, allowing organisations to recover from ransomware attacks without impacting business operations, regardless of the scenario.

HPE has also annunced the Edge-to-Cloud Adoption Framework, which aims to support customers in creating an effective cloud operating model by being able to evaluate it using categories such as Strategy and Governance, People, Operations, Innovation, Applications, DevOps, Data, and Security.

Beside it's three main new offerings, HPE has also showcased a new addition to its AI Ops for infrastructure, HPE InfoSight. Known as HPE InfoSight App Insights, the tool is capable of detecting application anomalies, providing recommendations, and preventing disruptions in application workloads. Customers looking to make smarter, data-based IT decisions across edge-to-cloud will also benefit from the new HPE CloudPhysics.

Commenting on the announcements, HPE president and CEO Antonio Neri said that “data is at the heart of every modernisation initiative in every industry”.

He adds, however, that many organisations "have been forced to settle for legacy analytics platforms that lack cloud-native capabilities, or force complex migrations to the public cloud that require customers to adapt new processes and risk vendor lock-in."

According to Neri, the big data and analytics software market, estimated by IDC to be worth $110 billion by 2023, “is ripe for disruption, as customers seek a hybrid solution for enterprise datasets on-premises and at the edge”.

“The new HPE GreenLake cloud services for analytics empower customers to overcome these trade-offs and give them one platform to unify and modernise data everywhere," he said. Together with the new HPE GreenLake cloud services for data protection, HPE provides customers with an unparalleled platform to protect, secure, and capitalise on the full value of their data, from edge to cloud.”

Customers can benefit from the HPE Edge-to-Cloud Adoption Framework starting today, while HPE GreenLake for analytics and HPE GreenLake for data protection will become available in the first half of 2022.

Northern Sky Research (NSR) has released version 2.0 of its proprietary Non-GEO Constellations Analysis Toolkit (NCAT).

The update adds to NSR's comprehensive analytical tools for benchmarking low Earth orbit (LEO) and medium Earth orbit (MEO) satellite constellations.

A multi-faceted toolkit, NCAT includes analytical models, input and output data tables, comparison charts, maps with satellites, orbital routes, footprints per constellation, and more.

According to the company, the latest version doubles input and output data tables to 56 sub-constellations and increases tool options to 15. NCAT2 also supports granular evaluation of mobile and fixed data applications.

“Two systems, Starlink and SES O3B, are now operational but many others, such as OneWeb, Telesat, and Amazon are in the pipeline,” said Christopher Baugh, NSR president.

Baugh added, “The rapidly increasing numbers in the Non-GEO sector drives need for tools to assess capabilities factually and in-depth. Driven by client feedback, NCAT2 incorporates new tools, including the ability to conduct assessment for fixed and mobility applications at global, regional or country-levels.”

NCAT2 comes preloaded with data, configurable filters, inputs, and visualizations. Specifically, the toolkit can be used alongside market data and insights drawn from relevant NSR research studies.

NCAT2’s other notable features include gateway housing and visibility assessment, exclusion angles, mobility heatmaps for 56 LEO/MEO constellations shells, and breakeven analysis of Non-GEO capital cost per Mbps

Commenting on outreach, NSR said its NCAT2 toolkit is intended for both business and technical professionals looking to understand Non-GEO high-throughput SATCOM in-depth.

“As thousands of LEO and MEO satellites beam tens of Terabits per second, they transform not only the space sector, but the adjacent telecommunications industry, making it vital for strategic decision makers of both value chains to evaluate potential developments. NCAT2’s configurable filters and visualizations allow that insight,” added NSR.

Just about any industry uses business analysts, from IT and telecoms to retail and financial institutions.

There is a growing demand for business analysts in many organizations. And anyone who loves crunching numbers and trawling through data to find insights may find this is the career for them.

But what do business analysts do and how do you become one? There are various ways to become a business analyst. As a first step into this career, you should determine if you are a good fit for such a role and figure out what skills, training, and qualifications you need.

What is a business analyst?

The job title sounds generic, but being a business analyst is certainly not monotonous. The role is diverse and wide-ranging and changes from business to business.

Depending on the organization, this can be an entry-level position or a role for experienced professionals. This means business analysts are engaged with a wide variety of tasks, and the job description from one company to the other can be very different.

A business analyst works inside an organization and reviews data so an organization’s plans align with business goals. This could be a one-off project or continuous development of the organization and act as a conduit between technology specialists and the wider organization.

Why are business analysts important?

Business analysts are important to many organizations as they make digital transformation easier, which is an essential undertaking in the modern business world. Their role is crucial to the future of any business. They do this by frequently assessing business functions to gauge the effect a proposed transformation or new solution may have on an organization.

What responsibilities does a business analyst have?

The business analyst’s role is varied, but they have several shared responsibilities across the board.

The key responsibility is to find business issues or inefficiencies and provide recommendations — usually through deploying some sort of technical solution.

There are a few important elements business analysts are responsible for:

• Investigation: Analysts must discover the principal business problems needing to be tackled and unearth any information connected to the project.
• Requirement analysis: Analysts must organize, specify, and model requirements to ensure they are comprehensive and clear.
• Requirement specification: Analysts must verify that documentation of requirements is formatted in an easy-to-understand way for stakeholders. They must also be easy to share.
• Requirement verification and validation: Business analysts must ensure requirements align with the business problem faced, they are ratified by all the applicable stakeholders, and meet important quality standards.

What are the key skills of a business analyst?

A business analyst must have a wide range of skills, but here are some key ones you’ll need to get your career going.

A technology head

The most important skills a business analyst can have are technical skills. Most analysts work in an organization’s IT department, and their recommendations usually involve a technical solution.

In today’s digital world, analysts must have a good understanding of what IT solutions are available and know about the latest key trends and technical innovations.

Research know-how

Projects are all about solving a problem for the business. Before any decision is made, all possible outcomes need to be explored. Identifying the most suitable outcome requires an analysis of the situation. This involves investigation and research.

To thoroughly research the problem requires asking probing questions, investigating all the angles, and looking at how other organizations tackled similar issues. All this knowledge is necessary when presenting findings back to the organization so it can make an informed decision.

Be a critical thinker

Research is essential to the business analyst role, but it is not just a case of presenting findings. To be a great business analyst, you must get the crux of the matter. This means thinking about all the means of fixing a problem.

An analyst must show how they have determined assorted possibilities and studied the most feasible ones, even if this means “thinking outside the box” and moving into uncharted areas.

When evaluating options, they should consider the effect and probability of each alternative. An analyst must also consider the risks of selecting or not selecting a specific result. A wide-ranging appraisal of several choices should ensure the organization is confident it has made a wise choice.

Be a people person

Business analysts spend a lot of time talking to people, such as users, management, clients, and other stakeholders, about these issues, so being a people person is a must-have skill for this job.

Being attuned to diverse opinions, inspirations and intentions are indispensable to balance the range of characters within an organization effectively. Knowing where within a business a person is, who they are responsible for, and who they report to will help figure out their risk profile and decide how to present assumptions and proposals.

Be a communicator

In line with being a people person, business analysts must be good communicators, as you must communicate your findings to everyone and gain buy-in to whatever outcome is eventually pursued. Having clear lines of communication will ensure all stakeholders are updated on the progress of a project.

What qualifications and experience do you need to become a business analyst?

Given that the job requirements are varied, relevant qualifications can vary from industry to industry and even from company to company.

While a business analyst needs no particular degree or qualification, one of the best ways of getting such a role is to have a computer science degree or an MBA. You can also get a postgraduate qualification in business information systems or business management.

Several organizations, such as the IIBA, IQBBA, IREB, and PMI, offer certifications that can boost your chances of becoming a business analyst. These certifications include, but aren’t limited to, the Certified Analytics Professional (CAP), the Entry Certificate in Business Analysis (ECBA), the Certified Business Analysis Professional CBAP certification, and the PMI Professional in Business Analysis (PBA).

Generally speaking, most business analysts spend three to four years earning a degree to gain an entry-level position. Even with an entry-level job, it will take a few more years in such a role to learn all about different aspects of the field.

What do business analysts earn and what are their prospects?

According to the US Bureau of Labor Statistics (BLS), management analyst employment, including business analysts, is expected to increase by 11% between 2019 and 2029. This is much faster than the average of all occupations.

The BLS also found the median pay for this category is $87,660 per year. The lowest 10% earned less than $50,990, and the highest 10% earned more than $156,840.

As a business analyst, the more experience you have will determine your salary. Location will also play an important part in salary.

The prospects for choosing a business analyst role are good, as businesses always need to change and reappraise their procedures to adjust to unpredictable circumstances. Being a business analyst, you can help organizations by asking questions, pinpointing the biggest problems, and answering them with new, data-driven solutions.

Approx. read time: 19 minutes

Over one-third of retailers now sell most of their products and services online and online selling is only becoming more business critical.

How can your business choose the right e-commerce platform to manage customer needs and future-proof your business against future changes?

Download this playbook to find out:

• Five must-have e-commerce capabilities, as identified by leading analysts
• Why these capabilities are critical to success
• How Adobe Commerce, integrated with Adobe’s enterprise-class marketing and analytics tech, can prepare you for the future

Customer intelligence platform Affogata has raised $5.5 million in a funding round led by Mangrove Capital Partners and PICO Venture Partners. Wix Capital and Fiverr Founder and CEO, Micha Kaufman, also participated in the funding round.

Affogata said it’ll use the funds to scale its business and develop new customer solutions. The Israeli startup also aims to strengthen its research and development department.

Affogata's artificial intelligence-driven platform gleans information from a wealth of open data sources about the brand and its competitors to provide real-time actionable insights.

"Our ability to provide customer opinions and actionable insights for multiple stakeholders in real-time is a major differentiator for any brand, especially with the continued rise of online communities like Reddit," said Affogata Co-founder and CEO Sharel Omer.

"This funding round is a big vote of confidence in our platform and will provide us with the ability to empower countless brands with a robust set of tools to improve their products, strengthen their online presence, and really stand apart when it comes to offering a superior customer experience while maintaining a strong reputation."

As an alternative to multiple tools focused solely on listening and responding, Affogata enables proactive responses and high levels of automation. Powered by millions of data points, Affogata’s platform eliminates bots and spammers with AI-powered moderation. This saves moderators countless hours of manual work.

Affogata also features competitor tracking, so companies have the information they need to make informed decisions. Businesses can also use the platform to find influencers and comprehend their reach, thereby channeling resources effectively.

"Being able to make decisions to improve product development and the customer experience based on expansive, real-time data is now mission-critical and Affogata's product has become a customer intelligence solution for leading global brands," said Claudia De Antoni, Operating Partner at PICO Venture Partners

"We're excited to join the Affogata team and look forward to seeing many more brands adopting the kind of customer-first approach their technology enables."

Not only is YouTube the most commonly used social media site, but it’s also growing much faster than other social media platforms since 2019, a new survey has found.

Except for YouTube and Reddit, most social media hasn’t grown very much since 2019 — not even Facebook, according to a survey by the Pew Research Center.

An eye-popping 81% of Americans said they use the video-sharing site, up from 73% in 2019.

Reddit was the only other platform that saw statistically significant growth during the same period, albeit on a smaller scale because Reddit is more of a niche site. Reddit use increased from 11% in 2019 to 18% today, the survey found.

Facebook’s growth has leveled off over the last five years, but it remains one of the most widely used social media sites among adults in the US. Nearly 70% of adults today are on Facebook — an impressive number, but the same as two years ago.

In the same vein, the number of Americans who reported using Twitter, Instagram, Pinterest, LinkedIn, Snapchat and WhatsApp are statistically unchanged since 2019.

Pew Research says this reflects a broader trend: The rapid expansion of social media sites that we saw over the last decade has slowed down significantly.

TikTok use is obviously expanding, but it isn’t on this list because the Pew Research Center didn’t even ask about TikTok during the 2019 survey.

Most of the poll’s results aren’t surprising: Most Americans use YouTube and Facebook, while Instagram, Snapchat and TikTok use is especially common among people in their teens and 20s.

Instagram attracts 40% of Americans, while 31% of Americans are on Pinterest and LinkedIn, 25% are on Snapchat, and 23% are on Twitter and WhatsApp. Newcomer TikTok has 21% share of Americans surveyed, and Nextdoor has 13%.

As always with these kinds of social media surveys, there are some noticeable age differences. A whopping 84% of Americans ages 18 to 29 are on social media compared to 81% of Americans ages 30 to 49. In comparison, 73% of people ages 50 to 64 use social media, while only 45% of those 65 and older do.

Amazon Web Services (AWS) is launching a new tool that uses machine learning for business analytics.

AWS’ Lookout for Metrics monitors key performance indicators (KPIs), such as web page views, mobile app downloads, numbers of active users, and income to detect anomalies and alert the organization so it can act.

It also makes action easier by grouping related anomalies together and offering potential root causes. It also ranks each anomaly by severity, so organizations know which issues require immediate attention and which can wait.

“From marketing and sales to telecom and gaming, customers in all industries have KPIs that they need to be able to monitor for potential spikes, dips, and other anomalies outside of normal bounds across their business functions,” said Swami Sivasubramanian, vice president of machine learning for AWS.

“But catching and diagnosing anomalies in metrics can be challenging, and by the time a root cause has been determined, much more damage has been done than if it had been identified earlier.”

AWS looks to make a splash in the world of business intelligence (BI), one of the most effective ways to use data. It can provide businesses with critical insights to help them make better decisions to foster a more productive and efficient organization, analyze how they can keep up with competitors, and grow their revenues beyond projections.

According to AWS, “In a couple of clicks, you can connect Amazon Lookout for Metrics to popular data stores like Amazon S3, Amazon Redshift, and Amazon Relational Database Service (RDS), as well as third-party SaaS applications, such as Salesforce, Servicenow, Zendesk, and Marketo, and start monitoring metrics that are important to your business.”

Lookout for Metrics is available to most US, UK, EU, and Asia-Pacific AWS customers via the AWS console and through supporting partners in the AWS Partner Network. Availability for additional regions is coming soon.

Amazon noted that Lookout for Metrics requires no specialist machine learning training to start using it.

AWS prices Lookout for Metrics in tiers according to the number of metrics per month your organization needs to track. The first 1,000 metrics cost 75 cents each per month. Metrics 1,001 through 5,000 are 50 cents each per month, and metrics 5,001 through 20,000 are 25 cents each per month. From 20,001 to 50,000 metrics are 10 cents each per month, and anything over 50,000 metrics is 5 cents per metric per month.

Investing in fleets of data scientists and expecting them to do all the heavy lifting in artificial intelligence (AI) is one of the most common mistakes businesses make, according to former IBM executive Phil Westcott.

Speaking at the virtual AI Festival, Westcott, who formerly lead the IBM Watson platform in Europe and is now co-CEO of AI consultancy firm Filament, told attendees that he had witnessed “a lot of overspending and wasted resources” during his career.

“A couple of the mistakes I've seen is to go out and buy a huge fleet of data scientists and pop them in in a large room and expect that the business outcome will flow magically,” he said.

Another “cautionary tale”, according to Westcott, is “going too far on the operational steps before you've proved the hypothesis within the state of the art of applied AI technology”.

Instead of wasting time and resources on these mistakes, businesses should invest in creating “more agile, cross-functional teams with the engineering skills and business analysts”, he said.

Businesses looking to build their AI capability on a budget must follow five key steps, which include setting a strategy, building AI assets, developing a culture of agility and collaboration, investing in skilled team members, and partnering with academic and industrial organisations.

“In your key strategic areas, where you want to make your own AI assets and build your capability, that's where you will need the help of consultants (...) to bring the benefits of the full ecosystem and bring their experience from other industries and other clients,” he said, before recommending potential partners such as the Google Cloud, IBM Watson, and the Alan Turing Institute.

Companies seeking to develop their businesses' AI capabilities will also benefit from turning their areas of expertise into a competitive advantage and focusing their AI programme on departments such as customer service, research, or HR recruiting.

“Because if they've already got a differentiated human capability there, they have an opportunity to turn that into a differentiating AI capability by effectively bottling that expertise into machine learning models that can be scaled, and can further augment that human capability,” he added.

Westcott was speaking at a keynote address during the first day of the newly-launched AI Festival. The virtual event, which is backed by BT, aims to explore the influence of AI technology on businesses, skills, and employment, with today’s keynotes focusing on issues such as the future of AI skills, ethical AI, and digital-based approaches to health.

When people talk about the use cases for edge computing, often they’re discussing situations where any level of latency is a huge problem and cable networking is impossible, such as in autonomous cars or deep sea oil platforms.

The reasons are obvious: If you’re in a fully self-driving car, you don’t want to wait for information collected by its sensors to be sent to the cloud for processing and then returned before it takes action to avoid crashing into a wall.

If those are the stakes down here on earth, then let me introduce you to the concept of latency in space.

It’s well known that if you put something in space it becomes more exciting and dangerous, with an added hint of mystery. Parasitic wasps go from being a grim threat to caterpillars but a negligible problem for humans, to being 7ft tall, bleeding acid, and wreaking havoc at the dinner table. Patrick Stewart goes from playing King Richard to being a cyborg drone. And let’s not even get started on large red LEDs.

Latency also becomes a more serious issue and even if it’s moving at the speed of light, data transfer is not instantaneous. If you’re a fan of The Expanse, you’ll know that the time it takes for data such as video and audio feeds to travel from one location to another is an important plot device throughout the series – images that have just arrived on a character’s screens may be several hours old at best.

These are real issues that face space science now and are potential hurdles to future space exploration. What’s the point in sending a manned mission to Mars, for example, if all the data processing for everything from weather forecasts to soil samples has to be done back on Earth and then relayed back to the red planet?

Enter our hero of the hour: Edge computing. If you can get a server to survive the 225 million kilometer trip between the two planets along with your astronauts and the rest of their kit, then some of the most critical data processing – things that are of direct importance to the mission – can be done in situ. This could increase safety for the crew and speed up the time it takes to make new scientific discoveries. More importantly, it could lend enterprise infrastructure a roguish air of mystique.

This isn’t some pipe dream from my lockdown-addled, server-loving brain either. HPE yesterday announced a follow-up to its 2017 Spaceborne Computer Project, which saw two of its Apollo servers sent to the International Space Station (ISS).

On 20 February, astronauts aboard the ISS will take delivery of an edge computing system dubbed Spaceborn Computer-2, which will remain in orbit for 2-3 years. It has the express purpose of carrying out real-time monitoring of the crew’s physical conditions by processing X-Ray, sonograms, and other medical data on site, rather than returning it to the planet’s surface, as well as processing data from the vast array of sensors onboard the ISS, which should hopefully speed up time to insight too.

As Mark Fernandez, HPE’s project lead for Spaceborne 2, told journalists: “Space is the edge of the edge” – the final frontier if you will. Now if we can work on keeping our spacefaring androids more Data than Ash, that would be marvellous.

Oracle has launched Digital Experience for Communications, a cloud-based customer experience (CX) management solution for the communications industry.

It consists of industry-specific applications to pull and analyze customer-interaction data from front- and back-office operations. Users will be able to better understand customers' buying behaviors and preferences to support the creation of compelling offers and products, improve customer service and increase sales.

"While every industry is unique, there is a common challenge facing all organizations: the need to unify data to address rapidly changing customer expectations," said Rob Tarkoff, executive vice president and general manager, Oracle Cloud CX and Data Cloud.

"To help the communications industry solve its toughest customer challenges, we have brought together our CX and industry expertise to develop Digital Experience for Communications. This isn't a wrapper on CRM; this is a new solution that has been developed based on extensive customer feedback."

Digital Experience for Communications supports the TM Forum's Open Digital Architecture and Open APIs, which will enable users to integrate current applications and provide a more accurate view of customer and billing data.

Users will have access to data and tools for improving how they create service offerings, sell across channels, monetize services and provide proactive customer care. It also offers operations support, billing and revenue management, and communications charging.

"Swisscom is very focused on delivering innovative new digital services and making it easy for our B2B and B2C customers to interact and transact with us," said Alexander Mohri, head of strategy, Swisscom. "Oracle Digital Experience for Communications gives us the agility and rich functionality to transform the way we engage with customers, improve our employee experience, drive operational excellence, and reduce costs."

Digital Experience for Communications provides a comprehensive view of what customers have purchased, what they’re using and how they’re interacting with service and digital assistants. This will help users provide more relevant offerings to customers.

For example, if a customer could answer billing questions via their mobile service provider’s self-service digital assistant, Digital Experience for Communications will use this data to suggest a customized and targeted offering for a new voice and data package. It can then automatically complete the order and update network and billing systems in real time.

"Other solutions lack critical communication industry capabilities, such as large-scale order management and a TM Forum-based open architecture, leaving customers to cobble together pieces and build their own APIs," said Jason Rutherford, senior vice president and general manager, Oracle Communications Applications. "Digital Experience for Communications integrates all of these components so service providers can focus their time on new opportunities, such as using the unprecedented volumes of data being created by 5G and IoT to fuel new business models."

Digital Experience for Communications is built on the Oracle Cloud infrastructure and offers two modules:

• Launch: Provides a customer dashboard and business user tools for automatically creating and launching relevant products and promotions
• Care: Provides natural language processing and digital engagement tools.

Within the next year, it will include the Buying module, which will provide data-driven recommendations to enable users to offer personalized omnichannel commerce experiences.

"TM Forum's Open Digital Architecture encompasses new approaches to strategy, customer centricity, and digital business operations that are essential in the next generation of 5G business models," said George Glass, vice president, Architecture & APIs, TM Forum.

"By adhering to the Open Digital Architecture and adopting TM Forum's Open APIs, solutions such as Oracle's can maximize the business and IT agility required for tomorrow's digital service provider."

Network intelligence company ThousandEyes has launched Internet Outages Map, a tool that provides real-time visualisation of the current state of the global internet health, based on ThousandEyes’ internet Insights.

ThousandEyes’ map provides consumers, businesses, industry analysts and other parties with an insight into ongoing and recent internet outages that may be affecting the experiences of their customers or employees.

The tool uses data from the company’s "vantage points" situated around the world, which perform billions of measurements each day to detect when traffic flows are disrupted within ISPs, public cloud networks and other service providers.

"Over the past couple of weeks, we’ve been inundated with requests from businesses, industry analysts and other various parties wanting to get a better understanding of global internet health during these trying times," said Mohit Lad, co-founder and CEO of ThousandEyes.

"Today, we’re thrilled to release the Global Internet Outages Map to give businesses and consumers alike a reliable source based on actual internet telemetry instead of public rumor to help them understand what’s happening on the Internet at any point in time."

The service was launched following a mass migration of employees from the office into working from home, due to the recommendations of governments and employers aiming to reduce to the deadly impact of the coronavirus outbreak.

This has caused an unprecedented level of internet usage, with Vodafone sustaining a 50% surge in demand for data services in some markets, while popular streaming sites such as YouTube or Netflix announcing that they will lower the standard of its streaming quality in order to prevent a much-feared internet-speed bottleneck.

“Despite massive traffic increases — particularly across consumer last-mile networks — we have not seen a significant corresponding spike in Internet outages, which can occur when traffic levels strain network capacity,” wrote product marketing director Angelique Medina in ThousandEyes’ blog post. “However, there has been an upward trend line in outages over the last three weeks compared with the previous three-week baseline.”

The company says that the Global Internet Outages Map is available at www.thousandeyes.com/outages to anyone at any time, and also provides information in the form of a historical timeline depicting outage volume over the last 24 hours.

Businesses at the top of their respective industries all have one thing in common: the knowledge that data, both structured and unstructured, forms the heart of their organization and is key to their success.

A comprehensive and nuanced data strategy is therefore vital.

Creating one, however, is not an easy feat. It isn’t just a matter of dealing with all the sources of data; it’s also having expertise in the tools required to manipulate and process that data and understand what needs to be structured and where a rigid model is inappropriate.

Do you have people that are experts in Excel, SQL, NoSQL, PostgreSQL and Oracle? Does your business have a suitable data lake so all corners of the company can benefit from the data itself and generate their own specific insights?

If you answered ‘no’ to any of these, then your data management strategy might not be up to scratch. How you feed that data into your business, or data lake, is up to you. But understanding how to store that data - either in a structured or unstructured manner - is crucial in making that next big step in your business’ journey to the top.

What is structured data and how is it managed?

When it comes to databases, structured data is often what first comes to mind.

Structured data - also known as relational data - is stored in tables with columns and rows. The structure is described by a schema, with relationships defined between the tables. For example, one table might contain a list of customers, another table might contain a list of telephone numbers with one row in the customer table potentially having many connected rows in the telephone numbers table.

Structured data is, by its very nature, already “managed”. The definition of the tables and columns means that data must be kept in an orderly fashion. Constraints can be added to the columns to, for example, ensure that only numbers are entered for telephone numbers and that no telephone number can be added without an associated customer.

The structured approach to data storage has both benefits and drawbacks. Users are able to understand and access data easily, with a wide variety of tools for querying and analysis available. However, it can also be regarded as highly inflexible when the data needing to be stored does not fit the structured data model.

It's also worth noting that a relational database management system (RDBMS) is often embedded in products that also offer far more bells and whistles than just managing data and making it available to queries.

For example, Salesforce, the cloud-based customer relationship management (CRM) platform, manages the structured data put into it, but also offers tools like chat, access to the Force.com development platform, analytics, and so on.

What is unstructured data and how is it managed?

Unstructured data is anything that can't be organised into a structured database. Common examples are free-flowing text-based interactions, such as email conversations or chat logs, word processing documents, slideshow presentations, image libraries, or videos.

Estimates vary for how much unstructured data lies in business. One recent projection estimated that 80% of global data would be unstructured in the near future. 

It contains a wealth of corporate information but, by its nature, was difficult to access until modern big data analytics and AI have become a reality.

Together with structured data, it's also one of the three Vs of Big Data variety (the other two being velocity and volume).

There are many benefits to using unstructured data. It is rapid to accumulate and there is no need for time-consuming parsing since there is no predefined structure. It can also be stored in its native format and lends itself well to corporate data lakes.

However, the benefits can also be pitfalls; specialized skills and tools are required to analyze unstructured data and users can be left alienated when faced with data that does not adhere to a uniform format.

Common tools for dealing with unstructured data (also referred to as NoSQL) include MongoDB and DynamoDB.

Unstructured data management (UDM) is essential for successfully making use of all this data. Rather than there being a handful of tools to point to for UDM, there are instead some basic tenets to be followed, which we outline below.

Indexing

This term, sometimes known as "discovering" as well as other related terms, means compiling your data to really see what's there, how frequently it is accessed, for how long it has existed, and more. 

The objective of indexing is to find out whether this information will potentially bring future value to the organisation and see if it is worth putting in an UDM system and archiving it.

This, however, can be a long process, and can take many weeks to sift and scan all the data. Be ready to dedicate a lot of effort and time to this process in the initial stage. This is also the section where you should add metatags so that the data is easy to search later on in the process.

Storage and availability

Now that the data has been organised, it now requires storing in a suitable location with the correct attributes that make it automatically and easily accessible.

There are a number of storage locations to choose from, which includes general cloud storage like Microsoft Azure or AWS S3 or on-premises data lakes. When the information resides here, it is able to be stored in its "natural" state, which means there is no need to store it in a database format, but also allows it to be available for automated querying through APIs.

When thinking about which type of storage to utilise, it's worth considering how frequently the data that is being stored is accessed. For example, if it's relatively frequent, it might need to be put in "cold" storage, which is usually much cheaper than if it is kept in storage that makes the information accessible at all times. However, in this "cold" storage it will be slower to access initially when you do need to sift through it and query it.

Semi-structured data

Semi-structured data can be regarded as a “halfway house” between structured and unstructured data. While it does not have the predefined model of structured data, it is easier to store and work with than unstructured data.

Examples of semi-structured include JSON or XML. Although the columns and definitions of traditional structured data are not usually present, the data does contain tags and markers to separate elements, making for simpler querying. These tags and markers are referred to as “metadata” and mean data can be better catalogued.

Vector databases

Also found in the category of unstructured data  - although increasingly meriting a section of its own - and becoming progressively more popular in the era of generative AI, are vector databases.

As with unstructured data, pretty much anything can be dropped into the database. However, an embedding function encodes the data into vectors to capture its meaning and context. The result is something that can be searched based on similarity or vector distance.

An example would be searching for an image based on content or style. Or a large language model (LLM) generating more relevant and coherent text.

Examples of vector databases include Pinecone and Weaviate.

We are living in an increasingly data-driven world, with access to unparalleled quantities of information. The cumulative nature of the digital economy is showing no sign of stalling. A company's ability to compete will increasingly rely on its capacity to make swifter, more educated decisions. Here, data can be the difference between keeping above water, and sinking.

The challenges for large enterprises with digital transformation projects are well documented, and many have executed tools to make use of the data they produce. But how are SMBs managing with digital transformation?

According to a study from SMB Group, 48% of small and medium businesses are preparing to engage in activities to support them in adapting and transforming their businesses for a digital future, with 36% already executing plans. SMBs are most likely to put the money towards enhancing employee productivity, streamlining the customer experience, and improving decision making.

This final point is an increasingly critical one. The more information and data a company has, the more able it is to make smart decisions and investments in technology and people. But information and data alone don't offer the full picture; so how can small and medium businesses use the information they have and turn it into actionable understanding for digital transformation?

Improved collaboration

If you've just invested in tools to aid collaboration, data can help you select what is being used effectively and what is redundant, the type of staff need more of. Even with small teams, data can highlight opportunities for improved ways of working, or whether additional support is needed for processes that are working well.

Data can also be used to gather information on current projects, for example marketing campaigns or internal workflows. When the data is collected across a project and has been analysed by the teams, it is much easier to identify methods for improving ways of working, which in turn can offer insight across different parts of the business.

An important part of this is enlightening employees about what data analytics is, and what business issues it can be used to solve. This, in turn, will help change the working culture from being one of seeing known facts, to concentrating on the unanswered questions and using data to drive those decisions based on the answers.

Collaboration is not restricted only to employees. Analytical insight inferred from data will improve the value of the wider business in the eyes of possible partners. Collaboration across businesses could result, with advantages offered in return from the sharing of data. Alternatively, data can be viewed as a commodity, and once it is centralised and analysed, it can be sold to raise profits.

An extra degree of caution should be wielded when using data as a tool to increase collaboration across businesses. Data spread across disparate digital applications can make it more vulnerable to cyber threats. The use of different defences from business to business can even detract from overall security, with IT departments having to navigate software they may not be familiar with. Moreover, issues regarding intellectual property rights have been known to arise.

Discover efficiencies

Smaller businesses can't afford to spend lots of money on suboptimal campaigns in the same way as larger organisations, and that means that identifying areas where cost and time savings can be made has a tangible value. Data can be used to highlight projects and tools that are working well, as well as ones that aren't being used to their full capacity, such as cloud storage or software licences. Using data in this way can add up to bigger savings in the long term.

Data showing usage patterns for software or tools in the business can also be a good way to identify ways to scale. It can be used to highlight periods of high demand for resources, and in turn where investment is needed in the future.

Digital transformation itself is a constant process, and therefore there will always be new efficiencies that data analysis can identify as the business and the technology within it evolve.

Introduce automation

Data can help identify areas where routine tasks can be automated in a business, however large or small. One of the advantages of introducing automation as part of a digital transformation strategy is that it can be used in just a few small areas without needing significant investment, and then scaled up across different parts of the business as and when it's needed.

Artificial intelligence (AI) applications that feed on data are increasingly found at the heart of digital transformation. Simply, the greater quantities of data that are channelled into AI applications, the more information that can be processed, producing business-insight and subsequently helping to induce wider digital transformation.

When it comes to practically implementing AI, a study from Salesforce has revealed that while just 8% of SMBs are using AI today, an additional 32% have plans to implement the technology, with uses ranging from automatic customer recommendations to predictive forecasting and automated campaign insights.

Enhanced customer insights

Data can provide insights into customer behaviour that will enable even small businesses to identify how they can optimise a customer's experience, whatever industry the business may operate in.

Keeping up with changing customer expectations can be tough - in fact, 34% of respondents to a Small & Medium Business Trends report identified this as one of their top challenges. But this is an area data can really help out.

Being able to deliver recommendations and special offers to customers based on factors like location and purchase history is much more likely to cultivate loyalty. Customer Relationship Management (CRM) systems can help manage expectations and scale the business, and many vendors have options that can be tailored to smaller businesses. Even simple data gathering tools can improve the customer experience for small businesses without needing a costly big data system.

In the long term, data is also valuable for predicting trends and identifying new business opportunities, whether that be economic trends or customer spending patterns.

A study from Econsultancy and Adobe found that data was a key area of investment for companies, with 65% of respondents saying that this would help them to better understand customer experience requirements. This was a more popular priority than optimising internal collaboration, or optimising workflows within the business, showing how important data can be in identifying points of friction and improving customer experiences.

None of the strategies above require huge investments in expensive software or hardware. Often, simple data analytics tools that integrate with existing technology can draw together enough data to be able to inform decisions in an SMB without needing to spend lots of money on all-singing, all-dancing systems designed for larger businesses.

What is vital, however, is that the data is of sufficient quality so that it can be understood and easily used by analytics programs and applications. Good business decisions will only be made on the back of good data.

Telstra has launched a platform to give customers the capacity to seamlessly share data from their Internet of Things (IoT) devices using Microsoft's Azure cloud platform.

The two companies have teamed up to co-develop the Telstra Data Hub, a modular system which aims to reduce the barriers that businesses face when it comes to sharing data, including the costs involved.

The technology will allow organisations, initially within the connected supply chain, water management and agribusiness industries, to share and exchange data securely.

The thinking is that by allowing businesses to harness their data through the platform, it'll lead to productivity gains.

"Our heritage is in building national infrastructure that benefits generations," said Telstra's group executive for product and technology Christian von Reventlow.

"We see more than $100 billion in incremental value to customers and the economy through digitisation and data-driven collaboration.

"We are excited to be partnering with Microsoft and unveiling this new innovation today at Telstra Vantage and we look forward to sharing further news as we continue to develop this exciting product."

The telecoms firm outlined a case study centred on the connected supply chain, in which one of Australia's largest supermarkets has used the system to track and monitor shipping containers to reduce the cost of missing cargo.

The supermarket, and its partners, can in future use data accessed via the hub to respond to ongoing operational problems, as well as understand chokepoints and improve efficiencies within the supply chain.

Telstra identified several problems it aims to solve, including a reluctance to share data for fear of losing control, high costs in setting up platforms, a lack of data standardisation and having to integrate various systems together.

The partnership is part of a wider effort across the industry to give business customers far more power to access and exploit the vast amounts of data gathered.

Salesforce and Amazon Web Services (AWS), for example, bolstered their existing partnership in September last year to simplify data sharing, and give customers better access to data to drive digital transformation.

Hadoop is one of the most popular open source cloud platforms used by developers, data scientists, and engineers to process enormous amounts of data, including analysing, sorting and storing information.

The platform is used to deliver key understandings into a company's health, whether financial or operational, allowing stakeholders to make decisions based upon data.

One of the main advantages of using Hadoop over other cloud platforms is that everything happens in real-time, enabling faster reaction times and meaning businesses can always keep one step ahead of competitors.

Debuting more than 10 years ago, Hadoop has undergone many changes, essentially evolving to take account of shifting demands from businesses, including the capacity to process much larger datasets.

For example, the first iteration was able to sort 1.8 TB on 188 nodes in 47.9 hours - revolutionary for its time. However, this has been increased considerably in a decade and much greater datasets can now be handled in less time thanks to contributions from the likes of Facebook, LinkedIn, eBay and IBM.

Hadoop was first developed by Doug Cutting and Mike Carafella, two software engineers that wanted to improve web indexing. It was built upon Google's File System paper and was created as the Apache Nutch project, after tons of experimentation by Cutting and the subsequent creation of Apache Lucene, previous to his tie-up with Carafella.

When Cutting started working for Yahoo in 2006, the platform's development accelerated thanks to a dedicated team to work on it. Fast forward to 2008 and it became clear that Yahoo was no longer the place for Hadoop to flourish.

At this point, Rob Beardon and Eric Badleschieler who had been heavily involved in the development of Hadoop made the decision to split Hortonworks, which was currently responsible for the evolution of the platform, from Yahoo. They created a team of developers that could focus primarily on its future.

Google hasn't completely abandoned Hadoop, but the evolution reigns are now managed by Hortonworks' founder Owen O'Malley.

Hadoop's main role is to store, manage and analyse vast amounts of data using commoditised hardware. What makes it so effective is the way in which it structures the data, making it extremely efficient to sort it autonomously.

Big data has huge potential, but only if you work out how to harness it. That means you must think about how you want to use it, and what answers you want from the data. That will determine your approach to data analytics, and help you reach the right insights that give your products and services an edge over the competition.

How does Hadoop work?

Four key elements comprise Hadoop; the Distributed File System, MapReduce, Hadoop Common and YARN. These 'modules' are each responsible for distinct tasks within the software.

The Distributed File System (DFS) provides the storage component; splitting files into fragments that it then distributes over many servers to improve high bandwidth streaming. This approach also makes it simple to increase storage capacity, compute power and even the bandwidth itself - all you need to do is install more commodity hardware.

Next, we come to the MapReduce module, the piece that performs the analytics on the datasets. It works by taking a large volume of data and crunching it into smaller datasets - perhaps the most crucial part of Hadoop's software.

MapReduce does two things - first, its map function crawls through the data, and slices it into key-value pairs. Reduce then collates the results of the mapping. We can see how this works on word-counting tasks.

Let's say we performed MapReduce on the 'she sells sea shells' tongue-twister, first of all the mapping part of the software would break the text down and count each word, which would look like this:

(she, 1)(sells, 1)(sea, 1)(shells, 1)(on, 1)(the, 1)(sea, 1)(shore, 1)(the, 1)(sea, 1)(shells, 1)(that, 1)(she, 1)(sells, 1)(are, 1)(sea, 1)(shells, 1)(i'm, 1)(sure, 1)

Once this operation has completed, the reduce function then groups the results together, returning an aggregate of the various values. In this case, that would be:

(she, 2)(sells, 2)(sea, 4)(shells, 3)(on, 1)(the, 2)(shore, 1)(that, 1)(are, 1)(i'm, 1)(sure, 1)

While this is an exceedingly simplistic example, it does illustrate the vital role MapReduce plays in breaking down huge datasets into more manageable formats.

The other modules that make up Hadoop are Hadoop Common, which refers to the utilities, libraries, packages and other miscellaneous files that the framework requires to function, and YARN (short for Yet Another Resource Negotiator), a cluster management tool that offers a central platform for research management across Hadoop infrastructure.

Why is Hadoop so popular?

While there are alternatives to Hadoop, it's unquestionably the most popular Big Data processing framework in the enterprise. There are a few very good reasons for this. The first is that it's fantastically economical because a Hadoop cluster can be built using commodity hardware, the setup and operational costs are extremely low compared with other technologies. It's open source, too, which means that there are no inherent licensing costs.

It's also highly scalable, which has been a boon to businesses that are looking to quickly spin up their Big Data operations. Hadoop also offers very high bandwidth across the cluster, allowing large workloads to be completed much more quickly and with fewer bottlenecks.

What are the main problems with Hadoop?

The biggest problem facing Hadoop is that it's still incredibly complex. While other elements of the enterprise IT stack have benefitted from increasing simplification and user-friendliness, Hadoop is still one of the more labyrinthine and confusing technologies in common use.

This makes it difficult for businesses to find people who can start working on Hadoop deployments or to up-skill existing staff in its implementation. It can also make it incredibly hard to sell the board on its benefits, as non-IT folks often have a hard time getting their head around what it actually does.

Over the past year or so, a maxim has crept into the IT industry's common lexicon: "Data is the new oil". In other words, it's a precious resource that has the potential to be extremely useful and incredibly valuable.

With this in mind, many organisations are now seeking to transform themselves into "data-driven businesses", making use of tools like data analytics and machine learning to extract value from the vast reams of information they generate on a daily basis.

This task isn't necessarily as simple as it sounds, though. Becoming a data-centric organisation can be a long and challenging process, with many potential hazards to navigate. In this month's IT Pro Panel discussion, we spoke to some of our expert panellists who have been through this journey to get their insights on how best to navigate the road to data nirvana.

Driving business outcomes

Speaking to our panellists, it quickly becomes clear that the wave of interest around data is more than just empty hype. It can have a real, tangible impact on multiple areas throughout a company, as Ritam Gandhi, founder of digital design firm Studio Graphene, explains.

"What I have realised is that 'data' touches nearly every part of our business," he says "We use data gathered on user engagement across websites we build to inform decisions taken by our digital marketing team. We use data collected as a result of user testing to inform UX decisions taken by our design team. We use historical data collected on a variety of tasks by our project management team to predict how long a particular project is going to take to complete."

This use of user behaviour data gathered from within applications and websites to improve and optimise them is one of the most universal examples of data analysis in action, but it can also enable business-critical functions such as security.

"As a business with a large online presence, William Hill uses data much like any online retailer," says William Hill's group CISO Killian Faughnan. "With a heavy focus on improving customer experience and service. From my own perspective in security, and perhaps where I can speak more authoritatively on the subject, we use it primarily for the automation of our protection platforms and security controls."

"We pull in data from every layer of our infrastructure so that we can kick off predefined playbooks to reduce the impact to our customers of directed attacks," he explains. "Without access to the vast quantity of data available through modern information systems we would simply be unable to protect the business in the way we do today."

In some cases, data analysis can help augment and support gut feeling, providing a solid foundation for business decisions when personal instinct cannot solely be relied upon. This can come in particularly handy if the business finds itself in a "brain drain" situation where it lacks in-house expertise in certain areas - with Addison Lee being a prime example.

"Addison Lee spent 38 years under private family ownership, where they knew the business and market inside out," chief data officer Graeme McDermott reveals. "However, when it was sold to a private equity firm and the many family members moved on, they needed to revert to data and insight to make decisions - not least as their market was disrupted by the arrival of Uber. Now they use data and tech to the extreme."

"Data is vital in the humanitarian sector," adds Oxfam International CIO Michael Duggan. "When a disaster happens, it's essential to get timely data in order to support critical decisions on what type of aid is needed, how to get it there and making sure it's used in the most effective way possible."

Big data, little data

One of the first hurdles for any business to overcome when planning a data strategy is deciding what data they need to collect.

"I think the starting point is for organisations to identify what they are trying to achieve from 'data'," Studio Graphene's Gandhi says. "They then need to identify what data sources they have access to and lastly what treatment needs to be applied to the data set in order to make it valuable to them."

For McDermott, the obvious starting point is data on your clients or customers, and on any transnational business you're doing.

"Clearly, as a data exploitation person at heart, I want it all. But the reality is not all of it adds value or richness," he says. "I suppose the crucial point is to evaluate systems you implement to support your product and ask 'where is the data, what does it collect and how easy is it for me to get to?'"

However, another important consideration raised by Duggan is that of safety and compliance, particularly in a post-GDPR world. Organisations, he says, don't just need to be legally compliant, but must also have a set of ethical guidelines about acceptable data use - something that he thinks will become a critical factor in organisational governance.

"Many data projects use a lot of personally identifiable information," he points out. "If you don't ensure you understand your data risks adequately you might set yourself up for a lot of pain later.

"Most of the data we collect is about people, many of whom are vulnerable. An expression I heard last year was that 'If data is the new oil, data protection is the new climate change'. "As a result we are putting a lot of energy into making sure the way we collect data is safe, and the way we store and share it is safe."

Ensuring the accuracy of the data they're analysing is also a key concern for all our panellists. As a CISO, the margin for error in Faughnan's data analysis is close to zero, so he goes to great pains to make sure it's as accurate as possible.

"I've not found a silver bullet that's for sure. From my own experience I've found I need to categorise data sources as either 'reliable' or 'unreliable' and act accordingly," he says. "Once you start watering down the quality, you begin to add in uncertainty to the decision tree, which, for us, means human intervention is required."

He does, however, accept that a certain amount of flexibility is required when it comes to the quality of data being processed in non-business critical analysis, arguing that "realistically, outside of mission-critical circumstances, unreliable data is the norm and businesses need to be cognisant and accepting of this."

"What I tend to say is that 'Big Data' is informed by 'Little Data'," Duggan argues, "which can give us an insight and potentially a way of understanding if our analysis of Big Data is right. Given the velocity of many of our operations, it's unlikely our Big Data set is as good as we would like it to be, so it's important to have strong check on making decisions. The best thing being people on the ground who can assess."

"Michael's point is so true," McDermott agrees. "A few years ago I saw a presenter talk about the 'data underdogs'. His summary was 'don't forget about the smaller, less important data items as a way of explaining what's really going on'."

A question of hype

This is one of the potential pitfalls organisations can fall into when looking at data, but it's far from the only one. The most common issue identified by our panellists as a possible threat to the success of a data programme is a poorly-thought out or unrealistic strategy. As Duggan points out, a data-driven organisation is in many ways a different beast from a more traditional company.

"A key question is if your business model is designed for data-centric decision making," he says. "A lot of organisations have a very linear value chain - a bit like Porter's value chain - which incrementally uses Big Data to make decisions but not in a transformational way. The models we see companies like Uber (and Addison Lee) shifting to are platform models where Big Data is used in real-time to influence things like capacity and pricing. It's a fundamentally different way of architecting your organisation."

In McDermott's view, it's imperative to strike the right balance between getting started with a proof-of-concept quickly, and adjusting it for long-term viability once the project has been approved. He points out that while delivering rapid time to value is important, if you don't take the time to reassess the technical architecture every so often, you can end up with an unstable end result.

"No one remembers that it was only a proof-of-concept until five years later, when the systems dies and someone asks why it was up on bricks. Trust me, it happened to me several times - with inherited systems, I might add," he explains. "If everyone's not on the same page about the final picture you'll have a crazy outcome. However, you can't be planned to the nth degree before starting."

For Faughnan, it's important to maintain a sense of perspective when looking at data projects, as many decision-makers tend to get carried away by the potential of data analysis without being realistic about what can actually be achieved.

"While we should definitely think strategically about where we want to get to, it's important to be pragmatic about how realistic it is to attain this nirvana, under what conditions, and in what timeframe. Give a glimpse of the blue sky distant future, but pitch a pragmatic solution that gets you some of the way there," he advises. "Be adventurous, but not irresponsible, in your approach."

In fact, all of our panellists agreed that although data analysis can have huge benefits, it has also been over-hyped to a certain degree.

"I think part of the challenge is that drawing inferences from a dataset isn't always as straightforward as one thinks," says Gandhi. "Organisations often jump to incorrect conclusions because of the way they've interpreted the data they have or often because they do not have a large enough data set to draw an accurate conclusion."

"I think there has to be a sense of realism here," Duggan muses. "Many might think that data is 'cheap' or easy but good, clean data is actually expensive to acquire ... this is why many data projects stall when they get out of pilot as organisations struggle to scale."

"Like every new innovation we overestimate the initial impact, but underestimate the longer term change it will make at industry level. I suspect Graeme is at the heart of this, as he's competing with an organisation built around data: Uber."

Fundamentally, however, data - and the insights that it gives access to - remains an integral part of all of the panellists' businesses. While it can be challenging to effectively exploit, it can deliver real value to organisations, assuming they approach it in the right way.

If you're a senior IT decision-maker and you'd like to apply to be part of the IT Pro Panel, please email panel@itpro.co.uk.

If you’ve ever had the chance to work for a larger company, especially one which has multiple offices, you may already know what subnetting is.

But many will go about their jobs without ever knowing the vital role that subnetting plays in day-to-day communication, collaboration, and network security.

Subnetting explanation and benefits

Data moving through a subnet can do so as directly as possible. Imagine you were looking to get from your town to one further north, but had to travel via a major city to the east, where you could board a train to your final destination.

Subnetting this example would mean the creation of a dedicated line between your town and the northern one, thereby removing the need to go east at all.

Subnet masks are a necessary component for TCP/IP, and split an IP address into its two constituent parts; a host address and a network address.

It is important to understand IP addresses in order to fully comprehend the process of subnetting. IP addresses are combinations of 32-bit numbers, unique for each device, giving a maximum of 4,294,967,296 possible unique addresses in IP address version 4 (IPv4).

They are split into four octets, which is a group of eight bits. The most prevalent visible format of an IP address is created by converting each octet into a decimal, separated by a single dot. 

In an IP address, you’ll find a Network Prefix (or ID) and the Host ID, which can be thought of as two separate fields. These are separated according to the five classes of networks to which the IP address has been assigned.

The classes are named alphabetically, ranging from A to E. In the majority of cases, IP addresses are likely to be placed in the A to C class. Class D is reserved for multicasting, in which a single host can send a stream of data to multiple hosts at the same time. Class E, meanwhile, is reserved for research purposes respectively.

Class A networks use a default subnet mask of 255.0.0.0 and have 0-127 as their first octet. Class B networks use a default subnet mask of 255.255.0.0 and have 128-191 as their first octet. Class C networks use a default subnet mask of 255.255.255.0 and have 192-223 as their first octet.

Although the IP system made finding specific networks easier, it also introduced hurdles to the transfer of data to specific machines within a network. The networks of large organizations can become particularly inefficient without subnet use.

Organizations can use IP subnets to divide networks by physical constraint, e.g. smaller broadcast domains, or by logical reasoning for features such as firewalls. In this way, subnets guide the routing choices that routers make.

Subnetting is also used to improve network security, as the divisions between each subnet allow organizations to enforce access controls - which also helps to contain any security incidents.

When a computer on one network needs to communicate with a computer on another, it uses a router. A router specified on a host, which connects the host's subnet to other networks, is called a default gateway. This passes traffic on one subnet to devices on other subnets. This gateway often connects the local subnet to the internet.

It's one thing to have access to data, but it is another to know what to do with it. This is where a business intelligence (BI) analyst can help by teasing put the insight that enables a business to scale.

An example of just how BI can lead to growth can be taken from the rise of Amazon; it is said that founder and former chief Jeff Bezos had an interest in data long before he even created the e-commerce giant.

Though it was often framed as a 'customer obsession rather than an interest in data, the results were hard to ignore. Simply put, fact-driven business intelligence insights can lead to growth on a global scale. We can't put interest in data analytics and business intelligence down solely to Amazon, but there is certainly a great deal more interest in the field now than there was before the online retail giant exploded onto the web.

Putting skilled analysts into ambitious companies has proven to be a successful path to profitability and also a sound method of becoming compliant with current data laws.

That last point has become increasingly important since the implementation of the General Data Protection Regulation (GDPR) and the UK's own Data Protection Act 2018. Since the GDPR came into force in 2018, businesses have been under more scrutiny around the way they collect and retain data, with greater urgency on transparency and accountability.

These are just some of the ways a dedicated business intelligence analyst can help an organisation, SMB, startup or even a government. So if finding the patterns and insights from high volumes of data is your bag, business intelligence might just be your next career move

What does a business intelligence analyst do?

BI analysts spend a lot of their time analysing data in order to identify company weaknesses and formulate solutions to these problems. To do this, BI analysts are responsible for some of the following tasks:

• Conducting tests to ensure that intelligence is consistent with defined goals
• Maintaining or updating BI tools and databases
• Understanding and communicating business requirements
• Developing BI and data warehouse strategies
• Analysing competitive market strategies through analysis of a related product, market, or share trends
• Presenting findings to all levels of management

BI analysts may also be responsible for competitor analysis, keeping up to date with industry trends, and exploring where their organisation can improve and reduce costs. Depending on the organisation involved, a BI analyst may be responsible for developing or researching a suitable business intelligence solution that is appropriate for the needs of the company.

What qualifications does a business intelligence analyst need?

An educational background in areas such as mathematics, computer science, or other technology subject is essential for this field and it's often required (or strongly preferred) that aspiring BI analysts obtain an undergraduate degree.

BI analysts should have training experience in various analytic processes, such as enterprise, SWOT, and PESTEL analysis, as well as have experience in technologies like SQL. It would also be beneficial for an aspiring BI analyst to be trained in areas such as management and scenario planning.

Whilst technically not necessary, it would be useful to get certified when participating in this field. The benefits of earning a certification include professional recognition, professional development opportunities, and an increased salary certified BI analysts see a 16% average increase. There are different programmes available with varying levels of certification, so it's important for a BI analyst to find one that closely aligns with their goals.

What skills should a business intelligence analyst have?

With a role so heavily reliant on data, it will come as no surprise to hear that anyone wishing to pursue a career in data analysis will need a strong analytical mind and technical skills to understand the meaning of the information.

A BI analyst also needs to possess strong people and social skills as much of their work is communicating with clients and customers to share information and suggest how the data can be used to develop their business.

Team management and problem-solving are some other secondary skills you should probably consider if you’re applying for a role as a BI analyst. It’s safe to say that in this position you’ll probably work closely with other analysts on your teams as well as other parts of the organisation to help and advise key stakeholders with their decision-making.

How much does a business intelligence analyst get paid?

If you’re based in the UK, then business intelligence analysts can usually make an average salary of £31,849 per year. The good news is that you can also expect to receive a bonus anywhere between £763 and £4,000, which is what the compensation research website Payscale has found.

A business intelligence analyst’s salary is also likely to increase over time, depending on how much experience you have acquired. This is also boosted by the fact that businesses are constantly needing to analyse more and more data, making the role fairly valuable. At the bottom end, entry-level BI analysts usually make around £27,000 per year, and can expect around an extra £1,000 raise for every year of work. Once you’ve worked for around five years, the salary should hit £35,000, while after working for 10 years it plateaus slightly to £38,000. However, analysts who have been in the industry for some time and have at least 20 years of field experience can look forward to earning around £50,000 each year.

Since organisations are constantly having to sift through more and more data to improve their operations, the demand for these individuals is increasing. It could translate into greater premiums for specialist BI analysis skills, so it might be worth improving your data analysis skills to capture this reward in the future.

One thing that candidates should bear in mind when browsing openings for this role is to ensure that their future employer is able to offer them training opportunities. If you’re able to learn new skills and better yourself, it not only means that the company can benefit from your training but also helps you to be up-to-date with the ever-changing technology and data landscape.

Commvault is a company in transition. While leadership questions linger, the last 12 months have seen a slew of changes at the data protection and backup firm, from a complete overhaul of its product line to a new channel strategy.

Simplification is king. And everything has come together with aplomb, as Commvault sees it. Hence its attitude going into the third annual flagship GO conference, hosted this year in Nashville, Tennessee, was buoyant.

The confidence levels Commvault exuded last week were, in fact almost stratospheric, as CEO Bob Hammer slapped his data protection firm into the same bracket as industry titans Amazon, Google, and Netflix, in his joint keynote presentation with COO Al Bunte.

This is because, as we learned from the 'Bob and Al show', those companies changed their users' expectations by harnessing AI, and vastly simplifying the user experience. It is precisely what the veteran duo believe they have just pulled off at Commvault, telling customers, partners, and the press that their 20-year vision is finally realised.

The biggest story to come was a revamp of its front-end user interface. After heavy criticism that its UI, product portfolio and pricing plan were highly vexing to navigate, the company's new Command Centre, which sits as an interface to its now just four products, will roll out in December as a streamlined single point of contact. The UI will also harness AI and machine learning to not just rewrite itself to fit around each enterprise's data lakes, but also slash backup times and notifications.

Elsewhere the company announced a handful of partnership extensions, especially with HPE and NetApp, that embody its reshaped channel strategy. The nine-month-long work in progress has seen Commvault move from a channel-friendly to a channel-centric company, with its channel leadership team "80% happy" with what they've achieved in the space.

GDPR, a hot topic for any and every firm, also featured prominently at GO, with a number of interesting discussion points to emerge. Take the right to be forgotten; what happens when a data subject requests their data wiped, but that data is in a backup, would you delete the entire backup, or instigate a 'delete on restore' policy to work around this?

Commvault's GDPR guru and head of data protection and governance Jo Blazey also highlighted how GDPR compliance may shift beyond what is required by law, based on consumer expectations.

Consent, for instance, is only required for a handful of processing actions. But companies may find themselves seeking user consent for everything because it's what their customers expect, and everyone else is doing it.

Uncertainty also lingers over the fate of Bob Hammer, who many expect has addressed his final GO. Despite a hunt underway to find his replacement, the near 80-year-old CEO certainly seemed bullish, with Al Bunte confirming in a press Q&A the pair had been given board confidence for three more years. But it's almost certain their time has come to an end.

Despite this, the company was keen to present itself as forward-thinking and innovative. This was embodied in a new keynote format, GOx, which saw seven industry chiefs and analysts speak for seven minutes each, including presentations from Microsoft Azure and HPE.

It was engaging enough, but the subject matter, which centred mostly on different aspects of AI implementation, warranted a much deeper-dive than seven minutes would allow. A live band playing country classics as delegates wandered into the main conference hall each morning, however, was a smash hit.

As Commvault's third annual flagship conference drew to a close, the messaging centred on a handful of ideas. These include simplified products and pricing, giving customers a broader understanding of their data, quality not quantity when it comes to alliances and channel partners, and implementing new technology, such as AI.

Call it confidence - call it arrogance - but the data management and backup firm certainly feels it's set a technological standard for the entire industry, and moreover, that it's finally cracked the code to catching up with its competitors.

Salesforce has revealed its plans for an Einstein voice assistant that will enable its users to talk to its mobile app or smart speaker and interact with their data on Salesforce's platform.

The company has also revealed the platform will allow users to build their own Einstein Voice Bots that provide customised interactions for use in their products.

The new voice assistant turns what users say into action by updating Salesforce records, notifying team members and creating tasks, the company explained. Einstein will also give users a daily briefing of key Salesforce metrics and priorities via their preferred voice assistant.

Daily Briefings can be configured by admins to focus on what each business user wants, such as the day's schedule and key opportunity updates, to an overview of their team's pipeline.

Voice commands will allow business users to get to critical information they need in Salesforce. Instead of manually opening and filtering dashboards, users can now pull up Einstein Analytics dashboards and go into further detail.

With Salesforce's Einstein voice bots, companies would be able to create custom voice-enabled interactions for their own customers and employees. It said that such bots could be created without any knowledge of coding and be deployed to Google Assistant and Amazon Alexa.

It gave an example of a rental car company allowing its customers to update a reservation with a simple voice command to the smart speaker of their choice. The Einstein Voice Bot would then source responses from knowledge articles in Service Cloud and share answers to help resolve the customer's case. The bots can also be customised to match an organisation's branding.

"We're in the midst of an incredible technological shift, where the power of voice is fundamentally changing the way we work," said Parker Harris, co-founder and CTO at Salesforce. "Einstein Voice will usher in a new era of conversational CRM, delivering new levels of productivity and redefining customer experiences with voice technology."

Einstein Voice will launch as a pilot within the next few months, although users will have to wait until next summer to start building their bots. Pricing for the products will be announced once they become generally available.

Databricks has partnered up with Microsoft to offer its Unified Analytics Platform on Microsoft Azure as an integrated service within the Microsoft portal.

Azure Databricks will make it easier for businesses to glean better information from their big data and implement artificial intelligence to analyse the information they hold.

“Our alliance with Microsoft is a major milestone for the growth of Databricks’ Unified Analytics Platform,” said Ali Ghodsi, co-founder and CEO at Databricks.

“There’s a large base of Microsoft Azure customers looking for a high-performance analytics platform based on Spark – and Databricks is already the leading Cloud platform for Spark. These organizations will be able to simplify Big Data and AI with Azure Databricks.”

The platform brings together a collaborative workspace for data science and engineering teams to work closely with business users, analysing varied sets of data including batch, ad hoc, machine learning and deep learning, streaming and graph from Azure SQL Data Warehouse, Azure Cosmos DB, Azure Data Lake Store and Azure Blob storage.

It's a fully managed service that Databricks says will save businesses significant costs, while offering the scalability organisations now demand. It's these customer requirements that shaped the development of Azure Databricks, Microsoft explained.

"Azure users will benefit from the fast and collaborative Apache Spark based analytics platform optimized for Azure, and through the integration with other Azure services, such as Microsoft Power BI, Azure SQL Data Warehouse, Azure Cosmos DB, and Azure Active Directory," John ‘JG’ Chirapurath, general manager of Data Platform Marketing at Microsoft said.

The integration of Databricks and Microsoft Azure offers businesses access via the Azure portal, making it simple to sign on and get started.

The news follows the launch of a new Databricks HQ in London last month, a move designed to take advantage of the UK's booming AI industry.

Salesforce has revealed Sales Cloud Einstein Forecasting, designed to give sales teams better visibility and intelligence to predict revenues at every stage of the sales cycle.

Combining Einstein Opportunity Scoring and Einstein Email, the offering will help businesses uncover their highest-value opportunities and conversations, helping them chase the most likely conversions that will bring in the highest revenues.

“Salesforce continues to lead the CRM market, delivering innovation, after innovation,” said Adam Blitzer, EVP and GM, Salesforce Sales Cloud. “As we celebrate our first year of Salesforce Einstein, we’re excited to introduce the next generation of Sales Cloud Einstein, an unparalleled sales platform. Now, equipped with predictive and intelligent capabilities, Sales Cloud Einstein has cracked the traditionally flawed forecasting model, bringing AI to every step of the sales cycle.”

It works by analysing historical CRM data, learning from the opportunities that have converted in the past and finding similar opportunities for the sales team to pursue. It can take into account factors such as seasonality and individual salesperson performance and style to make accurate sales forecasts specific to the organisation and team.

“Predictable and profitable business growth is something that every company strives to achieve,” said Sheryl Kingstone, Research Director, 451 Research Customer Experience and Commerce.

“However, accurate sales forecasting has been a longstanding challenge for many companies. By combining intelligence, pipeline transparency and key sales data, Einstein Forecasting provides a crucial view of sales revenue indicators that helps business leaders correctly predict business growth.”

Salesforce also unveiled the introduction of a $50 million Salesforce AI Innovation Fund to help the development of new AI solutions to help businesses across the entire organisation. Highspot, Squirro and TalkIQ are the first three businesses to take a cut of the fund, helping them create groundbreaking products for the Salesforce platform.

“Artificial intelligence has the potential to make every company and employee smarter, faster, more efficient and more productive,” said John Somorjai, EVP of Corporate Development and Salesforce Ventures, Salesforce. “The new Salesforce AI Innovation Fund will help accelerate the development of transformative AI solutions that extend and complement Salesforce, helping our customers reach new levels of success.”

Cloud computing and big data systems have without doubt been among the most disruptive technology trends of the past decade. These developments have irrevocably changed the way that technology companies operate, and how value is delivered to stakeholders.

With cloud computing, firms have been able to optimise IT operations through significant reduction of the need to invest in on-premise hardware and software, as well as provide businesses with more strategy options than ever before. Digital transformation and the shift to the cloud continues to show momentum, with the public cloud services market growing 29% from the previous year in 2021 according to IDC.

Simultaneously, big data technologies have provided enterprises with an unprecedented avenue of data value from assets. In the past, high-volume, diverse data proved troublesome for enterprises that were used to working with traditional database technology. But great technical developments allowed businesses to reduce the overhead required to transfer raw data into sorted information within a data store, as well as drastically increasing the efficiency, accessibility and speed of mass data processing.

These innovations have allowed for actionable analysis on a range of once-challenging data sources, including web logs, documents and machine sensors. Even so-called ‘dark data' has been revitalised through these technologies.

Big Data systems help organisations solve the trickiest problems, but they generally require a significant up-front and ongoing IT investment, in addition to dealing with the sheer amount of data in more ambitious projects.

Therefore, it makes sense that enterprises are turning to cloud providers with expertise in managing and maintaining scalable, and flexible, computing and storage infrastructure. While on-premise data systems are far from defunct, organisations are starting to effectively push the limits of big data analytics at scale by tapping into big data systems hosted on cloud infrastructure.

While many of these early cloud projects involve high volumes of structured data, there are 3 key components that are already enabling extraction of value from massive, diverse data sets on cloud infrastructure:

Cloud Analytical Databases

These cloud-based services, such as Amazon RedShift, are elastic data warehouses optimised for analytics with existing Business Intelligence tools. This type of analytical database includes management as well as solutions monitoring for the provider.

Hosted Hadoop Services

Hadoop clusters are often hosted in the cloud, which negates the need for on-premise infrastructure. Some Hadoop cloud offerings come with additional managed services, like job troubleshooting, software installation, testing and other such services.

Data Integration and Analytics

Databases built for high-performance analytics can make use of cloud techniques like compression, column-based storage and high-speed inserts of structured data, perfect for sophisticated queries and truly insightful business analytics.

Early adopters are already demonstrating how the cloud can improve upon the value proposition of big data, delivering elastic and cost-effective solutions for analysing data at unprecedented scale.

This article was first published on 12/09/17, and has since been updated

Cloudera has announced the acquisition of AI research form Fast Forward Labs, which it says it will use to help large scale businesses adopt cloud technologies.

Fast Forward Labs, as US-based AI startup founded in 2014, offers advice to businesses about how to apply machine learning technologies to their organisation to solve "real world business problems."

This is complementary to the services that Cloudera provides - signing people up to open source cloud technologies. Together, the two businesses will be able to provide an all-in-one solution for companies looking to start using open source technologies and develop innovative products using the power of machine learning.

"A machine learning product requires data, algorithms (our specialty!), and infrastructure to all work harmoniously," Hilary Mason, founder of Fast Forward Labs wrote in a blog post.

"We’re delighted to join forces with a company that drives progress in the foundational technologies our work relies on. By joining Cloudera, we will be able to bring the opportunities discovered in our research to life in new ways, at the scale of the Cloudera platform."

It's not clear yet how the two businesses will operate together - whether Cloudera will simply integrate Fast Forward Labs' technology or whether the AI firm will operate as a business within Cloudera, although Mason made it clear existing clients won't be affected just yet.

"We will continue to offer our research and advisory services, and our existing clients will see no significant changes," she said. "The enterprise is more excited about machine learning and applied artificial intelligence than ever, and in order to meet this growing opportunity, we are heading in a new direction.”

Alteryx Server is now available for businesses running Microsoft Azure, helping them gain and share insights from their cloud environment using a single platform for analysis and deploying new workloads.

Combined with Alteryx Designer, the company explained it's easier for businesses to install and configure their self-service analytics platform, without requiring any hardware modifications.

“We’re seeing increased demand from enterprises looking for cloud-based analytics solutions that meet expectations for flexibility and scalability, without sacrificing the security or performance of on-premises solutions,” said Bob Laurent, vice president of product marketing at Alteryx.

“With Microsoft Azure, we are making it easier, quicker and safer than ever to deploy analytics in the cloud across an entire enterprise.”

Alteryx Server has been designed to make it easier for Azure users to keep tabs on their businesses while Alteryx Designer helps companies build the workflows to prepare, merge and analyse datasets. This information can then be published on Alteryx Server for all relevant parties to view and action.

Users within an organisation can use the insights provided to customise and execute workflows based on the insights, independently to the original data making it more efficient for changes to happen.

“Microsoft Azure provides Alteryx with global scale, a broad portfolio of services and market-leading data management capabilities,” said Nicole Herskowitz, senior director of product marketing, Azure at Microsoft.

“We are pleased to see our mutual customers benefit from the combined power of Alteryx Server and Azure."

Alteryx Server and Alteryx Designer are now both available to download from Microsoft's Azure Marketplace, enabling organisations to share their data and make better business decisions as a result.

MariaDB hopes to go up against data analytics players like Teradata and Amazon's Redshift with its new open source columnar storage engine, MariaDB ColumnStore.

The platform enables customers to merge their transactional and analytical processing using one single interface, making it much easier to stay on top of data, without having to wander outside of the familiar MariaDB interface.

MariaDB claimed its product can significantly lower the cost of data warehousing compared to proprietary solutions such as Teradata and Vertica, because it doesn't need separate licensing and hardware agreements to use.

Additionally, MariaDB's ColumnStore takes advantage of flexible deployment options, giving customers the choice of using the cloud or on-premise hosting.

“Data warehouses are famously expensive and complex - requiring proprietary hardware which makes it nearly impossible to deploy on the cloud, or on commodity hardware. MariaDB ColumnStore makes data analytics more accessible, on premise, in the cloud or in a hybrid deployment,” said David Thompson, VP of engineering at MariaDB.

“Costing on average 90% less per TB per year than the leading data warehouses and offering a scalable solution for the cloud, MariaDB ColumnStore presents a new, open source model for big data analytics that is designed to meet today’s enterprise needs.”

ColumnStore also makes it simpler for administrators to manage and execute their data analysis, with the same security set-up as MariaDB Server, including encryption for data in motion, role-based access and auditability. Analysis can be triggered using existing queries without modification thanks to full ANSI SQL capabilities, not supported with Hadoop solutions.

"The demand for robust analytics technologies has exploded as companies look to derive greater value from increasing data volume," said Jason Stamper, data platforms and analytics analyst at 451 Research.

"MariaDB's ColumnStore is a new option for high-performance analytics to big data, which aims to enable faster and more efficient queries while eliminating delays and reducing enterprise data warehousing expenses."

MariaDB ColumnStore was announced in April, with the beta released to testers in May.

Gatwick Airport has implemented Splunk's software platform to help in better analyse data to improve performance and the customer experience.

Splunk's software analyses information provided by the airport's customer touchpoints including its transit system, ground handling, airlines and security services, using this data to optimise how the customer moves through security and onto their boarding gate.

It can also inject outside sources of information into the process, such as passengers' social media activity, the Highways Agency and Network Rail to predict the pressure on resources ahead of time.

“Splunk Cloud enables our teams to closely monitor real-time performance through an easy to use dashboard which plays an important role in the smooth running of the airport,” said Chris Howell, head of business systems at Gatwick Airport.

“By monitoring a wide range of passenger information and performance data, our teams are able to stay ahead of the game and effectively deploy resources in real time to deliver an enhanced service for our passengers.”

The way the information is processed has enabled it to meet and exceed its targets of processing 95% of its passengers through security within five minutes

“Gatwick Airport is the model for airports worldwide, and ultimately, any venue looking to maximise efficiency while keeping customers happy,” said Shay Mowlem, vice president of product marketing at Splunk.

“Innovative organisations see machine data as a strategic asset to gain a competitive advantage through digital transformation. In this case, Gatwick is able to drive efficiencies by correlating flight schedules, passenger movements, ground team information and other operations data.

"It’s an exceptional story of innovation through analytics, and we are pleased Splunk Cloud can be the platform to help enable this kind of transformative change.”

HPE has launched a managed service SAP offering, with the aim of helping companies make better use of their SAP resources on demand using automation.

Enterprise Solutions On Demand (ESO) for SAP utilises a web-based Service Catalogue, enabling users to increase or decrease their capacity without losing budget. The solution is payable on demand too, ensuring only what is used is be billed for.

The agile business platform provisions SAP resources quickly - within minutes or hours depending on workload, rather than days. They can be scheduled in advance or triggered on-demand, offering much greater flexibility, which is essential when companies need to react more quickly and when building development and testing environments.

This is especially evident in ESO for SAP's “detect-to-correct” predictive capabilities that reduce the amount of time it takes to fix incidents. This reactive environment means companies can be more innovative, ensuring they can respond to the latest demands and ensure business continuity.

Additionally, ESO for SAP is compliant with most industry best practices, with encryption supported for data at rest and in transition. To be further compliant, it uses a local datacentre, ensuring in-country hosting regulations are always adhered to.

“Most businesses are constrained by their traditional IT run-time environments, which possess built-in barriers to innovation and business flexibility such as long lead times for deployment, high total cost of ownership, and skills shortages,” said Eugene O’Callaghan, vice president of workload and cloud at Hewlett Packard Enterprise.

“ESO for SAP revolutionises how users consume SAP in the cloud by providing a ‘full stack’ managed cloud service enabling users to directly manage their SAP resources on demand. With ESO for SAP, SAP customers can optimise their IT spend for their SAP ecosystem and become more agile in adding new features to their SAP environments consequently allowing them to focus on their business needs.”

Facebook will not let Admiral analyse users' data to determine customers' car insurance prices.

The social network confirmed to IT Pro that users' accounts will only be used for login and verification purposes on Admiral's firstcarquote app.

The app, which Admiral has been working on for some time, would offer first time drivers a discount of between 5% and 15% on their car insurance in exchange for access to their Facebook data.

An analysis of their posts and likes over the last six months, allegedly including whether they use exclamation marks and certain words, would determine any discount, with Admiral saying that there is a "proven link between personality and how people drive".

It was due to go live this week, but was put on hold yesterday evening, which Admiral said was to address "a few outstanding issues".

But Facebook today confirmed that users' data would not be available to Admiral whether or not users granted the insurer permission to access it, as it breaks Facebook's usage policy.

A spokesperson told IT Pro: "Protecting the privacy of the people on Facebook is of utmost importance to us. We have clear guidelines that prevent information being obtained from Facebook from being used to make decisions about eligibility.

"We have made sure anyone using this app is protected by our guidelines and that no Facebook user data is used to assess their eligibility. Facebook accounts will only be used for login and verification purposes."

Instead, Admiral will now have to ask users of the app to answer a series of questions that will assess their eligibility for a discount instead.

Facebook's statement comes after privacy organisation, the Open Rights Group, raised concerns about the app.

Executive director Jim Killock said: "We need to think about the wider consequences of allowing companies to make decisions that affect us financially or otherwise, based on what we have said on social media.

"Young people may feel pushed into such schemes because of financial constraints. The right to keep things private shouldn't be the preserve of those who can afford it."

Meanwhile, UK data watchdog, the Information Commissioner's Office (ICO), told IT Pro it will ask Admiral about the techniques the insurer wanted to use on data.

An ICO spokesperson said: "The law says that the use of personal information must be fair. A key part of that fairness is ensuring that people are informed about how their data will be collected and used and it is processed fairly. This applies to using personal information acquired from social networking sites. We are paying particular attention to the increasing use of new 'social scoring' techniques to ensure that these developments proceed in accordance with the law."

Admiral's lead on the project, Dan Mine, told the Guardian it was not an invasion of people's personal data, saying: "It is incredibly transparent. If you don't want to use it in a quote then you don't have to."

The insurer's FAQ still tells applicants that it's up to them how much data they share, but adds that "what we have access to will impact how we understand and calculate safety and the discount we're able to offer".

Any data collected would be a "static snapshot" of the last six months and would be stored in Amazon Web Services, but Admiral admitted that older data could sometimes be taken into account.

Admiral is yet to respond to IT Pro's questions, but it did post on Twitter to say it has not yet used Facebook data to set prices. AdmiralUK tweeted: "In response to today's coverage, we'd like to assure customers Admiral doesn't use social media data to set prices for its policyholders. We haven't and don't access Admiral policyholders' Facebook data."

IT Pro asked Facebook whether or not it had stripped the app of its ability to access profile data during a final review process all Facebook-related apps go through, but the company declined to comment further.

This story was originally published at 11.15am on 2 November but was updated at 11.55am with comment from Facebook and the ICO. The story was updated again at 12.45pm to include Admiral's Twitter comments.

Doncaster Metropolitan Borough Council (MBC) claims to have saved £2 million by implementing an Advanced financial management system back in 2013.

The council opted for the e5 finance, procurement management, budgeting and forecasting solution, dubbed Collaborative Planning, while document management software was provided through V1. The software is all hosted in the cloud by Advanced.

Steve Brown, financial systems manager at Doncaster MBC, said the council had previously been heavily reliant on paper and Excel-based systems, with manual data entry required for numerous solutions.

The council, which serves more than 300,000 citizens, selected the system from Advanced for several reasons. Brown told Cloud Pro that Advanced's "very competitive price" and "flexible approach" made the implementation work within a local government context.

Brown declined to say which other products were considered, but said that there up to 10 providers on the Local Government Sprint II framework, including sector leaders, were invited to bid for the contract.

Out with the old

Previously the council had a solution for general ledger and processing supplier invoices as well as a bespoke mainframe for raising invoices and managing debt collection, while its spreadsheets for bank reconciliations and purchase management processes were paper-based or non-existent.

Using e5 for its general ledger system, Doncaster MBC now "has a single version of the truth", said Brown, giving staff access to one accurate, up-to-date source of data.

"With electronic purchase-to-pay processes we have greater visibility of spend and can ensure compliance. By choosing to have our solutions hosted by Advanced we were able to take advantage of their resources and expertise on this complex project," he added.

Automation has played a big part in improving processes - requisitions are raised within eProcurement, which automatically updates the general ledger in e5. Meanwhile, invoices received by suppliers are scanned using Optical Character Recognition (OCR) technology with V1 Capture, and matched to the purchase order within e5 to be processed for payment.

Documents are electronically archived and can be accessed through the finance tool.

The Collaborative Planning tool is used by all financial management staff as well as around 300 budget holders for revenue monitoring and forecasting.

"Our budget holders are empowered to self-serve and manage the budgets themselves, while finance staff are now able to do more analysis to support strategic decisions," Brown explained.

In September 2015, the council gave staff at 50 schools whose payments were processed by the council access to the budgeting and forecasting solution.

This has enabled them to view the data - including access to invoices - without having to wait for monthly reports from the council.

Brown added that from mid-2017 the council will also be using Collaborative Planning for capital monitoring, which is currently managed on spreadsheets.

Money matters

The council has saved £2 million so far as a result of implementing the Advanced tool, with a remaining £100,000 anticipated in the 2016/17 financial year, when new modules and upgraded systems are implemented.

"By implementing this integrated suite of software we have increased visibility and control of finances, streamlined process efficiencies and decommissioned several legacy systems, including a 20-year-old mainframe, which led to reduced licensing and support costs," said Brown.

"We recognise the need to utilise technology to deliver process efficiencies and release significant savings. We have transformed our financial systems, and are continuing to invest in Advanced's technologies by adding new modules and upgrades to the solutions we have," he added.

Brown explained that as the solution is still being implemented, it was difficult to work out exactly how much it has cost so far.

As a result of the success the council has seen from its existing system, in 2015 it shared the entire system at Doncaster Children's Services Trust, to run the council's children's services.

IBM's revenues in areas such as cloud computing and analytics rose in the second quarter of its financial year, despite an overall dip in growth.

Revenue from public, private and hybrid cloud grew by 30 per cent in Q2 2016, while mobile revenue went up by 43 per cent. Analytics revenue also had a slight uptick of four per cent, while security revenue was up by 18 per cent.

"IBM is pioneering new business opportunities beyond the traditional IT marketplace," said IBM CEO Ginni Rometty. "And we continue to invest for growth with recent breakthroughs in quantum computing, Internet of Things and Blockchain solutions for the IBM Cloud."

The results indicate that IBM's "strategic imperatives" are paying off. The company has been making significant investments cloud technologies, big data and artificial intelligence, as companies move away from legacy infrastructure.

In total, these imperatives grew by 12 per cent, while the company's overall revenue dipped by three per cent. IBM's cloud business and strategic imperatives now make up over half of its revenue.

However, as the revenue dip shows, an increase in IBM's cloud revenues is not enough to completely plug the gap in its finances caused by the steep decline in its legacy hardware business, and the company's systems division reported a revenue reduction of 23 per cent.

TechMarketView analyst Kate Hanaghan noted that while IBM is going in the right direction, it needs to be careful about its pace.

"Wrestling with the balance between legacy and emerging IT services revenue is an industry-wide challenge," she said. "We believe IBM has the right approach but this level of transformation takes time - especially when you're moving into (and creating) new markets."

Hanaghan added: "To become the type of supplier that will be around (and will thrive) for years to come, you have to be different and be disruptive. However, you must also show patience - something that shareholders are less likely to do."

Hewlett Packard Enterprise (HPE) has announced its Operations Bridge Suite (OpsBridge) analytics-driven IT management software.

OpsBridge gives IT managers key insights about the performance of their hybrid applications and platforms, meaning they can ensure everything is running to maximum efficiency.

“To keep pace with constantly evolving business environments, IT teams need tools that harness big data to optimize their fluid infrastructure landscape,” said Tony Sumpster, senior vice president and general manager, IT Operations Management at Hewlett Packard Enterprise.

“HPE Operations Bridge Suite helps customers transform IT organisations from a cost function to a value creator by simplifying and automating IT operations, and gives unique real-time visibility to help executives make decisions faster.”

OpsBridge is powered by HPE Vertical to automatically finds and manage applications running both on-premise and on cloud services such as Microsoft Azure, Amazon Web Services (AWS), Docker, HPE Helion Openstack and other OpenStack distributions.

It makes managing such applications a more automated process, so IT managers don’t have to waste time making sure their applications are running to full capacity manually. It also includes integration with collaboration platforms such as Slack and HipChat via ChatOps so departments can keep on top of what’s happening at all times.

HPE added that the software will help streamline hybrid application management, breaking departments free from the fragmented services that make it more complicated to ensure systems are running at optimum efficiency.

“As we move forward in our digital transformation journey, we are continuously challenged by legacy systems that support some of our operations,” said Jay Rooney, system analyst at Vancity Credit Union.

“Hewlett Packard Enterprise’s Operations Bridge solution gives us tools we need to automatically sense what’s happening, analyse our data and then adapt through remediation.”

Yandex Data Factory's data and analytics services are now available on SAP's HANA Enterprise Cloud platform.

The services allow those working in customer-facing industries such as retail, e-commerce, banking and telecommunications sectors to predict what customers will require and present personalised offers, recommendations and understand churn in their business.

"While the advantages of big data analysis are well-understood, many in the retail, e-commerce, banking and telecommunications sectors will have concerns over integrating data analysis technology with their existing systems," Alexander Khaytin, chief operating officer at Yandex Data Factory said.

"We wanted to remove this obstacle and therefore deliberately partnered with SAP – one of the leading providers of data and business automation tools in these sectors – so that we can offer our clients advanced predictive and prescriptive analytics, without additional integration costs."

Yandex Data Factory utilises unique algorithms that can identify patterns in user behaviour and then apply these to customers with similar browsing histories to ensure they are relevant. This method reduces the costs of acquiring new and retaining current customers by up to 10 per cent, Yandex explained.

“Synergy with Russian companies that are developing innovative services and products is one of the key tasks for SAP," Natalia Parmenova, chief operating officer at SAP CIS, said. "The partnership between our companies allows us to create highly accurate predictive business intelligence services, especially important in fast-changing industries, and the wider economy."

She added the solution would be particularly useful for middle-sized businesses that need to take action fast to attract new customers, but may not have the extensive budgets of large businesses.

A UK children's hospital is using IBM Watson to answer kids' questions about their stay and to analyse patient data in a bid to provide better care.

Alder Hey Children's NHS Foundation Trust is developing an app to let children and their families answer a range of questions before their stay in hospital such as their favourite food, games and films, and how they wish their bedroom to look.

They can also ask questions about clinical procedures and surgery, which IBM researchers and scientists from the Science and Technology Facilities Council's Hartree Centre will use to train Watson into providing useful responses to patients before they come to hospital.

Iain Hennessey, a paediatric surgeon and director of innovation at Alder Hey, said: "This is an unprecedented opportunity for Alder Hey to pilot this groundbreaking technology and learn how to transform IT capability and working practices in healthcare, not just in the UK but across the world.

"Helping our patients and their families prepare properly for coming into hospital will really reduce their anxiety and could mean we can get them better and home faster."

The first version of the Watson platform should be live by the end of the year, with funding from a 115.5 million pot the government made available last year. The app will be developed in parallel with money from the hospital's charity.

Alder Hey will spend the next few months collecting patients' questions to make Watson's responses as useful as possible.

Lee Hannis, head of business development at the Hartree Centre, said: "Our aims are to improve the quality of the precious time patients have with clinical staff and extend the care before and after the patient visit."

Analysing patient data

But both IBM and Alder Hey see more potential uses for the technology namely in collecting patient data to improve the hospital's quality of care.

Watson will provide analytics to the hospital to deliver insights on the data it collects, using it to match patients to clinical studies and monitoring admission patterns to help plan the availability of beds.

Another option is to create apps for patients with chronic illnesses, notifying them when symptoms reach a point where they need to seek medical help.

The Hartree Centre's Hannis said: "We are extremely excited about applying these new computing techniques to help improve the experience and quality of care provided at Alder Hey Children's Hospital and look forward to seeing the early interactions between the children and the hospital in coming months."

IBM's European director for Watson, Paul Chong, called the project a "significant milestone".

He added: "Alder Hey Children's Hospital has set a truly inspiring vision for the future of paediatric care. I'm thrilled to see IBM Watson technology applied to help doctors and their patients in the effort to improve the lives of children and their families."

Picture courtesy of Science and Technology Facilities Council

Kimberly-Clark Professional has chosen IBM's cloud technologies, including its Bluemix platform, to help digitally transform its intelligent facilities management app to help clients keep tabs on their toilets, in a bid to improve the customer experience and lower operating costs.

Sensors integrated into soap dispensers, air fresheners, entrance doors and other amenities monitor what's happening and this data is sent back to managers who can then address any issues more quickly, such as an air freshener not triggering often enough to keep bathrooms smelling fresh or dispensers running out of soap.

Everything can be viewed and managed from a single dashboard, making it a more time-efficient way of checking up on multiple sites. Both mobile and desktops are supported, so facilities managers can check up on their bathrooms while on the move.

"The restroom and supplies management have always been important factors in facilities management, however, we now know they're critical to maintaining the business itself," said Bryan Semkuley, Kimberly-Clark Professional's vice president of global innovation.

"We wanted to help our clients reduce tenant churn, lower costs, and improve the customers' experience along the way. That's when we turned to innovations in cloud and IoT from IBM that can be operated from facilities managers' smartphones."

Initial results show the amount of supplies needed in the toilets reducing by up to 20 per cent, demonstrating its power to keep customers happy and businesses saving money.

"Kimberly-Clark Professional products are used by one fourth of the world's population on a regular basis," added Rachel Reinitz, IBM distinguished engineer and CTO, IBM Bluemix Garage. "IBM Cloud, from development platform through IoT, enabled them to develop and deploy the innovative Intelligent Restroom from the ground up."

IBM has taken the wraps off three new APIs that aim to enhance Watson's emotional and visual capabilities.

The APIs: Emotional Analysis, Tone Analzyer and Visusal Recognition are currently in beta, while a fourth - formerly known as Text to Speech (TTS) - has been updated with new emotional functionality and rebranded as Expressive TTS.

One particularly novel, but useful, application of Tone Analyzer has been created by Watson Ecosystem partner Connectidy and focuses on how to make online dating communication more effective.

"We're using Watson's personality insights to use your own data and what matters to you," said Dineen Tallering, president of Connectidy.

"How often have you asked a colleague to read something over your shoulder to check everything is ok? Some 67 per cent of the time when people send out text messages or emails they are completely misunderstanding [what people will think] they mean. We call this [use of the API] a spellchecker for your emotions. Men and women communicate differently... Tone Analzyer steers people away from writing the wrong thing as they are writing it."

Tallering claimed what her company is doing is a first for the industry, adding: "As we expand the technology we will be going into many other verticals (such as work or choosing a college). For us the opportunities are endless using the Watson API."

Watson Tone Analzyer has previously tried to understand nine other traits across emotions, social propensities and writing style (agreeable, analytical, angry, cheerful, confident, conscientious, open, negative and tentative). It will now add new analytics capabilities and be able to determine a range of new emotions (disgust, fear, joy and sadness), new social propensities (including extraversion and emotional range) and is moving away from considering single words analytically to whole sentences.

Tone Analzyer, together with the one refreshed and other two new APIs aim to help developers unleash the power of cognitive analytics when creating apps.

Emotion Analysis forms part of the AlchemyLanguage API suite. It uses sophisticated NLP techniques to help users better understand and differentiate between emotions and positive/negative sentiment.

Visual Recognition, meanwhile, lets systems recognise and classify images rather than just tag them.

"Our systems can respond with nuance and inflection. It gives you a 'sensitive chip' and marks the next step in helping your developers, said David Kenny, IBM Watson general manager.

"Come and build with Watson. Find out how bringing all that emotional intelligence to your data can make a huge difference."

Data is becoming a major business asset that can be central to workplace innovation, says the chief technology advisor at SAP UK.

In an interview with Cloud Pro, at SAP’s annual Innovation Forum, in London, Mark Darbyshire says that organisations are increasingly realising that “there’s a lot of value in data”, and, when used in the right way, it can lead to significant efficiency savings for enterprises.

“You’re seeing the emergence of data as a business asset within organisations – and not just something that we have for legal or operational purposes,” says Darbyshire.

Competing taxi service companies GetTaxi and Hailo both offer customers the ability to order taxis from an app – much like Uber. However, he says the reason why Hailo has succeeded is because data is “very important” to Hailo.

SAP wants to help businesses innovate, and Darbyshire explains that the company’s cloud platform, HANA, which allows companies to collect, process and analysis data in real-time, is at the forefront of enabling it to do so.

HANA is SAP’s central data store. Companies are given access to this, and then SAP connects its other services – analytics, user experience data, mobile platforms and APIs, collaboration tools, integration, and so on – to the cloud platform.

During his keynote at the SAP Innovation Forum, Pat Bakey, the company’s president of industry cloud, gave examples of how SAP has given companies more room to innovative, often by reducing the time it takes for existing processes to be completed.

Darbyshire explains: “If I can take 10 per cent out of a company’s travel and expenses, and I can take half an hour of every working persons’ week, actually that’s quite a lot of innovation for a financial company. That frees up a lot of time for people. Similarly, I can give employees apps that allow them to do their jobs better.

“We’ve done this with [energy provider] National Power. They said they’ve seen a 20 per cent increase in employee productivity – I like to call that having Friday’s off.”

What works for some companies won’t work for others, though. Darbyshire’s view is that businesses must leverage what they already have.

“It’s about leveraging existing capabilities and communities,” he says. “But also, you can leverage the data, and I think that’s also why things like HANA Cloud Platform are attractive. Because it is a platform where process and capability are as important as data and analytics.”

Innovation through cloud adoption

When it comes to encouraging its customers to move their infrastructure and pipelines to the cloud, SAP’s core business applications have been taking off at a solid pace. It saw strong adoption of its HCM and CRM software in 2015, with more than 1,000 customers adopting the former. And the company revealed last month that the latest version of its ERP solution, S/4 HANA, now has more than 2,700 organisations using it.

Still, plenty of companies, including some of SAP own customers, have cold feet about moving certain areas of their business over to the cloud.

“It’s very difficult for a lot of companies,” says Darbyshire. “A lot of companies want to innovate, but the one thing they want to do more than innovate is not fail.”

Just turning up with flashy technology is not enough, says Darbyshire. He believes the reason SAP has been in business for more than 40 years, and why its customers trust in it to supply them cloud services, is because it assure its customers that it can make good on its promises, delivers them in a timely manner, and maintains a strong relationship afterwards.

From a personal point of view, Darbyshire says ongoing challenge he faces today is how to make things “consumable” in the greater shift towards data and analytics. “I think that changes the skill sets to some point within IT,” he says. “It’s just as important to be able to analyse the database as it is to be able to install it. And for some organisations, that can present a skills challenge for the IT department.”

Moving to their business systems to the cloud is an opportunity to “spring clean,” says Darbyshire.

“And that’s not spring clean the technology, that’s spring clean their own data, processes and their ways of doing what they’ve been doing,” he says.

“In moving to the cloud, companies need to understand what they are moving, the integration applications and think about how they are going to get at the data underneath it all. All of those are opportunities to innovate.”

Big Data firm and the RBS 6 Nations have teamed up to bring rugby fans greater insight into the 15 games of the annual tournament through analytics.

The company has brought together a team of experts, including Italy and South Africa coach Nick Mallett and former England internationals Ben Kay and David Flatman.

The analysis team will bring together their own expertise and insight and combine it with the analytics and business intelligence (BI) tools offered by Accenture to provide a data-driven perspective on the games of the 2016 tournament.

Nick Mallett said: "The importance of data in sport has been growing steadily for well over a decade, with coaches all around the world seeking to gain even the smallest advantage on the field.

"I believe that by utilising the smartest analysis ... fans will be able to review not just game-changing moments but the reasons behind them; the game plan, how single moments affect results and, more importantly, have an improved fan experience during the RBS 6 Nations."

Nick Millman, managing director of Big Data and analytics delivery lead Europe, Africa and Latin America at Accenture, added: "Bringing Ben Kay and David Flatman onto the team to supplement Nick Mallett's extensive international rugby experience will ensure the analysis team has the very best rugby knowledge to sit alongside our technical expertise.

"Our team will study two million rows of data per game using Accenture analytics tools to provide fans with insights that not only help them see the game in a new way but also enrich their RBS 6 Nations experience, for what is shaping up to be a very exciting Championship."

The use of analytics to improve fan experience is becoming more and more popular in sport, with the Women's Tennis Association and SAP pairing up in 2015 to offer new insights into the game.

Analysis from the Accenture team will be tweeted via @AccentureRugby throughout the RBS 6 Nations 2016 tournament.

Information and knowledge are no longer viable business currencies. They have been usurped by the need to gain greater insight from a range of data sources and that's how true business value will be derived in the future.

So claims IBM, which used its Insight conference in Las Vegas this week to talk up the power of being able to connect the dots between users and behaviour to enhance customer and company relationships.

"Insight is the new way to create business value," assembled delegates were told by a video opening the keynote session. "Insight is the new competitive advantage. Are you a player in this new economy? Or are you about to be disrupted?"

That video set the tone for the rest of the keynote, which also talked up the advancements that have been achieved in hybrid cloud and hybrid cloud infrastructure as helping businesses to unlock the value of data regardless of where that data resides.

"All of you are here today because you have helped ignite this data revolution. You know that competitive advantage depends on data rather than biases, instincts and past experiences. Many of you are leading with analytics and data to disrupt your business models and your industries. So during this week, we have a lot to learn from each other and I'm very much looking forward to it," said Bob Picciano, senior vice president of IBM Analytics.

"It's all about unlocking value, unlocking the game-changing insights that are waiting to be unleashed from all this diverse data around us. This includes dark data data we haven't been able to eliminate in previous generations of analytics capability. It also includes data generated by devices on the IoT."

The opening keynote session talked about how Apple shook up the music industry with iTunes and likened the current shift to that and the changes the photographic and publishing industries underwent when digital film and e-books respectively arrived on the scene.

What's more, attendees were told that all economic revolutions are primarily driven by science and technology, leading to massive disruption and leaving behind winners and losers, disruptors and disruptees.

Picciano also said we had entered a new era of business that focused on cognitive analytics that would help enterprises "outthink challenges and outthink the competition."

"As every business rushes to become a digital business, cognitive will be the critical differentiator. What is truly extraordinary is that these cognitive systems are not programmed to anticipate every possible answer or action needed to perform a certain function or set of tasks. Like us, they learn. They learn from experts, they learn from every interaction," he said.

"Cognitive businesses learn by continually ingesting data. Using AI and machine learning, cognitive systems are trained to sense, to predict, to infer. To ultimately augment human intelligence. Helping us to think better and enabling us to make more informed and unbiased decisions.

"To lead in the insight economy, every client needs to harness as much data as possible from diverse sources. To put it to work to help you answer the toughest questions that you have."

Informatica has announced a suite of tools for organisations using Tableau to manage data, driving productivity, with substantial time savings too.

The services include data integration, data preparation and a range of visual templates for Tableau customers in entire organisations without requiring any training or input from specialised teams.

Customers can collect the data from hundreds of data sources - whether in the cloud, on-premise or hybrid environments - then process the sources including cleansing and standardising the data and using one of the integrated templates, create visualisations to distribute around the organisation.

“Informatica is expanding our support by bringing every Tableau user in the cloud, desktop and server the ability to explore any data and get answers," Ronen Schwartz, senior vice president and general manager, Informatica Cloud said.

"We are offering wide access to applications and databases, with point-and-click simple data extraction, for on-premise and cloud data through easy visual cleansing and blending. Only with timely and trusted data can Tableau users realise the potential of powerful data visualisation without having to argue whether the data is accurate.”

Informatica also launched an app for the Informatica REV platform, meaning Tableau customers can access third party cloud apps including Salesforce and Netsuite to help visualise other data such as sales or marketing performance.

“Informatica shares Tableau’s mission to help people see and understand their data,” said Kelly Wright, Executive Vice President of Sales at Tableau. “Informatica helps customers create and clean governed data, then bring it together in one place where it can be easily analysed in Tableau. This partnership brings together what customers need to create agile, self-service analytics.”