A technology revolution is transforming the healthcare industry, changing everything from how patients are diagnosed and treated to our battle against some of the world's most serious diseases. It's a revolution fuelled by new sources of healthcare data and powered by big data analytics and it's being pushed even further by new developments in AI. Between growing populations, ageing populations, drug-resistant microbes and pressures on staff and budgets, healthcare faces some enormous challenges. Yet with data, analytics and AI supported by new cloud, storage and processor technologies the industry is moving in the right direction to meet them. This revolution will change and save patients' lives.

On the other hand, clinicians are finding ingenious ways to make use of the wealth of data collected by fitness trackers, smart watches and healthcare apps on smartphones not to mention information being freely and publicly shared over social media. While privacy concerns won't melt away overnight, researchers hope that, given assurances, the public will back the wider sharing of health information, particularly if it can help us fight diseases or make more informed choices about our diets, our sleep and our exercise regimes. With anonymisation and other appropriate safeguards in place, plus the legalities dealt with, there are endless applications.

From precision to prevention

Many of these harness the power of big data analytics, using these massive datasets to spot patterns or even predict outcomes based on certain factors or criteria. One large study combines genetic information with data from other studies and canSAR, the world's largest database for cancer drug discovery, to identify pathological mutations and match them to potential drugs. Such studies are finding that, by picking out new genes involved in the development of, say, prostate cancer, they raise the chances of creating bespoke drugs to battle specific mutations. Similar studies hope to isolate the impact of diet and exercise on diabetes, so that sufferers get more motivation to make potentially transformative lifestyle changes.

Clinicians and data scientists refer to this approach as precision medicine' using analytics to find out what fuels specific variants of a disease in specific individuals, then identifying the right individual treatment path to manage or cure it. Nor is this the only way analytics is transforming healthcare. Researchers fighting antibiotic resistant superbugs hope that analytics could find answers there in the long term, and that, in the shorter term, mathematical modelling could help estimate the global impact of antibiotic resistance and make a powerful case for increased funding.

Meanwhile, new healthcare apps, like Sentimento Ltd's My Kin, are working to help prevent illness. They do so by bringing in information from smartphones and wearable devices, including physical and social activity, sleep and environmental factors, and then using analytics to pinpoint behavioural changes that could help reduce health risks and prevent the users from developing serious conditions later.

Putting AI into practice

These approaches are only being improved by developments in AI and machine learning, as clinicians and researchers use new techniques to spot patterns faster or get a more accurate diagnosis in less time. At both MIT and the University of Pisa, smart algorithms are enabling MRI image scan comparisons that used to take up to two hours to be done in one thousandth of that time, or to cut down the time patients spend in discomfort during vital MRI screenings. Similar work is being done by Intel and the AI company, MaxQ, to analyse CT scans of stroke and head trauma patients to reduce error rates, or by Intel and the med-tech company, Novartis, to analyse thousands of images of cells during drug research and identify promising drug candidates. By augmenting manual analysis, the technology can reduce screening times from 11 hours to 31 minutes.

It's even hoped that by combining data analytics and AI, the kind of cancer drug treatment research mentioned earlier could go on to not just target the right treatment but prevent the disease from establishing a foothold. Machine learning and deep learning techniques could spot molecular drivers or mutations early and suggest appropriate action.

These developments make heavy demands on today's technology; whether you're working on large datasets in memory or pulling data from disparate sources in the cloud, storage speed and processing power count. Here fast flash storage arrays and persistent memory, like Intel Optane DC persistent memory, is delivering the kind of high-performance, high-capacity storage these applications need and making it more affordable and accessible.

Much the same is happening on the processing front, where Intel has teamed up with Philips to show that Intel Xeon Scalable processors can perform deep learning inference on X-rays and CT scans without the specialist accelerator hardware usually required. Using AI in medical imaging has been challenging up to now, because the imaging data is high-resolution and multi-dimensional, and because any down-sampling to speed up the process can lead to misdiagnosis. New deep learning instructions in Intel's 2nd Gen Xeon Scalable processors enable the CPU to handle these complex, hybrid workloads. Through this research, Intel and Philips are bringing the use of AI in medical imaging down to a lower cost.

In doing so, Intel is helping supercharge the new technology in healthcare revolution, providing the industry with the compute and storage performance it needs to transform raw data into personalised treatment plans and great patient outcomes. What's more, it's doing so in forms that will only grow more affordable and accessible with time. Combine that with the explosion in healthcare data and there's potential here for something truly special. Technology might not kill the world's superbugs or defeat cancer straight away, but it could forge major breakthroughs in these and many more of the world's biggest healthcare challenges.

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Cities of the world are buckling. The UN estimates 55% of the planet's 7 billion people live in urban areas and it's believed a million people join this list on a daily basis. Infrastructure is feeling the strain, there's unrest, congestion is polluting our lungs and crime is prevalent. What's more, if this rise continues as expected, cities will be home to some 6.1 billion people by 2050. Something has to give.

Speed and safety

In the UK, the economy as a whole lost 8 billion due to staff being stuck in traffic jams last year, or 178 hours per driver, according to research by Inrix. As cities become saturated, authorities and industry are turning to big data and tech to ease this load. In London, for instance, Transport for London's (TfL) Open Data project provides more than 80 data feeds through a free API. These feeds share details about air quality, tube times and delays, the number of passengers flowing through the network as well as data on live traffic disruptions. Some 600 third-party apps are now being powered by these feeds, used by 42% of Londoners, and it's reported to be putting 130 million a year back into the capital's economy. On a wider scale, it's estimated that by using open data effectively, 629 million hours of waiting time could be saved on the EU's roads and energy consumption could be reduced by 16%.

"Open data is changing our everyday lives and how organisations like TfL work," said Jeni Tennison, CEO at the Open Data Institute. "Data is becoming as important as other types of infrastructure, such as roads and electricity, which means building strong data infrastructure is vital to economic growth and wellbeing."

Beyond roads, tracking pedestrians is key in keeping a city moving. In Glasgow and London, mobile phone data can be used to track passenger numbers on public transport, while sensors in lampposts can track footfall. The Netherlands has even begun trialling smart traffic lights that give the elderly extra crossing time or change automatically when they detect an approaching cyclist.

In China and Singapore, authorities are taking things a step further. Through the use of IoT devices and sensors, alongside advanced 4G data networks and AI, not only are they monitoring and improving traffic flow, they use the data to track road violations and even predict crime. Singapore, for instance, uses data from RFID-equipped travel cards, CCTV and anonymised phone data to identify problems before congestion can take hold. Its AI can spot patterns and run algorithms that highlight issues some 10 or 20 steps down the line.

Elsewhere, the Chinese province of Zhejiang is using 1,000 sensors to capture more than a terabyte of data every month. Stored on Intel servers running on Intel Xeon processor E5 series and holding an incredible 198TB, this data is easy to access and analyse by large numbers of users who can search for a licence plate on the network in less than a second, from 2.4 billion records. In particular, products such as those developed as part of Intel's Vision Accelerator Design use deep neural networks to analyse such video footage quickly and accurately.

It's not just traffic violations being caught using next-level technology and analytics. CCTV video link-ups, license plate scanning, smart mapping and even real-time facial recognition are also helping save lives, cut down on vandalism and prevent robberies. The London Mayor's Office for Policing and Crime (MOPAC) recently partnered with Greater London Authority's Strategic Crime Analysis team to launch SafeStats. By feeding more than 20 million crime and safety records from the police, ambulance, fire brigade and transport authorities into advanced AI software, emergency services can detect patterns and identify crime hotspots. This AI can even offer solutions and guide authorities on policy. For example, when cross referenced with records from 25 hospitals, it can be used to create heat maps that help steer local policing strategies and funding.

The connected city

Once crime is being tackled, and transport delays are managed, cities become more attractive to tourists; another area in which big data, AI and analytics are playing a significant role. In Manchester, the Beacons for Science app lets tourists use virtual and augmented reality to unlock experiences at landmarks across the city. In London, Mastercard has been hired to produce a series of smart city initiatives including the Visit London Official City Guide app. This app taps into real-time data feeds to help tourists navigate the city, using geolocation to flag nearby places of interest and transport routes.

Elsewhere, the West of England Combined Authority was recently awarded 5 million in funding to trial a 5G network at tourist destinations in Bristol and Bath. This network complements the Bristol Is Open smart city scheme designed as a city-wide private network testbed, powered by an Intel Xeon equipped Blue Crystal II supercomputer, on which companies and organisations can test smart city solutions.

"The vision behind Bristol is Open was to see how we could make the city smarter and quicker than any other," explained Julie Snell, Managing Director of Bristol is Open. "We can offer a test network that's run on gigabit fibre. It's got everything from Wi-Fi to 2G, 3G, 4G, massive MIMO (multiple-in multiple-out), LTE and even some 5G. We also have 1,500 Wi-Fi meshed network lamp posts, allowing us to bounce signals around the city without us needing constant fibre connections."

This network, consisting of hundreds of Internet of Things connections, can help people in areas of poor connectivity get online easily, and cheaply. Data from this network can be fed into a 4K, 180-degree 'data dome' and used to track Met Office weather patterns in the region, monitor mobile usage, and record air pollution levels as part of a feasibility study by the University of Bristol. This could see the city become the first to let people identify their individual exposure to pollution, and it's a similar setup to that used by the Sensing London project which used Intel Galileo-based end-to-end Internet of Things infrastructure to measure local air quality and human activity. Beyond the sensors, Intel and Bosch recently teamed up to develop the Air Quality Micro Climate Monitoring System (MCMS) which takes the data from such sensors and uses software to measure air quality, providing councils with meaningful insight.

Looking to the future, the global rollout of 5G is expected to accelerate not only the adoption of smart city technology but its capabilities. It will exponentially increase the number of sensors, the strength of the connections and the speed at which data can be sent and analysed. Combine this with the ongoing advances in data collection and analysis, and the expansion of the IoT, and it looks like we're at a critical juncture in the pursuit of a truly connected utopia.

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When the athletes dramatised in the 1981 Oscar-winning film Chariots of Fire were competing nearly a century ago, a stopwatch was one of the few devices producing data to measure their sporting achievement. But these days sport is all about measurement and analysing the data it produces. Whether it's tracking your location, heart rate, oxygen saturation, or nutrition, huge amounts of information are being collected from athletes, including amateurs. But professionals in particular are finding that data collection and analysis could be what gives them the edge in competition.

Sports sensors sit where two of the biggest trends in contemporary computing meet: big data and the Internet of Things. The latter is a significant driver of the former. On the one hand, you need the connected devices that can track the relevant parameters to assess the factors behind sporting excellence and measure improvements. On the other hand, you need powerful data analytics to take the information produced, make sense of it, find trends, and help inform how athletes can do better.

Sales of running watches alone are growing five per cent every year, according to MarketWatch. These have now gone well beyond pedometer wristbands like the original Fitbit, and can include a GPS, heart rate monitor, and even a pulse oximeter to measure blood oxygen levels. They can also link wirelessly to cadence sensors in running shoes and on bikes, monitor your sleep patterns, and then automatically transfer the data collected to the internet. Some sports watches can use an accelerometer to detect which stroke you are using when swimming and when you push off at the beginning of a length, so the number of lengths you swim can be counted automatically.

Even consumer-grade systems can tell you useful things about your exercise ability that can guide how you train, such as VO2 Max, which measures the maximum amount of oxygen a person can utilise during intense exercise. This provides an assessment of cardiovascular fitness and can help you track your progress getting fit as you implement a distance-running programme. However, whilst the amateur trend for tracking exercise is driving device ubiquity and the sheer volume of data, professionals have access to systems that can provide much greater levels of detail, and with it a real edge in performance.

Data gets results in the beautiful game

For example, data is being used to improve football performance by analysing opposing team strategy and finding ways to combat it. Scottish football team Hearts used information from the InStat database to predict that a high-pressing game using players who could keep the pressure on for many kilometres of running would help them beat Celtic and it worked. They are not alone, as more than 1,500 clubs and national teams use InStat, giving the company information on more than 400,000 players.

But clubs also build up data on their own players using sophisticated devices from companies such as Catapult Sports that can collect up to 1,000 data points per second. Similarly, the STATsports Apex can calculate more than 50 metrics at once, such as max speed, heart rate, step balance, high metabolic distance and dynamic stress load. This goes beyond the pure numbers but adds interpretation about how this is affecting an individual athlete. The data is collected in the cloud for historical comparative use. Teams now use this information to help decide which players to purchase to achieve their objectives for the season, employing services like InStat and Opta to provide the details they need.

InStat collects data for football, ice hockey, and basketball, whilst Opta includes these plus cricket, rugby union, baseball, golf, motorsport, tennis and handball amongst others. Although team games have many variables that can make player statistics only part of the picture, sports that focus on individual performance such as athletics can rely heavily on data to provide clear insights on how to aid improvement. This goes well beyond GPS tracking of outdoor events. Wearable devices with accelerometers, magnetometers and gyroscopes can track hundreds of data points to describe an athlete's physical motion.

Stryd's running shoe attachment can capture cadence (steps or cycles per minute) and ground contact. This can be used to analyse running style, which can be compared to previous sessions and other athletes. This information can spot nascent talent or help an athlete hone their style so they can emulate what makes the most successful sportspeople win. It can also detect warning factors like asymmetric movements that might cause a future injury or imply an impending one. EliteHRV's sensors can provide high levels of detail on heart rate variability to see the physical effects of different levels of performance, so that athletes can recover adequately from their sessions and not over-train.

The secrets of the perfect golf swing

Another individual sport that is gaining considerable benefit from wearable sensors and data analytics is golf. Any sport using a bat, racquet or club can gain benefit from analysing a player's swing, but in golf, the swing and body posture are particularly constrained, without also having to take into account additional factors like cross-court movement or ball spin, although atmospheric conditions will have an effect. Systems collecting golf performance data include Opta and ShotLink. The latter has results data dating back to 1983 and tracks 93 events a year.

GolfTEC, in contrast, is more focused on how an individual achieves their performance. The company has developed a SwingTRU Motion Study database of 13,000 pro and amateur golfers that includes information on 48 different body motions per swing. The analysis found six key areas that indicate an excellent player. These factors were discovered by correlating swing data with performance. Similarly, TrackMan uses cameras to analyse a player's swing to aid training.

Rather than just analysing the past, predicting the future is where the application of big data analytics to sport will prove particularly valuable. As with every area of big data analytics, this will be dramatically affected by the application of AI and machine learning. Any massive store of unstructured data can potentially benefit from AI technology, which can help structure the data and find patterns in it proactively. First you feed in performance data, physical metrics during the activities, nutrition, sleep, atmospherics, plus anything else available. Then AI-empowered analytics will look for patterns that could provide strategies that make a difference, particularly when the margins for winning can be so small.

The systems we've discussed here are just the beginning. Data-driven sports are still only in their infancy, with much more to come in the next few years to help athletes find a competitive edge. Sport is just one area where big data and analytics are having a major influence, too. Healthcare, smart cities, and our understanding of the natural world are all seeing dramatic contributions.

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Changes to the planet are happening at an alarming rate, and it's no secret that we're partly to blame. But one way in which we are making positive changes to the Earth is through developing technology that can help us protect our planet. And how best to do this? By understanding exactly what is happening now, so we can help predict and influence future events.

This is where cutting edge technologies come in. From large scale global mapping to tracking individual species, big data analysis and deep learning are now being used to analyse the Earth and its life.

Macro scale mapping and modelling the Earth

Forests and oceans cover the majority of the planet. Their health is crucial to the stability of the ecosystem, but monitoring them is no easy feat. Enter digital maps and models. By using big data analytics to create high-resolution maps of the planet, many of which are updated in real time, scientists can literally get the bigger picture.

Global Forest Watch is a UN sponsored web application that uses live satellite images to assess the health of all forests globally. It has provided the tools and data to support projects such as the Amazon Conservation Association, which works to protect biodiversity in the Amazon, and Forest Atlas, which helps manage forest resources in Liberia.

Rezatec, an analytics company, has developed similar forest intelligence and the data analysis techniques and machine learning algorithms to produce crucial insights. In 2017, Rezatec partnered with the Forestry Corporation New South Wales to add value to its existing data by using multiple datasets to create more detailed and useful maps. This gave forest management the tools to spot pests and diseases and environmental changes early.

Similar projects are underway to understand our oceans. Ocean Data Viewer is another resource supported by the UN that collects and curates a wealth of data on coastal and ocean biodiversity from a variety of trusted scientific research facilities. Multiple global data sets and maps can then be viewed and downloaded for research and analysis.

71% of our planet is water, but we have better maps of Mars than we have of the ocean floor. Now, thanks to data gathered from ships, buoys, and satellites, oceanographers have plenty of information at their disposal. From analysing sediment samples with AI to employing cutting-edge laser techniques, they can now map out features on the seabed such as rivers and underwater volcanoes and detect changes as and when they happen. Advanced analytics, powered by Intel, are helping to make these developments faster and more efficient.

Micro scale monitoring individual animal species

Decline in an individual species is often an early indicator that something is wrong, as many animals are essential to the Earth's balance. By analysing animal behaviour, scientists can give governments, business leaders and the global community the tools they need to encourage species to thrive.

From counting whales from space to tagging elephants with AI trackers, there are plenty of new and exciting technological developments giving scientists more insight into our wildlife than ever before. However it's the smaller, more elusive creatures that can be the trickiest to understand. Luckily, technologies are now being developed to provide solutions for these species.

Take bees, for example. Through the pollination of crops, they perform an essential role in the global economy. However, their numbers are in worrying decline. In 2016, Intel teamed up with Australia's Commonwealth Scientific and Industrial Research Organization on a groundbreaking bee-tracking project. Tiny RFID 'backpacks' were attached to 10,000 Tasmanian bees to monitor their every move. Meanwhile, their hives were fitted with an Intel Edison board to collect data on internal conditions and honey production. From the data gathered, scientists deduced that the use of pesticides, climate change and the loss of wildflower habitats were all contributing factors to the species' decline.

Bats are another crucial, but elusive species. They are a great indicator of biodiversity, the loss of which has been likened to "burning the library of life". As bats only thrive when insect species are in abundance, understanding their behaviour and numbers can help scientists assess the health of the local environment.

In an Intel and UCL project, automatic smart 'Echo Box' detectors were fitted around the Queen Elizabeth Olympic Park in London. These boxes picked up ultrasonic bat calls, allowing scientists to acoustically track bats in the area. The Intel edge processor in each box then processed and converted the sound files into visual representations of the calls and deep learning algorithms analysed and logged each pattern. On some nights, over 20,000 calls were detected, indicating a reassuringly high level of bat activity.

All of these projects highlight the amazing capabilities and flexibilities of today's technology. From big data analysis to deep learning, the same groundbreaking tools used to empower businesses globally are being applied to make a real difference to the health of the planet.

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With its move to the Xeon Scalable architecture, Intel began a revolution in its enterprise processors that went beyond the normal performance and energy efficiency selling points. Combined with new storage, connectivity and memory technologies, Xeon Scalable was a step change. The new 2nd Gen Intel Xeon Scalable processors don't just continue that work but double down on it, with a raft of improvements and upgrades some revolutionary that add up to a significant shift in performance for today's most crucial workloads. Whether you're looking to push forward with ambitious modernisation strategies or embrace new technologies around AI, 2nd Gen Intel Xeon Scalable processors should be part of your plan. The revised architecture doesn't just give you a speed boost, but opens up a whole new wave of capabilities.

More cores and higher speeds meet AI acceleration

It's not that this latest processor doesn't bring conventional performance improvements. Across the line, from the entry-level Bronze processors to the new, high-end Platinum processors, there are increases in frequency, while models from the Silver family upwards get more cores at roughly the same price point, not to mention more L3 cache. For instance, the new Xeon Silver 4214 has 12 cores running at a base frequency of 2.2GHz with a Turbo frequency of 3.2GHz, plus 16.5MB of L3 cache. That's a big step on from the 10 cores 2.2GHz and 2.5GHz of the old Xeon Silver 4114, which had just 13.75MB of cache, and one that's replicated as you move on upwards through the line.

At the high-end, the improvements stand out even further. The new Platinum 9200 family has processors with up to 56 cores running 112 threads with a base frequency of 2.6GHz and a Turbo frequency of 3.8GHz. By any yardstick that's an incredible amount of power. What's more, these processors have 77MB of L3 cache and support for up to 2,933MHz DDR4 RAM the fastest ever natively supported by a Xeon processor. Put up to 112 cores at work in a two-socket configuration, and you're looking at unbelievable levels of performance for a single unit system.

From heavy duty virtualisation scenarios to cutting-edge, high-performance applications, these CPUs are designed to run the most demanding workloads. Intel claims a 33% performance improvement over previous-generation Xeon Scalable processors, or an up to 3.5x improvement over the Xeon E5 processors of five years ago.

Yet Intel's enhancements run much deeper. The Xeon Scalable architecture introduced the AVX-512 instruction set, with a double-width register and double the number of registers over the previous AVX2 instruction set, dramatically accelerating high-performance workloads including AI, cryptography and data protection. The 2nd generation Intel Xeon Scalable processor takes that one stage further with AVX-512-DL (deep learning) Boost and Vector Neural Network Instruction; new instructions designed specifically to enhance AI performance both at the data centre and the edge.

Deep learning has two major aspects training and inference where the algorithm is first trained to assign different weights' to some aspect of data being input, then asked to infer weights for new data based on what the AI learnt during that training. DL Boost and VNNI are designed specifically to accelerate the inference process by enabling it to work at lower levels of numerical precision, and to do so without any perceptible compromise on accuracy.

Using a new, single instruction to replace the work of three of the old ones, it can offer serious performance upgrades for deep learning applications such as image-recognition, voice recognition and language translation. In internal testing, Intel has seen boosts of up to 30x over previous-generation Xeon Scalable processors. What's more, these technologies are built to accelerate Intel's open source MKL-DNN Deep Learning library, which can be found within the Microsoft Cognitive Toolkit, TensorFlow and BigDL libraries. There's no need for developers to rebuild everything to use the new instructions because they work within the libraries and frameworks DL developers already use.

The next-gen platform

Of course, the processor isn't all that matters in a server or system, which is why 2nd Gen Intel Xeon Scalable processors are designed to work hand-in-hand with some of Intel's most powerful technologies. Perhaps the most crucial is Intel Optane DC Persistent Memory, which combines Intel's 3D XPoint memory media with Intel memory and storage controllers to bring you a new kind of memory, with the performance of RAM but the persistence and lower costs of NAND storage.

Optane is widely known as an alternative to NAND-based SSD technology, but in its DC Persistent Memory form it can replace standard DDR4 DIMMs, augmenting the available RAM and act as a persistent memory store. Paired with a 2nd Gen Intel Xeon Scalable processor, you can have up to six Optane DC Persistent Memory modules per socket partnered with at least one DDR4 module. With 128GB, 256GB and 512GB modules available, you can have up to 32TB of low latency, persistent RAM available without the huge costs associated with using conventional DDR4.

The benefits almost speak for themselves. With such lavish quantities of RAM available, there's scope to run heavier workloads or more virtual machines; Intel testing shows that you can run up to 36% more VMs on 2nd Gen Intel Xeon Scalable processors with Intel Optane DC Persistent Memory. What's more, this same combination opens up powerful but demanding in-memory applications to a much wider range of enterprises, giving more companies the chance to run real-time analytics on near-live data or scour vast datasets for insight. Combine this with the monster AI acceleration of Intel's new CPUs, and some hugely exciting capabilities hit the mainstream.

Yet there's still more to these latest Xeon Scalable chips than performance it's the foundation of a modern computing platform, built for a connected, data-driven business world. Intel QuickAssist technology adds hardware acceleration for network security, routing and storage, boosting performance in the software-defined data centre. There's also support for Intel Ethernet with scalable iWARP RDMA, giving you up to four 10Gbits/sec Ethernet ports for high data throughput between systems with ultra-low latency. Add Intel's new Ethernet 800 Series network adapters, and you can take the next step into 100Gbits/sec connectivity, for incredible levels of scalability and power.

Security, meanwhile, is enhanced by hardware acceleration for the new Intel Security Libraries (SecL-DC) and Intel Threat Detection Technology, providing a real alternative to expensive hardware security modules and protecting the data centre against incoming threats. This makes it tangibly easier to deliver platforms and services based on trust. Finally, Intel's Infrastructure Management Technologies provide a robust framework for resource management, with platform-level detection, monitoring, reporting and configuration. It's the key to controlling and managing your compute and storage resources to improve data centre efficiency and utilisation.

The overall effect? A line of processors that covers the needs of every business, and that provides each one with a secure, robust and scalable platform for the big applications of tomorrow. This isn't just about efficiency or about delivering your existing capabilities faster, but about empowering your business to do more with the best tools available. Don't let the 2nd generation name fool you. This isn't just an upgrade; it's a game-changer.

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If there's one resource the world isn't going to run out of anytime soon it's data. International analyst firm IDC estimates the Global Datasphere' or the total amount of data stored on computers across the world will grow from 33 zettabytes in 2018 to 175 zettabytes in 2025. Or to put that in a more relatable form, 175 billion of those terabyte hard disks you might find inside one of today's PCs.

That data pool is an enormous resource, but one that's far too big for humans to exploit. Instead, we're going to need to rely on deep learning to make sense of all that data and discover links we don't know even exist yet. The applications of deep learning are, according to Intel's AI Technical Solution Specialist, Walter Riviera, "limitless".

"The coolest application for deep learning is yet to be invented," he says.

So, what is deep learning and why is it so powerful?

Teaching the brain

Deep learning is a subset of machine learning and artificial intelligence. It is is specifically concerned with neural networks an interconnected graph of "digital neurons" inspired by the human brain.

An artificial neural network is comprised of layers of "digital neurons," says Riviera. "The more layers you have, the deeper you go, the more powerful is the algorithm."

There are two key steps in deep learning: training and inference. The first is teaching that virtual brain to do something, the second is deploying that brain to do what it's supposed to do. Riviera says the process is akin to playing a guitar. When you pick up a guitar, you normally have to tune the strings. So you play a chord and see if it matches the sound of the chord you know to be correct. "Unconsciously, you match the emitted sound with the expected one," he says. "Somehow you're measuring the error the difference between the two."

If the two chords don't match, you twiddle the tuning pegs and strum the chord again, repeating the process until the sound from the guitar matches the one you are expecting to get. "It's an iterative process and after a while, once all the chords are playing as expected, you can basically stop iterating and drop the guitar, because is ready to be used," says Riviera. "What song can you play? Whatever, even chords that weren't used to check the tuning steps."

In other words, once you've used a specific dataset to train a neural network, it will be able to evaluate new-unseen data samples independently.

"Once the neural network is giving the expected answers or the error is very close to zero, we have completed the training process. Then it's time to take that artificial brain and deploy it wherever might be relevant for our application: in a big server within a data centre or embedded in portable devices at the edge."

Deep learning as a concept isn't new indeed, the idea has been around for 40 years. What makes it so exciting now is that we finally have all the pieces in place to unlock its potential.

"We had the theory and the research papers, we had all the concepts, but we were missing two important components, which were the data to learn from and the compute power," says Riviera. "Today, we have all these three components theory, data and infrastructures we just need to be creative in order to benefit from the opportunities that deep learning is opening."

Deeper learning

That's not to say that deep learning isn't already being put to amazingly good use.

Any regular commuter will know the sheer fist-thumping frustration of delays and cancelled trains. However, Intel technology is being used to power Resonate's Luminate platform, which helps one British train company better manage more than 2,000 journeys per day.

Small, Intel-powered gateways are placed on the trackside, monitoring the movements of trains across the network. That is married with other critical data, such as timetables, temporary speed restrictions and logs of any faults across the network. By combining all this data and learning from past behaviour, Luminate can forecast where problems might occur on the network and allow managers to simulate revised schedules without disrupting live rail passengers. The system can also make automatic adjustments to short-term schedules, moving trains to where they are most needed.

The results have been startling. On-time arrivals have increased by 9% since the adoption of the system, with 92% of trains now running to schedule.

Perhaps just as annoying as delayed trains is arriving at the supermarket to find the product you went there for is out of stock. Once again, Intel's deep learning technology is being used to avert this costly situation for supermarkets.

The Intel-powered Vispera ShelfSight system has cameras mounted in stores, keeping an eye on the supermarket shelves. Deep-learning algorithms are used to train the system to identify individual products and to spot empty spaces on the shelves, or even products accidentally placed in the wrong areas by staff.

Staff are alerted to shortages using mobile devices, so that shelves can be quickly restocked and lost sales are kept to a minimum. And because all that data is fed back to the cloud, sales models can be adjusted and the chances of future shortages of in-demand products are reduced.

Only the start

Yet, as Riviera said earlier, these applications of deep learning are really only the start. He relays the story of the Italian start-up that is using deep learning to create a system where drones carry human organs from hospital to hospital, eliminating the huge disadvantages of helicopters (too costly) and ambulances (too slow) when it comes to life-critical transplants.

It's not the only life-saving application he can see for the technology, either. "I'd like to see deep learning used to build an autonomous system robots that can go and collect plastic from the oceans," he says. "We would already have that capability, it would just be matter of developing and enabling it."

"The best [use for deep learning] is yet to be invented," he concludes.

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Whenever artificial intelligence, aka AI, comes up in conversation, the usual image that springs to most people's minds is a threatening killer robot along the lines of Terminator that has nothing but murderous intentions towards humanity.

But these days the AI acronym is being liberally sprinkled far and wide, often referring to things that stretch well beyond its primary meaning. In this feature, we look at some of the common myths and misconceptions, compared to the real scientific situation in each case.

1. AI will create a malevolent Skynet-style system that will destroy humanity

Let's look at the most popular myth first the scary robot elephant in the room. Terminator is the most well-known example, but it is a recurring sci-fi theme from 2001: A Space Odyssey to the latest season of Star Trek: Discovery. On the one hand, technology has been automating the delivery of ordnance for decades, with in-missile video footage from the 1991 Gulf War just one watershed moment in a process towards greater autonomy that dates back to the German V1 and V2 rockets of World War II. The US army has been deploying Unmanned Air Vehicles (UAVs) for decades, and now has around 10,000 of them in regular use. But all of these still have human operators for key functions. The Defence Advanced Research Projects Agency (DARPA) has been awarding grants for the development of UAVs that can navigate themselves indoors. But as the RAND corporation points out, very few countries use armed drones just yet, and there is much controversy about their central value in warfare compared to conventional weapons systems. So even if fully autonomous fighting machines are developed, there are still many hurdles before they are deployed without human oversight, let alone take over the world.

2. AI systems and robots will eventually replace all jobs, making most people redundant

According to a report published by the UK's Department of Work and Pensions, 8,820,545 jobs could be wiped out by 2030 because of AI, particularly in the retail sector. Aside from the strangely specific number of job losses predicted, it's worth noting that it won't just be menial labour that gets replaced. Complex intellectual activities are already being replicated by expert systems, such as legal and medical advice. AI has been making inroads into healthcare to allow earlier diagnosis without the need for consultation with specialists, who are always at a premium. You can even put your job title into the Will Robots Take My Job website to see how likely you are to be replaced by AI. In reality, though, similar arguments have been made since the agrarian and industrial revolutions. On the one hand, many jobs will be automated by AI, but on the other, people can retrain, or young people educated in a different direction for the new jobs that are emerging potentially designing and building those AI-powered robots.

3. Siri, Google Home, Cortana and Alexa are AI

Voice-activated speakers have been a Christmas hit for the last couple of years, and more people are getting used to giving the smartphones verbal commands, too. These are undeniably clever, convenient systems (when they work), but in reality, they are just advanced natural language processing (NLP) recognition algorithms akin to dictation software like Dragon Naturally Speaking. There is no original thought going on, just a lot of pre-programmed responses to verbal commands.

4. AI is a computer version of the human brain

We now get to the main underlying myth of AI that computers model the human brain. This could be the subject of multiple PhD dissertations, but in a nutshell (and just for starters), computers are still based on the Von Neuman machine model of the mid-1940s. This reads data from memory, operates on it, and writes the result back to memory. This is not how brains work. Even multi-core processors, or HPC datacentres full of them, are still much more serial in their operation than a brain, which in contrast has a slow frequency (around 200Hz compared to multiple Gigahertz) but is massively parallel. Not just massively parallel, but inputs and outputs are connected in complicated feedback loops, with workings that we still don't understand completely yet. This isn't to say that computer AI isn't amazingly useful, or that we won't ever fully understand the human brain. But current AI is at best a very rough simulation, not even close to a digital facsimile of the cerebrum of homo sapiens.

5. AI systems can learn for themselves

Another two-letter acronym often found alongside AI is ML (Machine Learning). The common myth is that ML is a fully autonomous process, which will potentially lead to AI that transcends human intelligence and eventually decides to get rid of us (see 1 above). However, ML still intrinsically involves teaching by humans. Every AI system needs to be fed source material chosen by people, and its outputs adjusted by human experts until they work. This is precisely the process that Google's self-driving car system is going through right now, and this won't stop even when it gets the green light as a commercially available system in new vehicles.

6. AI systems will be much more impartial than human beings

As a result of AI's ML being fed by humans, there's no reason to believe that it will be any more impervious to prejudices than the humans that taught it. Early facial recognition systems had trouble identifying ethnicities, and the Tay Twitter bot was rapidly turned into a rabid racist by the tone of social media conversations. On the other hand, an AI that has been trained to be as impartial as the best human examples will consistently be better in this respect than the worst humans, which is where there is clear value for the legal and medical professions, amongst others.

7. AI will soon be smarter than human beings, or never will

Because AI runs on computers that are not an exact replica of the human brain (see four above), this is a bit of a trick question. On the one hand, there is no sign that a general AI will transcend overall human intelligence anytime soon, because we still don't know exactly what the latter is. But on the other hand, much more narrow AI has been beating humans for some years now, such as defeating the best human chess player, and surpassing the best players of Jeopardy!. In 2004, none of the vehicles in the DARPA Grand Challenges completed the course, but in 2005, five did, and now we have self-driving cars being tested on public roads. So AIs will likely be smarter than humans in many key areas (and already are in some), but may never be in a general, overall sense whatever that even means.

8. The technological singularity is approaching, or will never happen

Related to seven above, there is a theory, originally presented by maths and computer science professor Vernor Vinge, that the development of artificial superintelligence will arrive around 2030 and then AI will upgrade itself beyond human understanding and the world will change unrecognisably. This has been dubbed "the singularity". For reasons already discussed above, the date already looks massively optimistic before we even get into the details, due to our lack of understanding of the human brain. But, on the other hand, the singularity doesn't necessarily have to involve completely human intelligence. We could be creating something that isn't human, just loosely based on us. Nevertheless, this idea still gives computers the ability to have their own intentions, which somehow emerge spontaneously from the advanced technology. Right now, no AI can do anything other than what humans told it to do, even if your flaky desktop PC might sometimes make you believe otherwise.

9. AI is just sci-fi and nothing that should concern business

Just as AI can create new jobs as well as replace old ones, it's a topic that should be central to all businesses that intend to survive and grow over the coming decades, rather than ignored. On the one hand, the scary sci-fi scenarios of human replacement are very distant if ever likely to happen at all. But, on the other hand, there are real opportunities to enhance business services and consumer interaction via the more limited systems that have been shown to be effective already. Business intelligence data analytics, forward resource planning, and automated customer service are just the beginning. Companies that embrace AI, whilst being realistic about its limitations, will be the ones that prosper.

Discover more about data innovations at Intel.co.uk

Whether it's pocket-sized mobile communicators, cars that can drive themselves or a global information-sharing network, scientists and researchers have a history of turning the marvels of technology dreamed up by science fiction writers into reality - and the composer of that endeavour is the creation of artificial intelligence.

Although we're still some way from being served by self-aware robot butlers that can reliably pass the Turing test, AI technology has progressed immeasurably in the last decade alone. AI has moved from being the sole province of research projects working with giant supercomputers to something that all of us carry around in our pockets, and cloud computing has been a huge part of that move from research to reality.

The most foundational change came when public cloud offerings like Amazon Web Services and Google Cloud Platform became widely available. The development of AI using methods such as, deep-learning and neural networks requires a considerable amount of compute power. Once it became possible to rent as many servers as you needed from a cloud provider, tasks that were once only possible by universities and science labs suddenly became accessible to everyone.

Moreover, these servers take advantage of best-in-class hardware from Intel, featuring technical developments specifically designed to enable AI. For example, high-performance Xeon Scalable chips and low-latency Optane Memory. On top of this, many cloud platform providers have, in recent years, started to specifically cater to machine learning and AI development, offering servers and services tailored to make training deep-learning models quicker and easier than ever.

As these barriers to entry come down, companies and hobbyists around the world have started experimenting with machine learning and AI, exploring the possibilities and pushing the boundaries of what it can do. Much of this research has been shared with the wider community under open source licenses. A key example of this is TensorFlow, a machine learning library developed internally by Google and shared freely with the rest of the world.

Alongside the compute power to train deep-learning models, the cloud has also provided the datasets on which to train them. The development of AI has gone hand in hand with the big data boom, as companies start gathering and storing exponentially more data for analytical purposes. A side effect of this is that there are now huge corpuses of data that can be fed into machine learning models in order to train them in tasks like pattern recognition, clustering and regression.

All of this makes it much easier to improve and develop AI technology, but there's one key reason that it's now a legitimate business tool rather than simply a technical endeavour, and that's the ease of consumption that cloud models offer. It's so much easier for customers and end-users to consume AI tools running in the cloud as part of a SaaS application compared to traditional on-premise software.

None of the processing is done locally, so there are no hardware requirements, and because the vendor is responsible for maintaining the AI on a day-to-day basis, there's no need to hire machine learning or AI specialists. With no extra effort or investment required, companies are becoming more and more comfortable with the idea of integrating AI processes into their day-to-day workflows.

The general public has also grown increasingly familiar with AI technology thanks to the growing prevalence of AI-powered digital assistants like Siri, Alexa and Cortana. These services have helped acclimatise people to working with AI, as well as opening their eyes to the benefits offered by the technology.

These factors have made developing commercial AI more viable, resulting in an explosion of AI-enabled tools and services, many of which have been snapped up by cloud giants like Google and Salesforce and integrated into their product portfolios. Most cloud storage companies, for instance, augment their search capabilities by using machine vision algorithms to accurately identify objects in photographs or text in documents.

AI is also increasingly being used by companies as an initial point of contact for customer service, with chatbots handling Tier One support queries and sales inquiries. A far cry from the long-established automated telephone menus, these programs are intelligent and responsive, and are becoming increasingly common.

It's not just public clouds that have spurred AI advancements; private and hybrid cloud deployments have also seen a great deal of change. Banking is one area where AI can have a huge impact in terms of analysing huge quantities of data very quickly, but for regulatory reasons, financial firms often can't - or won't - use public cloud providers. Instead, these institutions use private clouds to run custom-built or specially-adapted machine learning algorithms to sort through their data.

Intel's advancements in processor technology have brought cloud-scale computing power within the reach of companies operating their own private cloud, meaning that you no longer need a sizeable data centre to run machine learning applications. Instead, you can run AI tasks on as little as one rack, depending on the size of the deployment. This enables you to keep total control of your data and infrastructure, whilst still taking advantage of cloud-style consumption and delivery models.

In the comparatively short timeframe that cloud computing has been a mainstream phenomenon, AI has gone from being the preserve of academics to a day-to-day reality for businesses around the world; used to perform all kinds of diverse tasks from data analysis to customer service. Machine learning applications are now being developed, deployed and delivered via cloud platforms, empowered and enabled by Intel's next-generation data centre technologies. Whether you're looking to run your AI applications on a public, private, virtual private or hybrid cloud, Intel is making your AI smarter, stronger and faster than ever before.

Discover more about data innovations at Intel.co.uk

Whether it's making a credit card purchase, messaging your friends or even simply ordering a pizza, virtually all of the things we do on a daily basis are powered and supported by data centres.

But the data centres we rely on today are a far cry from the technology of the past; they've changed almost immeasurably since digital computing took its first early steps in the 1950s and 60s. Processing power and capacity have increased exponentially over the years and the infrastructure needed to support modern applications has grown ever more complex.

These advances have been driven by the growing demands of both businesses and consumers. First, the birth of the internet led to an explosion in the amount of people consuming online services, which necessitated vast increases in the amount of processing power and capacity that data centres had to offer.

Before long, the need for server capacity spawned the creation of third-party providers, who would host companies' servers in their own facility, thus removing the initial expense and ongoing overheads of setting up an on-premise data centre for companies. Eventually, as network technology and connectivity improved, this gave way to the cloud computing model where companies rent space not in a data centre, but on the server itself.

Cloud computing has been a major catalyst for change in the data centre; not only have many operating models fundamentally shifted as a result as a result of its rise, but it's also driven technological advancements like multi-tenant systems, lightning-fast storage and AI applications.

Processing

One of the most foundational changes in data centre technology was the advent of multi-core processors around the turn of the millennium. By fitting two or more processing cores onto a single die, chip manufacturers could radically boost the total performance of data centre hardware, allowing the same workloads to be run with fewer machines.

Multi-core processing also brought huge advantages to virtualisation, which has been a linchpin of the data centre's growth. Because each processing core runs in parallel with the others, multi-core systems can run huge amounts of virtual machines simultaneously with minimal drops in performance, vastly increasing the amount of applications that can be run at once.

Containerisation has had a similar impact; each VM can host multiple containers within it, each of which can host its own application. This allows data centres to exponentially multiply their capacity for applications. As well as spearheading the continued advancement of multi-core processing, Intel has also been a leader in developing virtualisation and container technology, working with engineering partners to make containers and VMs lighter, faster and more resilient.

Cooling

Data centre equipment is highly powerful, but all that power generates large amounts of heat. Unfortunately, server processors are highly sensitive, and need to be kept below a certain temperature in order to ensure optimal performance. In order to maintain this, data centres have to be very carefully climate-controlled, relying on complex and expensive cooling systems that are often the second-largest consumers of power.

While these cooling systems are still very necessary, Intel's continued advancement in processor technology has made server processors more thermally efficient, generating less heat and therefore requiring less cooling. On top of that, the company also introduced sensors to its server chips in 2011 which allow data centre administrators to measure the temperature and airflow within a data centre. This enables them to better identify hot and cold spots, modelling the placement of new racks and equipment according to temperature conditions.

Along with preventing costly outages, increasing thermal efficiency throughout the data centre also prolongs the lifespan of the servers themselves and reduces the amount of overall cooling necessary, thereby saving administrators money in terms of the substantial operational costs incurred by cooling efforts.

Power

Intel has also steadily improved the power efficiency of its data centre products. Newer chips like its Xeon Scalable range offer greater performance than previous generations, while consuming less electricity. As with improved cooling performance, this saves data centre operators money in operational costs, but it also allows more chips to be packed into the same physical space.

This means that companies can eke more computational power out of the same resources, without needing to invest in more cabinets, increased power consumption or more cooling. Space efficiency is a key concern, too; floor space within a data centre is often in high demand, so the more physical components that can be packed into a single rack, the better.

Storage

The move from traditional spinning-platter HDDs to SSDs was a huge leap forward in this regard, as it meant that storage drives could take up much less space inside a server, albeit at a higher cost. SSDs were also much faster than HDDs at accessing the data stored on them, greatly speeding up overall server operations and enabling much faster performance for tasks like data analytics.

Intel has been instrumental in advancing storage technology through its partnership with Micron, which involved introducing data striping for increased performance and pioneering high-reliability enterprise drives. It also led the workgroup that developed NVMe technology and, more recently, co-developed 3D Xpoint memory technology, which offers unparalleled speeds for low-latency workloads. You may be familiar with Intel's Optane range of memory and storage products, all of which are powered by 3D Xpoint.

The end result of all of these numerous changes, developments and advancements has been modern data centres, which are capable of supporting complex, cloud-native workloads. Gone are the days of monolithic mainframes supporting single applications; now, data centres play host to hundreds upon hundreds of sophisticated, multi-core, multi-processor servers, each making use of advanced software-defined networking and low-latency solid state storage drives to power millions of simultaneous applications and processes.

Intel has been at the heart of this change for decades, drawing on its engineering heritage and world-class research expertise to push the boundaries of what data centres are capable of. Whether it's Optane storage technology, high-performance Xeon Platinum processors or the intelligent software supporting virtualised and containerised applications, Intel remains at the bleeding edge of enterprise processing technology.

Discover more about data innovations at Intel.co.uk

Data on its own can't drive business decisions; it can't help companies compete in challenging markets, push productivity or deliver a better service to its customers. Combine data with analytics, however, and it's a different story. Analytics applications can unlock the valuable insights hiding within all that data and most importantly turn them into something a company can use to take action, so that they can make their products stronger and more reliable, enhance their services and anticipate their customers' needs. In short, it's not having the data that counts, but what you do with it that matters.

This is crucial. A 2016 parliamentary report found that despite data driven companies being 10% more productive than those that do not operationalise their data, most companies estimate they are analysing just 12% of their data.' A 2016 report by Keystone Strategy for Microsoft found that companies who used data effectively outperformed their peers across a range of core business metrics, including profits, margins and employee productivity. At the same time, analysts at Gartner have claimed that anywhere between 60% and 95% of big data projects fail, primarily because companies find it hard to integrate analytics into their existing processes.

Combined with analytics and integrated into company processes, big data can be transformative. The UK insurance company, Markerstudy, saved millions and lowered customer cancellation rates by 50% by using Apache Hadoop and Spark to analyse hundreds of millions of quotes in seconds, then using Zoomdata to deliver those insights in easy-to-understand visualisations. It's not just beating its rivals on speed, but also on accuracy.

Transport for London is using data from Oyster card and contactless payments, along with bus location and ticketing data, to optimise its transport networks and minimise the impact of closures or diversions. Meanwhile, developers are using its real-time data streams to power hundreds of transport information apps.

Tesla has been a pioneer in in-car data collection, using data captured to spot, anticipate and predict potential problems before they can harm the firm's reputation, but it also ties this information into the data captured about its customers, so that it can target its resources to improve their experience. While other high-end manufacturers are responding to shifts in customer sentiment, Tesla is actively anticipating them and working out how to handle them.

Meanwhile the European clothing manufacturer and retailer, Zara, uses data captured in real-time on fashion trends and customer activity on the shop floor to work out what their customers are looking for and rush new designs from concept stage to retail within three weeks or less. Where less data-driven rivals have to gamble on a fashion trend or may take months to bring a key look into their lines, Zara can do it within 21 days.

Most UK supermarkets now use analytics to work out which products make the most profit in which locations in a given store layout, then use that information to drive sales. Many also use analytics to isolate promotions that can act as loss leaders plus the higher-value sales they'll drive.

From project to process

How, then, can you make sure you're putting your company's wealth of data to good use? It starts with preparation. Data needs to be broken out of company siloes, refined and combined into streams that analytics applications can use. It's not so much a question of separating the wheat from the chaff as getting some idea of what business challenges you're hoping to solve and which data sources will give you the most current, accurate and relevant information.

You then need to work out how and when to operate on that data. Early analytics projects focused on taking periodic snapshots in order to analyse what had happened, highlight trends and help companies plan for the future. Increasingly, though, companies are moving to systems that analyse incoming streams of data at a faster rate or even in real-time in order to spot patterns that predict what's most likely to happen and make active recommendations about how to take action.

This, however, entails a further stage; to bring these recommendations into the everyday business process in a form employees will actually understand and use. This may mean using the results in daily meetings or transforming insights into a narrative to follow, or increasingly it means creating dashboards with performance indicators, visualisations and automated data and recommendation feeds.

A recent McKinsey Report, Analytics Comes of Age, spoke to numerous executives at large enterprises. A CEO of an investment bank suggests that "by relying on the statistical information rather than a gut feeling, you allow the data to lead you to be in the right place at the right time". Another executive describes how their sales teams resisted using leads generated by the analytics model until these recommendations were integrated into daily workflow dashboards, so that they were simply presented as leads to follow. When analytics become part of the daily workflow, so do the results.

In the end, this means cultivating a data-driven culture, where insight, not gut-feeling, drives things forwards, and where companies can move faster to seize on opportunities and counter issues.

Powering the programme

One of the keys to making this successful is reducing friction and optimising the speed of the systems involved. If end-users have to wait for insights or their dashboards prove laggy, then there's a risk they'll ignore the systems and revert to using their gut instinct instead. What's more, there's no point collecting data at speed or in real-time if your systems don't have the performance to match.

This is where Intel's latest Xeon Scalable processors come in. They're built to handle the most demanding, data-intensive analytics applications and extract insights in seconds rather than minutes. Their new AVX-512 instructions accelerate performance in analytics workloads, with speed improvements of between 1.4 times and 4.6 times over the previous generation in common advanced analytics applications. What's more, Intel Xeon Scalable processors are designed to work hand-in-hand with the latest Intel Optane storage technology, where enhanced performance in write-intensive tasks can result in a 2x speed improvement in SAS.

Meanwhile, Xeon Scalable and Intel Optane DC Persistent Memory enable a wider range of enterprises to make use of powerful, real-time analytics, so that they get the information they need to make the right decision at the speed today's business demands. You can think of Optane DC Persistent Memory as delivering the capacity and affordability of flash with near-DRAM levels of performance and in a standard DIMM form factor. This gives firms what they need to run real-time analytics on an in-memory database, but at a fraction of the cost this once involved.

Using analytics to make better decisions involves a combination of the right data, the right process and the right technology to power it. The data and the processes are up to you, but Intel has the technology portfolio, for the data centre, to deliver on the rest.

Discover how analytics can transform your business at intel.co.uk

Big data should mean big opportunities for every business, yet for many the potential is never realised. Data that could be mined for value ends up resting, unused in archives. Insights that could be fuelling sales or enhancing customer experiences are never uncovered, or never make it to the screen of someone who could make the difference. Every day, some 2.5 quintillion bytes of data is created. The volume of data being produced is expected to be 44 times greater in 2020 than it was in 2009.

Yet in 2016 the analysts at Forrester estimated that, on average, between 60% and 73% of all the data within an organisation went untouched by analytics or BI applications, and in some industries the figure may be closer to 95% or more. To call this a shame is an understatement. EU studies have shown that companies that adopt big data analytics can increase productivity by 5% to 10% over companies that don't, and that big data practices in Europe could add 1.9% to GDP by 2020.

For many companies, some of this will come down to a lack of processes to capture, refine and structure the right data, or to a lack of the skilled workers needed to extract the most value. Yet technological issues also have a large part to play.

For a start, too many big data initiatives are held back by poor performance and underwhelming results. Big data analytics applications are extremely hardware-intensive, pushing not just compute resources to their limit running complex operations on huge datasets is never easy but also storage and network resources. Processor cores can be sitting, ready to go, but they're being bottlenecked by slow transfers of cold' data in slower, mass data storage devices to hot' data resources, specifically DRAM, in more direct contact with the CPU. Today's business thrives on speed and agility, and when mining data for insight takes too long, the excitement around these new applications dwindles. BI and analytics projects that should be powering growth become unloved and under-used.

What's more, these initiatives are expensive. Smaller and medium-sized enterprises baulk at the cost of the hardware needed to run these applications, and the storage and supporting infrastructure required to store, move and transport all that data. Worse, the real-time, in-memory applications being used by larger enterprises to analyse data at the point it flows into the enterprise are, financially-speaking, out of reach. This isn't simply because of licensing costs there are less-expensive and open-source alternatives to the big names but because the processing power, storage and high-capacity DRAM required doesn't come cheap. It's a sizable investment perhaps too sizable for many smaller businesses.

Meeting the Big Data Challenge

So, what can businesses do when confronted with these issues? There are clearly steps they can take in terms of data capture and preparation, processes and recruitment of data specialists, but technology also has its part to play in solving our big data problems, and more specifically, the technology in Intel's new Xeon Scalable processor architecture and Intel's Optane storage and memory products.

Let's start with Xeon Scalable. Its new mesh architecture, where CPU cores are linked to each other and to memory, network and storage resources by a mesh of connections, enables Xeon Scalable processors to handle demanding, data-intensive tasks more efficiently than the old Xeon architecture, where everything connected through a single central ring. Its new AVX-512 instructions are purpose built to accelerate performance in the compute and data-focused workloads characteristic of big data analytics. Running batch analytics, new Intel Xeon Scalable processors performed 1.4 times faster, on average, in comparison to the previous generation Intel Xeon, while enterprises running Cassandra NoSQL databases have seen up to 4.6 times the number of operations per second. Running SAP HANA in-memory workloads, Xeon Scalable shows nearly a 60% boost in performance.

Yet there's more to this than just running workloads faster. With Intel Xeon Scalable behind them, enterprises can think about using machine learning and predictive analytics to find, refine and manage data before its processed, helping to speed up analytics operations and deliver more accurate, more useful and more actionable results.

With 48 lanes of PCIe 3.0 connectivity per CPU, Xeon Scalable makes the perfect partner for Intel's latest Optane storage devices. Storage has always been a bottleneck for big data, but with reduced latency and stronger performance on write-intensive jobs than conventional flash SSDs, Optane drives dispatch with the old limitations. Optane's resilience up to 10x that of a standard SSD makes it a better choice for caching or short-term storage, creating a zone for warm' data between the existing hot' DRAM and cold' storage zones. Put Intel Xeon Scalable and Optane together, and you can see performance in SAS workloads double over Intel's previous best-of-breed platform.

Last, but certainly not least, Intel's new Optane DC Persistent Memory combines the capacity of flash with near-DRAM levels of performance, at significantly lower costs than DRAM but in a standard DRAM DIMM form factor. With support from the new Xeon Scalable processors, this enables enterprises to run real-time analytics in-memory without anything like the same levels of investment. Potentially, this could see a much broader range of enterprises using these applications and turning data into insight at the speed of modern business. It's a step that enables more companies to make more effective use of the data flowing through the business and one that levels the playing fields a little, so that smaller organisations can make the most of their agility and compete.

Can technology alone solve your big data problem? No, but it can help you mitigate the issues, prepare your data and develop approaches, applications and processes that work for you. The hardware is here to smooth out your analytics journey it's far too soon to stop it now.

Discover more about Intel's portfolio of solutions for Big Data Analytics at intel.co.uk

For too long storage has been a data centre performance bottleneck, limiting the applications businesses could run and slowing down crucial operations. With Intel Optane technology that bottleneck falls away, enabling organisations to harness the full power of a new generation of Intel Xeon Scalable processors, both to optimise their existing workloads and explore new applications that could enhance their business.

Optane is Intel's next-generation solid state storage technology, harnessing the performance characteristics of Intel and Micron's 3D XPoint non-volatile memory technology to create SSDs with radically faster and more consistent read/write speeds and up to 100x the durability of standard flash technologies. As well as enterprise-class SSDs, it's powering a whole new class of persistent memory DIMM that combines RAM-like performance with the affordability and capacity of flash: a real breakthrough for data-intensive applications.

Intel Xeon Scalable is Intel's latest family of enterprise-grade processors, aimed at workstations and the data centre and built around a new mesh architecture that creates more efficient data flows between cores and between cores and RAM, and that's optimised for the demanding or data-intensive workloads. Individually, both Optane and Xeon Scalable have much to offer in the data centre. Put them together and they're near-unstoppable.

That's partly because the two have been developed in tandem, Intel's hardware platform delivering 48 lanes of PCIe 3.0 connectivity per CPU, virtua NVMe RAID control and other features that support Optane hardwired in. As Justin Wheeler, NSG Storage Solutions Architect at Intel puts it, "the R&D is designed around optimising Optane storage for and with Xeon Scalable Processors, and actively looking at the architecture of the CPU to increase the efficiency, the capacity and the performance of the storage. It's very much a hand-in-glove approach." On the one hand, Wheeler explains, the speed, low latency and enhanced write performance of Optane lends itself to more processor and data-intensive tasks. On the other hand, the Xeon Scalable architecture and platform are designed to deliver the high-bandwidth connectivity Optane needs to deliver that performance.

"All three of those components compute, network and storage form part of an overall solution," says Wheeler. "We can't look at them as individual components anymore. We have to see them as one unit working together." Before Optane, you could have a fast CPU never reaching its potential because the storage couldn't maintain pace. Not any more. Wheeler believes that the next revolution in the data centre is very much around storage maximising the CPU."

This isn't just a case of performance for performance's sake. For one thing, the performance advantages of Optane play into specific workloads, including HPC, Hyper-converged infrastructure, Content Delivery Networks, large-scale databases, machine learning and real-time analytics. This ramps up efficiency, giving you the performance to handle existing operations at greater speeds. Backups and snapshots that used to take many hours can be handled in a fraction of the time. But it also helps companies build the right hardware platforms for data-intensive applications at a lower cost you can handle more transactions with smaller, cheaper Optane SSDs than with a significantly-more expensive flash-based SAN.

Perhaps most importantly, these new architectures add flexibility. In Wheeler's words, "This technology provides you with performance, but it also provides you with agility. There are a lot of unknowns out there in the business world; how do you know who your clients are or what their needs are going to be. You need to be more agile and responsive, and this technology is an enabler for that."

For concrete examples, we spoke to Dr Chris Folkerd, Chief Technologist at UK Cloud Services provider, UKFast. Folkerd and his team have recently rolled out Intel Optane data centre SSDs across three areas of the enterprise, beginning with the firm's backups environment. "We use CommVault as our main backup platform," says Folkerd "and when that's ingesting data there's a huge amount of disk I/O in the deduplication area, so what we've done is place the deduplication database on Optane." The performance improvements have been dramatic, he notes, thanks to Optane's faster write speeds and latency reductions.

Having built confidence in Optane through its implementation in backup, Folkerd rolled it into the caching tier of UKFast's journaling databases. "They're write-intensive and Optane has a much greater write endurance than SSDs," he explains. "Caching destroys SSDs. We can go through an SSD in less than two months, but the Optane drives are much more stable." Not only is this reducing cost and maintenance burdens, but UKFast expects to ramp up delivery of vSAN services during 2018 and into 2019, partly because the increased performance of Optane means that data doesn't have to be held for so long in the cache tier. This means the business can use smaller drives, enabling it to scale up without a huge hardware investment.

And while Xeon Scalable and Optane are helping UKFast improve their existing operations, they're also helping them broaden their services portfolio. The company's now harnessing the technology to power new high-tier hosted SQL services and move into a higher performance space. "It enables us to have a more diverse range of product offerings," says Folkerd. "Before, if you wanted to get the level of IOPS you can get with Optane it would have meant investing in a much larger SSD installation, whereas now that can be consolidated down you're not spending more money to get space you don't need just to get the IOPS that you do."

Powering new capabilities, Xeon Scalable and Optane give businesses the right platform for tomorrow's workloads. In UKFast's case, Folkerd adds "it gives us a much broader product portfolio for the same price point, so we can do a lot more on the high-performance side that would either have been out of reach for our customers from a cost perspective, or that, technologically, would have been very challenging to implement."

Perhaps best of all, these are technologies for the future. As Intel's Justin Wheeler says, "We're at the bottom of the wave, and there are going to be many more advances on the way." Folkerd concurs. "You're future-proofing a lot of what you do with storage. Looking at the roadmaps for other companies there isn't going to be anything that's as quick as this for a significant amount of time, so the overall investment in the switch to Optane isn't that big for all the benefit you get from it and from the longevity of the platform."

Discover more about data storage innovations at intel.co.uk

For the last few years GDPR has been striking terror in the hearts of organisations around the world. While data protection legislation is nothing new, GDPR has extended and clarified both the rights of individuals and the duties of organisations that store and process their personal data. What's more, GDPR has upped the ante in terms of what happens when organisations fail to meet their obligations and the penalties that might result. The discussion around GDPR has resembled, at worst, a business horror story, at best a corporate cautionary tale. And though the legislation came into effect back in May 2018, many companies are still working their way through how to meet their GDPR commitments.

Here, enterprises that use data analytics can find themselves right in the GDPR crosshairs. After all, GDPR goes a long way to clarifying not just how an organisation stores data, but the uses it can put that data to. This might lead you to believe that, if your business relies on data analytics, then GDPR is all bad news. Yet there's an alternative approach to GDPR that turns those negatives into positives, enhancing how analytics works for you. GDPR isn't simply a set of onerous requirements to be dealt with, but a catalyst for change.

What does GDPR mean for data analytics?

Much has been written about GDPR and we're not going to delve deep into the legislation here. However, there are some elements of the legislation that have a particular impact for companies that capture, store and process data for analytics a data processor in GDPR terms and companies that use the services of a data processor to gain the benefits of analytics (or data controllers).

As a data controller, companies need to make sure that they collect only the data they need and have a clear consent process, where users are aware of what's being collected and what it will be used for. Those users also need a choice or choices that enable them to opt-in or, later, opt-out of your data storage and processing activities, not to mention the means to control the data you hold to view the information you hold on them and request its deletion.

Data processors, including vendors of analytics services, have their own obligations, which include a need to comply with the terms of GDPR and guarantee that compliance with a Data Protection Agreement between them and any company they provide such services for. They need the means to support the rights mentioned just above, so that those whose personal data is stored can view their information and have it deleted.

Both controllers and processors have responsibilities in terms of safeguarding personal data and in terms of notifying data authorities and any affected individuals in the event of a data breach. What's more and this is the scary bit GDPR sets higher penalties for failure to meet these requirements. The maximum fine can be up to $20 million or 4% of annual global turnover, whichever is the highest.

Dig deeper and you'll find specific requirements that are particularly relevant to analytics. Firstly, there's a principle of minimisation, where any personal data collected has to be adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed'. Secondly, that personal data is only to be collected for specified, explicit and legitimate processes, and not further processed in a manner that is extra to those purposes. Finally, GDPR is particularly sensitive around issues of automated processing and profiling in a way that might seem contra the whole point of many real-world applications of analytics. If data is used to create a profile of an individual, and that profile could be used to analyse or predict aspects of their performance at work, economic situation, health, personal preferences, interests or behaviour, then that individual has a right to object to such profiling. Individuals can choose not to be subject to any decision based solely on automated processing particularly where there may be financial or legal effects. In fact, where decisions are made on that basis, a mandatory impact assessment is usually called for.

In short, it might seem that GDPR prevents or at least limits companies in capturing data for analytics and doing anything that interesting with it. Yet there's also a more positive way to look at the situation. In the past, many companies have had a tendency to collect every scrap of data they can, then repurpose that data or combine it with new data however they saw fit. This hasn't always been a transparent process, and it hasn't always led to a relationship of trust between organisations and their customers. What's more, this hasn't been a particularly effective way to store and process data, in some cases actually reducing its value. Perhaps most seriously, it's made it easy for companies to lose control of that data and who has access, making it harder to safeguard.

GDPR as an opportunity

Viewed from this perspective, GDPR becomes an opportunity to clean up, focus in on high-quality data and make it work better for the business. Instead of collecting everything and hoping to use it later, enterprises can look at their incoming data streams, capture what's useful and discard what isn't. This isn't just good for analytics operations, but for data security as well. GDPR forces businesses to consider what they store and process and how it's stored and processed and ensure that appropriate security measures are in place. It's an opportunity to bring in hardware-enabled security and authentication, using the technology built into Intel Xeon Scalable processors. Personal data encryption and key management can also help minimise both the chance of a breach and the potential effects.

In some situations, it could make sense to either anonymise or pseudonymise data you're not using for direct business to customer interactions. Anonymisation means stripping out all information that might identify the subject, which is all you need if you're running business intelligence programs on, say, seasonal purchasing habits or social media sentiment.

Pseudonymisation means processing the data so that it can't be attributed to a specific subject without the use of additional information, with that information kept separately and protected by appropriate technical and organisational security measures. Both approaches relieve you from some of obligations of GDPR in regard to the rights of data subjects, while also helping protect you in the event of a breach (as anonymisation and pseudonymisation mean less personally identifiable information is affected in the breach).

By storing and processing data, not in its raw state, but in a more targeted and actionable form, you're making the job of analytics easier, separating the insight-rich wheat from the unnecessary chaff. Combine this with the analytics processing power of the latest Intel Xeon Scalable processors, with Intel Advanced Vector Extension 512 technology speeding up analytics workloads, and Intel Optane storage, which enables low-latency processing of larger datasets, and the road from raw data to insight becomes significantly shorter.

Better still, because individuals need to opt in to storage and processing, with the purposes of that storage and processing clear, enterprises can use it as a means of building trust and a stronger relationship between the business and consumer you're effectively saying this is the data we need from you, and these are the services we can provide as a result.' Early results in the field of targeted email already bear this out, where both Manchester United Football Club and The North Face have lost subscribers from their email database but had improved engagement through the emails sent and fewer spam complaints.

As businesses extend their use of analytics, this will be crucial. Chatbots, recommendation engines and other automated service features all require a deep knowledge of the customer and their specific needs to make interactions both more personal and more valuable for both parties. Some users will inevitably find this strange or creepy, but many others will find the balance between privacy and benefits pays off. In fact, companies need to ensure that it pays off.In short, GDPR isn't a horror or a hindrance, but a chance to clean house and redefine relationships. Minimising the risks and handling the new obligations can go hand in hand with extracting more value from your data, while building trust between the enterprise and its customers. Now, that's not nearly so scary.

Discover more about data storage innovations at Intel.co.uk

As a driver, you never really think about the inner workings of your car's handbrake. Nor should you. Whether you're parked on a flat road or a hill, it just works and ensures your car doesn't roll.

Drivers and their passengers don't concern themselves with questions about whether the handbrake will work they just trust it to do its job. Until it doesn't and that's when panic sets in...

The same is true of tech security. It's importance cannot be understated, yet to ensure it truly works requires it to be so sophisticated and intuitive enough that users don't need to think about it.

If users start to think about it, they will question it. It's human nature. They may even start to doubt it or work against it. The reasons behind this are unknown, but real nonetheless.

As such, business and IT decision makers in organisations of all sizes need to recognise and deal with the reality of being insecure about security and ensure they focus on security by design rather than allowing vulnerabilities through being indignant.

People talk about transparency a lot, but when it comes to security, in a strange twist of events, you almost want the opposite. It needs to be opaque to users something they know is there but don't overthink.

But, behind that opaque facade lies a great deal of sophistication. A lot of work has gone on behind the scenes to bring about a level of security that users aren't even aware of, but is doing the utmost to keep your data and company info safe.

The value of data

Data and people remain the two biggest assets any organisation possesses. As such, companies must focus on protection, retention and optimisation of both elements.

"Anyone properly valuing a business in today's increasingly digital world must make note of its data and analytics capabilities, including the volume, variety and quality of its information assets," said Douglas Laney, vice president and distinguished analyst at Gartner.

Digital transformation is big business, with fellow analyst firm IDC predicting some $2 trillion will be spent worldwide on related technologies come 2022. This significantly surpassses spend levels to date, showing the appetite for digital transformation is real and growing.

Gartner concurs with the focus on digital and what else needs to happen to support businesses on their journey there.

"Security leaders are striving to help their organisations securely use technology platforms to become more competitive and drive growth for the business. Persisting skills shortages and regulatory changes like the EU's Global [sic] Data Protection Regulation (GDPR) are driving continued growth in the security services market," said Siddharth Deshpande, research director at Gartner.

"Security and risk management has to be a critical part of any digital business initiative," he added.

The role of security in digital transformation

Everyone understands the importance of security. However, it's only the few who recognise its significance when it comes to digital transformation, it would seem.

"All too often, security is an afterthought in the digital transformation effort. As organisations transform their business digitally, it is imperative that they take security into account early in the cycle," said Christina Richmond, programme director of IDC's Worldwide Security Services.

There's another reason the industry has a responsibility to make security a default rather than an add-on, too. Despite a universal recognition of the role security should play in digital transformation, just 31% of decision makers put it high up on their list of concerns relating to the topic, according to a study by Forrester Research.

"The data that is being generated by companies is a source of value for companies and that should be first and foremost in all of their [CEOs] conversations around how they're going to transform their business. [It's about] using technology as an enabler and taking advantage of the data being generated to make better business decisions, to be more competitive and to give them an opportunity to grow their business," said Richard Curran, Intel's chief security officer for EMEA.

"There have been fundamental changes in data protection, in the industry that is going through a major transformation as well. We talk about transformation in most vertical industries and security needs to go through a transformation too."

It may have been a prediction from 2016, but Gartner's suggestion that 60% of digital transformation projects will fall down due to a security service failure by 2020 is no less relevant.

"Cyber security is a critical part of digital business with its broader external ecosystem and new challenges in an open digital world," according to Paul Proctor, vice president and distinguished analyst at Gartner.

"Organisations will learn to live with acceptable levels of digital risk as business units innovate to discover what security they need and what they can afford. Digital ethics, analytics and a people-centric focus will be as important as technical controls."

Security as standard

We all know users can be the weakest link - whether they want to admit that or not, the fact remains.

Indeed, research commissioned by ObserverIT and carried out by the Ponemon Institute, in early 2018 found that insider threats are on the rise, with the average annual cost being not far off $9 million per organisation.

"This research reveals that ignoring the growing threat posed by insiders can be costly for businesses of all sizes and in all industries," said Dr. Larry Ponemon, chairman and founder of Ponemon Institute.

"The increasing cost of insider threats whether caused by negligent or malicious actors is extremely detrimental for organisations, potentially costing them millions of dollars annually."

So, then, what can be done to counteract the ill effect of users well-intended or otherwise?

"Rather than looking at security as something we put at the back end we need to look at an upfront and security-by-design philosophy. In all transformational strategies and decision making, one needs to put security as an embedded part of the discussion," Curran advised.

"[There needs to be] an understanding that up front about personal data loss or a loss generated by machine to machine or data in general. Because, fundamentally, it's the data being generated that is going to provide you with the source of value. And if that is compromised in any way, there are serious consequences," he added.

This is where Intel has somewhat of a USP. It recognises that security layered purely on software can only go so far in the fight against software-based security threats.

"For CIOs around the world nothing today demands more attention than protecting the enterprise against increasingly advanced cyber security threats. To detect and protect against modern threats, today's security solutions continually update themselves to stay ahead of the game," said Jim Gordon, general manager of platform security at Intel.

"Some rely on spotting known signatures, others rely on spotting suspicious behaviours, but they all use a layered approach, with each layer working in a different capacity to provide protection. Even so, modern and evolving threats are increasingly evading current solutions and advanced attack techniques can circumvent software-only approaches. To reduce the likelihood of that happening, Intel is innovating by coupling CPU data and software to detect threats, making such detection easier and more difficult to circumvent."

Gordon added: "Intel brings a fundamentally different approach to the industry with Intel threat detection technology. Using silicon to improve the efficacy and performance of security solutions. That is the power of hardware-enhanced security."

Intel's approach makes use of CPU data, AI algorithms and GPU offload to handle and optimise security workloads by offloading certain tasks from the CPU to the GPU. As a result, users benefit from a better experience as well as enhanced battery life and performance.

Security evolution towards revolution

There is still some debate as to who is or what should be responsible for security in an organisation. That's why a co-operative approach where decision makers work with vendors and, where applicable, government to unite in the fight against security threats, could be key.

"The CISO is also going through a transformation. They're very much in a reactive position and that needs to change. Ultimately, you have in the industry a huge shortage of skills and you have a dramatic increase in the amount of threats and the complexity associated with those threats," Curran added.

"You have increased governance like GDPR, and that increases the responsibility for the management of that data As a result, one needs to understand how you're going to incorporate a fundamental security strategy that's going to be part of the strategic direction of the company from a security perspective."

This, Curran said, means the role of the CISO or whoever is charged with security in the organisation regardless of their moniker has evolved from being purely reactive to much more strategic and forward-thinking. They now, in essence, really have a seat at the table in ensuring your organisation remains secure.

"Security is very much like business outcomes in any other part of the business. One needs to understand the risk associated with making decisions about where to put your investment. As a result of that, one needs to understand, from a business outcome perspective how you're going to create and implement a strategy around data protection. [It's that] rather than buying point solutions to protect your data. That is a reactive approach," Curran said.

"CEOs need to look at data protection officers or CISOs who are going to be a fundamental and integral board member rather than someone who is going to keep the lights on."

Curran added: "Software encryption is really good. There's a lot of really good products out there. We have many partners who have developed really good security based platforms. However, ultimately, we believe if you're looking at the growth of data, and how one needs to have a multi-cloud strategy, and the future of data centres and how you manage that capability all the way out the the edge and that edge being client and IoT we believe one needs to ensure that you have an attestation capability that ensures you can ensure that particular device no matter what it is has a trust-based capability and that the environment knows that that device can be trusted. And that the data being generated by that device or being sent to that device needs to be trusted."

Software-based threats are increasing and spending on security products and services is expected to surpass $114 billion this year up 12.4% on 2017, according to Gartner. Fast-forward to 2019, and that figure is expected to hit $124 billion. So, it's clear that new models of prevention rather than just cure are essential as things intensify.

"We are seeing a fundamental shift. It's not only a software driven value proposition in the security market. Now, the hardware and the security-by-design philosophy is looking at the capabilities of the hardware and [ensuring] the capabilities of that hardware are working together with the different types of software solutions out there," Curran said.

"We have got technologies today we make available to the software industry they can take advantage of the security to provide a route of trust, to provide a better attestation capability, to provide a geo-boundary or geo-location-based capabilities so that one knows when data or workload is being used on that device it is directly accredited to that device. So, if the data is moved to another location that data is a) encrypted and b) it won't be used. It's mapped and attributed directly to that device and that will increase your foundational security-based philosophy and that maps into security-by-design."

That way, according to Curran, threats can be more easily identified, quarantined and dealt with. And, he suggested, change is afoot with greater levels of intelligence being used in the fight between good and bad.

"Over time we are going to see devices make intelligent decisions to know that their state has been changed so they can actually move off the network, to self diagnose and ensure that before they go back on the network they've either been modified or the breach has been fixed," he said.

"The intelligence associated with that will come from AI and its ability to be able to analyse data faster and better. There's going to be a huge opportunity within the security market in terms of AI security and the ability to look at threat detection and analyse data down beyond the packet to be able to [tell] if that data has been manipulated in some way or some type of malware injected within that particular data set. As a result, the products within that whole framework can actually detect that as quickly as possible and either isolate it or correct it."

The times are a-changing and we all need to ensure we keep pace, especially where security is concerned. Hopefully, though, we will emerge stronger and more secure as individuals, businesses and industries if the glimpses of the future we have seen so far resemble wider reality.

"There's going to be so much data and so many products and devices, that the traditional way in which we manage security through a SoC or SIM based platform will be outdated," Curran added.

So, hold tight. But, remain afraid, too though. For that is what fuels our ability to fight. But, also, learn from experience and use that knowledge to avoid making the same mistakes again and again. After all, for every advance in technology we have that is good, several bad use cases are emerging just around the corner.

Discover more about data innovations at Intel.co.uk

Get ready for a revolution in enterprise computing. Until now we've had flash memory and DRAM as distinct entities, one for system storage, one for system memory. Both might now use solid-state technologies, but in terms of architecture, capabilities, costs and applications, they couldn't be more different. DRAM has always been fast, highly resilient and connected to the CPU by the fastest-available interconnect, but it has also been expensive, capacity-constrained and volatile the moment the power goes off, all the data is lost. Flash memory is cheaper per GB and wins on capacity, but even the fastest SSD technology can't compete with DRAM for high-speed bandwidth or overall read/write performance.

In the past, this wasn't a problem. When processors, applications and platform architecture were the compute bottlenecks, neither memory constraints nor SSD performance really held us back. Now, however, businesses are working with bigger datasets than ever before and striving to pull insights in near-real time from that data. They're investing in virtualisation, building flexibility and scalability into our IT, working to maximise the utilisation of every CPU core and system resource. They're building private and hybrid clouds designed to run next-generation business applications; applications that transform customer relationships and optimise all aspects of our operations. The streams of data running through are growing exponentially, and organisations need all the compute resources they can get their hands on to get the maximum value.

That's not such a problem for the world's largest enterprises, which have the capital to splash out on huge pools of DRAM, running vast multi-terabyte databases in-memory or maxing out the capacity of their hardware. However, medium-sized and smaller enterprises don't have that choice. DRAM is too expensive. Flash isn't nearly fast enough. In the new era of computing, the gap between flash memory and DRAM is preventing them from getting where they need to go.

Intel Optane DC Persistent Memory fills that gap. Designed to work with Intel's next-generation Intel Xeon Scalable Platinum processors, this new form of memory isn't just a big step forward, but potentially a bona-fide game changer.

Developed from Intel and Micron's cutting-edge 3D XPoint memory technology, Intel Optane DC Persistent Memory combines many of the advantages of DRAM and flash. The revolutionary architecture gives you higher densities and capacities than DRAM at a significantly lower cost. Yet it offers performance that's close to DRAM levels and far beyond anything conventional NAND-based flash technologies can deliver. The modules look and feel like DDR4 modules, albeit high-end performance modules with an enterprise-grade heatsink. However, where DDR4 capacities would normally stop at 64GB (vastly expensive) or 128GB (close to extortionate), Intel Optane DC Persistent Memory DIMMs start at 128GB and go up to 512GB and at a fraction of the price.

What's more, as the name suggests, this new kind of memory is persistent. Run it in the first of its operating modes, Memory Mode, and it acts just like a large-scale pool of DRAM memory, the Intel Xeon Scalable processor intelligently using DRAM to cache the most frequently accessed memory while the Optane DC Persistent Memory delivers bulk capacity. This is transparent at the application level, giving you the benefits of large memory capacity at a far more affordable cost.

Run it in the second operating mode, App Direct Mode, and the application and operating system work with the two types of memory independently, the Intel Xeon Scalable processor directing operations that require low latency to use the DRAM and those that need capacity to use the Optane DC Persistent Memory. In this case, the data held in the Optane DC Persistent Memory is retained even when the system is powered off or rebooted. While the OS and applications will need to be reloaded, in-memory databases and analytics frameworks may not.

What does this mean for enterprise computing? For a start, applications that involve both large datasets and heavyweight compute power can take advantage of an architecture that gives you huge capacity at the system memory level without the latency of moving data to and from the storage level. Artificial Intelligence, Machine Learning and High-Performance Computing applications will all see the benefits. We've already seen how organisations like the University of Pisa have harnessed the performance of Intel Optane storage; using memory-intensive undersampling techniques to shrink MRI examinations from forty minutes down to two. Intel Optane DC Persistent Memory should take this to another level.

It's a similar story with in-memory databases and big-data analytics or any application where you want to operate on a large dataset in close to real-time. Intel has claimed a 7x to 9.1x performance advantage when handling in-memory databases on an Intel Xeon Scalable Platinum 8170 and Optane DC Persistent Memory, as opposed to the same system with a more conventional DRAM plus SSD architecture. With Intel Optane DC Persistent Memory, you can put an entire Redis database in-memory and have sub-millisecond latency without the vast expense of doing so with DRAM. This has led Redis CTO and co-founder, Yiftach Shoolman, to say that we believe the next-generation server architecture will be all persistent memory. This is going to change the entire database market.'

And the impact will be every bit as big on virtualisation and, in turn, private and hybrid cloud. At the moment hardware servers offer a wealth of raw compute power, but are held back by the constraints of the DRAM that support them; you might have the cores and cycles to deploy more VMs or containers, but that means little if you don't have the RAM to do so. With Intel Optane DC Persistent Memory, you can add system memory in much greater densities, running up to three times as many virtual machines on the same hardware or as many as four times the number of database containers. For organisations deploying their own private or hybrid cloud, it becomes much, much easier to create pools of compute and storage resources that can be provisioned and scaled on demand. And as this memory is persistent, shutting down or rebooting a server doesn't have to mean serious downtime. In fact, reboot times change from a matter of minutes into seconds.

This isn't simply changing the way enterprises run mission-critical high-performance applications or operate virtualised IT, but also changing which enterprises can do so. By bringing down the cost of running some of the world's most memory-intensive applications, Intel Optane DC Persistent Memory brings them in reach of a much wider range of organisations. Startups and medium-sized enterprises aren't shut out of the A.I. and Machine Learning revolutions. They can harness next-generation analytics to compete. If Intel's new memory tech lives up to expectations, we could be seeing the start of a new era for the datacentre.

Find out more about Intel Optane DC Persistent Memory

The financial services sector, once one of the most stable and slow-moving industries in the world, is currently in the middle of a vast upheaval. A status quo which has remained largely unchanged for decades if not longer is being turned on its head, and it's all thanks to fintech.

Fintech (or financial technology) has undergone an explosion in recent years. Widespread connectivity and the growing ubiquity of mobile devices have enabled an unprecedented growth in the number, sophistication and accessibility of fintech tools and services, and it's having a noticeable impact on the financial services sector.

These tools have allowed new digital-native companies to disrupt the market, as well as letting existing companies bolster their offerings with an increased range of financial services. Digital-only challenger banks' like Monzo, Starling and Revolut, for example, have started drawing customers away from traditional consumer banking firms, and mobile-based trading platforms have opened up the stock market to everyday people who otherwise would never have considered buying shares.

Established financial institutions are now having to rethink how they operate in light of these new developments. Customers are demanding faster, more convenient and more secure experiences from their financial providers, and many are choosing to ditch their existing banks when they don't meet these new, evolving standards.

It's not hard to see why; the new capabilities being unlocked by emerging fintech are very attractive. The combination of AI-driven chatbots and advanced data analytics allow customers to have 24/7 access to their own personal financial adviser who can give them insights into their personal spending patterns. Moreover, it can help them set and control their budgets, while a digital-only bank means no longer having to make inconvenient in-branch appointments to manage your money.

Fintech offers the key to increasing customer loyalty and satisfaction, but having the right technology is key. Even if you introduce the kind of advanced features that customers demand from their financial service providers, a poor experience thanks to long wait times, dodgy security or frequent errors is just as likely to drive them away as not having them in the first place.

So how can you ensure that your fintech tools are speedy and reliable enough to keep up with your users' needs? Many businesses in other sectors have turned to the cloud for this, but in many cases, compliance and regulatory issues will mean that financial services companies simply aren't able to enlist the services of a public cloud provider like Google, Microsoft or Amazon.

Instead, financial firms should be looking to modernise their own in-house data centres. Extra investment in data centre transformation projects can pay dividends for the performance of financial applications in particular, such as data analytics. The bedrock of any modern business strategy, data analytics is especially essential for financial services, allowing organisations to rapidly analyse market trends and real-time transaction data, as well as their customers' banking information in order to provide them with new insights into their finances.

In order for this to be effective, however, your analytics model needs to perform at lightning speed, crunching through your datasets as quickly as possible to ensure your business retains a competitive edge. Similarly, you need to ensure that transactions and transfers are processed as quickly as possible; there's nothing more frustrating for a customer than having to wait hours or even days before the money that a friend sent over for their share of a bill arrives in their account.

Achieving this is no mean feat, however, and it's not just about adding more racks to your data centre. Making sure your data centre is running on the right infrastructure can be at least as important as its size, if not more so. Intel's Xeon Scalable platform, for example, is built specifically to support the kind of high-intensity data analytics workloads that modern financial services firms depend on.

Xeon Scalable's all-new Mesh architecture enables faster and more efficient data transfer between CPU cores an essential benefit when dealing with high-volume datasets and scales easily. In addition, Intel's new AVX-512 instructions offer a 1.6x performance boost for data-driven HPC workloads and 2.2x speed increase for AI and machine learning tasks.

It's also designed to couple seamlessly with Intel Optane storage and memory, slashing latency to the bone and processing instructions in a flash. It's also super high-density, allowing you to put more memory in a given rack, thereby reducing data centre expenditure.

Another feature that will be particularly useful for financial firms is the added efficiency that the new Xeon Scalable generation brings to security operations. In particular, the new instructions can process encryption tasks much faster than previous components thanks to a huge increase in the amount of floating-point operations that can be processed.

Encryption of network connections and data is essential for any financial services firm, but up until now, companies would be forced to suffer an increase in transaction time of up to 10% as a trade-off. Now, however, the improvements introduced by Xeon Scalable and Optane mean that this time penalty is significantly reduced, resulting in faster and more secure transactions for your customers.

Financial firms are now more at risk of being overtaken and disrupted than they've ever been, but the same tools that are allowing their competitors to threaten them so greatly can also open up a world of new opportunities, increased revenues and more satisfied customers for businesses that can embrace them.

Discover more about the technology powering Fintech at Intel.co.uk

According to Domo's annual Data Never Sleeps research, by 2020 there will be 1.7MB of data created every second for every single person on Earth. With the global population estimated to be around 7.6 billion people by 2020, that's more than 12,000 terabytes of data a second. Our storage needs are increasing every year, and when you consider that the first computer was built scarcely 80 years ago, we've come a long way in a short space of time. In this feature, we trace the journey of data storage through its key stages to the present day's bleeding edge.

Data storage has always been a key part of the way computers work, right back to when John Von Neumann defined his eponymous architecture in 1945 for the ENIAC (Electronic Numerical Integrator and Computer), an architecture that is still the basis of today's PCs. In this conception, the main job of the Central Processing Unit was to read data and instructions from storage, perform operations, and write the results back to storage with dynamic memory and longer-term capacity not necessarily differentiated. Initially, the inputs and outputs were punch cards designed to be compatible with IBM's 405 accounting machine. Each card could hold 12 rows of 80 columns, although the encoding system wasn't necessarily binary so this doesn't directly translate to 960 bits.

Nevertheless, in 2013 XKCD's Randall Monroe calculated that Google's 15EB of data would require an area the size of New England and a depth of 4.5km if it were stored on punch cards. Aside from the space required, imagine how long it would take to sort through that many cards for a specific piece of data. Clearly, this form of storage couldn't cope with today's data needs, considering that by 2020 we will be creating this much data every 21 minutes. But, fortunately, our storage technology has developed to match.

By 1953, the ENIAC added a 100-word magnetic-core memory system built by the Burroughs corporation, which performed the same function as today's RAM. Around the same time, in 1951, the first tape storage system arrived for the UNIVAC I, the first computer system commercially available in the USA. Using a half-inch wide nickel-plated phosphor bronze recording medium, this UNISERVO drive could store 128 characters per inch at 12,800 characters per second.

However, IBM's magnetic tape design became the standard, with tape lengths up to 2,400ft, moving up to 3,600ft in the 1980s. The capacity increased over time, starting at 200 six-bit characters per inch for the first seven-track designs and culminating in 6,250 8-bit characters using the group-coded recording encoding system and nine-track tape. With a 2,400ft tape this equates to 5MB capacity for the initial version, rising to 140MB for the final iteration, which was still in use in the 1980s. Modern descendants of tape remain a valid choice for huge storage, with the roadmap for the LTO format extending to 192TB cartridges and Sony demonstrating tape storage cartridges capable of 330TB capacity in 2017.

In the 1970s and 1980s, punch cards and magnetic tape were still being used for storage and data entry. But a much faster alternative had already been in existence since the mid-1950s the hard disk. However, the first IBM 350 RAMAC was the size of two full-height refrigerators and contained 50 platters each with a 24in diameter wider than a dart board. Each IBM 350 had a similar 5MB capacity as a 2,400ft reel of tape during the same era (equivalent to 64,000 punch cards). But it could access a record in 600 milliseconds, whereas a tape drive might take six minutes to get from one end to the other of a 2,400ft reel.

By the 1960s, hard disk platters had shrunk to 14in diameter and a removable format providing 2MB per pack had arrived, with each pack about the size of a cake box. In 1980, Shugart Technology (later to become Seagate Technology) created a much smaller 5.25in variant with 5MB capacity, but most personal computers in this era relied on the floppy disk. This started off as an 8in design in 1973 with a 237.25KB formatted capacity, then shrank to 5.25in in 1976 with 87.5KB formatted capacity. In the early 1980s the 3.5in floppy arrived, although this coexisted with the 5.25in variety for some time. The most popular PC version offered 360KB capacity in single-sided variant, eventually reaching 1.44MB in high-density format and 2.88MB in extra-high density format.

There have been numerous variants on the themes of these technologies over the years, including the LS-120 floppy offering 120MB, the magneto-optical MiniDisc, SyQuest removable hard drives, Iomega's Zip and Rev drives, as well as rewritable optical CD, DVD, Blu-ray and HD-DVD formats that seemed very important for a few years but are scarcely used today. However, during the last decade the main storage choice for personal computers and servers has been between new generations of hard disk and Flash memory-based solid-state disks (SSDs).

Hard disks are still readily available in 3.5in and 2.5in formats, with standard rotational speeds ranging from 5,400 to 15,000rpm. The demise of the hard disk has been considered imminent for at least 15 years, but it hasn't happened yet. For a start, the longitudinal magnetic grain technology used in hard disks appeared to be reaching the extent of its abilities according to the laws of physics. But reorienting the grains in a perpendicular fashion has allowed hard drives to reach 12TB in capacity in 2018, so HDDs continue to progress as a cost-effective mass storage medium.

Nevertheless, ever since SSDs became available as desktop and server drives around 2006, it has been clear that the dominance of the hard disk would slowly be eroded. Although the cost per megabyte of SSDs is still an order of magnitude greater than HDDs, the latest NVMe M.2 SSDs offer sustained reading throughputs in excess of 3,000MB/sec, 10-15 times what the fastest hard disks can muster, and access times are 100 times quicker.

Performance is not the only reason to choose SSDs, either, particularly for data centres. A typical hard disk will consume 5-10W when in use, and not that much less when idle unless spun down. An SSD, in contrast, uses less than a Watt when idle and a few Watts in use. This has a knock-on effect for how much heat they produce as well. As a result, all-Flash SSD arrays are gaining ground for business storage, since the running costs will be a lot lower and performance much higher, whilst over-provisioning alongside sophisticated controller technology can provide mean time before failure (MTBF) ratings on par with hard disks as well.

Perhaps the most significant recent development for SSDs has been 3D XPoint memory, sold by Intel under its Optane brand. This is a relative of Flash memory with characteristics that make it particularly attractive for enterprise applications. Whilst raw reading and writing throughput aren't a noticeable improvement over traditional SSDs, the read latency is two or three times lower, and write latency less too. This means that with random access of smaller files, Optane SSDs can easily outperform regular Flash equivalents. Enterprise-focused Optane SSDs also support 100 times as many write cycles over their lifetime than conventional Flash SSDs.

Optane technology is only getting started, too, as it can also be incorporated into RAM modules, which Intel launched around the middle of 2018 and will be shipping in volume in 2019. These Optane DC Persistent Memory modules can be installed just like regular DRAM DIMMs, except that they're non-volatile, so they won't lose their data when the power goes off. This is a potent feature for database applications, when allied with the much faster interface of memory DIMM slots.

Even though our data needs are expanding every day, storage technology appears to be keeping pace very well. Emerging SSD technologies like QLC and increased 3D NAND layering will bring the cost per megabyte of commodity SSDs below that of HDDs in a few years, perhaps as soon as 2021. PCI Express 4.0-based NVMe interfaces will allow even faster sustained throughput. Although we're producing more data than ever before, we will be able to store it more cheaply and access it more quickly.

Discover more about Intel's Optane data storage innovations at Intel.co.uk

Spare a thought for the humble data centre; while flashy technologies like cloud computing, machine learning and big data seem to get all the attention these days, it's data centres that make it all possible, quietly humming away in the background.

Whether you're a consumer or a business, data centres support and enable everything that you do on a moment-to-moment basis, whether it's talking to friends, watching a film or even catching the train. Technology has interwoven itself into every aspect of our daily lives, and none of it would be possible without data centres.

Even in this brave new world where almost every organisation is exploring the potential of the cloud, the data centre is still essential which is demonstrated by the fact that according to research from CBRE, demand for colocation in European data centres has hit all-time highs this year.

After all, all your application workloads, test environments and corporate files need to live somewhere; cloud computing means you don't have to maintain your own data centre, but it doesn't negate the need for one altogether.

In fact, the advent of widespread cloud means that businesses need to be more concerned than ever about the state of the data centre they're using, whether it's theirs or their cloud provider's. Organisations using their own on-premise infrastructure need to ensure that their hardware is capable of keeping up with their cloud-hosted competitors, while those in the cloud should be using the increased freedom that that entails to demand the very best from their cloud partners.

You see, not all data centres are created equal. As any IT veteran knows, it's not about how many racks you have, but what's inside them and it comes down to much more than core counts and clock speeds. Faster components and higher capacities are all well and good, to be sure, but the real heart of data centre transformation is ensuring that all the constituent elements of your data centre are working together in harmony.

The pace of modern business is increasing by the day, and in a mobile-focused, software-driven economy, split-second advantages can mean the difference between failure and success. To keep up with this rapid pace of change, organisations are increasingly turning to the powerful combination of data analytics and machine learning; using advanced AI to rapidly sort through the vast corpuses of information they generate on a daily basis and generate actionable business insights.

This strategy can be a real game-changer for business agility, but the downside is that it can put a huge strain on an under-equipped data centre. Analytics workloads rely on the speed at which the system can query a dataset; the faster a query can be returned, the more insights can be delivered within a given timeframe.

Where this typically hits a bottleneck in conventional data centres is the link between storage and compute. Regardless of how fast your processor is, older storage technologies take too long to move the data from storage to memory where it can be analysed, so faster processors are left underutilised because the storage can't perform enough IOPS to keep up.

Data-driven analytics isn't the only way that our use of data centres is evolving, though. One of the more interesting recent developments in enterprise IT has been the concept of edge computing' the concept of de-centralised data centres. In essence, this involves moving computational power away from the bulk of your IT system and putting it at the edge of your network, closer to where your data is collected.

This allows organisations to reduce the amount of time and bandwidth they spend on moving data between their data centre and their endpoints. It's commonly used in remote locations like oil rigs, for example, where internet connections can be expensive and difficult to maintain. In order to facilitate this, companies are effectively having to deploy miniature data centres, with portions of storage, compute and networking all packed into a single, hard-wearing package for deployment in hostile environments.

"With the recent increase in business-driven IT initiatives, often outside of the traditional IT budget, there has been a rapid growth in implementations of IoT solutions, edge compute environments and nontraditional' IT," explained Gartner research vice president David Cappuccio in a recent blog post.

"There has also been an increased focus on customer experience with outward-facing applications, and on the direct impact of poor customer experience on corporate reputation. This outward focus is causing many organisations to rethink placement of certain applications based on network latency, customer population clusters and geopolitical limitations (for example, the EU's GDPR or regulatory restrictions)."

As organisations adapt to the new landscape, these new use-cases are also changing the way that data centre hardware is being built. Intel's newest family of Xeon Scalable processors, for instance, has been built to enable these kinds of high-intensity workloads, featuring a new Mesh-based architecture which improves on the previous ring model by allowing more efficient multi-tasking and greater scalability, as well as providing an overall performance boost.

Where things really start to get interesting, however, is when you combine Xeon Scalable processors with Intel's Optane memory. Its flash-based architecture means incredibly low-latency, allowing it to perform much more queries per second than traditional drives. Optane DC Persistent Memory modules also offer DRAM-level speeds, meaning that data analytics workloads can be performed in-memory. Because information doesn't have to be shuttled back and forth between memory and storage, results can be delivered with much greater speeds.

Intel's Optane and Xeon Scalable technologies are engineered to work in concert with each other, eliminating bottlenecks and maximising the time-to-value from your data centre. Both have been designed with next-generation use-cases like machine learning, data analytics and IoT in mind, allowing your business to scale effortlessly without worrying about ageing infrastructure.

So what does the future of the data centre really look like? Despite the growing appeal of cloud computing, the data centre itself isn't going away any time soon. In fact, it's evolving, becoming more powerful and versatile than ever, as organisations demand the ability to run real-time analytics workloads and advanced machine learning algorithms. In the modern age, the cloud may be king - but the data centre is most definitely the power behind the throne.

Discover more about data storage innovations at Intel.co.uk

Aside from people, information remains the most prized asset of organisations large and small across the globe. As such, ensuring that data is accessible when needed but only by those who are authorised to view it is paramount. In essence, businesses must ensure they are in control of that data, because it could become even more powerful should it fall into the wrong hands.

In days gone by, cloud computing was associated with a lack of control and a lack of security. It was a brave business that was willing to place its most precious commodity in the hands of a third party. It's akin to leaving your new-born under the guardianship of a babysitter you've never seen let alone sought references for.

But times have changed. Cloud is no longer seen as the enemy. Far from it. Indeed, today, cloud is an essential part of the modern enterprise's success toolbox. Today, the majority (90%) of firms have embraced the cloud in some form, according to research by the 451 Group.

Cloud rights

By 2020, analyst firm Gartner believes a no-cloud policy will be as unusual as a no-internet policy. It's difficult to imagine any business that could operate, let alone be successful, without the internet. It would be an unthinkable omission in even the most basic of business strategies. The same is now true of the cloud.

Much like the vast array of internet consumption and delivery options available, the cloud presents many choices. Perhaps one of the first considerations and choices to be made is whether public or private cloud is best suited to your organisational needs.

Public is just that think of how you're paying for only the electricity or gas that you use and no more or no less - and can be a scary label for many businesses for a myriad of reasons. It's a trade off between management and control and is generally considered OK for non-mission-critical or everyday stuff.

On the other hand, the Private' moniker sounds expensive and resource intensive. But, both clearly have their merits and use cases as both in different ways essentially connect datacentres and network to intelligently automate and orchestrate the movement of data.

"IT now enables business transformation, helping organisations compete, grow and innovate. I know how disruptive trends and technologies can present challenges, but they can also be huge opportunities to transform how IT supports and impacts the business," says Bill Giard, CTO, IT transformation at Intel's enterprise and government group.

"Enterprises are realising they can benefit from public and private cloud. We talk to customers a lot about workload placement because it's so critical for getting the best ROI. Defining a strategy that puts the right workload in the right place is essential."

He adds that there are four areas of concern that tend to crop up when it comes to cloud:

• Size of data
• Level of integration
• Security
• Performance

"Based on these considerations, some workloads work better on-premise, while others are best suited to public cloud," Giard adds.

Which way to go?

But, what if you don't want to or can't go all in with one or the other? That's where hybrid cloud comes into play. It's an increasingly popular choice for businesses that want all the of the benefits that cloud offers, but also need to keep some data on premise for various reasons whether that's compliance, customer demand or just an irrational fear of letting go.

What's more, opting for hybrid gives organisations the best of both worlds. They get to enjoy all of the plus points along with peace of mind that the information that flows through their business is accurate, secure and accessible by the right people when needed.

Many firms choose to keep the data they hold dearest in an on-premise datacentre, while essentially outsourcing the safekeeping of other information and workloads to a public cloud provider. That's not to say the latter option is lacking in security public cloud providers have resources, whether compute, security or otherwise, that the average business can but dream of.

It's really no different to how many companies now operate. You can have a dedicated building or space you own or rent for certain functions, or even physical storage, which is totally under your watchful control you even get to choose the wallpaper. You are the only tenant. This can be complemented by shared workspaces that are much more cost effective while also providing the agility and flexibility you need as your workforce grows or the tasks being completed change. But, in the latter scenario, it's a house in multiple occupation (HMO).

Just as hybrid workspace models are proving popular with those who want the oft-paradoxical characteristics of flexibility and security, hybrid cloud is definitely gaining momentum. By 2019, 451 research suggests 69% of enterprises will have adopted multi cloud/hybrid IT strategies.

What's in it for me?

MarketsandMarkets research concurs with the upward trajectory of hybrid cloud, suggesting the market will grow to $91.74bn by 2021.

We've already outlined many benefits of embracing hybrid cloud rather than going all in on public or private, but what else needs to be considered?

In a recent article, IT Pro suggested that there is much to gain from an understanding of the benefits hybrid has to offer.

"With true hybrid cloud, organisations are freed from the shackles of the mundane and complex so they can focus on their core business objectives and move from the daily grind to innovating for future success," the article states.

Furthermore, by essentially expanding the reach of their datacentre under the guise of hybrid, companies can likely enjoy:

• Building new enterprise next-gen apps rather than being limited to traditional elements
• A more efficient test and development environment, freeing up time and resources and ensuring a reactive and proactive stance going forward.
• A solid DR plan for third-party backups as well as test environments and seasonal variation.

"Over 80% of the respondents to this study currently use multiple cloud environments, with varying amounts of integration, migration and interaction between them," said Liam Eagle, 451's research manager for cloud, hosting and managed services, in reference to a 2018 study on the subject.

"Perhaps most significant is that approximately a quarter of companies already use some form of hybrid cloud using the definition of seamless delivery of a single business function across multiple environments.

When software and hardware meet in the cloudDespite the plethora of benefits and plus points, it's important that organisations don't necessarily see hybrid as a panacea. Yes, it can be the solution for many firms, but in isolation, without proper thought or attention paid to certain other areas, it could actually prove more of a hindrance than help.

Indeed, 451's research suggested hybrid strategies often fail to move from theory to fact due to overlooking the need to retool' certain elements such as security - for a hybrid environment.

Just focusing on control or security may leave other issues hiding and growing in plain sight, though. While organisations can be confident that internal SLAs and other T&Cs are watertight as far as availability and security are concerned, they still need to make sure their public cloud provider is on the same page. After all, the cloud is just datacentres connected to a network and virtualised rather than on-premise, but that doesn't mean standards can or should slip.

This highlights the importance of not overlooking physical requirements in an increasingly virtualised world. Ensuring you have the right datacentre infrastructure and being confident your public cloud provider has done the same is paramount to ensuring optimum system performance and security.

If we reconsider the point that data is the lifeblood of any organisation, it stands to reason that modern apps are likely to be more data-intensive than ever before. As such, enterprises will need an added level of confidence that their private cloud infrastructure passes muster.

Similarly, smaller businesses crave the sort of agility historically the preserve of enterprise-level IT budgets. But, now, it is possible for them too to enjoy both secure and cost-efficient performance.

This has been made possible by another blending of the best of both worlds RAM and storage in the form of Intel's Optane platform. As enterprises focus ever more on analysing huge data sets, being able to access that data as quickly and efficiently as possible is critical.

On top of the storage innovation provided by Optane, Intel has taken compute to the next level in the data centre with Xeon Scalable. The latest Xeon chips are designed to cater to the needs of the modern enterprise, with in-built acceleration of core functions like encryption and decryption, data compression and analytics.

Any business that needs an on premise data centre, needs to be investing in the best possible hardware platforms to deliver the best possible performance. And if that on premise data centre will be supported by a public could provider in a hybrid configuration, that same level of performance should be demanded from said provider.

Gartner's research director DD Mishra concurs with 451's observations regarding complexity and toolset consideration, saying: "As the demand for agility and flexibility grows, organisations will shift toward more industrialised, less-tailored options."

"Organisations that adopt hybrid infrastructure will optimise costs and increase efficiency. However, it increases the complexity of selecting the right toolset to deliver end-to-end services in a multisourced environment."

Let's not forget though, with hybrid, there's also the added ability of being able to create an app or service with uncertain levels of performance demand and being certain you're able to seamlessly move between private and public to meet any spikes in compute requirements.

Where next?

Data sovereignty and protection remain key concerns for businesses and consumers alike and, post 25 May this year, when the GDPR came into force, such concerns are only going to intensify.

Individuals care more now than ever before about the data that organisations hold on them. Similarly, organisations need to demonstrate that they, too, care about protecting that data and also erasing all versions of it should that so be requested.

Hybrid cloud offers companies a way to ensure they are keeping all parties happy. By working with a trusted public cloud provider, they can be sure records are retained and removed when required in a timely fashion with the same levels of ease as they can if they were just heading to the filing cabinet to obtain Mr or Mrs Smith's customer file.

If hybrid can help organisations get the best of everything the cloud to offer, it will also help them ensure they can meet the needs of the business without compromising or falling short on customer needs it really is the best of both worlds.

Discover more about Intel's hybrid cloud solution here.

New enterprise applications are growing more data-intensive, as organisations push to make more of their inputs and outputs, looking to get to the insights and the value faster to gain the greatest competitive edge. The largest enterprises want architecture that promotes agility; that gives them the scope to innovate and react faster, transforming operations or the customer experience. Smaller enterprises are looking for technology that can help them compete in the big leagues, delivering cutting edge performance at a lower cost.

On both counts, traditional memory and storage architectures are failing. Conventional NAND-based solid-state storage seems to offer the performance, but while it's blazingly fast in certain circumstances, when you have a large number of pending input/output requests (what we call a high queue depth), those speeds don't necessarily hold up across a full range of scenarios. Latency creeps in and performance falters. Applications that manipulate large datasets in real-time or handle complex High-Performance Computing operations at speed are held back when the data they rely on hits the bottleneck.

For some platforms and workloads, working entirely in-memory is the answer, but if so it's the kind of answer that involves a major investment one that might be difficult to justify for many businesses. The sheer costs involved can ensure these applications remain a pipe dream for small and medium-sized enterprises.

With Optane memory technology, Intel has found a better route forward, offering the data centre a killer combination: the performance of RAM with the persistence and costs - of storage. Optane brings ultra-low-latency and significantly higher performance than NAND, even without a higher queue depth. Optane's performance also scales faster, so that you're not waiting for the speed to ramp up as a data operation runs it's there almost from the word go.

How does Intel Optane work?

To do this, Optane combines 3D XPoint memory media with Intel storage software, Intel Memory and Storage controllers and Intel Interconnect IP. 3D XPoint media uses a different structure to conventional NAND-based solid-state memory, where perpendicular wires connect microscopic columns containing the individual memory cells, the wires on one axis connecting at the top, and on the other axis connecting at the bottom. These structures are stacked to maximise density. The advantage is that each cell can be addressed individually by selecting the two wires that connect to it specifically. Data is read and written by varying the voltage sent to each selector, and there's no need for a transistor at each memory cell, enabling the cells to be packed even more densely.

Like NAND, 3DXpoint is a non-volatile form of memory, which makes it great for storage. It's very durable, enduring more read-write cycles than NAND without any significant impact, and its density eight to ten times that of DRAM makes it possible to cram more memory into less space, reducing cost. Combine that with fast switching material and an interconnect design optimised for low-delay, and you have a storage technology that can outperform NAND SSDs on random read/write performance, under write load or at low queue depths; scenarios that arguably better reflect the way that demanding data centre applications use storage.

To really maximise the benefit of Intel Optane, you can partner it with Intel's new Xeon Scalable architecture. The biggest step-change in twenty years of Xeon CPUs, Xeon Scalable thrives in the kind of big-data, high-performance, low-latency workloads where Optane also shines. Xeon Scalable isn't designed around just brute performance, but around a synergy between computer, network and storage capabilities, where Optane has the characteristics to work hand in hand.

READ MORE: IT Pro's Intel Xeon Scalable deep dive

Optane in the data centre

Optane's advantages sound exciting on paper, but what are the benefits in real-world use? Well, while much of the hype around Optane has focused on the performance benefits of Optane memory in high-performance desktops and workstations, Optane has just as much to offer the data centre in high-performance, high-capacity SSDs, or in Optane DC Persistent Memory modules that combine the persistence of non-volatile storage with the speeds of DRAM.

Optane makes a tangible difference when you're running high-end Business Intelligence or analytics workloads, particularly when working with high-capacity, in-memory datasets. Optane's low-latency characteristics make it a natural fit for any application where latency can bottleneck performance. Storage remains more responsive even under extreme workloads, latency is reduced, and applications can deliver more transactions per second, consistently, over sustained periods.

What's more, Optane enables you to create a pool of memory beyond DRAM capacity, enabling you to grow your memory footprint without actually buying more RAM. This enables severs to deliver in-memory levels of performance across many workloads, even when DRAM is only supplying between one-third and one-tenth of the overall capacity. That's big news when Optane costs around half as much per GB as the equivalent quantity of DRAM.

That's great in heavily virtualised environments, but also superb for high-performance computing or AI and machine learning applications, where working in-memory is crucial to performance. Optane could help run complex simulations at higher speeds for lower costs. It's already enabling scientists at the University of Pisa to use undersampling and interpolation in MRI exams, reducing exam times from 40 minutes down to two minutes without affecting the accuracy or utility of the results.

You can tackle projects that would normally involve huge amounts of DRAM with a combination of DRAM and Optane, reducing the initial investment and lowering operating costs. Applications that used to be the preserve of the largest enterprises trickle down within reach of smaller organisations, while existing applications can scale up at lower costs. In its own quiet way, Optane could be revolutionary. It doesn't just boost performance in the data centre but gives engineers a more cost-effective platform on which they can innovate and build. Higher speeds, lower latency, enhanced endurance, reduced costs Optane is delivering the future of data centre storage now.

Read more about data storage innovations at Intel.co.uk

Over the last few years, it has become clear that data is an incredibly valuable asset, particularly now the arrival of GDPR has put a focus on how much it could cost companies that don't comply with regulations regarding the protection of personal data. Client databases have become the lifeblood of successful sales strategies, user behaviour data has allegedly been employed successfully to interfere with election outcomes, and just-in-time manufacturing revolves around comprehensive feedback on every stage of the supply chain.

Most of the biggest new companies of the last decade Google, Facebook, Uber, Airbnb, Palantir, Didi Chuxing base their businesses on the value of their data and how they use it. But unlike physical assets such as raw commodities or manufacturing equipment, it's very hard to put a definitive value on the data itself. Whilst the idea that "data is currency" has become something of a catchphrase over the last few years, and blockchain technologies have attempted to weld money and information together algorithmically, the true denomination of this data-currency remains opaque.

Normally, the value of data is not separated from the overall value of a company and its business. The data aspect has generally only been separated out when it has been lost or compromised in some way. For example, in 2017 the consumer credit agency Equifax suffered a data breach involving 143 million users. The resulting class action could cost the company up to $70 billion. But that's only one way of valuing data, revolving around how much losing personal information might damage the user. It doesn't say very much about how much that data might be worth to Equifax's business. With 2.5 quintillion bytes of data being produced every day globally, according to the World Economic Forum, getting a handle on the monetary value of information has become essential.

The problem, however, is that the value of data isn't just a factor of quantity or volume, but how it can be used, and that's based on how the data is structured and analysed, amongst other things. Time is even more influential than it is with hardware that depreciates as it becomes obsolete. Not only does data need to be fresh and up to date; it also has to be delivered at exactly the right time. It may even be collected and used in real time. Internet of Things sensor information controlling an environmental system must be delivered and processed in a timely fashion to maintain settings, whilst data about the locations of taxis, customers wanting to travel, and where they want to go is only useful when it's still true. There's no point sending a taxi when the traveller left 20 minutes ago.

Because the fact that data is valuable is obvious, but the method for working out just how valuable is obscure, there have been serious attempts to quantify this for insurance purposes, albeit a very complex process. Gartner analyst Doug Laney has defined six different ways of valuing data. With financial methods of estimation, data can be valued according to how much it would cost to replace, how much it would fetch if sold, or how much it contributes to everyday revenue. With non-financial methods of estimation, data can be valued depending on its intrinsic worth to the company, its usefulness for business purposes, or how it drives other aspects of the business.

Since Laney's system is aimed at the insurance business, it involves complicated mathematics, marketplace valuation, and revenue modelling to provide as much precision as possible. There are complex formulae revolving around the accuracy of the data, how complete the set is, how easy it is to access, and whether it's a scarce resource nobody else has. Relevance is key, as is how much the data contributes to business objectives and key performance indicators.

This is all quite esoteric, but it highlights the fact that one of the most important underlying factors in data's value comes from the speed and accuracy of its analysis. This is the point where the static ones and zeros of the data itself blend into the hardware infrastructure that stores, delivers, and performs useful operations on that data. The faster data can be delivered and processed, the more relevant it will be to the person accessing it. So, a rapid, reliable hardware platform for your data is an integral part of realising its value.

Technological improvement can enhance the true value of data at every level. The general rule of thumb is that the more data you have, the better, but that leads to massive storage requirements. The cost of solid state disks (SSDs) continues to fall, making all-Flash storage arrays increasingly affordable. These also provide much faster random access than traditional hard disks, but this can be at the expense of hardware longevity. Recently, however, 3D XPoint memory, as used in Intel Optane SSDs, has arrived boasting even faster transactional speed and much better durability than traditional SSDs. This makes it ideal for keeping data safe and readily accessible, enhancing its value.

You also need to know that once data has been loaded into an application server, it is being operated on as quickly as possible. The faster your analytics can be performed, the more valuable that data will be to your business. This necessitates a hardware platform capable of high core density, high processing power per core, and extensibility for when your requirements grow. Intel's Xeon Scalable architecture provides all of these features.

Few would deny that data is becoming one of the most valuable assets many companies have. But on its own, without the hardware platforms to do it justice, data's true value will be reduced or even wasted. There's no point spending a fortune amassing a wealth of information without also spending adequately on the hardware to take full advantage of it. That's the truly wise way to spend the currency you have created with your data.

Is your business ready for IT Transformation? Discover more from Intel here.

There are big changes afoot in the enterprise data centre. Many organisations are going through a widespread transformation based on online services and data. They're harnessing that data to powerful AI and analytics applications that can turn it into business-changing insights, then rolling out tools and services that put those insights to work. They're looking to migrate key on-premise capabilities to the cloud, and steadily finding the right balance between public and private cloud. They're searching for new ways to deliver strong security at scale and speed. All this demands a new kind of server and network infrastructure, optimised for AI, analytics, massive datasets and more, powered by a new and revolutionary CPU. That's where Intel's Xeon Scalable line comes in.

Intel Xeon Scalable represents arguably the biggest step change in twenty years of Xeon CPUs. Not simply a faster Xeon or a Xeon with more cores, it's a family of processors designed around a synergy between compute, network and storage capabilities, bringing new features and performance enhancements across all three.

While Xeon Scalable offers a 1.6x average performance boost over previous-generation Xeon CPUs, the benefits extend beyond the benchmarks to cover real-world optimisations for analytics, security, AI and image-processing.* There's more power to run complex High-Performance Computing workloads, plus changes designed to speed up your network infrastructure. Where the data centre's concerned, it's a win in every way.

A new architecture

Perhaps the biggest and most obvious change is the replacement of the old, ring-based Xeon architecture, where all the processor's cores connected through a single ring, with a new Mesh architecture. This aligns cores plus associated cache, RAM and I/O, in rows and columns connecting at every intersection, allowing data to move more efficiently from one core to the next.

If you imagine it in terms of a road transport system, the old Xeon architecture was like a high-speed circular, where data moving from one core to another might have to move all the way around the ring. The Mesh is more like a grid of highways, only one that lets traffic flow at maximum speed from point-to-point without congestion. This optimises performance in multi-threaded tasks where different cores might share data and memory, while also ramping up energy efficiency. In the most basic sense, it's an architecture purpose built to move large amounts of data around a processor that might feature up to 28 cores. What's more, it's a structure that scales up more efficiently, whether we're talking multiple processors or new CPUs with even more cores further down the line.

New Instructions

If the Mesh architecture is about moving data around more efficiently, then the new AVX-512 instructions are about optimising how it's processed. Building on the work Intel started with its first SIMD extensions back in 1996, AVX-512 allows even more data elements to be processed simultaneously than with the last-generation AVX2, doubling the width of each register and adding two more to enhance performance. AVX-512 enables twice the number of floating-point operations per second per clock cycle, and can process double the number of data elements that AVX2 could in the same clock cycle. It's a genuinely massive speed boost.

Better still, these new instructions are designed specifically to accelerate performance in complex, data-intensive workloads such as scientific simulation, financial analysis, deep learning, image, audio and video processing and cryptography. This helps a Xeon Scalable processor handle HPC tasks over 1.6x faster than the previous-generation equivalent or speeds up AI and deep learning operations by 2.2x.*

AVX-512 also helps with storage, accelerating key functions like deduplication, encryption, compression and decompression so that you can make more efficient use of your resources and strengthen the security of on-premise and private cloud services.

New Accelerators

In this respect, AVX-512 works hand-in-hand with Intel QuickAssist Technology (Intel QAT). QAT enables hardware acceleration for cryptography, authentication and data compression and decompression, increasing the performance and efficiency of processes that place heavy demands on today's network infrastructure and that will only grow more demanding as you roll out more digital services and tools.

Used in conjunction with software defined-infrastructure (SDI) QAT can help you recapture the lost CPU cycles spent on security, compression and decompression tasks, so that they're available for compute-intensive tasks that bring real value to the business. And because a QAT-enabled CPU can handle high-rate compression and decompression, almost free, your applications can work with compressed data. Not only does this have a smaller storage footprint, but takes less time to transfer from one application or system to another.

A new platform

Intel Xeon Scalable CPUs integrate with Intel's C620 series chipsets to create a platform for balanced, system-wide performance. Intel Ethernet with iWARP RDMA connectivity comes built-in, delivering low-latency, 4x10GbE communications. The platform delivers 48 lanes of PCIe 3.0 connectivity per CPU, with 6 channels of DDR4 RAM per CPU supporting capacities of up to 768GB to 1.5TB per CPU and speeds of up to 2666MHz.

Storage gets the same generous treatment. There's scope for up to 14 SATA3 drives and 10 USB3.1 ports, not to mention virtual NMMe RAID control built into the CPU. Support for next-generation Intel Optane technology boosts storage performance even further, with dramatic positive effects for in-memory database and analytics workloads. And with Intel Xeon Scalable, support for Intel's Omni-Path fabric comes built-in without any need for a discrete interface card. As a result, Xeon Scalable processors come ready for high-bandwidth, low-latency applications on HPC clusters.

READ MORE: IT Pro's Intel Optane deep dive

A new performance benchmark

With Xeon Scalable, Intel has delivered a line of processors that cover next-generation data centre needs, but what does all this technology mean in practice? For starters, servers that can handle bigger analytics workloads at higher speeds, pulling insight faster from larger datasets. Intel Xeon Scalable also has the compute and storage capabilities for cutting-edge deep-learning and machine-learning applications, enabling systems to train in hours, not days, or infer' the meaning of new data with increased speed and accuracy while processing images, speech or text.

The potential for in-memory database and analytics applications, like SAP HANA, is enormous, with up to 1.59x higher performance when running in-memory workloads over the last-generation Xeon.* When your business relies on pulling insights from vast datasets with real-time sources, that might be enough to give you a competitive edge.

Xeon Scalable has the performance and system and memory bandwidth to host bigger and more complex HPC applications, finding solutions for more complex business, scientific and engineering problems. It can deliver faster, higher-quality video transcoding while streaming video to more clients. Yet it's in the nuts and bolts of the datacentre that some of the most exciting optimisations and efficiencies are to be found. Xeon Scalable can deliver faster, smoother-running communications networks, so that enterprises can build more effective and scalable software-defined infrastructure using fewer but more powerful machines.

A ramp-up in virtualization capacity could allow organisations to run four times as many virtual machines on a Xeon Scalable server than on a last-generation system.* And with near-zero overhead for compression, decompression and encryption of data at rest, companies can use their storage more effectively, while strengthening security at the same time. This isn't just about the benchmarks; it's about technology that transforms the way your datacentre works and, in doing so, your business too.

Is your business ready for IT Transformation? Discover more from Intel here.

* Intel technolgies' features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. No computer system can be absolutely secure. Check your system manufacturer or retailer or learn more at intel.com

Software and workloads used in performance tests may have been optimised for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit intel.com/benchmarks

All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest Intel product specifications and roadmaps.