What is big data analytics?
We explain the differences between descriptive, predictive and prescriptive methods of looking at data
"Big data" seems like a simple term for the way we power modern life, but it is far more complex than that suggests. Essentially, it's a mass collection of information that we use to make decisions, train models, enhance public-facing technology and much, much more.
The methods used to collect the raw data used in big data analytics range widely; the rise of the internet of things (IoT), cloud computing, and increased smartphone use all enable the harvesting of information. For some, such as bad actors, this is used for ill-intent such as identity theft. For businesses, however, data harvesting forms a cornerstone in their quest for increased profit.
Analytics are used to identify the insights, patterns and strategies present within datasets. Specialist software or systems tailored to the task are often used to analyse high volumes of data quicker than any team of people could. These are then used to inform business decision-making.
What is big data?
To appreciate big data analytics, you first need to comprehend what's being examined.
Big data is defined by three 'Vs' - volume, velocity, and variety. Huge tracts of information are produced every second of the day, and depending on one’s focus may be represented in any number of formats.
When it comes to big data analytics, what is most important is this last component. More diverse sources of data are now accessible than ever before: organisations may obtain information from areas as diverse as loyalty card schemes, website interactions, CCTV cameras, reviews, app use data, and more. This data can all be separated into two categories: structured and unstructured.
Structured data is what might immediately come to mind when you think of "data" — information neatly stored in a database or spreadsheet, for example.
Unstructured data, in contrast, is the kind of information found in emails, phone calls, online interactions and other such seemingly opaque forms that defy easy analysis.
Big Data analytics programmes, such as Spark, Hadoop, NoSQL and MapReduce, can analyse both structured and unstructured data from a wide variety of sources, recognising significant patterns that can be used to drive new business proposals or adjust strategies.
Additionally, companies such as Google and Meta offer their own analytics insights, although these typically lack the raw insight offered by datasets sourced in-house.
Types of big data analytics
Understanding of the three primary types of analytics that can be deployed with big data is key to using it most effectively.
The first is descriptive - for example, notifications, alerts, and dashboards. These tell you what has previously happened, but don’t elaborate on the causes or what may change as a result.
Next is predictive, potentially a more useful form of analytics. This uses past data to model what could happen in the future. For example, how sales could be affected by marketing conditions, or how a marketing campaign might influence customer behaviour.
Finally, there's prescriptive analytics. This uses techniques such as A/B testing or optimisation testing to advise managers and employees on how best to fulfil their roles within an organisation. For example, it could help a salesperson decide what types of discounts to offer customers, or give a developer insight into which form of advertising would work best on a webpage.
Trends in big data analytics
Tools to analyse data, whether it is in a data lake that stores data in its native format or a data warehouse, are still emerging. A number of different factors will determine how big data and associated analytics will operate in the future.
First is analytics in the cloud. As with a lot of things, big data analytics is increasingly hosted on the cloud. Hadoop can now process large datasets in the cloud, even though it was originally designed to do so on physical machine clusters. Among the companies offering Hadoop-based services in the cloud are IBM Cloud, Amazon's Redshift hosted by BI data warehouse, Google's BigQuery data analytics service and Kinesis data processing service.
Predictive analytics is also becoming more-commonly used. As technologies become more powerful, larger datasets will be able to undergo analysis and this, in turn, will increase the ability for change to be anticipated.
Video analytics is also a good example of where big data is being both produced and deployed. Cloud-based CCTV systems extract billions of data points each day, and these are used to power facial recognition systems, manage crowd control at events and even aid smart town and city planning. Similar systems are also found in cameras and sensors used by driverless cars, many of which are used to improve this technology and make it safer for eventual use on real roads.
Finally, there's deep learning. This is a set of machine learning (ML) techniques that use neural networks to spot interesting patterns in massive quantities of binary and unstructured data, and infer relationships without needing explicit programming or models. This is critical for training artificial intelligence (AI), and right now is one of the most scrutinised areas of development in the tech sector.
The combination of Big Data and analytics is an important part of keeping organisations one step ahead of the competition, especially as cloud computing becomes a ubiquitous backbone for business. But firms must also foster the right conditions to enable data scientists and analysts to test theories based on the data that they have, in order to reap the most valuable results.
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