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 used to make decisions, run AI models, enhance public-facing technology and much, much more.
How we collect all this information is even more varied; the rise of IoT, cloud computing, and even our use of smartphones all enable businesses, governments and, yes, unfortunately, bad actors, to collect information. For businesses, however, it's a key tool in their quest for operational efficiencies and profits.
We use analytics to tease out the insights, patterns and strategies within datasets. This is through specialist software and or systems that process high volumes of data quicker than any team of people could. These are then used to inform or feed other systems to get that big business win.
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. There's an enormous quantity of information being produced virtually every second of the day and as it's coming from various sources, it's also in diverse formats.
When it comes to big data analytics, what is most important is this last component. The diversity of data sources available now is vast: organisations may obtain information from many areas such as loyalty card schemes, website interactions, CCTV cameras, reviews, app use data, and more. All this data can be divided into two categories: structured and unstructured.
Structured data is what might come to mind when you think of "data" as a concept of information stored in a spreadsheet or database for example.
Unstructured data, on the other hand, is the kind of information found in emails, phone calls and other more freeform arrangements that cannot easily be analysed using traditional data analytics.
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.
Types of big data analytics
Businesses need to be aware of the three types of analytics that can be deployed with big data.
The first is descriptive - for example, notifications, alerts, and dashboards. These tell you what has previously happened, but don't give the reasons why it happened or what may change.
Next is predictive, which is 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 customer might respond to a marketing campaign.
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 police officer predict criminal activity, inform a salesperson on what types of discounts to offer customers or tell a web developer what ad will work best on a webpage.
Trends in big data analytics
Tools to analyse data, be it in a data lake that stores data in its native format or a data warehouse, are still emerging. There will be several trends that 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 moving to 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.
The use of predictive analytics is also increasing. As technologies become more powerful, larger datasets can be analysed and this, in turn, will increase predictability.
Video analytics is also a good example of where big data is both coming from and being used. 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 street and town planning. Similar systems are also found in cameras and sensors used by driverless cars, many of which are used to improve the technology and enable it to be used in real-life.
Finally, there's deep learning. This is a set of machine-learning techniques that use neural networks to find interesting patterns in massive quantities of binary and unstructured data and infer relationships without needing explicit programming or models. One deep learning algorithm has been used to look at Wikipedia data to learn that California and Texas are US states.
The combination of Big Data and analytics is an important part of keeping organisations one step ahead of the competition. But these businesses must also create the right conditions to enable data scientists and analysts to test theories based on the data that they have.
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