IT Pro is supported by its audience. When you purchase through links on our site, we may earn an affiliate commission. Learn more

What is big data analytics?

We explain the differences between descriptive, predictive and prescriptive methods of looking at data

Big data has been part of the tech consciousness for around a decade, with businesses realising that collecting data generated from websites, POS terminals, devices, machines, Internet of Things (IoT) devices and more can enhance profits and reduce inefficiencies.

While it's accurate that big data is a valuable asset, with some labelling it the "new oil", much like any resource it's useless if you don't do something with it. 

This is where big data analytics comes into play; by employing specialist software and systems to process these vast collections of information, businesses can acquire insights they can then use to assist them towards those legendary mega margins.

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.

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.

Featured Resources

Four strategies for building a hybrid workplace that works

All indications are that the future of work is hybrid, if it's not here already

Free webinar

The digital marketer’s guide to contextual insights and trends

How to use contextual intelligence to uncover new insights and inform strategies

Free Download

Ransomware and Microsoft 365 for business

What you need to know about reducing ransomware risk

Free Download

Building a modern strategy for analytics and machine learning success

Turning into business value

Free Download

Most Popular

Russian hackers declare war on 10 countries after failed Eurovision DDoS attack
hacking

Russian hackers declare war on 10 countries after failed Eurovision DDoS attack

16 May 2022
Researchers demonstrate how to install malware on iPhone after it's switched off
Security

Researchers demonstrate how to install malware on iPhone after it's switched off

18 May 2022
Windows Server admins say latest Patch Tuesday broke authentication policies
Server & storage

Windows Server admins say latest Patch Tuesday broke authentication policies

12 May 2022