Predictive Analytics and Business Intelligence

Predictive Analytics and Business Intelligence

When most people talk about predictive analytics and Business Intelligence (BI) they usually frame it as an either/or discussion. Which is best, why you should choose one over the other. That is a fundamentally flawed approach, however. The two are not mutually exclusive, and in fact, complement each other fantastically. A much better way to look at predictive analytics is in what ways it can make BI more effective and how it is driving the technology forward.

Predicting the future is something every company would benefit from. Being able to prepare for all eventualities or, even better, prepare for specific eventualities. An increase in the capability of data-gathering and processing tools means that predictive analytics enables companies to create models to predict what future situations might look like. Unsurprisingly, this is popular technology and Gartner has predicted that by 2020 predictive analytics will account for 40% of enterprises’ net new investment in BI.

How Does Predictive Analytics Work?

Predictive analytics takes advantage of historical data and advances in machine learning and artificial intelligence (AI) to create data models that predict what can happen in the future. The learnings from this historical data are then used on data that has been collected recently, preferably in real-time, to predict more accurately what could happen next. It can be used to suggest operational changes, such as when machines in a factory will require preventative maintenance, or even suggest changes in pricing for certain times of the year. The beauty of machine learning and AI is that it will adapt to its inputs.

Predictive Analytics Use Cases

The benefits of being able to predict future trends via the analysis of historical and real-time data are obvious. Business decision-makers will be given greater insights and empowered to make smarter and more evidence-based decisions. Businesses that shift from being reactive to proactive rarely have a bad thing to say about it.

Traditional uses of predictive analytics are very much focused on increasing revenue, with a particularly strong focus on customer behaviour trends. i.e. when people are most likely to spend their money, and how they usually choose to do this. As the technology becomes more understood and more commonly offered by BI vendors the variety of applications will naturally also increase. Already there are cases of forward-thinking businesses using predictive analytics to help HR with recruitment, and monitoring employee well-being. Every advantage available will be used.

The Future of Predictive Analytics

Predicting the future of predictive analytics certainly has a sense of irony about it. Although we cannot say with as much certainty as some predictive analytics tools what the future might hold, in general, though the future will follow similar trends to the rest of the BI industry. Real-time data collection, analysis, reporting and now prediction will become more commonplace. Indeed, it could be argued this is not so much a prediction as a status report. More advanced, more user-friendly and more accessible machine learning and AI tools will also mean that, combined with predictive analytics, some smaller decisions and mundane tasks will require absolutely no human input at all. Ultimately this leaves decision makers with more time and resources to drive the business forward.

What Next?

Predictive analytics is one of the most powerful tools in the arsenal of decision makers. It is a future-focused strategic capability that offers insights simply not available from other methods. BI and data visualisation have long been used to report on what happened and why, predictive analytics allows businesses to ask, ‘What Next?’

It is tempting to simply focus then on the ‘what next?’ But ignoring the ‘what happened’ and ‘why’ is to devalue BI and make the entire picture confusing. Data is most effective when the entire story is told – especially in the hands of machine learning and AI tools. The more complete the picture of what happened, the more accurate the answers to ‘what next?’ will be.