Which Analytics Do You Really Need?- A Boeing 787 aircraft generates half a terabyte of data on an average flight. That’s an enormous amount. Though it’s filled with meaningful insights, the sheer quantity can make it seem like an endless maze.
The best way through the maze is to determine a path to follow. A recent survey by NewVantage Partners reveals that 97 percent of executives are investing in analytics projects. What this figure does not reveal is that each of those projects is unique, incorporating different tools in pursuit of individual goals. Business analytics are a powerful tool, but only if you embrace the type that is right for you.
Analytics come in four distinct types, and each builds off the other. Imagine a pyramid, with each level supporting the next: descriptive, diagnostic, predictive, and prescriptive.
The base of the pyramid is descriptive analytics, which report what happened and allow analysis of past performance data in order to identify known strengths and weaknesses. A descriptive report for United Airlines, for example, could show how many tickets the airline sold last month in different major markets.
The next level is diagnostic analytics, which report on why something happened and reveal what factors drive positive and negative performance. If United’s descriptive report shows that sales are down in one market, a diagnostic report might show that it’s because of reduced marketing spend.
With those as our foundation, we can use predictive analytics to report on what could happen, based on past performance. If United wanted to increase revenue, its predictive report could show where higher marketing spend would go farthest to increase revenue.
At the very top we have prescriptive analytics, a report on what should happen. Here, artificial intelligence and machine learning mine past data to inform future decisions. Prescriptive data could tell United’s marketers exactly which price points to push in their latest sales copy.
Analytics from the bottom up.
Companies often prioritize a single tier of the analytics pyramid without first securing more primary tiers.
The process starts with stabilizing your operational data. Only once operational data has been properly structured and organized is it possible to think about more advanced analytics. Companies that fail to build an analytic foundation are often saddled with huge tech costs, business intelligence software that can’t meet its full potential and weak overall insights.