The Journey From Descriptive to Prescriptive Analytics

Across the board, businesses sit on top of a goldmine of data that has the potential to derive important insights and drive vital decisions about their business processes. But, due to its raw state, most businesses treat data like a landmine instead of a goldmine, preferring to minimise their engagement with it due to a lack of a cohesive data strategy.?

Data engagement needs to be done the right way. Relying on raw, unstructured data to inform future plans of action is like throwing a line in the ocean hoping for any kind of bite, never mind what kind or from where.?

Approaching data can understandably be intimidating, so many businesses either do it minimally or don’t do it at all. With this in mind we need to ask ourselves, how well do we understand the value of our data? Do we have a data management strategy in place, and do we understand its limitations as well as capabilities?

Busting the Myth of Homogenous Big Data - Descriptive and Prescriptive Analytics

You’ve probably read and heard the term “big data” numerous times, but its overuse has somewhat turned into a catch-all buzzword, which erodes its meaning. At its core, big data is simply vast datasets that arrive in high volumes from numerous sources.?

Utilising big data is all the rage right now for businesses looking for the next trend to jump on but the truth is, without understanding the nuances of big data and correctly interpreting the insights it provides, it won’t have much impact at all on your business outcomes.

Big data is often confused as being homogenous thanks to its all-encompassing terminology but under the lens of data analysis, it stratifies and tells different, but equally important stories.

Descriptive analytics allows businesses to assess what’s already happened through data aggregation and data mining.?

Predictive analytics uses forecasting tools and statistical models to help businesses predict what could happen in the future.?

Finally, prescriptive analytics further analyses the data from predictive analytics to make specific recommendations for action.?

Why You Need More Than Just Descriptive Analytics

Descriptive analytics, while important, only consists of static data telling the story of what’s already happened. While there’s nothing wrong with looking at retrospective data (in fact, it’s a necessary part of data management) descriptive data is limited in what it’s able to present. It’s able to tell us what happened but not what will happen.?

For this reason, it’s vital to have both predictive and prescriptive data to inform your business decisions and strategies. Predictive analytics makes use of mined and historical data, figures and statistics to generate potential outcomes and future scenarios.?

Prescriptive analytics assesses predictive models already generated to recommend suggestions for the most optimal outcomes. It provides the guidance and intelligence you need to ensure you’re making the best possible decisions for your business at all times.?

Making the Journey From Descriptive to Prescriptive Analytics

It’s important to understand that prescriptive analytics and data serve a different but equally important purpose to predictive and descriptive analytics. They work together to serve and optimise your business efforts.?

A good data management strategy needs to make room for descriptive, predictive and prescriptive data analysis.

Even though prescriptive analytics has the power to help change the course of your business actions, it can’t act alone. You need descriptive data to feed the prediction models of predictive analytics as well as the recommendations that prescriptive analytics makes.?

Conclusion

Understanding the role that these different datasets play is critical to your business’s success. Comprehensive data management is an ongoing process of analysis, refinement and evolution from descriptive to prescriptive analytics, not a destination or end-point.