Moving from Predictive to Prescriptive Analytics

Business analytics can be divided into three key components: descriptive, predictive and prescriptive. Descriptive analytics involves an analysis of the raw data. Predictive and prescriptive analytics take this data and turn it into actionable information. Predictive analytics is currently being used by organisations to improve processes in retail, manufacturing, customer service and many other areas. Using machine learning (ML) technologies businesses can achieve higher prediction accuracy, allowing them to take action earlier and make informed decisions on the future of their organisations.

When it comes to analytics, businesses are faced with these key problems:

• Incomplete datasets
• Lack of analytical resources
• Insufficient time allocated to deep data analysis
• Insufficient compute power
• Lack of statistical analysis and resulting biased outcomes

 

Machine learning technologies provide a solution to most of these issues. ML algorithms can be used to analyse complex, large sets of data at scale, providing predictive analytics that tell you not only what is likely to happen, but what you should do about it.

Predictive analytics aims to identify events before they occur using statistical modelling. With the right algorithm, real-time performance data and historical data can be analysed to pick out warning signs to predict events such as system failure before it happens. The process relies on two analytical capabilities:

• Descriptive analytics – identifying an occurring event
• Diagnostic analytics – determining why the event occurs

 

Using a combination of both descriptive and diagnostic analytics, business can now get deeper than ever understanding of key processes that impact their business and take meaningful action before it is too late.

Prescriptive analytics offers specific actionable steps to solve the issues brought up by predictive analytics. Prescriptive analytics applies machine learning algorithms to suggest ways to take advantage of future opportunities or mitigate future risks; it optimises the process for a better future outcome.

 

Prescriptive analytics systems have the ability to sort through, analyse, learn from, and build on datasets too large for humans to process. ML-powered prescriptive programmes can continuously learn by taking in new data to provide more accurate predictions. Any decision made on the back of a recommendation becomes another datapoint being fed into such system.

Most industries apply predictive analytics in one form or another. Here are some of the most popular real-world examples.

• Predicting market fluctuations to inform a trading strategy
• Weather forecasts
• Inventory planning and stock control
• Predictive maintenance
• Demand prediction to inform likely footfall in retail stores
• Route optimisation

 

Having rich data sets for predictive analytics will yield better results for prescriptive recommendations. Prescriptive analytics builds on the results from predictive analytics to understand what variables can be manipulated to achieve the desired outcome and how.

Predictive analytics uses real-time and historical data to find future outcomes, while prescriptive analytics analyses that data and finds potential results to recommend certain actions. An increasing number of industrial companies are actively leveraging ML-powered prescriptive analytics solutions to increase their operational intelligence, and those that do it well will likely outsmart their competition.