By Southard Jones
As the awareness around the possible gains that can be afforded to organizations by leveraging analytics grows, so does the awareness of the various categories of analytics. When you look at the Fortune 1000, I would estimate that 75 percent of all large corporations are familiar with “prescriptive analytics.” These enterprises have IT teams that know prescriptive analytics exists and they understand the benefits this approach offers. However, I would also say less than 25 percent are using prescriptive analytics at scale.
The primary reason for this comes down to the data challenge. This area of analytics is complex and today’s organizations usually keep it within the realm of data scientists or data analysts. It’s currently difficult to update prescriptive analytics programs in a timely manner, and nearly impossible to then have that information accessible to business users so that it can be used in a meaningful way.
The key to successfully implementing prescriptive analytics is part technology and part expertise.
However, this is a growing opportunity and it will expand as tasks can be automated. The important thing here is making prescriptive analytics suitable for everyone within a business, rather than it being the preserve of the data scientists alone. As this automation takes place, I think we’ll see use of prescriptive analytics double within the F1000 over the next year.
Predictive analytics helps a business predict what is likely to happen, whereas prescriptive analytics helps an organization influence what is going to happen. This is the next step in using data to run a business better. A great example of this is Amazon’s usage of prescriptive analytics for recommendations around a purchase: when a shopper buys an item, Amazon uses data to suggest other items for the shopper to buy based on the original purchase. This approach helps Amazon take a greater share of the customer’s wallet and provides a better user experience.
Prescriptive analytics has the most impact when you can get data into the hands of ordinary people across a business, rather than keeping it within the data science team. If you get people to use data as part of their decisions on a daily basis, their individual performance can improve. Over time, this adds up to a huge benefit for the whole company, whereas predictive analytics tends to improve a small piece or segment of the business.
Prescriptive can be very useful for sales environments, where data can be used to improve the chances of success. What would be the best price to ask for something? Do you think they would pay more or less if the deal was different? Which product has the best fit with the customer? Which customers are most likely to buy that given product based on their demographics and previous purchase behavior? Getting uplift in profitability or winning ratio can have a huge impact on the business.
In order for a business to be successful, its approach to data has to go through a maturity curve; first, getting the diagnostics analytics right is critical, after this step, it is key to implement predictive analytics and then the business can follow that with prescriptive analytics. Diagnostic analytics helps a person understand what is happening in the business and why. Predictive analytics involves using that past data to ‘predict’ with some confidence what might happen in future. Prescriptive takes the “What might happen?” scenarios and helps you load the dice so that it comes true – actually influencing the future so that it rewards the company.
Southard Jones is Birst‘s VP of Product Strategy. Southard was previously Vice President of Products at SCIenergy, a leading provider of Energy Management Analytics to commercial buildings, where he transformed product and go-to-market strategy, leading the company to a five-fold growth in quarterly ACV bookings. Prior to SCIenergy, as Vice President of Products, he led Right90, a pioneer in SaaS sales forecasting, from start-up to acquisition. His software career started at Siebel where he ran Performance Management and Workforce Analytics product lines in Siebel’s fastest-growing business unit, Sibel Analytics. Southard holds a BS in Mechanical Engineering from Cornell University and an MBA and MEM from Northwestern’s Kellogg School of Management and McCormick School of Engineering. Connect with him on LinkedIn.
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