Two of the hottest terms in the business world right now are “Predictive Analytics” and “Data Science”—we all know that they are the keys to making better decisions and ultimately improving business performance, but how do they actually do it? And if they are so crucial, why isn’t everyone implementing them?
Most organizations are already well down the path of their analytics strategy, but have been focusing mostly on descriptive analytics—or what we typically call business intelligence (BI). The real difference between descriptive and predictive analytics is simply in how they achieve their goal of “surfacing insights.” BI tools make heavy use of visualization techniques and rely on human interaction to drill, slice, dice, and draw data whereas predictive tools leverage mathematical algorithms guided by humans to look at data in ways and at speeds not humanly possible.
Barriers to Predictive Analytics
The real barrier preventing many enterprises from adding predictive analytics to their existing analytics landscape is the perception that it will require a mountain of cash, a library of knowledge, and mythical data scientists that everyone knows are scarce. We’re trained to consider everything as a cost, to de-risk projects so far they are no longer innovative, and to prove tangible value before you even start. Now is a good time to start unlearning those bad habits!
Predictive analytics is not a total cost of ownership (TCO) discussion—it’s a return on investment (ROI) one. How? Consider that if you had to make a very important business decision and a magical genie popped out of a lamp and could tell you with great certainty which option will make you more money, would you want to know how much money you could make or would you ask how much the lamp costs first?
That said, predictive analytics isn’t a genie in a lamp—but neither is a data scientist. Even if your business employs data scientists, they likely do not have the domain knowledge for the business problems you are trying to solve. By considering descriptive analytics (BI) and predictive analytics (data science) as separate, it’s very difficult to have a coherent analytics strategy that leverages the best of both worlds.
Adding Predictive to Your Analytics Strategy
Fortunately, integrating predictive into your existing plans doesn’t have to be difficult and best of all, it doesn’t always require a data scientist (although they can be useful if you have them). The key for determining where to start is by focusing on a defined business problem that is small, yet important. It may be a problem you don’t know how to solve, or one that you are currently struggling with by using a BI solution.
For example, the question “Why are my customers cancelling their service with me?” is a common problem that gets to a BI analyst’s desk. The BI approach is to slice, dice, drill, and graph characteristics that those who have already cancelled might share so we can filter on the remaining customers and reach out to them before they call us to cancel. We rely on the analyst’s knowledge of the market, customer base, data set, and personal experience to choose how to successively narrow the dataset.
The predictive analytics approach is to “train” a predictive model by feeding it data on customers who have cancelled and those who have not so the model understands each characteristic of the customer and how much each would contribute to their decision to leave us. We can then use the predictive model to “score” each customer on their propensity to leave and even understand the key influencers to their potential decision. The BI analyst can then filter on the score— a weighted variable that takes all of the customer’s characteristics into account, resulting in a far more accurate prediction than cascading filters in a BI report.
Making It Happen
You may be wondering how to “train” and “score” and do all that predictive stuff without a data scientist. Automated predictive algorithms have come a long way and SAP has pioneered a number of technologies that encode many of the steps that data scientists currently do by hand or through scripting today. Think of it as “Data Scientist-in-a-Box.” At the core of the solution are automated predictive algorithms that have had over 18 years of data science refinement both in the mathematics lab and in the real world where hundreds of customers who rely on it for mission critical decisions every day.
While it may sound like automated predictive analytics is only for the non-data scientists, it’s quite the opposite. Automated predictive technologies make quick work of the boring, tedious, and error-prone tasks that make up “data science.” A skilled data scientist can tweak and optimize automated algorithms to make them even better, and at a fraction of the time it would take them to do even a basic predictive model by hand. This frees up the data scientist’s precious time so they can work on more projects and provide value to more parts of the business.
Regardless of where you are in your analytics journey and whether you have data scientists on staff or just really smart business intelligence experts, there’s a 100% chance that predictive technologies can either remove some analytical obstacles— or at least make them much easier to conquer.