Congratulations! Your company has amassed a collection of data. That’s what organizations are supposed to be doing, right? Well, yes, but there’s a lot more to it than that. What really matters is how you interpret that data and allow it to inform business practices.
“What I see across most industries is that a lot of people want to have a good strong presence around the application of analytics,” says Donald Spaulding, Design and Innovation Partner at a leading telecommunications company. “They think that that is related to having a lot of data, [but] I find that most companies are very data-rich but very business application-poor.”
Fortunately, the tools available to data scientists are more versatile than ever before. However, that means nothing without forward-thinking leaders who possess a clear direction on a business’s goals. There are four distinct phases of business analytics: descriptive, diagnostic, predictive, and “the final frontier,” prescriptive. While the first three offer valuable information on where a company is and where it expects to be, prescriptive analytics takes it to the next level by turning evaluation into opportunity.
In order to fully understand prescriptive analytics, let’s first take a brief look at each phase:
- Descriptive analytics
Descriptive analytics looks at existing data to see what has happened, currently and in the past. For example, a retailer can see how well a particular product sold in a given period.
- Diagnostic analytics
Diagnostic analytics ask why the data turned out as it did. If that product sells more units at a given time of the year, is it related to holidays, weather, or another factor?
- Predictive analytics
Predictive analytics showcase probable outcomes based on current patterns. If we notice that our product sells twice as well when it’s snowing, we can expect a surge in sales if there’s a blizzard in the forecast.
- Prescriptive analytics
Prescriptive analytics allow organizations to go beyond estimating future events by manipulating aspects of the data to determine the ideal strategy (or strategies). If we know a blizzard is coming up and offer a deal on another item that complements our product, will it boost profits? What sort of discount will maximize sales? What demographic is most likely to take advantage of this offer, and how do we target them?
“When you’re [using] prescriptive, you kind of orient towards action,” says Ksenija Draskovic, Chief Data Science Solutions Officer at KDSS Analytics. “You start adjusting and optimizing and going back and forth until you connect all the dots.”
Demographics and Trends
Data is fruitful at every stage of the supply chain, but the chief reason it is so prized is for its enlightening profiles of consumers. Companies can track trends among their customer bases, allowing them to allocate targeting efforts based on age, geography, and just about any other category imaginable.
For example, “they happen to be millennials, they happen to be moving around, they happen to be interested in this genre of music,” says Draskovic. “You can personalize the message because this is what we found out. That’s when you have that conversation when it’s more action-oriented and prescriptive, using those insights to better achieve those goals.” The ability to adjust the customer experience provides a major competitive advantage, and in some cases, directly correlates with profits.
“Even in the in the industries that have historically not really been able to justify investment around how you apply data, I think that’s changing,” says Spaulding, citing utility companies in particular. “One of the things that have become important to them is customer satisfaction now factors into whether or not they can actually get successful approval for rate increases. It has a financial bearing on them now.”
Beyond Programming
Because the field can be both so powerful and so easy to mishandle, there are numerous confusions surrounding data analytics. One of the biggest, according to Draskovic, is when companies equate data scientists with programmers.
“[Programmers] want input, and they produce output,” she says. “They really don’t care what this input’s saying. They just get the task to do, which makes sense. A data scientist is something a little different. They need to understand the business problem.”
A core difference is the ability to conduct the “sanity check.” “[A programmer] will say, ‘Okay, I have the result. Here we go,’” says Draskovic. “But you have to ask does this make sense? Does this story make sense? Does the output make sense? Does this connect to the business?”
“[Many companies] almost hoard this data, and often you find that the data is not in a form that’s accessible,” says Spaulding. “It’s not in a form that can be applied. If it is in a form that can be applied, it’s being misapplied.” In many ways, this is worse than not having the data at all.
Katalyst Can Help
Most enterprises are not using their data as wholly or wisely as they could. Don’t let your organization be one of them. Implementing prescriptive analytics can be a departure, but the experts at Katalyst are here to help. Reach out to schedule a consultation and see what your data can teach you.
