Stagnation is a familiar yet unfortunate trap for businesses. The early business model proves successful, profits soar, and a “don’t fix what isn’t broken” mentality sets in. But trends come and go, the market evolves. In order to survive, and even thrive, companies need to consistently seek out new and innovative ways to improve themselves. In that never-ending search, many have turned to the opportunities found in leveraging big data.
Across a number of industries, the C-suite often asks itself, “how can we best use the wealth of information we’re already gathering?” Here enters prescriptive analytics. Once implemented, collected data is run through an engine to identify hidden gems of value and is then translated into insight and actions outlining how to maximize efficiency of current business practices. This crowd sourcing method helps professionals understand what exactly is working in the field. Simply put, it helps reduce waste and raise the top & bottom line. What is making prescriptive so attractive is that it does not discriminate between internal and external behaviors. For example, a retailer might leverage prescriptive to determine which sections of a store are receiving the most attention from customers and how to capitalize upon that (i.e. external behaviors). Versus a supply chain manager who uses prescriptive to identify average shipping times which can increase the efficiency of deliveries (i.e. internal behaviors). Furthermore, it democratizes analytics by delivering the information in plain English, right to the person who should see it, rather than requiring a trained professional for interpretation.
But prescriptive also has the potential to go beyond simple practice improvement. As solution providers create more intelligent engines, they are able to actively identify problem areas that are costing the organization in revenue. To use the retailer as an example once again: prescriptive is able to flag excessive lead times when hot items aren’t being replaced on the shelves fast enough to meet consumer demand. Over time, if retailers don’t act upon these insights, it can cost a small fortune.
So if prescriptive analytics has such potential to improve operations, why isn’t it the superstar of big data? There are, unfortunately, a few misconceptions which are hindering adoption:
- Time to implementation. This is an issue that applies to a number of analytics solutions, not just prescriptive. Despite a desire to leverage analytics for big data, many decision makers are hesitant to pull the trigger due to a belief that time-to-launch may require up to six months. In fact, there are providers who can have a solution fully integrated and working in two weeks.
- Return on investment. As with any new technology, business leaders worry that it would not be worth the investment in the long run. However, prescriptive has proven its value time and time again. In one instance, prescriptive was able to save a national grocer $1.8 million annually by recognizing a packaging issue pattern.
Ultimately, as businesses decide how to best leverage their big data, how exactly to implement an analytics strategy will become a necessary conversation among decision makers. With demand growing for a reliable solution, prescriptive holds the keys to efficiency with the least amount of risk and the fastest time to value. For businesses, it is simply a matter of time before market trends force change. Prescriptive analytics can help them stay ahead of the curve.
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