Data is at the heart of business decision-making. However, until recently, most of that data came from sources such as customer feedback and marketing testing. The rise of big data, however, has created a situation in which more businesses can launch products based on predictive analytics rather than experiential testing. It’s a different marketplace.
Are there demonstrable differences between research-driven business decisions and those made based on predictive analytics? Here’s what a closer look can tell us about how big data is shaping marketing.
A Bigger Pool – With No People
In the past, pre-launch market research required businesses to gather a pool of subjects to test a product or ad campaign – but often, these pools were limited, leading to insufficient data. The metrics available from big data, however, allow businesses to use modeling to test new ideas. Because the data pools available for modeling are much larger than those found in most market research, they have the potential to be more accurate than limited pre-launch tests.
On the other hand, statistical modeling may make it more difficult to differentiate between correlation and causation. With a pool of actual subjects, you can interrogate them and gain further insights into their responses to your idea; when using statistical modeling you just have to run test after test to ascertain what’s happening within the data pools. If you miss an important test, you may end up misunderstanding what the market forces call for.
Do You Have Enough Data?
Another problem that many businesses are encountering, in this era of predictive analytics, is that of insufficient data. While large, well-established companies have an overwhelming amount of information to work with, new, smaller businesses often lack the key information needed to make smart decisions. But if small businesses are going to use older research styles to make decisions, they’ll never keep up with e competition. What can they do?
Because big data’s statistical analysis systems are now so strong, businesses can do a lot more with limited data. By successfully defining the terms of the problem and exploring the scope of potential solutions, companies can build a tenable plan out of sparse scaffolding. The testing phases may still be longer than they would be for more established companies, but the potential for success is greater.
From Predictive To Prescriptive Analytics
Ultimately, big data’s ability to shift businesses away from predictive analytics – defining what could happen – to prescriptive analytics – suggesting what the company should do – may be the real game changer in how modern decision making works. With prescriptive analytics, companies have data-based assurance that they’re on the right path in the face of many possible outcomes. It’s a much more measured approach to decision making, replacing human error with calculated machine judgments.
The rise of prescriptive analytics has brought many business leaders to believe we’re moving into an era where data will lead problem-solving efforts. This may well be true. Human problem solving is much more biased and can’t hold nearly as much data in mind simultaneously; we’re notoriously unreliable. Whether machines fair better since we automatically code in some of our own biases, is yet to be seen, but all signs point to yes – big data will push business decision-making to new heights, making it faster, smarter, and more comprehensive than ever before.
