This is the final article in our series about big data analytics, and it answers the most important question of all: What impact will data analytics have on how we make decisions?
The question in today’s business landscape isn’t whether big data analytics has value or will be picked up by various industry leaders. More than 70% of CSPs have invested in big data analytics, says Ovum. While we’ve addressed some of the challenges inherent in adopting data analytics in other posts, let’s ask the big question in this one:
How will big data analytics change our decision making?
To understand that, we have to understand what business decision makers want. In short, they want Google Maps.
Why Decision Makers Want Google Maps
Google Maps does one thing, and does it well: it tells you where to turn when you’re going somewhere. It doesn’t tell you what satellites it’s using, what the wind speed is, and what other cars are doing. It gives you simple information to act on.
This is what decision makers need. They’re not looking for extraneous information. They just want to know when to turn left. They want actionable information.
How Big Data Analytics Is Changing Business
Let’s examine this concept in the context of a realistic business conundrum. For example, say that an online retailer wants to maximize revenue by minimizing churn. In the past, a churn model would be deployed to tell them the likelihood of any individual customer leaving their ranks. But this information only addresses part of the problem: it doesn’t give them insight on keeping the customer. And it doesn’t tell them if the customer is even worth saving.
Big data analytics adds two more dimensions to this approach by adding two more independent models. These models calculate the lifetime value of the customer (and identify high-value customers) and predict their responsiveness to various marketing activities.
Combining these models can be tricky when you add the human error element into the equation. That’s why Absolutdata believes so firmly in decision engineering – a method that takes the output from several models and weighs it in the light of possible options. In the end, decision makers are given a set of actions and a probability of success for each action.
So, for our example, suppose our online retailer runs Customers A and B through the decision engineering system. Instead of merely learning that Customer A is likely to stay, while Customer B is likely to leave, they get a set of actions to evaluate. Customer A is likely to stay, but they’re not a high-value customer. Customer B is a churn risk, but they’re also a high-value customer. Based on past history, offering Customer B free shipping only has a limited effect; offering them a loyalty discount is far more likely to bring them back to the online retailer.
This multi-dimensional look delivers not only insights; it delivers possible outcomes. And because all the information is available, a logical decision can be reached.
Is 2016 Your Big Data Year?
As technology becomes ever more ubiquitous, decision makers are demanding more from it. They want the power of data analytics available when and where they need it. They want the results clearly displayed and delivered promptly. Most importantly, they want this to be fueled by the most advanced processes available.
Big data analytics is evolving to meet these challenges by providing insight into possible decision outcomes, not just on isolated factors. Is this the year that big data analytics becomes part of your organization?
