This chapter is from the book
This chapter is from the book
This chapter is from the book
Is Big Data a Strategy?
Here’s the problem with this so-called strategy. Suppose that reams of data are captured. All that we’ve done is kick the can down the road. Deep in the recesses of the warehouse we’ve built, in the multitude of haystacks we’ve collected, is the golden nugget that we’ve been looking for. The problem is that we now have to go through each of those haystacks. And, more likely than not, we’ll find something. Whether we can do anything with what we find is another story. The focus on collecting more and more data has obscured what we should have been asking ourselves from the start: What actions are we going to take?
It’s true that data are necessary to derive insights, and those insights inform the actions we take. However, thinking strategically requires that we work backward. Asking first what it is that we’re trying to do, we can then identify the insights needed to inform such actions. Based on the insights we need, we can back into the data that are needed to yield such insights. If we start blindly by compiling data without considering where we’re trying to end up, we run the risk that we’ve created more work for ourselves because we now have to sift through mountains of irrelevant data that have been captured in our dragnet.
The challenges faced by many organizations, from city governments to publically traded corporations, don’t require Big Data. Rather, these organizations should be taking a look at the key issues they’re facing and considering how those issues can be investigated. It’s not that more data are necessarily better. Sometimes more data are just more.
What the conversation should be focused on are the data that will lead to bigger insights. Sometimes this does in fact require more data or different types of data. In other cases, it requires rethinking the assumptions we currently hold and applying a different type of analysis. In specific circumstances, Big Data may be the raw fuel powering these insights. However, on its own, Big Data doesn’t tell us what we should be doing. It doesn’t tell political campaigns in which media markets to place advertisements. It doesn’t tell retail stores to whom to send coupons. It doesn’t tell city agencies how best to use their limited resources. Making such recommendations is where the artistry in data science comes into play. If Big Data is a natural resource, then the advanced statistical tools employed by analytic professionals and data scientists serve as the means of extracting and refining the raw material into something of value.
A colleague has suggested that Big Data is like “rocket science.” There are actually people who are doing rocket science, but the common use of the phrase extends beyond the well-credentialed few. There are indeed organizations that are knee deep in Big Data, but the phrase has become a catchall for most things involving data. Like it or not, we are living in the age of Big Data.