3 Ways to Make the Most of Big Data & Machine Learning

When I talk to executives at credit unions about big data, I inevitably get two reactions. On the one hand, they’re excited about what big data means for the future of banking. On the other hand, they’re wary about the promised return on these investments. They wonder if the demand is simply ahead of the technology, or if the impact of big data simply isn’t as great as many people have assumed.

These executives are often right to be disillusioned. Far too many efforts with big data suffer from what might be termed a “garbage in, garbage out” mentality. Such efforts prove that it’s not enough to gather reams of data. The data also has to be relevant, cleansed, contextualized and well structured, even if that structure is a derivative of an unstructured data set. Without these features, these companies end up with garbage. And getting access to lots of garbage – which is what “big data” often equates to – won’t help with a single business decision. In fact, in cases like these big data can often just be a distraction.