There is a lot of hype surrounding data and analytics. Firms are constantly exhorted to set strategies in place to collect and analyze big data, and warned about the potential negative consequences of not doing so. For example, the Wall Street Journal recently suggested that companies sit on a treasure trove of customer data but for the most part do not know how to use it. In this article we explore why. Based on our work with companies that are trying to find concrete and usable insights from petabytes of data, we have identified four common mistakes managers make when it comes to data.
Mistake 1: Not Understanding the Issues of Integration
The first challenge limiting the value of big data to firms is compatibility and integration. One of the key characteristics of big data is that it comes from a variety of sources. However, if this data is not naturally congruent or easy to integrate, the variety of sources can make it difficult for firms to actually save money or create value for customers. For example, in one of our projects we worked with a firm which had beautiful data both on customer purchases and loyalty and a separate database on online browsing behavior, but little way of cross-referencing these two sources of data to actually understand whether certain browsing behavior was predictive of sales. Firms can respond to the challenge by creating “data lakes”, holding vast amounts of data in their unstructured form. However, the very fact that these vast swathes of data now available to firm are often unstructured, such as in the form of strings of text, means it is very difficult to store them in as structured a way as could occur when data was merely binary. And that often makes it extremely difficult to integrate it across sources.