Data Explained

In organizations of nearly all levels, there’s now a firm reliance on data. Business professionals, strategists, engineers, and scientists all use data to get their work done. With the heavier reliance on data, there’s a risk of data inundation. That is when there is too much data for an individual or team to make sense of. To manage this risk, a new set of startups will rise to service the need to explain the data.

As physical work is replaced by automation and digitization – all of it backed by data – more of the work done in offices will shift towards the complex comprehension of data feeds and settling on decisions. That is what I mean by explaining data. To facilitate this shift in work, new tools will need to be created.

Both Google Analytics and Adobe Analytics, for example, have built-in anomaly detection. Tableau takes this explaining approach one step further and allows users to easily get data-driven responses to questions like “Why are bike rentals spiking in August?”

In time, more businesses will be built around leveraging the millions of observational data points to provide more explanations on everything from automated machine lines in a manufacturing facility to page views and click rates. I believe the opportunity exists in developing a unique form of data capture to facilitate the explanation of data in areas that are currently not being looked at by incumbents.