Jan Bosch is a research center director, professor, consultant and angel investor in start-ups. You can contact him at jan@janbosch.com.

8 June

This week, I had an interesting discussion about data with the CEO of one of the startups I work with. The challenge is that many companies are collecting vast amounts of data, storing it and then leaving it as an unused asset. It surprises me that so many companies are maintaining such amazingly large data stores without finding good ways of using them.

The key underlying reason, in my opinion, is that collecting data is easy, but generating actionable insights out of it is hard. It requires a deep understanding of the domain, as well as insight into what provides value within the domain’s context. This calls for a mindset different from the one present at most companies, where the focus often is on doing what we’ve always done, but a little bit better or faster.

The company of the CEO I talked to operates in the media domain and has reached a point where key employees of its customers receive a daily email listing the highest-priority tasks that they should focus their energy on that day. These tasks are identified by collecting data from the relevant media properties of the customer, analyzing this data to identify deviations and anomalies and then recommending the most likely mitigation actions that will address the identified concerns. These mitigation actions are the tasks that the key employees receive in their daily to-do list. To me, this is the hallmark of being a data-driven company: it’s not about the data, but about generating actionable insights from the data that you can use to your advantage.

Of course, in this case, the insights are generated based on the customer’s own data. The next step is to get some or all of the customers to agree to anonymously share their data with the company. This allows the company to compare each customer to all the others, which allows for the next level of insights to be generated. Now, it’s not just deviations and anomalies identified within your own scope, but also those identified through comparison with others. By comparing your performance with others in the same industry, it’s much easier to gain insight into where energy should be invested to improve.

In many industries, there are mechanisms in place for companies to compare themselves to others. However, these mechanisms tend to be manual and often run infrequently. The typical example is the yearly employee survey where companies compare their employee engagement score with others in the same industry. Here, we’re talking about continuous, quantitative and fully automatically generated insights that give you daily input on where to spend your time.

Concluding, to be a data-driven company is not about having lots of data. It’s about generating valuable and actionable insights from that data continuously and using those to generate, preferably automatically, the actions for your team that will have the most impact. It’s not about the data; it’s about what you do with it.