A few years ago, it seemed that every pitch – from early-stage VC decks to banker composed CIMs – ended with a proclamation that the firm’s core operations would generate a valuable data set that could be sold to others.  This was to be a soft of alchemical tailwind that made good business models great.

The sobering reality, however, has been that most firms aren’t actually able to sell their data. This can be for a diverse set of reasons, but most often, the information simply isn’t useful enough to others to justify a meaningful price.

It would therefore be tempting to draw the conclusion that most firms’ data isn’t valuable because it can’t be packaged and sold.  Simply put, this is the wrong conclusion.  The truth is that many firms simply aren’t doing the right things to extract the value of their data.

Shift the Focus to Data-Powered Operations

 To do so, companies first need to re-frame the question from “How could my data be valuable to others?” to “How could my data be valuable to my operations?”

At an introductory level, many firms have addressed this second question via executive dashboards.  These views, which unify disparate data sets to provide a lens into both leading and lagging business indicators, can be powerful navigation tools for those steering the ship at the highest levels.

The more powerful – but less celebrated – lever, however, comes at the operational level.  By embedding the power of data into everyday processes, workforce activity can be intelligently guided to the most optimal actions.

Example: Data Driven Customer Service

As a simple example, consider a customer service rep responsible for supporting hundreds of customers.  If left to their own devices, they might choose to reactively communicate with clients who have reached out with an issue.  If they have extra time, they might additionally choose to supplement this reactive posture with proactive, round-robin outreach.

But what happens, instead, if customer interaction with a company’s products is catalogued and analyzed for patterns?  Leveraging relatively simple regression analysis, companies can proactively classify customers as “healthy” and “unhealthy”.  These classifications can provide a highly valuable intervention trigger, thereby reducing the rate of customer churn.

Use cases such as this are many, and often involve leveraging data firms already record and store.  Establishing a culture that Identifies these opportunities, models them with data and integrates them with processes and applications can establish a competitive moat that separates companies from the competition.