By Audrey Tregaskes, Strategic Solutions Executive


Some might be familiar with the company Nielsen, who has been selling data since the 1950’s.  Back then, households would be given a meter to put on their televisions which would provide data on the programs being watched so that television networks could set prices on advertising for commercials.  Nielsen has grown their business since then, expanding into other areas of entertainment (streaming, apps, sports, movies, as well as retail) but they were very early adopters of data monetization, and very successful at it!

Gartner has a simple, yet accurate definition of data monetization: “The process of using data to obtain quantifiable economic benefit”.  Simple definition? Yes.  Simple to implement? Not so much.  Let’s dive in a bit to understand the various ways of monetizing data and preparing data for maximum value.

According to MIT Sloan Management Review, only 1 in 12 companies are monetizing their data at its fullest extent.  Thinking outside the box on how to maximize the use of data is how companies will set themselves apart and gain competitive advantage.  There are two ways of monetizing data, in-direct and direct.  In-direct is where an organization leverages its own data internally to: improve operations, products and services; cut costs; or increase revenue.  Examples include a procurement team using vendor data to optimize negotiations or sales teams using data to upsell or cross-sell into their customers to expand their portfolio.

A direct technique is where an organization extends its data externally, as a revenue stream, for customers and partners to use in a variety of ways. Data-as-a-Service is a type of direct monetization where anonymized, sometimes raw, data is sold to a company who analyzes the data for insights.  An example of this is grocery stores selling customer loyalty card data to CPG companies for them to better understand patterns in end-consumer location and purchasing behavior so that they can provide products to their customers for a better customer experience.  Insights-as-a-Service is where a company captures data, sometimes combines it with 3rd party data, adds data analysis tools, and serves it to their customers.  Logistics companies provide traffic, weather, truck, driver, and route time insights to their customers so that they can better plan their shipments, optimizing revenue and providing an enhanced customer experience.

Making sure the right teams are aligned to a company’s data monetization strategy is very important.  Sales, marketing, product, finance, IT, and data teams must all collaborate towards one single goal – from the top-down.  Everyone must come together to conceptualize and fully understand the value and importance of their data, along with how to go to market with it.  Data increases exponentially and doesn’t deplete, which allows the same data to be used repeatedly in diverse ways, offering different results for different purposes, so the value of data is endless!

Customers want to trust the quality and security of data, so it must be prepared to derive the most value.  The right platform needs to be implemented to support scalability for future growth, data ingestion, and analytics for insights.  All of this must be easy for an end user to understand, so not only do companies need to enable education so that employees understand this new revenue stream, but customers must also be educated to utilize the data to its maximum effectiveness.

Think about the many industries that didn’t adapt to this new world of digitization and lost market value (i.e., taxi companies vs. Uber – Blockbuster vs. Netflix), I predict that the same can be said for companies that don’t start thinking about how they can maximize the value of the data they have today for customers and partners in a whole new revenue stream – the early adopters here will be the ones with the most success.