Perhaps the most obvious way to monetize data is simply to sell it to other organizations. But that isn't the only data monetization path, nor is it the most likely one for companies that aren't information services providers at heart. For such businesses, the more common approach is embedding data along with tools for analyzing it in the products and services they sell.
And there are real opportunities to do so, "often quite significant ones," in most companies, according to MIT researchers Barbara Wixom and Jeanne Ross. In an article published by the MIT Sloan Management Review in January 2017, the two wrote that "wrapping" products and services with data that enriches them can help ward off commoditization and improve customer satisfaction. Ideally, that leads to increased sales and stronger customer loyalty, even with higher pricing on the enriched products.
In most cases, however, companies also need to upgrade their IT and analytics capabilities to avoid possible data-related problems that could damage their standing with customers, Wixom and Ross cautioned. New investments may be needed in things such as data quality programs, big data platforms and data science skills to keep efforts to monetize data on track, they said.
Gartner analyst Doug Laney similarly urged IT and business execs to think more broadly about prospects for monetizing data in a July 2016 blog post. But organizations should still quantify the financial impact of what he characterized as indirect data modernization methods. Otherwise, Laney asked, "how can they claim they're monetizing it?"
Positive results, Wixom and Ross wrote, "stem from a clear data monetization strategy, combined with investment and commitment." This handbook offers guidance on how to build that kind of an initiative as you move to monetize data.
Other articles in this handbook:
Missions for monetizing data need lift from upfront groundwork
Organizations launching data monetization strategies should factor some key initial steps into their plans to develop revenue-generating data products and analytics services.
Building data science teams takes skills mix, business focus
In a panel discussion at Strata + Hadoop World 2016, managers of data science initiatives discussed how to structure and lead teams of data scientists for effective big data analytics.
Why physicists are a good fit for data science jobs
With data scientists in short supply, physicists and other academic researchers from hard-science disciplines are increasingly finding places on data science teams -- and even leading big data analytics projects.