There are many ways for organizations to monetize data, including selling “data products” directly to consumers. A seven-step model shows the way real-life companies are developing those products and services.
In their recent MIT Sloan Management Review article, “Designing and Developing Analytics-Based Data Products,” authors Thomas H. Davenport and Stephan Kudyba note that a large variety of data products now enhance the consumer experience. LinkedIn’s “People You May Know” feature is one example. So, too, is Zillow’s Zestimate, which uses publicly accessible housing data to predict what price a homeowner might get for the sale of his or her house.
But as ubiquitous as this kind of incorporation of data and analytics into the consumer experience now feels, relatively little has been written about the process of developing these new generations of data products, say Davenport and Kudyba. They’re changing that.
To find out what leading companies are actually doing in the field to create, refine, and generate value from data products, Davenport and Kudyba interviewed data scientists, met with representatives from large companies that are exploring data- and analytics-based products and services (including State Street Corp., GE, Monsanto, the World Bank, Thomson Reuters, and Caterpillar), and interviewed managers at more than 40 companies that had some data product development activities underway.
The result is a model by Davenport and Kudyba of seven steps that companies go through in the development of data products. Taking their lead from a 1996 article by Marc H. Meyer and Michael H. Zack that outlined specific steps in designing and developing information products, Davenport and Kudyba have augmented and updated the Meyer-Zack model. “Data product development activities today are rarely undertaken in a traditional product development sequence that involves identifying the need, developing the product, and then taking it to market,” they write. “Rather, product development activities often take place in a continuous, iterative fashion, with the important activities conducted in parallel.” The sequence also includes a few new steps that they have identified.
On Dec. 1, 2016, Davenport, the President’s Distinguished Professor of Information Technology and Management at Babson College and a Fellow of the MIT Initiative on the Digital Economy, and Kudyba, an associate professor of Business Analytics and MIS at the Martin Tuchman School of Management at the New Jersey Institute of Technology, participated in a webinar expanding upon what they found and how other companies can get into the business of data-based products and services. The webinar was hosted by MIT Sloan Management Review and made possible with sponsorship support from Xively.