Stephan Kudyba, associate professor of business analytics and management information systems at NJIT’s Martin Tuchman School of Management, not only co-created a model to help companies develop analytics-based “data” products and services for consumers, he also co-wrote a paper on the subject, and co-produced and appeared in a webinar about it this past December. Kudyba’s collaborator is Thomas Davenport, distinguished professor of information technology and management at Babson College and a fellow of the MIT Initiative on the Digital Economy. Their paper, “Designing and Developing Analytics-Based Data Products,” was published by MIT Sloan Management Review, which also hosted the webinar, “IoT and Developing Analytics-Based Data Products.”

“An increasing number of companies are creating products that combine data with analytical capabilities. Creating an effective development process for these data products requires following well-established steps — and adding a few new ones, too,” they write in the article, which includes LinkedIn’s “People You May Know” feature as an example.

Kudyba and Davenport spoke with data scientists, managers involved in data-product development and reps from large companies investigating this new data-centric consumer arena. Their resultant seven-step model for the development of data products is an update of the standard five-step model for manufacturing information products, with a closed-loop process that incorporates product conceptualization and market feedback and considers smart devices and the Internet of Things (IoT) (see graphic).

Associate Professor Stephan Kudyba's seven-step data product development model.

“There has always been a trade-off in product development between having structures that ensure that the product addresses market needs and is of high quality, and being able to introduce products quickly and remain responsive to customer needs. Though the pendulum in data products has clearly swung in the direction of responsiveness, there is still a need for structure and method in developing new offerings,” write Kudyba and Davenport.

During the webinar, discussion turned to the growing need for resources for becoming qualified in business-focused data analytics. NJIT recently heeded the call by establishing a Ph.D. program in business data science, one of the first in the country. The program integrates business analytics and management systems theory with statistics, computing science and engineering.

“In the United States, there are so many universities that are just gearing up on the analytics side,” remarked Kudyba. “NJIT is ‘going after it in a big way.’”

To access the webinar, visit http://sloanreview.mit.edu/article/iot-and-developing-analytics-based-data-products/.