Monetizing products and services that are based on big data analytics seems like the next logical step to fully developing the capabilities of smart products and the Internet of Things (IoT). Yet it has proven to be an elusive goal until very recently.
Many online businesses like Google, LinkedIn, Zillow, and Facebook are building out data-based services that extend their platforms. And we’re starting to see more diverse examples of data analytics marketing, as well, said MIT IDE Fellow, Thomas Davenport (pictured above), during a recent recent MIT Sloan webinar.
But even if market leaders are ready to capitalize on data- and sensor-based products, taming the complexity of massive new data sources and integrating them with traditional business models represent some of the biggest challenges of the data economy, he said. For these data-intensive products and services to proliferate, analytics must make sense of the data and guide intelligent go-to-market strategies. Additionally, substantial data integration efforts are necessary to incorporate the mass of unstructured data with high-level IoT platforms such as cars and homes. Today’s cars alone may have 200 sensors and195 different data formats, Davenport said.
The Promise of New Revenue Streams
The webinar’s premise is that “connected devices and the data and analytics that drive them are fundamentally changing how companies think about and plan for product development.” In fact, many companies are establishing new revenue streams “by capitalizing on the huge demand for analytics-based data products. And this new category of products requires a reworking of the traditional phases of product development.”
Davenport explained that product development and monetization can take several forms. One is selling data directly, such as Fitbit data being used by health insurance companies, or shipping data made available to logistics stakeholders. Another form is embedding data and analytics in new or existing products and services, such as Delta airlines using RFID tags for bag location, or Schneider trucks monitoring driver safety. Data and analytics can also provide access to platforms and improve internal business processes. “These are all early signs that we can get dramatic value of the IoT,” Davenport said.
At the same time, “we are just beginning to think about business models to monetize the billions of devices now connected.” We need structured processes and analytics to make sense of the data and make sure it is ready to use, according to Davenport.
As he and co-author, Stephan Kudyba, Associate Professor at the New Jersey Institute of Technology, wrote in a recent SMR article: “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.”
A Structured Approach
Davenport and Kudyba have come up with a seven-step framework to help businesses plan and develop their data-intensive products and services. During the webinar, Kudyba explained that they built upon a standard five-stage structure for the manufacturing of information products–data acquisition, refinement, storage/retrieval, distribution, and presentation—and then added product conceptualization and market feedback stages to reflect the requirements of smart devices and the IoT to create a closed loop process cycle. (See chart).
Businesses also need to hone new employee skills and to foster “intensive collaboration” among stakeholders to manage data that’s more complex—and more robust—than in the past. “Think of all the value this data can add,” Kudyba said. “The potential is huge, but we have to figure out how to harness and make it coherent as a product” while still meeting customer needs.
Of course, an updated model also needs to reflect new “time-to-market” expectations in the product development process. “Product development activities today often take place in a continuous, iterative fashion, with the important activities conducted in parallel,” the authors wrote. The popular “lean startup” model with periodic refinements over time is the best path to follow and it shouldn’t slow down development.
“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,” according to the article.
In sum, the professors offer this practical advice to those seeking to develop analytic-driven services:
- Get good data and make sure you have rights to it
- Develop strong integration, cleaning, and analytics capabilities
- Try the products internally, first
- Understand your customers’ decision needs and processes
- Employ automation techniques
- Anticipate security/privacy problems
- Address pricing/bundling/terms issues
- Handle conflicts with your existing business
Also see the McKinsey & Co. report, The Age of Analytics: Competing in a Data-driven World