A Product Management Approach to Data Monetization

Part II of III

(US and Canada) In Part I of this series, I addressed how to evolve from data project management to data product management, how to borrow from the traditional product management playbook to do so, and what to look for in a data product manager. In this next part, let’s take a look at how to manifest a data product vision and understand the multitude of ways organizations are squeezing value from available data assets. 

Refine the Data Product Vision by Working Backward

Legendary golfer Greg Norman says he plays each hole backward in his mind. “As I step onto the tee, my mind goes to the green. Before I decide which club to hit or how to play my tee shot, I want to know the exact position of the flag — once I know that, I play the hole backward in my mind.” Similarly, as a data product manager, it helps to start with a vision of what you want to produce. This is the approach companies like Amazon take.

Ian McAllister, the former director of Amazon Day, says that working backward begins by “[trying] to work backward from the customer, rather than starting with an idea for a product and trying to bolt customers onto it.” For each new initiative, a product manager writes an internal press release announcing a finished product. “Internal press releases center around the customer problem, how current solutions (internal or external) fail, and how the new product will blow away existing solutions,” comments McAllister. “If the benefits listed don’t sound very interesting or exciting to customers, then perhaps they’re not, and shouldn’t be built.” And if not, then the product manager should continue revising the press release until they have come up with something better. 

It may seem to be a lot of work for an idea that may never see the light of day. But as McAllister explains, “Iterating on a press release is a lot less expensive than iterating on the product itself … and quicker!”

A Meta-Analysis of Organizations Squeezing Value from Data

Certain patterns emerge across the spectrum of hundreds of real-world stories I have compiled of organizations generating real, measurable value from data assets. These meta tendencies,  in and of themselves, are quite instructive: 

Adapting Advanced Analytics 

An organization need not be big to leverage big data, and an organization does not have to have big data to do big things with it. Many high-value data products leverage large-scale data integration across multiple or unique data sources that have small volumes or velocity of data. Or, they make use of advanced analytics techniques on limited data sources. Consider how the organization Thorn has made use of a broad array of harvested web content and other data sources along with advanced text analytics to identify hundreds of sex-trafficking victims and traffickers. 

Many stories also involve the use and mining of unstructured content, not just structured customer or transaction data. For example, Lockheed Martin continually analyzes project communications and documentation to have three-times greater foresight into project issues than traditional status reporting methods, and is saving hundreds of millions of dollars a year on cost overruns. 

Data from Beyond Your Four Walls

Many organizations incorporate external data, including syndicated data, open data, social media, web content, or data from partners often in combination with their own data, to generate monetizable insights. Or, they find valuable uses for “dark data” they have collected, used for a single purpose, and forgotten about or archived. 

For example, Minute Maid has blended a quintillion data points from social media, consumer surveys, satellite images, and across its supply chain into a precise dynamic formula for how to blend orange juice consistently — irrespective of the season or any supply anomalies.

Beyond Basic Benefits

Most use cases cite multiple benefits, sometimes even multiple measured benefits. Remember, data is a non-rivalrous, non-depleting asset. That is, it can and should be used multiple ways simultaneously and over-and-over to maximize its value. 

Also, most indirect data monetization implementations focus on improving revenue or margin, but the most clever ones and those with the most ancillary benefits focus on other drivers like customer experience or agility — which lead to primary financial benefits. Case in point, The North Face increased revenue 23% after converting its online search to incorporate natural language processing from EasyAsk. And Walmart reduced online shopping cart abandonment by 10-15% by incorporating social media trends into its search engine algorithm.

Infrastructure Considerations

High-value implementations do not necessarily require enterprise-wide data warehouses or data lakes, but are targeted and vocational, focusing on a single problem or opportunity with a limited set of data. Even with its performance limitations, the concept of a data mesh or fabric is becoming popular for limited, low-volume analytic solutions and data products. 

L'Oréal, for example, continuously scours social media platforms for mentions of beauty-related products or topics, classifies and assesses these posts in real-time, then routes them internally to those who can interact with them. The company claims that this “Voice of Beauty Command Center” has transformed how brand awareness and loyalty are monetized. 

Integrate to Be Great

Most data products involve integration, not just data integration — integrating the analytic output or data streams directly into business processes or operational systems. Like how the biotech company Sigma-Aldrich collects and aggregates competitor pricing data via web content harvesting technology from Import.io into an SAP data warehouse to continually optimize prices for some 200,000 products. 

But, keep in mind that digital solutions are an increasingly popular form or evolution of data products. For example, in Burberry stores, sales people can identify customers the moment they walk in, and electronic tags on clothing trigger interactive videos with product details. 

Measure Data to Monetizing It

Even though they are not data brokers, many organizations are finding ways to productize and license their data. Dollar General, for example, has a self-funding data lake as a result of licensing its sales, inventory, shopping basket and other data available to its CPG partners. (A self-funding analytic foundational layer should be the goal of any business!) And, the Chicago Mercantile Exchange (CME) now has a thriving business that automates the creation and sale of tens of thousands of distinct data products to trading companies and others via a data monetization platform from Ticksmith.

Using data as a form of collateral is an emerging form of data monetization. The upstart venture, Gulp Data, after automatically generating an estimated data valuation, matches data-rich organizations with banks willing to lend against these assets. 

Borrowing from the Best 

Most high-value stories are driven by business leaders, not IT leaders. In data monetization workshops I facilitate for clients, most of the great ideas come from those with knowledge of particular business functions or the data itself, not from technologists. 

In addition to borrowing some of these best practices above, in Part I, I discussed how to borrow from traditional new product introduction and project management approaches to conceive and create data products. But that’s not all you should borrow. Clients often ask, “What are others in our industry doing with data and analytics?” My flippant response to that question is, “Why do you want to be in second or third place?” Why not adopt and adapt ideas from other industries, both those adjacent and far afield from your own? 

In Part III, I will explore the various patterns of data monetization — both direct (or external) and indirect (or internal), overcoming the various myths of data monetization, along with some final thoughts on embracing the unique economic qualities of information (infonomics). 

About the Author

Doug Laney is the Data & Analytics Strategy Innovation Fellow at West Monroe where he consults with business, data, and analytics leaders on conceiving and implementing new data-driven value streams. He originated the field of infonomics and authored the best-selling book, “Infonomics,” and the recent follow-up, “Data Juice: 101 Real-World Stories of How Organizations Are Squeezing Value From Available Data Assets.” Laney is a three-time Gartner annual thought leadership award recipient, a World Economic Forum advisor, a Forbes contributing author, and he co-chairs the annual MIT Chief Data Officer Symposium. He also is a visiting professor at the University of Illinois and Carnegie Mellon business schools, and sits on various high-tech company advisory boards.