Data Monetization: Meaning & Use Cases

Last Published: Aug 05, 2021 |
Stephan Zoder
Stephan Zoder

Over the course of the last year increasingly more clients have been inquiring with us about the topic of “Data Monetization”. In other words, they wanted to learn more about how Informatica enables their ability to “sell” their existing data from operations instead of just archiving them away or deleting them. As you can imagine, this is especially difficult for industrial B2B firms, which do not consider themselves peers of firms like Amazon. Just like Digital Transformation, Data Monetization is a tremendously gooey subject. It can mean so many different things and almost everybody has a different twist on it. Moreover, there is no good reading available on it besides some high flying marketing fluff. 

Today, I will remedy this situation by starting a couple of posts about how we at Informatica view Data Monetization. The goal is to go a few levels deeper than you may traditionally find in your Google searches. 

First, what is it? Monetizing an organization’s data is already happening all over today. The good news of today is that by investing in your data management infrastructure, you are effectively monetizing your data. If you are not in the business of (digitally) transforming your internal operations with data already, get started with it first. 

How, do you ask? De-duplicating, standardizing, linking, keeping lineage, transforming, moving and securing your data with a software tool enables your organization to be more productive by decoupling yourself from the legacy hand coding effort. It also ensures transactional integrity when it comes to order and service management, billing and other operational processes by ensuring missing, conflicting or incorrect data elements are minimized, thereby reducing process failure and rework. A secondary effect of this lack of errors is revenue assurance from process integrity, i.e. orders don’t linger, equipment does not fail, bills get mailed, etc. Third, by eliminating errors and legacy systems responsible for some of these, you are reducing cost; cost of service, cost of operations, cost of acquisition and so on. The question now is how your data can help your clients (B2B or B2C) achieve the same? What is the secret sauce you have within your data repositories? After all, research has shown; there is money in data

This leads us to the “external” monetization of your data. This means leveraging your internal operational data to create a new revenue stream for your organization by selling it in some way to your existing clients or prospects. These parties could be within your general market, a channel partner or even a completely unrelated sector. The good news here is that you can leverage your internal data monetization effort and infrastructure you have painstakingly built over years for external use. 

To understand the opportunity, let’s look at a historic example: the hedge fund industry. If you look at its average returns over the last twenty or so years, it has been steadily declining, once being as high as 20% and now at a dismal 4%. Certain funds were able buck this downward trend but how did they do it? 

They outperformed their peers by using new data and exploring new uses of data. This search for new “Alpha Sources” became the Holy Grail of the hedge fund industry. In the Fifties, long and short hedging strategies developed based on new data sets followed by new mathematical models and computerization in the Eighties. The Nineties saw the advent of high frequency trading, which became the driver for the Big Data boon. Since 2016, we have put Big Data on steroids by ingesting "alternative data", such as weather, consumer sentiment, footfall, water consumption, etc. into these increasingly complex and unsupervised trading models. 

Still, this data’s value decays quickly due to economic cycles and competitors leveraging the same capabilities because of general data democratization. Today, companies overshadowing their peers’ performance compete based on the broadest use of data sources with multiple latencies and degrees of fidelity. 

The new currency is how quickly and effectively a hedge fund can develop new uses of existing data sets when combining it with massive amounts of historical data. 

Realizing that you are likely not a hedge fund manager, when would it be prudent to execute on this approach? There are 6 major factors you should evaluate:

  1. Market Share: Is your organization managing at least 20% of your core market’s transactions?
  2. First Mover: Are you the first, second, or at least in the top 50% of your market’s competitors introducing such a product? Because if you are, 80% of the available profit pool will go to you.
  3. Usage Rights: Do you have legal rights to re-use your client’s (B2B and consumer) data?
  4. Privacy: Do you have a deep understanding of required privacy regulations governing this data?
  5. Readiness: Is your organization ready to operate the required IT, sales and service infrastructure?
  6. Value Proposition: Do you fully understand how the consumers of your product/service value it in terms of net-new benefit to them to price it accurately?

If you have these covered, you should be in good shape to start a more formal, meaning budgeted evaluation of your go forward. You may have also noticed that I spoke about a “product” in this list. The reason: what you are ultimately providing your customers is precisely that – a new Digital Product.

Yes, data becomes a product.

Companies like Nielsen, Acxiom, D&B, TransUnion, Equifax, Bloomberg and IMS run their business on licensing their data in raw format or as part of an application infrastructure. Your goal should be to become like them. 

What will your new digital product do for your clients? It will likely fall into one or more of this six – yes, six again – digitization categories.

  1. Automation
  2. Supply Extension
  3. Distribution
  4. Digital Feature
  5. Repackaging/Customization
  6. Platform

All these facilitate the development of one or a combination of the following consumption mechanisms:

  • A consumable, enriched and clean data set (think a marketing list)
  • An analytical application (think dashboard or predictive model about customer churn)
  • An operational application (think product failure alert and maintenance scheduling system)

No matter which flavor or combination of consumption you choose, you must come up with a set of use cases for at least one of the six digitalization categories. More about these in my next post. 

First, let’s look at automation. It basically enhances productivity for existing processes. Good examples here are robotics, order recommendations, self-service analytics or insurance online quotes. All these should leverage your “secret sauce” data to improve the productivity and effectiveness of the user. This is all about making judgments more efficient; knowing why certain industrial robots break down less often given certain conditions, what order patterns typically trigger other purchases, knowing what good comparative data sets are for a customer satisfaction prediction or what input parameters are to appropriately assign risk premiums to a new driver. 

The next category creates new marginal revenue streams by widening the supply base. An example for this are the myriads of second hand clothing market places like Poshmark.  The ability to analyze large sentiment data sets, supply and demand categories can be directly used for pricing and promotions. 

Distribution is all about moving your product portfolio offline to protect your market share and reduce physical inventory/infrastructure cost. Zappos, E*Trade, Geico, Khan Academy or New York Times Online Edition fall into this category. They use demographic data to better target customers and enhance their offering for online purchasing. 

Digital Feature is next on our list. Simply put, it is the addition of a wireless capability to a legacy hardware product. Good examples are a NEST thermostat, a RING doorbell or a Samsung connected refrigerator or Netflix movie downloads (I don’t think anybody still remembers their legacy business model). 

Now let’s look at Repackaging/Customization. Here it starts to get a bit more complex. Organizations following this paradigm restructure their legacy products to fit usage (subscription) based requirements to avoid a CapEx-oriented sales cycle. GE Aviation’s program to bill clients based of its jet engine’s time-on-wing performance rather than sell the airlines thousands of $24 million turbines fits perfectly here. They chose to go this path to take advantage of the continuing trend of soaring passenger miles traveled and 9-year carrier buying cycles. Their ability to utilize atmospheric, airframe, customer, contract, location and engine IoT data in mid-flight allows customers to reroute planes to conserve fuel and reduce blade wear & tear. On the flip side, GE Aviation always knows about the health of its engine fleet, thereby predicting maintenance work, scheduling parts manufacture and shipping as well as the impact on contractual obligations and pricing. 

Lastly, we have the platform play. This is the highest form of digital product sophistication. Amazon, Apple AppStore, Spotify, OpenTable, AirBnB, Uber, LinkedIn, Facebook and so on are the darlings of Wall Street for a reason. They have canned the ability to use a digitally integrated platform to create and/or distribute physical and digital products alike on behalf of consumers, partners and employees. There is no better example why it matters if you are first or second in your respective market niche than these. Anybody still remember Plaxo? Right, I thought so. While these companies provide applications with rich functionality, they are primarily data companies. Go on the Uber developer connection website and you see the in-depth travel analytics they can run on your data. 

These firms have already transformed themselves to the highest form of digitalization to monetize their data even more effectively. They not only utilize their partner or consumer behavior data but actively encourage their users to contribute to the stockpile of data directly via postings, shares, likes, opt-ins, etc. The same is possible for B2B models, which can accelerate and create new insights by circumventing the channel partners and going direct to consumers. This is something CPG firms are already trying through their own loyalty programs. Layer on top some AI to further predict an entity’s next move and you just may have created the next killer app! 

So far, so good. Next time I will get a bit more into the weeds about the digital product release cycle, use cases, some examples of my recent customer conversations and the concept of a digital product factory. Until then, please share the best examples of the above six digitization categories you are aware of.

First Published: Mar 06, 2018