How Master Data Management Enables Two Types of Data Monetization

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Click to learn more about author Bill O’Kane.

According to Gartner, data monetization refers to the process of using data to obtain a quantifiable economic benefit. Internal or indirect methods include using data to make measurable business performance improvements and inform decisions. External or direct methods include data sharing to gain beneficial terms or conditions from business partners, information bartering, selling data outright (via a data broker or independently), or offering information products and services (for example, including information as a value-added component of an existing offering).

Whether internal or external, at the end of the day, data monetization is about achieving direct financial results by accessing and leveraging data that is stored, maintained, classified, and made available in an optimal manner. Trusted master data is key for two reasons. First, it is easier and safer to monetize than “raw” data. Second, it is the key to monetizing the bulk of the remaining transactional and interactional data in an organization’s ecosystem as it is used to categorize, segment, and target the event-driven data itself.

As a result, organizations now view Master Data Management (MDM) solutions as an absolute requirement for data monetization. MDM enables the business to maximize the benefits of data monetization while reducing the risk of earning revenue by utilizing data that is misidentified, misclassified, or outright inaccurate. Let’s look at the two most direct ways of monetizing data: selling data and wrapping data around products and/or services.

Selling Data

Creating revenue from data that you have already collected and enriched is the good old-fashioned way of making money from data. This is what the likes of Dun & Bradstreet, postal services around the globe, and many others have been doing for years, either as their main purpose or as an important part of the business. In that way, the data becomes third-party data to others. In MDM implementations, third-party data is often used as input in order to achieve better Data Quality and competitive advantage. The same holds true for the data you have compiled internally. This data can also be provisioned, provided it is well-arranged master data, transactional data described by well-defined master data, and/or big data identified against well-understood master data.

Wrapping Data Around Products and/or Services

Another way to create value is to consider the tangible products as well as services that must be supported by data. In pharmaceuticals, for instance, we see digital therapeutics where medicine comes with devices and apps for measuring the effect through data. In hospitality, the accommodation must be described using a lot of data, and so must the surrounding location. Now quite common, ridesharing data must divulge things like: When will your ride arrive? How can it be identified? What is the rating of the driver — and what is your rating as a passenger? In each of these examples, it is clear that one’s organization cannot be successful in any industry without a solid way to handle its master data.

Thanks to COVID-19, the problem (or opportunity) is exacerbated as more and more products are sold online. This has been going on in the B2C world for a long time and is now also becoming the norm in B2B transactions. This shift means that products must be described in customer-friendly terms and hold all the specifications to support a buying decision based solely on the data. If you are the manufacturer or brand owner, you must supply this data to your merchants or marketplaces. If you are the dealer or retailer, you must get the data from your suppliers, and it must be completely trustworthy. In that way, product master data becomes an ancillary domain to party data. The completeness, accuracy, and consistency of this data determines the potential revenue from the products they describe.

Regardless of whether you have the opportunity to sell data or to incorporate data into products or services with extensive and consolidated data sources, the starting point for data monetization is well-managed master data that accurately and consistently identifies and describes the involved parties, products, and locations. Failure to effectively manage the master data elements involved in these revenue opportunities not only decreases their effectiveness to the buyer but substantially increases multiple types of risk to the seller and buyer of the data. Master Data Management is key to ensuring maximum return on investment and reducing both the transactional and reputational risk for all parties looking to derive profits from their data assets.

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