What is data monetization?
Data monetization refers principally to the use of enterprise data for revenue generation. It also encompasses how an organization quantifies the bottom-line economic value of this strategic practice.
The data monetization market
Industry projections for the data monetization market provide solid evidence of its vitality: A May 2022 report from MarketsandMarkets predicted this field would grow from its approximate 2022 value of $2.9 billion to reach $7.3 billion by 2027, at a sizable 19.5% compound annual growth rate (CAGR). Drivers of this expansion range from the overall value jump of big data to the rise of first-party data solutions for digital identity management.
Types of enterprise and customer data monetization
Any project characterized as a data monetization initiative falls into one of the following two categories: direct and indirect.
Direct data monetization
Also known as external data monetization, the direct monetization of data involves the selling or trading of information to another party.
The sale of customer data to adtech firms or other third parties is arguably the best-known type of direct data monetization. Retailers have done this for many years. Also, companies whose flagship product is free, like Facebook and Google, became profitable throughout the 2000s and 2010s by selling user data.
The sale of information services and products—such as data as a service (DaaS) tools or embedded analytics platforms—is also a form of direct data monetization. Other examples of this practice include organizations managing advanced analytics operations for their clients or selling reports rich with actionable insights to companies that lack the resources to conduct their own data-gathering operations.
Data sharing between two partnered businesses—with the goal of obtaining mutual benefit—is still another variety of direct information monetization. This can be between enterprises uniting through a merger, a parent company and its subsidiary, or a business and its clients.
Indirect data monetization
Indirect data monetization refers to the use of an organization's own data for process improvement, product development, sales efforts, marketing campaigns, or any other purpose that encourages revenue generation or profitability. Consider the following example that touches on most of those areas:
- An automaker reviews fuel efficiency reports for its flagship sedan.
- Analysts identify several poor-performing components in the vehicle's engine that lead to greater fuel use and fewer miles per gallon.
- Designers and engineers review this data, determine necessary adjustments, and implement them for next year's model.
- The marketing team highlights improved fuel efficiency, which appeals to consumers' concerns about gas prices and growing interest in sustainability.
- The new model arrives at dealerships and sales exceed their projections.
Each action results from careful gathering, analysis, and actionable application of product data. The gains aren't instantaneous, but they may be better for the automaker in the long term than, say, directly selling some consumer data.
Internal customer data—especially customer satisfaction information—is another incredibly valuable driver of indirect monetization. From this data, an enterprise can determine many potential improvements, ranging from greater availability of technical support to more efficient supply chain strategies. Data-driven business models in which businesses build analytics capabilities into products—cars, smartphones, exercise machines, and many others—also allow customers and companies alike to benefit from data without directly selling it.
Data monetization benefits and use cases
Advantages enterprises can realize because of data monetization include:
Creating revenue sources
If an enterprise wasn't selling its data before and then starts doing so, that can be just as—or more—valuable than putting a new end-user product or service on the market. On the indirect end of the spectrum, associating a data-driven business model with certain offerings creates the opportunity to provide options like tiered subscriptions—e.g., analytics services at varying levels of granularity for customers who want different degrees of insight.
Finding competitive advantages
Most enterprises monetize their data in some way, but not all of them do so as effectively or broadly as possible. An organization that searches for, identifies, and acts on opportunities for both direct and indirect data monetization can easily develop an advantage over its competitors. For example, companies leveraging first-party data can gain more detailed insights into customer and website visitor behavior than rivals relying on batches of third-party data, which can lead to better sales growth over time.
Improving customer experiences
Both direct and indirect data monetization can benefit customers. The former often informs marketing strategies and helps consumers discover the most relevant products, while the latter can be the root of business improvements that meet consumers' demand for better product functionality or ease of use.
Strengthening internal productivity
When companies use data to discover ways in which employees, systems, or technologies can be improved for greater productivity, they sow the seeds for potential long-lasting bottom-line gains.
Monetization use cases
Specific data monetization applications exist in many industries. Here are just a few notable examples:
Saudi Telecom Company (STC), a Teradata customer and leading mobile wireless and broadband provider, follows an analytics-based approach to drive various service improvements: better internet connectivity and bandwidth speeds, fewer unsuccessful or dropped calls, improved call quality, and more.
Telecom firms can also monetize data more directly. For example, a provider could use mobile network data to track customers' movement, then sell it to clients ranging from retailers to gas stations that in turn improve their targeted advertising.
Spain-based banking group ABANCA worked with Teradata to improve the agility of its data platform. By using logical layers for organized, efficient data management, business users could more easily access the information they needed based on their level of technical expertise: non-expert, analyst, data scientist, and so on. Ultimately, ABANCA's application of analytics contributed to internal improvements and a 300% increase in its customer satisfaction Net Promoter Score (NPS).
In other corners of the finance world, organizations can monetize data by using it to assess risk. For example, institutions might purchase data from mobile telco firms and use mobile purchase and payment data as a barometer of loan applicants' creditworthiness.
Building an effective, compliant data monetization strategy
A company's data monetization strategy should revolve around making the most of the data it has or can readily obtain, serving short- and long-term goals while remaining compliant and ethical. Data privacy laws like the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA)—and cases that galvanized their passage, such as the Facebook-Cambridge Analytica scandal—must be observed along with any other applicable guidelines.
Arguably the best way to mitigate such risks is to focus as much as possible on first-party data for direct monetization efforts. Use third-party data only in compliance with applicable laws. Also, a direct data monetization strategy must be closely monitored to ensure it creates a sustainable new revenue stream. Otherwise, the initiative should be reevaluated.
Meanwhile, companies indirectly monetizing data to improve internally should focus on identifying business units where sound data management and data sharing practices will have the greatest impact. Selecting the most valuable internal data sources is also critical: For example, unstructured data from sales call records is helpful for future sales and marketing, but data from customer service or account management can inform everything from product to delivery logistics.
Teradata VantageCloud, the most complete cloud analytics and data platform for AI, is an ideal hub for any data monetization strategy. Its unparalleled data integration and expanded analytics capabilities, along with built-in first-party data management tools, allow data teams to create a single source of truth from which company leaders can identify the most valuable, monetizable data.
To dig deeper into VantageCloud, connect with us today.