Data Monetization: How to Do It So You Can Get More Value Out of Your Data

According to Gartner, “Data and analytics can be a valuable business asset that will not only improve business decisions and drive digital business transformation, but even generate new revenue for your organization.” Whether they realize it or not, most organizations can monetize their existing data. It starts by identifying the data’s value-add to a new audience.

In this blog, we’ll discuss the basics of data monetization, explain why the concept made our top 2022 trends list to include in your business strategy, and cover best practices to get started.

Why is it Critical to Think About Data Monetization in the Context of Business Strategy?

Data is likely the most valuable asset your organization owns, so it’s time to start thinking about how data can work even more for you.

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According to a McKinsey analysis, data monetization efforts are proving to be a source of differentiation in many industries and seem to correlate with industry-leading performance. And the barrier to entry for data monetization has never been lower—whether you’re building a product or service to offer at a premium or looking to make better, more informed business decisions.

What is Data Monetization?

Data monetization is using your data to add to or increase your revenue stream. This isn’t a new concept, but it’s never been easier to do. So many modern data and analytics tools make it easy to “white label” an analytics solution or package your dataset as a viable product.

Data monetization can be achieved through both internal and external methods:

• Internal data monetization is the method of using data and analytics to make informed business decisions that turn into measurable improvements to the way a company does business. By modernizing their data and analytics ecosystem, any organization can use their data to improve internal processes and practices, such as more targeted marketing campaigns, identifying upsell opportunities, and improving the customer experience—all of which maximize company profitability.
• External data monetization is the method of creating a product or service using your internal data assets and selling them to a third party. This can be in the form of benchmarking or forecasting reports, survey data, one-off datasets, or any kind of consumer interaction data. As long as you have a data asset that you can quantify, you can monetize your data externally. For example, if your business collects consumer data, you can package up the insights and provide premium access to benchmarking data as well as guided analytics to your customers.

Examples of internal data monetization include upselling or cross selling opportunities with targeted marketing, supply chain planning to improve inventory and transportation management, or hiring and retaining staff based on data and analytics. External data monetization examples include creating benchmark reports; selling lists of contact information, or verified address and property data; or providing access to academic research or survey results behind a paywall.

Learn how global HR recruiting firm Cielo started with a data strategy to help identify data monetization use cases.

How to Identify Opportunities for Internal Data Monetization

To take advantage of data monetization internally, start by reviewing your data strategy—because anytime you take a holistic look at the role data plays in your company, you will uncover areas for improvement and optimization. Next, look at the capabilities of your data stack—are you taking advantage of everything your technology has to offer, or do you need to consider new technology and infrastructure to get more from your data? Make sure you are considering your whole business, from tools and resources to people and processes.

What are the Different Ways You Can Monetize Data Externally?

There are two ways to monetize your data externally—either by offering data as service or product, or by offering insights as a service or product. Data as a service is access to raw and governed data sets—information that consumers can use however they want. Insights as a service pertains to a guided analytics solution where insights are presented in pre-built visualizations and reporting to guide analysis.

• Data as a Service provides data either as a one-time product or as a subscription service to a data set that is constantly updated. For example, Gartner—which is considered the industry standard for grading data analytics tools and vendors—sells access to its premium data about the specific tools and vendors it ranks. As an organization, you can provide access to your own data in a variety of ways, including in one of the online marketplaces, a downloadable data dump, or via an API that you make accessible to other organizations.
• Insights as a Service provides analytics and expertise attached to your data that you can either sell as a one-time product or as a subscription-based service. For example, Meta for Business (formerly Facebook) sells insights on how customers interact with advertisements on the platform. This can help guide the marketing department within an organization on how to improve campaign or expand reach. Your organization can provide access to insights you’ve gathered either through an embedded analytics solution, a data visualization, or even banded PDFs or a report sent via email. The key here is that you are providing guidance toward a relevant point within your data set—making it easier for your consumer to understand the data you own and act on it in a meaningful way.

In terms of how you deliver your data and insights to your customer, most BI tools (Looker, Qlik, Tableau, Power BI, etc.) have embedded capabilities baked into the platform, or they offer specific embedded-only licensing tiers. A lot of them also offer a marketplace for you to sell your data sets or visualizations.

Learn more about the embedded analytics capabilities within Power BI, Looker, Qlik, and Tableau.

A McKinsey survey finds that many companies are launching data-focused products or services, and that data monetization correlates with industry-leading performance. There are multiple ways in which you can monetize your including adding new services to existing offerings, developing new business models, and joining with similar companies to create a data utility. Photo credit: McKinsey & Company

What Are Best Practices to Get Started with Data Monetization?

Drawing as much value out of your data as you can is key to optimizing an asset you already own. Although each use case is different, we’ve identified five best practices to get started with data monetization.

1. Quantify the Value of Your Data: Before you invest in the time and effort required to monetize your data—especially externally—you need to ensure there is a market for it. There are costs—software subscriptions, licensing, labor, R&D, marketing, etc. —associated with data monetization. Often the end product can offset those costs—but you need to do your due diligence initially to determine whether there is interest for what you are putting into the market.
2. Manage Your Data: You need to have a plan in place to ensure that your data and the insights derived from it are useable to the end consumer—internal or external. That means you need to consider data quality, data governance, data dictionary, release notes, updates, etc. The work that you do to get your data ready for data monetization will always have internal benefits as well.
3. Access and Security Considerations: If you’re providing direct access to your data, you need to consider access requirements as well as consumption capacity. You don’t want a lot of people querying your cloud instance everyday as that will shoot costs way up. As far as security, think about how people will access your data, consider requirements around single sign on authentication or anonyms authentication, and make sure you are aware of governmental data regulations such as GDPR.
4. Assess Your Current Technology Stack: Assess your current software subscriptions to see if you can leverage any tools or technology you’re currently paying for. Ensure that your tech stack can feasibly support the use cases you have in mind. For example: If you’re looking to present real-time, streaming metrics in your product, do you have cloud-based technologies in place to support it? Finally, where you find gaps in your technology stack, are you willing to make the investment needed to fill those gaps so that you can build a desirable product?
   

   How to pick the right BI and data analytics tools for your business.

5. Get Buy in from the Organization: Monetizing data will require cooperation from multiple departments—including sales, marketing, legal, finance, IT, and more. For data monetization to be successful, it needs to be viewed as an organizational effort up front, and responsibilities need to be clear for everyone involved. It is also critical to have dedicated product owners who champion the project and see the vision executed all the way through.

Your organization’s data is valuable—it is the key to business transformation. Take the time to assess how you can use it to drive business growth and identify additional revenue streams within your organization.