Data is frequently described using cliches like “the new oil” or “the new air.” Whatever you call it, data monetization is becoming increasingly important in every industry.

Forward-thinking businesses see data apps as both a revenue source and a unique service for increasing consumer loyalty. In fact, 44% of surveyed organizations claimed that embedding analytics in their app or product would increase revenue, while another 43% said it would improve engagement and retention—and that’s only one facet of data monetization.

Gartner says, “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 businesses can monetize their existing data. It begins by identifying the data’s value-add for a new audience.

This begs the question: what is data monetization? In this article, we’ll explain what it means to monetize your data, look at ethical concerns, learn about basic components, and look at some actual implementations. By the conclusion, you’ll understand why and how businesses realize the full financial value of their data assets.

Why Data Monetization Is Important?

Organizations that invest in data collection might increase their earnings. Good data monetization strategies ensure that organizations maximize the value of their data both internally and externally. They can sell the data externally to enhance earnings, reduce internal costs, and maximize opportunities for the firm.

Creates New Customer Opportunities

Several organizations are recognizing the value of their data. With an adequate data volume, they leverage the tapped and untapped market to create new sources for revenue. By further refining market segments, they can better target their ideal customers.

Increases Data Value

Tech giants and social media platforms collect all the activities associated with a user. This means they identify many features about their users such as their interests, shopping preferences, and level of income. These attributes enhance internal data and maximize the value of the data they collect.

Provides Market Information on a Broad Scale

Customer data provides businesses with insights such as market trends, geographic demand patterns, impact of competition, and the shelf life of customer data. Is the data worth as much in six months, or is it stale and useless by then?

Increases Internal Productivity

Data can maximize productivity as well as decrease the amount of waste or excess consumption.

Creates Competitive Advantage

Successful companies monetize data by understanding their customers’ preferences. This helps them offer products or services that are highly relevant to their customers and create a competitive advantage in the market.

Boosts Profitability

Data is essentially valuable, but it is the comprehension derived from data that builds value for an organization. It can help segment customers to allow better targeting, predict demands, optimize price, and manage costs—resulting in overall profitability.

Enhances Customer Experience and Bolsters Customer Loyalty

Understanding customer needs and preferences improves customer experience. This makes the customer remain more loyal to the product offering and reduces customer churn.

Uplifts Revenue Streams

Data monetization helps segment the customer database based on gender, industry, preferences, demography and a range of other socio-economic groups. These classifications allow business owners to deliver customized messaging, provide a better user experience, and increase revenues.

Strengthens Partnerships

The process of purchasing and selling data happens in a data marketplace. Data owners can set data prices, and consumers can choose from whom they wish to buy data. This improves data collaboration and sharing between internal and external stakeholders.

Streamlines Decision Making and Planning

The data marketplace divides audiences and offers the right set of consumers for the right kind of data. It gives a depth of insight that allows decision makers to understand their business better, anticipate changes in the market, and manage risk better.

Identifies and Mitigates Risk and Improves Compliance

Data is a key asset for any organization in today’s world. However, it needs to be leveraged as per individual privacy rights. Data monetization requires an organization to have their data organized, legally obtained, managed, and protected. If a business wants to sell its data, it needs to be completely compliant with regulations.

What is Data Monetization?

Barb Wixom concisely defines data monetization as “the generation of financial returns from data assets” in her book “Data is Everyone’s Business.” Wixcom goes on to underline the universal significance of data across many industries. Gartner adds on this by describing data monetization as the process of exploiting data to generate quantifiable economic advantages.

Read Also: Data Monetization Use Cases: Real-Life Examples

This is what it looks like in the real world. Every day, your business generates more data, much of which is a result of its fundamental operations—sales transactions, marketing operations, order fulfillment, logistics, and shipping, to mention a few. You monetize data by converting the data generated by these real-world activities into positive bottom-line impacts. The impact can be expressed by generating revenue, reducing expenses, or lowering risks. 

Despite its potential, the term “data monetization” stirs controversy among some stakeholders.  Consider, for instance, a healthcare company. They might be hesitant to use the term data monetization based on the appearance that they are generating revenue from private healthcare data—when in reality, they are using their data to reduce expenses or lower risk, not to generate revenue. 

How to Use Data Monetization

When data monetization is employed effectively by a business, it broadens the scope and flexibility for making the most of big data from multiple sources. However, as the company expands, customers must select which monetization technique best suits their data plan. This implies that it is critical to analyze various techniques and choose which are best suited to present and future company objectives, as well as which platform offers the data monetization capabilities that are appropriate for their purposes.

Data as a Service

Data as a Service (DaaS) is the most simple and straightforward data monetization method. The data is sold directly to intermediates or customers, in either an aggregated or raw form. Buyers can then mine the data for insights relevant to them. These data buyers do not get insights or analytics from the data, but instead, derive this information themselves.

Insight as a Service

The organization merges external and internal data sources and applies analytics to derive insights. These insights can be directly sold or transformed and sold in different formats. These insights are limited to the context, datasets, and specific information purchased

Analytics-Enabled Platform as a Service

This is one of the most flexible types of data monetization, and it can provide a significant amount of value to customers. Here, a business intelligence and analytics platform is installed and implemented to provide customers with scalable and highly versatile data analytics in real time.

Embedded Analytics

This is the most advanced, and often the most appealing way, of data monetization. It provides the most value to customers. In simple words, embedded analytics involves adding features associated with business intelligence software such as analytics tools, dashboard reporting, and data visualization to existing applications. Using this technique, product teams can create and implement customized, actionable analytics apps at scale and integrate them into other applications that the company uses. This opens up new revenue streams and provides a strong competitive benefit.

For any company, data is precious. But how to find its worth? A company’s data value can grow in three main ways:

• Gain more insights about customers to create higher value sales
• Sell insights to third parties
• Generate more data

Irrespective of the company’s domain, data monetization pays off. There are many examples of how companies can increase revenue from data value analysis.

E-Commerce Data Monetization

E-commerce companies in particular are known for making the shopping experience easier for customers. But this is also a way of increasing their customer data. Users save their addresses, other contact information, what items they’ve searched for, and their preferred payment methods. While these functions are all helpful to the user, they are all valuable data for the organization too.

These companies continue to optimize their platforms through customer data, which they reinvest back into their platform. Search suggestions like “Customers who bought this also bought that” and “you might also be interested in” are helpful to customers while generating more revenue.

They ensure customers visit their platform more frequently by creating helpful, personalized features.

Location-Based Analytics

Data monetization can also be location-based for services like rideshares. With customers’ permission, many rideshares sell location-related data to other businesses. Other businesses then use this data to offer location-based discounts, vouchers, and advertising.

Telecommunication

Telecom players typically adopt external data monetization methods through partnership models in B2B and B2C segments. In a few cases, companies have also acquired startups for collaboration and assistance. The gathered data allows promoters and advertisers to target messages to specific users better.

Data Monetization Challenges

Every firm creates potentially useful content and data. Companies, like all developing new technology, are responding to new opportunities, albeit not always successfully. With data monetization, business owners frequently confront various strategic, organizational, and technological hurdles.

Strategic Challenges: New Opportunities in Unfamiliar Markets

For companies operating outside the information services industry, processed data is generally a by-product of their core activity. In this case, the monetization opportunity represents a new business area with product offerings, revenue models, and regulatory constraints the company may be unfamiliar with.

Strategic Challenges: So Many Possibilities, Such Little Time

With so many information assets, organizations must decide where they want to play in the data value chain, which in turn raises numerous questions about the data value, products, services, internal assets, and technology choices. So, with different implications and options, the problem is often about finding quickly which products or services to pursue before the opportunity window closes. Without proper prioritization, companies may not achieve their desired result.

Organizational Challenges: Technology-Driven Decisions

With technology-driven decisions or capabilities, new products often display unnecessary or complicated features and prove disconnected from actual end-user demands or needs. When this happens, business owners or executives trying to monetize data find themselves in a tough situation: finding an issue with the solution they just developed.

Organizational Challenges: Requires New Skills and Expertise

For many data providers, new opportunities means moving up the value chain. This involves building off existing data and shifting to productivity tools and workflow solutions more embedded in customers’ businesses. Such migration assures new revenue streams, longer-term relationships, and higher margins. However, this process typically needs more sophisticated skills, technology, and expertise.

Organizational Challenges: Data Ethics

Is this data collected legally? Stored correctly? Is it allowed to be sold, or used in the manner that’s intended? This is an ever-changing and challenging area of regulatory compliance.

Technological Challenges: Data and Content Are Locked Away

In companies that have already invested in prior-generation technology, data is often designed to be locked within the company’s firewall. However, turning these contents into revenue-generating products may prove expensive and present several technical and operational issues.

Technological Challenges: Lack of Scalability

Companies that are new to the content or data space typically have traditional infrastructure that lacks scalability. For any business, flexibility and scalability are critical to support self-service subscriptions and increased business velocity.

Best Practices to Get Started with Data Monetization

Drawing as much value from your data as possible is critical to optimizing an asset you currently have. Although each use case is unique, we have identified five best practices for getting 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?
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 holds the key to business change. Take the time to consider how you might use it to generate business growth and identify new revenue sources for your organization.