With the increasing amount of data being streamed and processed on a daily basis, several companies have discovered non-traditional and novel ways to exploit this information, such as data monetization. Data monetization is the process of using company-generated data to achieve a measurable economic gain. This can include selling data to other parties or utilizing data internally to improve procedures or capitalizing on new innovation opportunities.

Companies that use data monetization reap benefits such as cost savings, revenue growth, and prospects for new data-related services. Because the present benefits of data monetization outweigh the initial financial and time expenditures, now is the best moment for your company to learn how to get a competitive advantage through effective data collection and analysis.

In this article, we will explain data monetization, show how it may significantly improve business performance, and provide the information you need to implement data monetization techniques.

What is Data Monetization?

Data monetization is the process of generating commercial and monetary value from raw data. Simply put, the process is ‘data out, money in’. However, the steps between input and output can differ. In other words, data monetization might take several forms.

Data monetization continues to be an increasing trend. As the amount of data generated and recorded globally grows by the second, so has data monetization’s popularity. Indeed, selling data has evolved from a marginally viable business strategy to a no-brainer for many businesses and individuals. Part of this is due to the generative AI revolution, which has resulted in LLMs and other machine learning models that require large quantities of training data.

As a result, data monetization is becoming increasingly popular. It has a tremendous impact on every company in technology, many companies in more traditional verticals such as retail and finance, and now even the individual consumer.

With only 1 in 12 companies currently monetizing their data to the fullest extent, why should your organization make the leap today? 

• Provides a Competitive Advantage: In mature industries, it’s difficult for businesses to differentiate themselves. Well-executed data monetization strategies can help to gain an edge on competitors who’ve yet to harness the power of their data effectively.
• Creates New Revenue Streams: Even if you aren’t planning to sell your data to a third party, data monetization can still generate new streams of revenue. For example, uncovering new customer trends within your data can precipitate the creation of a brand new product to meet those newly-discovered demands. 
• Streamlines Operations: For those of you in the manufacturing industry, in-depth analysis of production data can help to streamline output. Companies get to enjoy the positive byproducts of reduced waste and a drop in unnecessary expenditure.
• Create Strategic Partnerships: Data monetization does not have to be limited to strictly numerical gains. You can offer your data analysis discoveries to interested third parties, such as banks and credit providers, to receive favorable terms in return.

What are Examples of Data Monetization?

Selling your proprietary data

The simplest way to monetize data is if you’re already in possession of data that other parties will pay for. Organizations’ internal receipts, contact details, health records, legal documents etc. all count as proprietary data containing valuable insights. Simply put, selling them is the easiest way to monetize data. The step beyond this is launching a fully-fledged business providing data.‍

Launching a data provider business

Becoming a commercial data provider might entail selling your proprietary data, as we explained above. Alternatively, you could buy a license for a dataset from another party and build your business by selling that. Usually, you build the software to help you sell this newly acquired data. For example, Forager.ai built a web scraping service that collects and then supplies constantly refreshed B2B data to end customers.‍

Refining internal processes

An often overlooked means of extracting value for data (ergo monetizing it) is one of the most obvious. That is, by using data to make internal processes more cost-efficient.‍

Consider when Satya Nadella became Microsoft’s CEO in 2014. His first move was to urge employees to question their current workflows using only their internal data. This included time salespeople spent with customers and their closed-won rates. Working with current sales data, Nadella’s executives were able to better predict successful sales and see where to improve productivity.‍

This is a non-obvious example of data monetization. No data was sold, but its value was realized as it was applied to cut Microsoft’s costs elsewhere and reduce its margins.

To help you understand how data monetization works in practice, consider a few implementation examples from firms across industries.

Data monetization in consumer goods

AB InBev is the world’s biggest brewing company. Throughout its expansion, they have purchased a large number of additional brands. Today, AB InBev has a portfolio of over 500 global and regional beer brands distributed throughout 100 countries.

Onboarding these many businesses has undoubtedly brought some issues. At one point, AB InBev used 27 separate Enterprise Resource Planning (ERP) systems and 20 different integration technologies to try to connect all of the disparate systems.

Naturally, AB InBev decided that creating a cloud-based data center would be the ideal method to centralize all of the data. With a consolidated data hub, AB InBev can now create more accurate projections and minimize product time to market, allowing them to dominate their business. They now produce the top three best-selling beers in the US market, thanks in large part to their new data strategy.

Data monetization in agribusiness

Farmers in a high-volume, low-margin company want real-time variable information, such as field-level weather and commodity pricing, at their fingertips. For more than 30 years, the Digital Transmission Network, or DTN, has provided these data points to the agriculture business.

Read Also: 10 Ways to Monetize Your Data

DTN, like AB InBev, had invested in a variety of data systems to deliver the information that huge firms such as John Deere, Monsanto, and Pioneer rely on every day.

However, DTN struggled with the ongoing expenditure required to manage and maintain a growing number of complex applications across several networks. This policy constrained future growth and product innovation.

As a result, DTN chose to develop a cloud-based data tool with a clear and uniform set of interfaces for each individual data field. This reduced the need for millions of expensive point-to-point integrations and enabled a significantly improved user experience.

DTN’s integrated platform is swiftly becoming as the industry standard for agricultural business data sharing. They monetize their knowledge by charging subscription fees and offering value-added services.

What are the Five Key Steps of Successful Monetization?

The most successful data companies follow these 5 steps to developing a successful, scalable data monetization strategy: parsing, products, pricing, platform, and partners.

1. Parsing

The phrase ‘data monetization’ combines the vocabulary of two distinct linguistic groups: analytics and business. Because ‘data monetization’ is a collaborative effort between business and analytics professionals. As a result, efficient data monetization necessitates proficiency in both analytics and business terminology. Business teams must comprehend the information assets they are monetizing, while analytics teams must grasp both the economic worth of the data being sold and the larger external data economy.

That is why data parsing is critical for successful data monetization. Parsing data converts an illegible, unstructured file into a language that humans can understand. And by ‘people’, we don’t just mean analysts and CDOs, but also sales reps, account executives, price strategists, and marketers who sell the data.

Parsing data into a globally intelligible format allows all parties involved in data monetization to recognize its value. Then comes the next step: developing useful information products and pricing them optimally.

2. Products

Data is just like any other product. It is the result of cognitive effort and, like a product, exists for a certain purpose. The goal is to answer queries by offering information. Software advancements such as Narrative’s Data Shops demonstrate the trend of treating data as a product that can be purchased from a shop.

However, many data vendors have not taken a product-driven approach to selling their information assets. Good products should be standardized so that the seller can mass produce them while also making them understandable and appealing to a wide range of customers. According to Auren Hoffman, it’s ” really advantageous if data providers and data consumers agree on a single standard.’

For the best data monetization plan, begin arranging your data assets into standard, digestible, and saleable products. You can use this internally to create a catalog of data products. Then, utilizing this catalog, you can generate product listings for all of the data monetization platforms that you use.

3. Pricing

The price of external data varies greatly depending on the type of data you provide. A weather data product will most likely cost more than a location data product. As a result, it’s critical that you evaluate your internal data before presenting it as external, easily purchased items.

After an internal review, undertake market research to identify the best price point for your data offerings. Your study should consider the market prices for the type and volume of information you’re selling, as well as competitive data suppliers.

4. Platform

Data monetization is only one component of the larger data commerce revolution. There has been a proliferation of data commerce platforms that are driving this change. Data commerce platforms, like e-commerce platforms such as Spotify, enable merchants to sell things online. They are channels meant to make your data monetization plan easier and more efficient.

Integrating systems like Data Commerce Cloud™ (DCC) into your data monetization strategy offers numerous benefits. DCC enables you to sell your data goods across several sales channels using a single central account. This decreases the overhead associated with managing a data business as you scale your data monetization activities.

5. Partners

The term ‘data’ is frequently followed by terms connoting partnership: sharing, collaboration, and exchange. The data industry is still, at its core, a people-powered environment. Strategic partnerships are the final, critical component for successfully monetizing data.

Data partnerships can take different shapes. First, you form agreements with other providers. By collecting and connecting your data assets, you increase the value of both parties’ data. This is because the combined data product is richer, cleaner (because to the ability to cross-reference datasets and remove anomalies), and capable of answering more questions. So it’s worthwhile to form connections with other data-as-a-service companies. It’s mutually helpful for your respective data businesses: everyone’s products are more valuable.

A second type of relationship is required to develop a scalable data monetization strategy, this time using the data commerce platforms discussed in Part 4. Most systems provide tiered plans, starting with self-service data seller accounts. However, the true benefit of data commerce platforms comes from purchasing a premium plan and genuinely collaborating with them.

This opens up opportunities such as active public relations and promotion, access to high-end buyers, and direct lead referrals. According to The Data Appeal Company, a top location data provider, “We love our partnership with Data Commerce Cloud™ because it gives us access to prospects around the globe across a variety of industries”.

What are the Benefits of Data Monetization?

Data monetization offers several benefits for data providers, and also for the wider technological landscape in terms of incentivizing innovation.

Generate net-new revenue

The most attractive part of data monetization is that it allows you to generate a new stream of revenue. This benefit is even more promising if you’re capitalizing on your existing data assets. Monetizing data gives enables you to extract revenue from assets that would otherwise lie dormant.‍

Audit existing data government

Data monetization entails auditing your existing data government processes. So it can also shed light on company’s internal data usage. This helps you improve overall data management practices within your organization, with a view to optimizing your data offerings to then sell them.‍

Supports economic and AI innovation

Lastly, data monetization contributes to a healthy business and technology landscape. With more data available, more innovation and AI development can take place. It also levels the economic playing field. If every company has valuable data to sell, small fish companies can disrupt the companies which overpower them in terms of other capital.