Types of data products and how to monetize them

Data, when viewed as assets, doesn’t automatically generate value. A favorite analogy of mine, taken from the book “Data is Everybody’s Business: The Fundamentals of Data Monetization,” is to see data as seeds. Simply planting the seed and relying on the natural effects of sun and rain without any effort might result in a plant: these are the insights we naturally obtain just by organizing data.

However, left to natural processes alone, it’s clear that the plant won’t grow as much as we’d like it to. To achieve this, we need to water, fertilize, and care for it: this action is equivalent to treating data to extract value. With this effort, the plant can bear fruit, which equates to the value of the data. But if these fruits aren’t harvested and utilized, they may fall and rot. So, in addition to creating value, someone needs to oversee and manage it.

Just like in our analogy, data products generate different levels of value. They can be categorized into three types: Improvements, Wrapping, and Information Solutions.

Improvements

This type of data product refers to the ability to generate data assets that people can find, use, and trust. Today, a significant portion of professionals’ time is spent searching for and validating the trustworthiness of data. Additionally, many actions are taken erroneously due to data errors. Here are some ways of offering improvements as a data product:

Providing Data

The most basic way to offer improvements is by providing more accurate, faster, and more integrated data to users.

Providing Insights

From structured data, we can extract insights in the form of benchmarks, reports, recommendations, and alerts. The data provided can also be enriched to facilitate decision-making.

Trigger for Action

Finally, when discussing improvements, the real value of data emerges when we make it easy for users to simply accept recommendations or even fully automate their actions.

Wrapping

Another type of data product is Wrapping, which involves the ability to capture, transform, and disseminate data securely and efficiently. Here are the different levels of wrapping:

Data Wrapping

At the first level, this involves generating reports, graphs, and dashboards that users can integrate into their systems. This is slightly more comprehensive than the basic level of improvements, where there isn’t necessarily a structured way to visualize data.

Insights Wrapping

With more structured visualization, it’s also possible to wrap insights by providing guidance on next steps for decision-making, recommendations, and alerts.

Action Wrapping

With structured visualization and insights, necessary changes can be determined and actions can be advanced as much as possible.

Example of a data wrapping product in the retail industry
A great example of a successful data wrapping product was implemented by Pepsi in a chain of convenience stores to boost soda sales. The company created visualizations related to the consumption of its products, as well as demographic data for each region, enabling the chain of convenience stores to make better marketing decisions. 

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Information Solutions

Finally, we have information solutions, where we use scientific methods, processes, algorithms, and statistics to extract meaning and insights from data and turn them into products. The information solutions can fit into one of these levels:

Data Solutions

We can see this as the transformation of assets into products that users can integrate into their systems to fill gaps in their own data.

Insights Solutions

At a second level, this data can be better structured to already offer insights in the form of decision-making support with results, benchmarks, alerts, and visualizations for information in a specific context.

Action Solutions

Ultimately, a level where the value of this data is realized is when it performs tasks automatically on behalf of the user.

Example of an information solution in the Health industry
A particularly interesting case of data monetization through information solutions is Healthcare IQ, a platform for managing client billing in the healthcare sector. Since each hospital has systems with different standards for procedures and products, the company’s first task was to clean and standardize patient data and verify compatibility between hospital products (data solution) to sell to its clients.

With all this structured data, they were able to improve their product by offering insights on expenses relative to other healthcare institutions (insight solution). Finally, to offer a complete solution, the company created a consultancy to support its clients in decision-making based on the data provided, with a remuneration model based on a percentage of the gains (action solution). This journey clearly illustrates how a data product can be built and developed to meet the needs of different clients.

Value realization by approach

To realize value from different types of data products, we must clearly understand the processes of creating and realizing value, measuring realized value, identifying who is responsible for the results, and understanding the risks of each approach. The following table illustrates each of these items.

As we can see, when we talk about improvements, we’re referring to operational enhancements. Wrapping involves enhancing the value proposition, while information solutions deal with the structured commercialization of data.

In the first approach, idle time is reduced. In the second, there’s potential for upselling and cross-selling within existing clients. In the third, new revenue streams can be created.

Regardless of the approach, all three types of data products directly impact profits by either reducing costs and increasing margins or by creating new products.

When thinking about data monetization, it’s crucial to understand which type of approach your solution fits into to align your strategy and have clear ways to calculate ROI.

How to become data-driven?

To advance in building a data-driven culture, the first step is to understand where your company is on its journey and create an action plan. This can be done through a data maturity assessment. Additionally, one of the best ways to accelerate the process is through real-world cases. Identifying a company challenge and understanding how a data strategy can support it helps increase team knowledge, generate value, and deliver tangible returns with the mindset of “Think Big. Start Small. Scale Fast.”

To identify opportunities for these initiatives, you can use the D2E (Data Driven Everything) methodology, which is based on Working Backwards. This approach helps understand customer challenges, whether internal or external, get to know the personas better, and design a roadmap starting with an MVP.

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Reference for this article: “Data is everybody’s business: The fundamentals of data monetization” (2023), by Barbara Wixom, Cynthia Beath and Leslie Owens.