As companies increasingly recognize the value of their data, many struggle to create meaningful revenue streams from it. That is because they approach data commercialization with a data-first mindset instead of a buyer-first mindset. Too often, data sales discussions devolve into conversations about technical specifications and engineering nuances when they should focus on how data can solve a buyer’s problem or enable them to capture an opportunity.

This “data-centric” thinking leads to poorly differentiated products that lack a clear value proposition, resulting in limited commercial success.

The commercialization conversation must elevate and focus on the problems being solved. Data commercialization is no different than selling physical products or software. The key to success lies in understanding what is in it for the buyer. Salesforce “brings companies and customers together,” Trello “helps teams work more collaboratively,” and Post-it “helps us all get organized”- these are outcomes, not inputs.

What makes data commercialization particularly attractive is the ability to generate multiple revenue streams from the same underlying data assets. Data’s non-rivalrous, non-depleting qualities mean that a company can create different data products for different buyers, with each product solving a particular problem based on the use case. The key to success is effectively articulating the relevant value proposition in each scenario.

To win in today’s increasingly competitive data market, companies must adopt a buyer-first mindset, focusing on the value their data delivers rather than the dataset itself. This article outlines 3 common mistakes to avoid and 5 actionable steps to begin commercializing data.

Note: The term “Data Commercialization” is used in this article to represent a company that is licensing datasets to another company. This process is a sub-element of Data Monetization, which represents the process of capturing financial value from your data via internal or external use cases. 

The misguided mindset: Focusing on what the data is rather than what it does

One of the most common pitfalls in data commercialization is the tendency to describe data based on its technical attributes — how much of it there is, how frequently it’s updated, or how it’s collected. A buyer’s primary interest isn’t that your dataset is 129 GB (gigabytes) and utilizes the latest tools for data prep. They want to understand the specific use cases your data addresses and what value potential that represents to their business.

The problem with a data-centric approach is that it emphasizes what the data is rather than what the data can do or what problem it solves for different buyers. This creates an unfocused offering, lacking a clear and buyer-centric value proposition.

Different end users have different needs, and a single dataset can solve very different problems depending on the buyer. This is why a one-size-fits-all approach to data commercialization doesn’t work. Instead, companies need to customize how they position, package, and price their data for different buyer segments. For example, different buyer segments will use, and therefore view, the same dataset differently:

• Retailers use foot traffic data to optimize store layouts or plan marketing promotions.

• Real estate developers use the same foot traffic data to assess potential locations for new projects.

• Financial institutions use the dataset as a factor in investment decisions, helping predict retail performance trends in certain geographic areas.

Despite all three segments using the same data, each buyer has unique needs. This is why it’s critical to think in terms of use cases rather than datasets — focus on what the data can do for the buyer, not just what it is.

Instead of asking “What is in this dataset?” ask “What value does my data provide? Why would buyers pay a premium for it?”

Reframing your data strategy — Buyer-first, not data-first

In a buyer-first approach, businesses shift from emphasizing the technical aspects of their data to focusing on how the data solves a specific problem for their target customers. As I discussed in my previous article, “Unlock the Potential of Your Data Assets,” the value of data comes from how it’s used, not from the data itself.

Instead of approaching data commercialization by asking, “What data do we have?,” the question should be, “What problem can our data solve?” In the example above, Post-It didn’t position itself as a “small piece of paper with adhesive”; the company helps its customers get organized.

In data commercialization, this buyer-first perspective allows companies to:

• Identify high-value use cases for different industries.

• Package their data into actionable solutions that deliver immediate benefits.

• Frame the value proposition around how the data can be used, not just the technical specifications of the data.

For instance, healthcare providers don’t want patient data just for the sake of accumulating more patient data — they want to use data to reduce readmission rates, predict trends, and optimize care delivery.

3 common mistakes to avoid in data commercialization

Mistake 1: Advertising data as the product

Many companies believe that once they collect and clean data, the data itself becomes a sellable product. But in reality, this mindset leads to undifferentiated products with no clear value proposition.

The reality? Data itself isn’t the product — the solution it provides is. For example, instead of focusing on the volume or format of sensor data, explain how manufacturers can use it to prevent machine downtime and streamline operations.

Mistake 2: Assuming data valuable to you is valuable to everyone

It is easy to assume that all data has intrinsic value. After all, you’ve invested in collecting and managing it, so why wouldn’t it be valuable to buyers?

The reality? Data only holds value if it solves a problem for the buyer. For example, temperature and climate data might be crucial to logistics and agriculture companies, helping them optimize supply chains and manage crops. However, the same dataset might be irrelevant to a hedge fund or retailer, unless framed as part of a larger weather impact analysis. Understanding the context in which the data will be used is critical.

Conversely, your unused (and at times nearly forgotten) “data exhaust” may be valuable to others. Archived datasets held in cold storage to comply with regulatory requirements may represent a novel training set for a tech company focused on developing a function or sector-specific next-gen AI solution.

Make sure to include your entire inventory of data assets when contemplating potential commercialization strategies.

Mistake 3: Limiting your commercialization efforts to your existing ecosystem

Many non-tech companies believe that data commercialization is a domain reserved for tech giants like Amazon or Google. They assume that because data isn’t their primary business model, they can’t successfully commercialize it.

The reality? Companies across industries can commercialize their data. In a world where there is an arms race for training data to create new and differentiated AI solutions, proprietary data is increasingly valuable. The key is to recognize that proprietary data, when positioned correctly, can drive value for use cases that may not be related to your current supplier, customer, and partner ecosystem.

5 tactical steps to start commercializing data

Here’s how companies can start taking a buyer-first approach to data commercialization:

1. Identify buyer segments and use cases: Begin by identifying who your potential buyers are and how your data could solve a specific problem for them. Is your data helping hedge funds improve trading strategies? Is it providing hospitals with insights into patient care optimization? Understand their pain points and map your data to those needs.

2. Engage with potential buyers: Don’t make assumptions about what buyers need — talk to them directly. Surveys, pilot programs, and interviews can help you understand how they want to use your data and what kind of product or insights they are willing to pay for.

3. Package and position the data: Data alone isn’t enough. Package it in a way that is easy to use and provides actionable insights. Whether it’s a dashboard, an API, or predictive analytics, the goal is to offer a solution that can be implemented immediately.

4. Ensure data quality and compliance: Buyers demand high-quality, clean, and compliant data. If your data has gaps or errors, it loses credibility and value. Ensure that your data governance and compliance processes (e.g., GDPR, CCPA) are solid to meet buyer expectations.

5. Start small and scale: Pilot programs allow you to test your data products in a low-risk environment. Work with a small group of buyers to refine your offering and gather feedback before scaling up.

Conclusion — Data commercialization is about solving problems, not selling datasets

The key to successful data commercialization isn’t in the data itself but in how the data can be used to solve a problem for the buyer. When companies adopt a buyer-first approach, focusing on the needs and challenges of their customers, they transform their data from an abstract resource into a solution that drives real value.

The companies that succeed in data commercialization are the ones that start with the end in mind, asking not “What data do we have?” but “What value will our data provide to the buyer?” When you shift your perspective in this way, you stop selling datasets and start delivering value.

About the Author:

Jeremy is Managing Partner at Gulp Data where he helps companies quantify and realize value from their data assets. Prior to Gulp Data, he was a Partner at West Monroe Partners, a management and technology consulting firm, where he led data science and data monetization efforts for Private Equity firms and Fortune 500 companies.