It’s been almost 20 years since British mathematician and data scientist Clive Humby coined the phrase “data is the new oil.” Businesses that generate massive datasets have been looking for ways to monetize the commodity ever since, and most have discovered what Humby already knew, which is that, like oil, data has to be refined before its owner can extract the maximum value.
The level of expertise needed to refine data into a consumable product — and the mere scale of the task— can be daunting, resulting in many vast reserves of data remaining untapped. It’s also why refining data isn’t usually approached as a side hustle. Most data suppliers are in the data business, but some enterprises in sectors like retail, financial services and insurance take on the role of data suppliers simultaneously because it can produce significant revenue streams.
If your organization generates large datasets, you’ll need to carefully weigh the pros and cons of a data monetization project. Considerations should include the data’s value as a proprietary competitive asset compared to its potential as a revenue generator. While the right decision will vary by industry and organization, here are some data monetization guidelines that apply in almost all scenarios.
Don’t Underestimate the Complexity of Launching a Data Monetization Product
If you package your company’s data for internal use, it may seem like offering it as a product would be fairly simple, but that’s not the case. If you plan to offer data as a product, you’ve got to know how to package, price, and license it. Licensing alone is incredibly complicated. For example, consider that big banks routinely pay millions to data suppliers in license violation fines simply because they find writing large checks simpler than efficiently managing the digital rights.
Packaging data for external use requires strict data hygiene, including the ability to handle data integration, transformation, and observability. It requires ongoing data hydration and mature data handling practices. A higher level of rigor than the practices currently used for internal data is often necessary for sales to third parties, which come with customer expectations attached. The expected returns must outweigh the cost of addressing these complexities, so make sure the ROI potential is solid before moving forward.
Do Understand the Challenges of Distributing Data
It’s crucial to understand how to distribute data in formats that work for your stakeholders. Everyone consumes data in different ways. Some want their data in Snowflake or Azure or Databricks. Others may receive it in BigQuery but want it in Cloud SQL instead to perform tasks in Postgres, or they need data to be compatible with SQL Server or MongoDB. If you’re leading a data monetization project, you’ll have to decide whether you will handle these requests in-house or find a partner to manage it.
Be aware that the vast majority of data exchanges are still file based and take place over legacy protocols like SFTP. You have to decide if you’ll perform that service or leave it to the customer to sort out “last mile” issues like managing flat file formats and delimited text files such as CSV. There are massive variations, and data consumers expect data in their preferred format. They rely on the data provider — you or a partner who specializes in external data — to perform services like unifying tables to make the data more usable. It’s crucial to recognize that a supplier’s decision to tackle formatting and delivery challenges directly impacts their GTM strategy. If data is formatted correctly, customers are more likely to buy it and could be willing to pay a premium because they don’t have the added burden (time = costs) of having their data engineers do the prep work.
Don’t Overlook Distribution Options
In some sectors, particularly financial services, the dataset owner has information that clearly has value for a specific set of customers and partners, and those tend to be the companies that are already monetizing their data. Businesses that are doing that, as well as those who are just getting their feet wet in data monetization can potentially broaden their pool of customers by building out a new marketplace or offering data for sale on an already existing third-party data marketplace.
Since data marketplaces like Google’s Analytics Hub provide a platform where organizations can buy and sell data, they are an attractive option because the data owner doesn’t have to build out their own platform, manage licensing, etc. An existing data marketplace platform can be a huge advantage from a discovery perspective. Using an existing data marketplace and partnering with an organization that specializes in data hydration can significantly streamline a data monetization project.
Do Understand the Benefits of Data Monetization Internally and Externally
Finally, it pays to fully understand the benefits of data monetization from an internal and external perspective. The most obvious internal benefit is that successful data monetization adds a new revenue stream. But there are additional internal benefits to consider. When your data-handling processes are more mature, your organization becomes a better data source for internal customers too.
Market goodwill may be another underappreciated external benefit. When you share data with customers, your organization is more transparent, and that generates greater trust, which can provide value. Making your company’s information available to customers is also valuable to the economy overall – fresh sources of information help businesses make better decisions, and a growing, thriving economy is better for everyone in the end.
As others have pointed out, data may be the new oil in the sense that it’s a valuable commodity that has fueled economic growth in the 21st century. But unlike dwindling oil stocks, the amount of data is only growing, and it’s reusable. So if you’re considering a data monetization program, keep these do’s and don’ts in mind and reuse your organization’s valuable data to generate even more value.
About the Author
Dan Lynn is the Senior Vice President of Product at Crux, bringing over 20 years of experience in building data operations software and founding data-centric startups. In his role, he enables the company’s development of greater self-service and external data products aimed at empowering data consumers with the assets they need – when they need it and how they need it.
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