Understand Legal and Reputational Impact

First and foremost, organizations need to consider whether they can sell data and whether they should. A legal advisor can provide guidance on whether existing contracts or Terms of Service permit monetization. While modifications are possible, there may be legal or practical restrictions on the frequency of changes, and whether changes would apply to existing or only future customers.

Data privacy is another requirement for any external data sharing. Consumer data privacy is regulated by legislation including GDPR, CCPA and HIPAA. Although business data is not subject to the same regulations, I encourage companies to apply similar best practices to protect customer names and other confidential data. In most cases, data de-identification and aggregation steps will remove sensitive details while retaining most of your data’s value.

Finally, companies should consider how their customers and other stakeholders will react. Lessons in reputation management can be drawn from successful data monetization efforts and high-profile data-related missteps. In my view, transparency is key. Inform stakeholders of your intent and don’t deviate from what you have told them. Thoroughly assess potential harms and benefits and respond proactively when concerns arise.

Assess Organizational Readiness

Companies contemplating a data monetization initiative should carefully assess their current product strategy and growth initiatives and decide where this one fits. Businesses in hypergrowth mode, or those in steep decline, are unlikely to have the bandwidth to focus on the technical, legal and commercial requirements. Conversely, companies with a steady business cadence that can prioritize the up-front development effort on their strategic roadmap and feel confident they can support ongoing product delivery are better candidates. It’s important to remember that data monetization is a product development process and should be treated as one: research customer needs, scope the work, and staff the necessary resources.

As a data scientist, I often remind business leaders that data maturity is another prerequisite to success with data monetization efforts. Typically, a data team or lead data practitioner with deep understanding of your existing data is required. Technical details such as data storage, baseline quality and writing the code to export data sets are owned by this team or person. Expect the journey from proof-of-concept to viable data product to be an iterative process that involves identifying potential data sets, producing sample output, and building and maintaining data pipelines that product the consistent, high-quality output expected by buyers. In some cases, a company’s data becomes more valuable when augmented with external data that links it to public company ticker symbols, brand names, geolocation, or other enrichments. Coordination with legal and security teams is essential during the data product development to ensure that the end-to-end process is secure, compliant and privacy-preserving.

Develop a Distribution Strategy

With confidence in your company’s external stance and internal organizational readiness, I recommend that leaders carefully consider how to package, price and distribute data products to interested buyers. As with any new product, begin by understanding who the potential buyers of your data might be. Evaluate their needs and their willingness to pay. In some cases, the organization already knows the buyers; they may be partners, suppliers or others in their industry ecosystem. In other cases, a data marketplace, reseller or aggregator may be used to reach a broader set of customers. Buyers’ needs range from detailed raw data to monthly or quarterly summaries, to distilled insights accompanied by graphs in a stylized user interface. I’ve found that a clear understanding of  buyers’ needs will more likely result in data products that are attractive in the marketplace while fitting the capabilities and privacy requirements of the company. Willingness to pay varies, but in all cases the buyer must feel that they value they derive from the data exceeds the cost. The more you understand buyer use cases, the better you will be able to package, price, and sell effectively.