Demystifying Data Monetization

by | Jun 21, 2021 | Data | 0 comments

Under All Is The Data

Okay, so you’ve managed to collect the relevant data you wanted. Now, the big question is “How do you monetize that data?”

I found some answers to that question as I was reading an article today in one of my favorite newsletters on the MITSloan Management Review entitled AI, Data & Machine Learning. The article’s catchy title was “Demystifying Data Monetization” –

I knew I had what I was looking for when I read this line: “Data on its own has value, but insights derived from data substantially increase that value.”

Some key excerpts from the article:

Every company operating today is a data company. Most have access to an array of data on their supply chains, operations, strategic partners, customers, and competitors. Yet most companies are leaving money on the table, with only one in 12 monetizing data to its fullest extent. Data on its own has value, but insights derived from data substantially increase that value. These insights can be used to direct activities as varied as customer segmentation, demand and churn prediction, pricing optimization, retention marketing, and cost management — and they can also command even greater margins when sold externally.

There are two primary paths to data monetization. The first is internal and focuses on leveraging data to improve a company’s operations, productivity, and products and services, and also enable ongoing, personalized dialogs with customers. The second path is external and involves creating new revenue streams by making data available to customers and partners.
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Using data, operators can proactively make better decisions, machines and equipment can be monitored digitally, and analytics can predict and diagnose issues early.
The external path to data monetization actually offers the greatest opportunities. The three primary external data monetization business models include data as a service, insight as a service, and analytics-enabled platform as a service.

Create a data platform. The architecture and technology stack that support a data monetization business model typically involve a robust enterprise data strategy, and a “data platform” with an intuitive interface to allow analysis, synthesis, modeling, and interaction with the data at a higher, more visual level. The goal is to create a “single source of truth” via data storage, harmonization, and processing. This enables data to be used by internal and external parties. Building the right data platform can require large-scale, multiparty data sharing and scalable computing, typically enabled by public or private cloud options.

Prepare for governance and compliance. Operationalizing a data monetization strategy requires a robust governance model that considers appropriate standards, guidelines, and compliance policies across teams.

Demonstrate cybersecurity and privacy. While leaders point to cybersecurity as one of their biggest concerns, it is often an afterthought when it comes to solution design.

Conclusion: All companies are data companies, and most have a substantial amount of untapped, underutilized data, which could unlock tremendous financial value.
Companies can increase their “earnings per byte” by reimagining a future where they not only maximize value creation internally, but also create a market for their highly valuable data and insights. This approach will mean they are not only changing the playing field, but reinventing the game, and securing market dominance early on.



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