Weekly Business Insights from Top Ten Business Magazines | Week 316 | Strategy & Business Model Section | 2

Extractive summaries and key takeaways from the articles curated from TOP TEN BUSINESS MAGAZINES to promote informed business decision-making | Since September 2017 | Week 316 | September 29-October 5, 2023

How to Have Better Strategy Conversations About Monetizing Data

By Barbara H. Wixom et al., | MIT Sloan Managment Review | September 27, 2023

Extractive Summary of the Article | Listen

Companies can’t manage what they don’t measure. They also can’t manage what they can’t discuss. Take the term data monetization: Definitions range from the narrowly focused “selling data sets” to the overly broad “creating benefits from data.” Too little consistency among curricula in academia and too much siloed business thought leadership only add to the proliferation of data babel. When leaders try to have productive conversations about a data monetization strategy within a complex business environment, they often reach an impasse. They need a simple, common language to break through.

Try using this definition: Data monetization is the conversion of data into financial returns. In their new book Data Is Everybody’s Business, the authors offer two simple data frameworks that — when combined — represent an easy yet comprehensive set of data products.

Three different approaches to converting data into money: Improve, wrap, or sell. If you sat down and began to list possible ways to create value from your company’s data assets, you likely could come up with hundreds of ideas. Fundamentally, however, you can organize your ideas into three buckets. Does the idea make a work task or process better, cheaper, or faster? If so, you’re looking at the improving approach. Does the idea make a product more valuable to customers? That’s wrapping. Does the idea identify information that a customer would pay for? You’re proposing selling. Improving, wrapping, and selling are very different in terms of risk, ownership, and requirements. When you know whether you are improving, wrapping, or selling, you can have smart conversations about data investments, capabilities, and returns.

Explaining the Value-Creation Journey Points: Data, Insight, and Action.  To truly create value from data, organizations need a person or system to take an action that would not have happened otherwise. There are three ways in which a company can help make this happen. First, it can give data to someone (or to a system) who uses it to derive insight and take action that generates benefits. Better yet, a company can give an insight directly to the consumer, who then needs to choose the appropriate action. Or, taking an even more active approach, a company can trigger or prompt action, and the consumer sees results without having to do much.  Leaders need to understand where and how the company should participate in the data value-creation process and consider the related trade-offs.

To continuously improve your strategy, consider using the matrix in these four ways: conduct a rough inventory of your crucial data-heavy initiatives and slot them into the matrix; use the matrix as a benchmark; look for opportunities to reuse and recombine data assets; and identify open spots on the matrix.

3 key takeaways from the article

1. When leaders try to have productive conversations about a data monetization strategy within a complex business environment, they often reach an impasse. They need a simple, common language to break through.
2. A new approach offer two simple data frameworks when combined in the form of a matrix — represent an easy yet comprehensive set of data products. The first framework offers three different approaches to converting data into money: Improve, wrap, or sell. The second framework reflects three points along the data value creation process: People or systems need to use data to develop insight that informs action. Combine the two frameworks, and you have a matrix that offers nine distinct product choices, each with its own set of commitments and outcomes.
3. Four ways to improve: conduct a rough inventory of your crucial data-heavy initiatives and slot them into the matrix; use the matrix as a benchmark; look for opportunities to reuse and recombine data assets; and identify open spots on the matrix.

Full Article

(Copyright lies with the publisher)

Topic:  Strategy, Business Model, Data Monetization