How to Have Better Strategy Conversations About Monetizing Data

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 the new book Data Is Everybody’s Business, we offer two simple data frameworks that — when combined — 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.

Leaders can use the data monetization matrix to explore current activities, future opportunities, and the interrelatedness of work. The simplicity of the matrix can help leaders be more transparent and open to breakthrough ideas. The objectivity of the matrix removes the heat from discussions and encourages conversations about how to monetize data instead of whether to monetize data.

Explaining the Monetization Approaches: Improve, Wrap, and 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.

Let’s break down each of the three approaches in plain terms, with examples and questions to ask yourself about each approach.

Improve. Most companies find it easy to identify ways to use data to do things better, more cheaply, or faster. Consider Femsa, a multinational beverage and retail company that operates 21,000 Oxxo convenience stores in Latin America. The Oxxo subsidiary’s senior management invested in creating a method of calculating SKU-level revenues and costs to arrive at a true measure of profit. In this way, they generated a data asset out of a host of revenue and cost data. With this new data asset, the organization could help its people understand the profitability of their decisions, leading to more profitable choices. Oxxo didn’t just develop a data asset. The company soon used it for myriad improvements in store management, merchandising, product assortment, and operations.

Companies often fail to establish the kind of internal accountability that Oxxo achieved and then miss out on having efficiencies materialize as financial returns. Conversations about improving with data need to explore who in the company can drive workplace change and how to support them. Ask these questions: Who has the experience to make an improvement viable, appealing, and scalable? Who else or what else is needed to reduce or redeploy slack resources to activities that hit the bottom line? Does the company have the willingness and ability to measure and track the financial impact of improvements?

Wrap. Almost any product can be wrapped. A tractor can be wrapped with a digital display that shows operational performance; a bank account can be wrapped with a chart that categorizes the account owner’s spending; and freight delivery can be wrapped with notifications of expected delivery times. Consider wrapping initiatives when products are being commoditized (a threat) or when products can be enhanced using new kinds of data via sensors, mobile apps, or surveillance feeds (an opportunity).

Wrapping conversations are customer-centric: How can the company use data to help customers better acquire, use, or retire its product? How can it use data to help its customers make money, save money, or better reach their goals? Such conversations need to include the owner of the product that is being enhanced because they are the person ultimately in charge of monetizing a wrap. How will wrapping be evaluated, monitored, and managed as a part of the company’s product management and development processes? How does the company ensure that wrapping delights rather than disappoints customers?

Sell. An organization might be attracted by the higher margins that selling information seems to promise, but it’s important to understand that this approach also comes with the greatest risks. Companies like Healthcare IQ, LexisNexis, and Verisk have learned how to solve specialized problems using their data assets after decades of investing in technology platforms, building algorithms, deepening subject matter expertise, cocreating with customers, and learning how to price solutions.

The crux of any selling conversation is whether it is worthwhile to invest in a new business model. Who in the company has the skills and experience to lead an information business? How can the selling initiative be established, protected, and nurtured while it gains traction? How can the company identify markets that want solutions based on its data? How can the company learn how much to charge them and how to sustain profitability in the face of competition?

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.

Data. Offering data involves putting new or better data into the hands of someone executing part of a process (for example, machine speed data, for an equipment maintenance supervisor), someone using an existing product (for example, medical history data, for a health care patient), or an entirely new customer (for instance, someone buying a list of registered voters). When offering data, consider if the recipient will know from the data alone what action to take in order to generate value. Will someone take the time to turn that value into money?

Insight. Offering insight, as a rule, involves analyzed or contextualized data. Using the same examples as above, insight might include machine speed compared against standards, the likelihood of a patient having certain medical conditions, or a comparison of voting behavior across ZIP codes. Insight conversations debate consumer behaviors and require exceptional subject-matter expertise. Your team will want to be well-versed in what common questions consumers ask of the data and how they ask them. Your company will also need a strong sense of the competitive marketplace and the latest analytical platforms. What exactly do consumers need to know and for what reason, and what inefficiencies can be removed from their analyses?

Action. Offering action prompts specific behaviors or automates tasks. Examples include automating the scheduling of machine service to prevent equipment downtime, providing a set of tailored dietary recommendations along with a food log to help a patient manage their health, or automating delivery of voter registration forms to selected recipients. Take caution here: Action always appears to be an ideal place to participate in data monetization because it positions the company closest to value creation, with a high likelihood of influencing it. However, leaders need to thoughtfully explore whether they have the capabilities, the rights, and the trust to offer action. Does the consumer want the company to trigger an action on their behalf, and would that be legal? Is the company confident that its data and insights are good enough to inform the correct action?

As you can see from these examples, leaders can deliver data, insight, or action using any of the three data monetization approaches (improve, wrap, sell).

The Data Monetization Matrix: Examples in Action

To help a team evaluate potential options, use the data monetization matrix to frame the discussion. Explore these real-world examples that a company might tackle and how the matrix helps explain the options.

Let’s start with an improving example. Consider an organization that has an employee performance data asset that includes employee evaluations, skill profiles, and recognition histories. Leaders believe that talent development could be improved by using this data to help supervisors identify, develop, and reward good performance more cheaply, easily, and quickly.

• A data product based on employee performance data might give supervisors direct access to this data, along with some features that allow them to sort and select from it.
• An insight product might provide supervisors analyzed and contextualized performance information — say, a dashboard identifying which employees performed above or below standard expectations. Supervisors might use this information to give feedback and monitor progress.
• An action product might automatically send a bonus and a recognition certificate to employees who consistently performed above expectations.

Now consider wrapping opportunities at an organization that has a device that collects historical blood sugar readings for patients and their doctors. The product owner believes that if patients received more benefits from the device, its price could be raised.

• A data product would add a display of the historical daily blood sugar level readings for the patient. Hopefully, the patient would see patterns and get insights about how to better control their blood sugar.
• An insight product would display the patient’s recent blood sugar levels relative to established standards for their age/gender cohort. Patients would need to learn how to use this insight to behave differently.
• An action product would send the patient an alert to have their midmorning apple snack within the next 15 minutes.

Finally, let’s review opportunities that a grocery store chain might investigate in the sell column. This company has a sales data asset consisting of purchase data and other customer-specific information for shoppers who have joined its loyalty program.

• The chain can develop a data product that provides data on items scanned at checkout that the company can sell to product manufacturers.
• That data, coupled with an analysis of customer purchase history (using loyalty card data and shopping cart items), can be the basis of an insight product that the company can sell to organizations that want information about trends in dietary habits.
• In the action box, the team could create a cross-selling algorithm that automatically suggests additional purchases to consumers — and charges the manufacturers who participate in the program.

Take note: Data monetization opportunities don’t need to flow in a straight line across the matrix. Your conversations might need to flip around. For example, leaders at the grocery store chain might decide that it’s not appealing to monetize by selling at all. Instead, they might want to engage in conversations about how to use the company’s sales data asset to improve store operations and make them better, cheaper, or faster. Or commoditization pressures might lead them to explore how to wrap existing grocery goods and services with the sales data asset to distinguish their services from those of other grocers.

4 Ways to Improve Your Strategy

As these examples show, you can use the 3×3 matrix to have more effective conversations about data monetization strategy. To continuously improve, consider using the matrix in these four ways.

1. Conduct a rough inventory of your crucial data-heavy initiatives and slot them into the matrix. Key questions: Have you tried improving, wrapping, and selling? Or just improving? Have you delivered data, insight, and action products? Or mostly just data products? You might find that your activities are weighted toward improving with data (pumping clean data from one system to another, for example, or revealing more data to a partner). If so, start conversations around how to evolve into wrapping or selling, or into insight and action.

2. Use the matrix as a benchmark. From time to time, ask whether you are expanding your repertoire of data monetization approaches. Key questions: Is data monetization activity spreading from pockets of the organization to broader parts of the enterprise? Is data monetization keeping pace with digital transformation goals and accomplishments? Do data monetization efforts have the right accountability, funding, and support?

3. Look for opportunities to reuse and recombine data assets. Key questions: Are the same data assets being used across the matrix or in just one cell, row, or column? Does each data monetization opportunity require new data preparation, permissions, and provisioning? If so, consider how data can be converted into liquid data assets that can be reused broadly in many data monetization efforts.

4. Identify open spots on the matrix. Key questions: Do empty cells represent hidden opportunities? Or are the open spots unachievable for some reason, like missing enterprise capabilities, skills, or customer connections?

All companies need a data monetization strategy, but creating, sharing, and executing the strategy can be fraught with misunderstanding and resistance. The solution is not more data or more AI but, rather, better communication. If leaders can get their teams up to speed on the matrix, they’ll encourage people to engage in productive conversations and get on track to manage the right data monetization strategy in the right way.