Let’s now dig into wrap types.

• Data Wraps consist of simple reports, dashboards, charts and data feeds. They create the least value for organizations and customers.
• Insight Wraps simplify customers’ decision-making or problem-solving with respect to a core offering. These provide recommendations, flags for irregular activity, alerts, etc.
• Action Wraps tell a customer to do something, or they act on behalf of a customer. In a financial institution they suggest a refinancing that meets a specific customer’s needs.

The marketing outcome for great wraps, say the authors, is found in increased unit sales, the ability to command higher prices, growth in market baskets and improvement in customer retention. Great wraps anticipate a customer’s needs. They meet customer’s need in a tailored manner. They do this with advice and evidence-based decision making and they perform action that benefits customers. They should make it more enjoyable to find, acquire, use, store, maintain or retire the offerings.

Data Monetization Strategies: Selling

The final data monetization strategy is selling. Here the seller provides an information solution for money. To be fair, selling is about more than selling data; it’s also about creating a sustainable advantage. Data businesses should sell insights and actions. Pioneers of this business model, say the authors, are Owens and Minor in Healthcare and Kroger with POS.

In this model, data may be the product, but the value comes from creating value for customers. To sell data, data companies must be cost effective, use sophisticated data science, add domain expertise and have customer empathy. Caterpillar uses data to tell farmers what to plant, when to plant it and when to harvest.

Meanwhile, GE has gone from a physical product to a redefinition of what they sell. They learned that the data from jet engines when aggregated allowed them to sell a jet engine service that keeps airlines flying versus selling jet engines. Because they could predict failure, it was transformational for them and their customers.

Creating the Data Capabilities Monetization Requires

Wixom, Beath and Owens suggest five capabilities are needed for producing data that is accurate, available, combinable, relevant and secure. The authors also suggest these should be enterprise capabilities.

So, let’s inspect each:

• Data Management produces data people can find, use and trust. This is accomplished through master data management, data quality and observability, data lineage and metadata for discoverability. This includes the ability to integrate and curate data as well as understand its relationships.
• Data Platform provides the ability to capture, transform and disseminate data assets securely and efficiently. Today’s data platform leverages contemporary, cloud-based software and architecture to ingest, process, secure, integrate and deliver data assets.
• Data Science provides the ability to extract meaning and insights from data assets. Machine learning and automated processes are increasingly an output. Data science goes from simple reporting to machine learning.
• Customer Understanding provides the ability to gather accurate and actionable knowledge about a customer’s needs and behaviors. This includes grasping what customers need and value and co-creating value with customers. This process can involve formulating and testing hypotheses about customer preferences.
• Acceptable Data Use provides the ability to gather, store and use data assets in ways that are compliant with existing laws and regulations and consistent with organizational and stakeholder values. The steps here are these: 1) policies and training; 2) agreement on appropriate use and auditing; and 3) allowing customers to self-manage their data beginning with establishing polices for customers to control data. This is like the authors of “Rewired” who argue for automating trust. “Automating trust is the process of turning trust policies into code such as compliance requirements.”

Related Article: Data at Work: Metadata Matters

Improving With Data

The starting point for data leaders whether they be CMOs or CDOs is creating a vision for increasing the value the organization creates and realizes from data. This should start with the state of data capabilities. Without question, many improving initiatives focus on making data more accurate, timelier or better integrated.

With this, it is critical to make the business a more involved stakeholder. This means involving them in fixing literacy and data skill building. Remember that the outcome is the ability to improve by offering insight, triggering or prompting action. As someone that did a healthcare IT startup, I was amazed with the authors’ case study of Trinity Health which used data to reduce patient falls. This matters to patient outcomes and experience but under healthcare reform has steep costs to healthcare providers for each fall.

Creating Data Democracy

Data democracy without question shouldn’t be a fuzzy organizational goal. It is core to enabling businesses to run better and transform. Data democracy involves more than adding data expertise. It involves connecting domain and data expertise. The authors suggest data democracy occurs when organizations have pervasive employee appreciation of, access to, and use of data assets and monetization capabilities.

Visually putting this together, if data people are red and domain people are blue, the goal is to create a purple population. Given this, data democracy is about creating data domain connections. To do this, leaders need to facilitate knowledge exchange between data and domain experts. Additionally, they should embed data experts and create more multidisciplinary teams. What is needed are two-way collaborations, conversations and joint learnings. Embedding a data person in marketing makes it easier for employees to exploit data. It also means marketing people will know more about the next-best-offer algorithms.

CMOs should ask for data people to be embedded with their teams. Meanwhile, chief data officers and CIOs should push for more multidisciplinary teams that can solve customer churn by blending data and domain perspectives. Maybe, use machine learning to find potentials early for churn?

Put simply, connections drive data innovation. Another important step is to have leaders who act as early adopters and signal their expectations. They provide an example of the importance of data by encapsulating it into social norms. What is needed is a virtuous cycle effect on social norms and motivation.

Getting after monetization will mean that others will want to be involved in the initiative. Organizations that become data democracies remove barriers for domain experts to become aware of, access, and use data assets and capabilities. People who do this right enable knowledge sharing, inspire learning and generate innovation.

Creating a Data Monetization Strategy

Data monetization strategies should communicate how the organization will improve its bottom-line using data assets. It should link business strategy, digital strategy, and data strategy. For each, there needs to be a vision including benefits such as cost savings, sales increase, or direct revenue from information offerings.

Achieving Monetization

Wixom, Beath, and Owens approach this like enterprise architects. They say to evaluate the current state and determine the future state. Here, organizations should determine what their key data assets are. With this, determine if they are accurate, complete, current, standardized, searchable and understandable. And with this, consider the organization’s data capabilities, initiatives and connections. This includes asking questions. How does the organization produce data? Are current data monetization efforts paying off? How extensive has the organization seeded or formed a data democracy?

Related Article: 5 Essential Skills for Today’s Chief Data Officers

Some Parting Words on Data Monetization Strategies

There are a lot of data books out there. I have reviewed many of them. What is nice about this book is it turns monetization on its head. Instead of mere calculation of value achieved through data, it prescribes how you generate data monetization strategies that make their organizations money.

In an era of frothiness for generative AI, Wixom, Beath and Owens do something important. They share how to generate money from data. Amazingly, a few years ago, I hired a major analyst firm to evaluate why chief data officers were being hired. In almost every case it was to achieve the ends the authors describe. So where is your organization in achieving them?

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