Monetizing “Dead Data”​ is the Business Opportunity You’ve Been Looking For

Data Monetization in Practice: Three Steps for Monetizing Data in Your Company

Why approaching “dead data” as a living opportunity is an investment win.

As the global volume of data increases, the challenge of monetizing data is only growing. In fact, data is projected to increase ten-fold by 2025, and 25% will be real-time in nature, requiring sophisticated systems and processes to capture and utilize effectively.

Approaching Data Monetization

One of the most common business questions we get at Cicero Group is how to leverage the value of “dead data.”  Data monetization is “the collection and packaging of data (or data insights) for delivering value-added services or creating revenue-generating products”. As the term “value” suggests, data monetization goes beyond just selling or transferring data assets. Instead, the best data monetization practices include both direct and indirect strategies. An indirect strategy may involve using data to improve customer experience, drive cross-selling, or improve performance, and a direct strategy may involve creating new sources of revenue with outside partners.

As the volume of data explodes, companies are finding creative ways to exploit this information. For example, retailers are harnessing customer browsing and purchase histories to perform targeted marketing. Insurance providers are using a customer’s driving data to inform premiums. Manufacturers are leveraging weather patterns to improve supply chain and logistics. The opportunities are truly endless.

Three Strategies

The good news is that you don’t need Rumpelstiltskin to spin data into gold; you can do it yourself using three simple, cyclical strategies:

1. Identify Data
2. Determine Value
3. Capture Value

1. Identify Data

The first step in any monetization effort involves identifying what data are relevant. For many organizations, however, this seemingly-simple question is harder to answer than expected. For instance, 63% of organizations either don’t have a data management or governance strategy in place, or they have various strategies scattered throughout the organization. Only 2% of organizations have a fully-centralized data management strategy, while another 35% tend to mix fragmentation and centralization.

This lack of governance makes it difficult—if not impossible—for companies to realize the value associated with their data. Just determining what data are available may require coordinating many groups and departments. Varied technologies and tools in each department might prevent the organization overall from combining data in valuable ways.

So, how can an organization identify data? Begin by documenting the existing data landscape—noting what data exists, where it is housed within the organization, which systems and tools are associated with it, and what quality it has.

If you find that your data is decentralized with no governance in place, then your data monetization efforts are the perfect time to design and implement such a system. After documenting the internal data landscape, examine market trends and data use cases that are external to the organization, focusing on those already receiving or providing value to your business (for example: suppliers, customers, and partners). This will help reveal previously-unthought-of uses for “dead data,” or ways to combine existing data into new datasets with significant value.

2. Determine Value

After identifying relevant data, develop monetization initiatives for each dataset.  The definition of an initiative is broad and encompasses anything with data as the key component. Some suggestions include the following:

• Selling raw data
• Developing analytics capabilities (for internal use or external sale)
• Creating a new product or service
• Improving operational efficiencies

When developing these initiatives, focus on answering two questions: 1) what problem does the data solve? and 2) How valuable is solving the problem? This should include direct monetary value from revenue generation or cost savings, as well as indirect value from areas such as positive publicity, higher employee job satisfaction, improved operational efficiencies, or better partner relationships.

Given that the definition of “value” will differ between organizations, you should develop a custom evaluation framework encompassing the unique objectives, risks, and priorities of your business. For example, is public perception top of mind, or are efficiency improvements the primary goal? Building out a framework that captures key criteria allows a company to assess each initiative accordingly and begin to narrow the list of ideas.

Even with such a framework in place, estimating the value of the data may pose a significant challenge. Many organizations pursuing data monetization for the first time lack the expertise to value their data outside of internal applications (such as efficiency improvements).

This is where a third-party firm such as Cicero Group—which has experience working across industries and functions—can add significant value by helping assess a company’s data. The end result of this value estimation process is a focused list of data with corresponding use cases that is worth pursuing for monetization.

3. Capture Value

With a list of opportunities in place, the final step for monetization entails creating a go-to-market plan for each opportunity. Here, an organization carefully considers key details such as which customer segments are targeted, what sales channels are utilized, and what kind of marketing is required.

Each idea should be thoroughly vetted and validated in this phase. This helps identify areas where internal capabilities must be developed, or where outside expertise is required for implementation. This is particularly true in organizations without a comprehensive data governance system in place, as the more valuable initiatives would benefit from implementing such a system first. With a fully developed go-to-market strategy, an organization is well equipped to begin monetizing its “dead data.”