Private equity firms are invested in a wide range of financial services companies: payments, broker dealers, wealth managers, fintech service providers, and insurance brokers, among others. Many of these subsectors, like payments, are enjoying significant organic growth and benefiting from the disruptive application of new technologies. Growth and technical disruption are, indeed, the primary value proposition driving these investments.


Research by the Massachusetts Institute of Technology shows three ways to monetize data: 1) improve internal execution, 2) wrap data, and 3) sell data. And while the digital economy has made every company a data company, most are only analyzing 12% of the data they have.

The largest amount of value to be captured, and the one with the most immediate returns, is through improving internal execution: putting data and analytics in the hands of employees who make decisions. Consumer-focused applications might include marketing effectiveness and customer retention. Operational and financial applications generally focus on improving processes, cost efficiencies, and margin analysis.

Wrapping information around products is the next highest value-added activity that a company can do with its data. Data wrapping enriches products, services, and customer experiences by using data and analytics to provide connectivity and customization. As the MIT researchers found: “Top performers in data wrapping please customers with useful and engaging wraps, design specifically tailored features and experiences, and measure the financial returns from their efforts.”

Finally, selling data is actually the hardest way to monetize this resource, often requiring a unique business model. Benchmarks across suppliers, end-customer consumption, and market intelligence are some of the data that companies package and sell to third parties. However, while this avenue receives the most attention in the media, new regulations and consumer privacy concerns, as well as simple business model challenges, can make it quite difficult.

However, the services provided by these companies are also commoditized in large part. The days when most consumers would pay a premium for wealth managers or insurance brokers are generally in the past, and most fintech business models center on driving cost efficiencies and enhanced customer value propositions through improved technologies.

To generate outsized returns on investments in the space, leaders will need to monetize the wealth of data many of these firms possess but currently underutilize.

"To generate outsized returns on investments in the space, leaders will need to monetize the wealth of data many of these firms possess but currently underutilize."


These monetization opportunities aren’t just academic. They are generating real value for financial services businesses.


Salesforce integration, benchmarking, and optimization are critical to properly servicing financial institutions customers but remain a challenge for many.

In post-merger integration, a common yet cumbersome issue is combining and rationalizing back office systems and customer information. Identifying and mapping the joint customer base, products, and relationship holders is crucial. Data formatting issues, including missing, invalid, poorly scanned, and differing information on the same customers create major logistical obstacles. New analytical tools, including AI applications, have automated what was a burdensome, often human-intensive process.

Companies can now more easily link client information, collect and organize KYC documentation, and merge even structured and unstructured data sets into a central repository. By doing so, they can drill down from global to customer level reporting within a few clicks, analyze pre- and post-integration views of performance, develop customized reports to provide insight into key areas, and monitor engagement progress.

As an ongoing matter, optimizing cross-selling opportunities and benchmarking best practices and salespeople are activities dramatically facilitated by the data, connectivity, and analytical tools now available. Existing, internal data can also assess profitability by product and customer and suggest ways to increase profitability.


Payment processing companies are using merchants’ own data, as well as aggregated information from within their ecosystem, to provide new business insights to help merchants market better and bring in new customers. This can be as simple as providing merchants with new visualizations of spending patterns in their stores, with comparisons over time across product and customer types. In more advanced forms, it can show how their customers are performing relative to other, similar businesses.

Effective data wrapping that makes a product both useful and engaging has been shown to generate greater customer retention and loyalty and potential new revenue streams and higher prices. MIT research shows that companies that report wrapping more effectively than peers achieve an average return on investment of 61% from wrapping projects, versus 5% that report wrapping less effectively than peers.


Despite its challenges, some financial institutions have found success in selling their company’s data to third parties through unique and specific business models.

One example of this is State Street Global Exchange, which combines State Street data and analytics with new research to provide entirely new products to frequently a new set of clients, offering research and advisory services, data solutions, and investment analytics. But to support this, State Street had to build a new operating model in a separate division and develop an entirely new sales strategy.

Another is Cardlytics, which has built a business around selling data, in a privacy-friendly way, on consumer spending patterns. They partner with financial institutions to run their banking rewards programs, which provides Cardlytics a view across institutions into where and when consumers are spending their money. This anonymized data is then sold to marketers to help them understand buying patterns, target likely buyers, and measure the impact of marketing campaigns. Two out of every five card swipes in the US, by some 120 million users, are captured in their analysis.

So, selling data is doable and can be quite attractive for the right company. But it is not the simple solution it appears on its face and requires a unique approach.


There are four primary challenges that financial institutions must overcome to profit from the rich amount of data they have available. However, the time is right to begin overcoming these challenges.


The technology exists today to easily and cheaply pull data from legacy systems into the cloud, integrate and organize them, and begin to utilize. The costs associated with this activity have dropped dramatically, and for a host of reasons beyond data monetization, legacy system remediation is something financial institutions should be undertaking.

Even for organizations that have already built platforms where they have dumped large 2 quantities of data without knowing what to do with it (the so-called “data bog” problem), finding a use for that pool of data should be a priority.


Artificial intelligence and fuzzy logic tools enable probabilistic matching of data identifiers that automate what was once a burdensome and manual process. These applications clean and normalize sets of structured and unstructured data and put it in a usable format.

One of the useful things about data lakes is that they can store data in various states of cleanliness. Data can be landed, then progressively cleaned and structured using AI applications. This makes handling poor data much easier than the days of relational databases.


Internal data can be augmented with external data provided inexpensively as a service. In the case of one client, we analyzed their best customers, then looked for prospects that looked like them in external data. This facilitated high return digital marketing from their very limited internal data.

Partnering and pooling data sets across portfolio companies is another way to build scale.


Through a combination of transparency with customers and the application of appropriate compliance controls and technology tools, this too can be overcome. New applications provide methods for consumers to opt in or out of these systems, as well as providing greater anonymization.

Machine learning, combined with other tools like natural language processing, can help categorize data and provide confidence in compliance with confidentiality standards and regulations.


Pursuing a pragmatic and incremental approach to data monetization is the best way to begin. Start small: Overly ambitious projects, particularly ones that impose entirely new operating models onto conventional businesses, are almost certainly doomed to failure.

Identify a business need, be that a current efficiency problem, customer gap, or customer demand, in which key deliverables can be implemented in no more than three to six months. Doing so is a two-step process:

First, assess the value of the data you have. Identifying and mapping data based on its accessibility, cleanliness, exclusivity, history, and specificity provides a baseline.

Second, armed with this knowledge, you can develop a prioritized list of data monetization opportunities. Combining the views of business leaders with data scientists, revenue enhancement and cost-saving opportunities can be identified and prioritized based on both the level of importance to the business and the difficulty in implementation.

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With priority given to those in the upper right quadrant, with the highest strategic impact and least difficulty, you can move quickly to implementation, beginning with pilot testing and then roll out.

In this practical and time-sensitive way, private equity firms can begin to realize the potential presented by the wealth of data at their financial services portfolio companies.

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