The data gold rush: Companies need the right models and capabilities to monetize data

For financial services firms, the opportunity to monetize customer and transaction is new and compelling – with revenues of US$175-300 billion per year. Financial firms can expect to see nontraditional players trying to muscle into the space. This report identifies five basic business models for using data profitably and effectively.

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The data gold rush Companies need the right models and capabilities to monetize data


Boston John Plansky Senior Partner +1-617-521-8801 john.plansky Carl Drisko Partner +1-617-521-8809 carl.drisko Jamie Solomon Senior Executive Advisor +1-617-521-8800 jamie.solomon

Düsseldorf Peter Gassmann Partner +49-69-97167-470 peter.gassmann São Paulo Roberto Marchi Partner +55-11-5501-6262 roberto.marchi

Sydney Vanessa Wallace Senior Partner +61-2-9321-1906 vanessa.wallace Peter Burns Partner +61-2-9321-1974 peter.burns



About the authors

John Plansky is a senior partner with Strategy& based in Boston. He specializes in IT strategy, large-scale transformations, and cost fitness initiatives for financial-services firms. He leads the North America digital business and technology practice for Strategy&. Jamie Solomon is a senior executive advisor with Strategy& based in Boston. He has 35 years’ experience in business and technology for financial-services firms, focusing on the monetization of data assets. Rebecca Karp was formerly a principal with Booz & Company based in Boston. Carl Drisko is a partner with Strategy& based in Boston. He leads the firm’s financial-services IT architecture team and specializes in the architecture, design, and implementation of large mission-critical applications.

This report was originally published by Booz & Company in 2013.



Executive summary

Few financial firms recognize the value of their data. One reason is the newness of the technology. Only recently has it become possible to mine in such quantity this fine-grained information about costs, profits, operations, supplier practices, and (most important) customer behavior. But though the opportunity is new, there is no time to waste. Strategy& estimates that the revenue from commercializing data will be US$175 billion in 2013 and will ramp up to $300 billion per year in the next three to five years across capital markets, commercial banking, consumer finance and banking, and insurance. That kind of opportunity is going to attract interest from many quarters, and financial firms can expect to see nontraditional players trying to muscle into the space. To seize the opportunity, leaders of financial-services companies must first pick an appropriate business model for an enterprise-wide data and analytics transformation strategy. This report identifies five basic business models for using data profitably and effectively. They range from improving your current core business to offering entirely new products and services. No matter which of these models a company chooses, there are five core design principles that it must put in place to pursue a data transformation strategy: understanding its customers, getting to know its data, understanding the value chain, sizing the value, and enhancing the infrastructure.



Data is a valuable asset

Opportunities can hide in plain sight. Consider, for example, the data available to financial-services firms about their customers’ behavior, their suppliers’ practices, their costs and profits, and their own operations. Data of this sort is so abundant that it is often overlooked. Its growth is so exponential that the companies are unprepared. Its operational challenges are so daunting that many financial-services companies fail to recognize that data is also one of their most valuable assets. In the next five years, fortunes will be made and lost based on which corporate leaders can grasp this fact; reorient their approach; initiate enterprise-wide, data-led transformations; and effectively monetize this new type of asset. But how do you get there? A financial-services company can take advantage of the data gold rush through a three-phase transformation. These phases can occur sequentially, but in some well-designed initiatives, they occur in parallel and reinforce one another’s impact. The transformation includes the following phases: 1. Process-led transformation: standardizing and simplifying the front- and back-office processes to be best in class, while focusing operations on the most valuable parts of the business 2. IT-led transformation: using digitization and emerging technologies such as cloud computing and machine intelligence to automate delivery of products and services and create straight-through processing 3. Data-led transformation: monetizing data assets, integrating outside data (perhaps from adjacent industries, new partners, or unstructured data sources) with internal information, and developing analytics to gain powerful insights into customer behavior and supplier capabilities to create new products and services



This report describes the design and practice of data-led transformation and the monetization of data assets. The value of this opportunity for any financial-services firm can be enormous. Strategy& estimates that the revenue from commercializing data and analytics will be US$175 billion in 2013 and will ramp up to $300 billion per year in the next three to five years across four key areas: capital markets (accounting for 30 percent of this potential revenue), commercial banking (20 percent), consumer finance and banking (35 percent), and insurance (15 percent). For many financial-services companies, this will be a game-changing move, allowing them to move beyond enhancing core offerings and enter new industries, reach new customer segments, and drive revenue, profitability, and higher valuations (see Exhibit 1). Recent technology advancements, regulatory developments, and consumer habits are all converging to set the stage for this opportunity. On the technology front, mobility and connectivity allow companies to know where customers are at any moment and to market more easily to individuals, providing such personalized services that they reach the “segments of one.” Meanwhile, cloud computing, Hadoop (an open source software project that enables the distributed processing of large data sets across clusters of commodity servers), machine intelligence, and other technologies allow companies to process vast quantities of data in real time (or very near real time) and to integrate external structured data, along with internal and external unstructured data, with their own internal structured data. For example, imagine the power of correlating keyword searches and global news mentions, which are both unstructured data sources, to a momentum-trading strategy.

Exhibit 1 Financial-services estimated data monetization market size


Consumer finance and banking

Commercial banking

Capital markets





Total estimated market = US$175M

Source: Strategy&



In the regulatory arena, new oversight requirements since the financial crisis have expanded the categories of data that financial-services firms need to manage, process, and disseminate. For example, new capital and risk analytics will be required to efficiently manage Basel III and Dodd-Frank. Finally, consumer habits and expectations are changing rapidly, driving some consumers to switch their banks and insurance providers. Social sites such as Facebook, Twitter, and Pinterest have whetted people’s appetite for more comprehensive information, and retail sites such as Amazon and eBay are broadly influencing customer experience expectations. Google and eBay are quickly entering into the payments space, demonstrating how nontraditional competitors can enter the landscape via a data advantage. The end result for the industry will be a new group of high-margin business opportunities. This is good news for the industry as a whole, but it is also likely to attract outside firms with expertise in monetizing data. Think of PayPal and Google Wallet as the first of these new competitors. Companies from the computer industry could easily give some established financial firms a run for their money. Strategy& estimates that leading financial firms risk losing 10 percent or more of their potential top-line revenue to nonfinancial competitors within the next few years if they do not move aggressively to transform the enterprise today.



Five basic business models

The first step in seizing the opportunities of data monetization is picking an appropriate business model for your data and analytics transformation strategy. There are five basic models — five ways to use data profitably and effectively (see Exhibit 2). Together they make up a spectrum of activity for transforming your approach to information: • Your current core business, improved through use of enhanced data • Better return on your marketing investment using insights about your customers or transactions

Exhibit 2 Data and analytics transformation spectrum
Enhance current business
Leverage enhanced data for core business Generate new insights

Enter adjacent businesses
White-label capabilities & infrastructure

Develop new businesses

Create new data

Create new offerings

– Seek opportunities to enrich existing service through new data sources – Develop and leverage new platforms – Deliver enhanced services (e.g., in real time)

– Understand deep client insights – Enhance marketing campaign ROI and conversion

– Monetize existing analytics capabilities via white labeling to clients and other partners across the value chain – Commercialize infrastructure to sell platforms as a service

– Partner with adjacent players across the business value chain – Identify new sources of data (e.g., unstructured) to join with existing data sets – Monetize new sets of data

– Develop new sets of analytics and data products (e.g., benchmarks, tools) – Develop new products that benefit from enhanced data and analytics (e.g., real-time net asset value, active non-disclosed exchange traded funds)

Source: Strategy&



• White-label capabilities and infrastructure delivered by other firms • Delivery of new types of data directly to customers • New products and services made possible by data and analytics All these business models will generate incremental revenue. However, some are more transformative than others — in other words, they are more likely to reshape your business overall. Financial-services firms that are creating new data and developing data and analytics products are truly evolving into something more like a technology business. They may even start to enjoy the higher valuation multiples associated with technology and data firms. Corporate leaders can ask themselves a series of questions to understand where they would best fit on the data and analytics transformation spectrum: 1. Does my firm have a strong product set but limited conversions, cross-sells, or marketing results? 2. Do I own the market in key categories but have trouble enticing customers with new products? 3. Do customers need my products in real time or integrated with common industry platforms? 4. Is my firm’s accessible data robust? For example, do I see 20 percent or more of the industry’s transactions or volume? 5. Can I partner with others to improve the quality, value, and delivery of my data? 6. Can I use data to develop products and services in adjacent or new business sectors? The answers to these questions will help a company identify the best business model for its data and analytics transformation strategy (see Exhibit 3, next page). Frequently, companies will pursue more than one of these models to reach various customers and address different needs.



Exhibit 3 Data and analytics transformation dimensions

Key questions

Leverage enhanced data for core business

Generate new insights

White-label capabilities & infrastructure

Create new data

Create new offerings

1. Does my firm have a strong product set but limited conversions, cross-sells, or marketing results? 2. Do I own the market in key categories but have trouble enticing customers with new products? 3. Do customers need my products in real time or integrated with common industry platforms? 4. Is my firm’s accessible data robust? For example, do I see 20 percent or more of the industry’s transactions or volume? 5. Can I partner with others to improve the quality, value, and delivery of my data? 6. Can I use data to develop products and services in adjacent or new business sectors?

Source: Strategy&



Case study: Equifax
On the surface, Equifax is a company that sells data and enriched data in the form of credit scores and ratings to risk managers, marketing officers, compliance heads, demand managers, and the like. It is also an anomalous success; its stock price has risen by 50 percent over the past two years, more than double the growth of the S&P 500. What sets Equifax apart is its ability to develop business opportunities across the entire data and analytics spectrum. Like many financial companies, Equifax interacts with a large and diverse set of data assets: customer wealth, fraud and authentication, securities compliance, telephone and utility exchange, and income and employment data. It already has a presence in each of the five business models, with its core business positioned on the far right of the data and analytics spectrum. Most recently, Equifax is expanding into adjacent businesses based on its access to income and employment data from more than 200 million employee records. For example, Equifax’s Suspicious ID product provides real-time fraud monitoring capabilities across the company’s network of financial institutions as well as related industries, a big improvement over the existing periodic report. Equifax is also focusing on tailored solutions. Its Analytical Sandbox product blends Equifax data and analytics with data and analytics from a specific customer to conduct back testing and scenario testing. The company’s ability to develop products and services across the entire data and analytics spectrum is clearly driving growth. In fact, Equifax predicts multiyear growth of 7 to 10 percent (with acquisitions), including 2 to 3 percent growth fueled by new products and innovation in data and analytics.



Design principles

No matter where your company currently sits on the data and analytics transformation spectrum, one goal should be to fill in areas of the spectrum you don’t currently occupy. This typically means moving to the right, where new data and new offerings can yield new business. There are five core design principles that companies must have in place to pursue such a data transformation strategy. Understand your customers: Continually learn about and manage customer needs, a process that should include a deep segmentation analysis to unearth customer requirements and preferences. This goes beyond enhancing products and services to fill unmet needs. It means understanding the delivery and integration models that clients require in order to benefit from those enhancements. In other words, it’s not enough to offer a useful product; the company must also deliver data using tools and platforms that customers can easily integrate into their advisory platforms, trading platforms, analytic desktops, order management systems, middle-office systems, and back-office systems. For example, a tool to help investment managers understand fund flows needs to integrate into their systems in real time to be useful at the point of sale. Get to know your data: Catalog and map data housed across all business lines to develop an enterprise-data taxonomy and identify opportunities. For example, one capital markets firm mapped its full range of data and analytics capabilities and channels to identify those it could use to build new products and services. The firm then prioritized those data sets, favoring the ones with the most immediate promise. For instance, it built a new business generating intraday risk measurements, combining its capabilities in investment risk management and its enhanced data storage capacity. This was linked with proprietary data that, in combination with outside data, generated additional product opportunities, such as an intraday risk analytics tool kit. Understand the value chain: Develop insights into partners and competitors across the value chain, including upstream suppliers and other partners of your clients, paying particular attention to where
12 Strategy&

Case study: Tesco
U.K.-based supermarket giant Tesco is a prime example of a nonfinancial company that’s using data to compete effectively with traditional financial players. Until 2008, the company ran Tesco Bank as a 50/50 joint venture with the Royal Bank of Scotland. That year Tesco bought out RBS and began developing a completely new infrastructure for the business, built a new team, and brought in new expertise. The transition was not always smooth — for instance, online customers were locked out of accounts for several days in 2011 when Tesco moved data from the RBS systems to its own — but it’s now complete. To fully exploit this treasure trove of data, the company took a significant stake in Dunnhumby, a U.K. data mining firm that will help Tesco monetize the consumer data from both the retail and banking operations. At its core, Tesco Bank is underpinned by the Clubcard. The insights the bank gains from the Clubcard customer data allow the company to understand customer needs and make the most relevant offers in the store and in the bank. The Clubcard credit card rewards customers with points whenever they use their card — one Clubcard point for every £4 (US$6.12) spent. Clubcard customers can also receive preferential deals when buying Tesco Bank products — including discounts on car, home, pet, and travel insurance — and can use points to buy Tesco Bank insurance. This year, Tesco Bank gave customers around £70 million (US$107 million) worth of points to spend in the store or on Clubcard rewards. In terms of systems and IT, Tesco’s new platforms significantly improve customer service. Instant decisions are now possible on loan applications, and customers can open and fund savings accounts in just 10 minutes rather than the two weeks required in the past. The conversion is still in its early days, but Tesco’s efforts are paying dividends in the form of increased market share across a range of products. In 2009, Tesco Bank credit cards made up 9 percent of all MasterCard and Visa credit card transactions in the U.K., and by 2012 that figure had grown to 12 percent. Meanwhile, from 2008 to 2012, the company’s car insurance gross written premiums increased by 39 percent and pet insurance gross written premiums rose 44 percent.

market adjacencies offer new opportunities to leverage your data. For example, by understanding how merchants interact with order management system vendors, a payments company can identify potential partnerships and opportunities for its data. One merchant might benefit from a better understanding of segment-specific demand patterns; another might benefit from understanding sales volume by time of day. To identify these relationships and opportunities, it’s helpful to map the data ecosystem to see your adjacent relationships, as well as those relationships’ adjacent relationships, and so on. Size the value: Understanding the market opportunity for commercializing data is a key design principle. Each of these business models can yield a substantial internal rate of return; indeed, each of them can be self-funded with the right design. One reason is that
Strategy& 13

investing to digitize your business can save 25 to 35 percent in operating costs. To size value, you need to understand your competitors and your own key inputs — such as the various costs — as well as the marketplace demand for the data products. Enhance the infrastructure: At the foundation of the data strategy must be an information technology infrastructure that is sophisticated, transparent, and flexible enough for a company to unlock the value of its data. At a minimum, the infrastructure needs to provide single sources of data truth, real-time data for messaging and transactions that can be monetized (even without additional analytics), and a highly available infrastructure. These five core design principles should guide your practice as you design and execute your strategy for monetizing data. You will invest and manage a host of new activities (see Exhibit 4). They will enable you to build the capabilities you need for the data gold rush.

Exhibit 4 Building capabilities for the data gold rush
- Invest in continuous learning and management of clients’ or customers’ unmet needs across the value chain. - Truly understand the delivery and integration models that clients require to benefit from enhancements to products or new products and services. - Understand, catalog, and map data housed across all business lines. - Map data and analytics services across business units to understand what types of capabilities can be leveraged to build new products and services. - Determine additional opportunities across the value chain by developing insights into adjacencies, for both data and partners. - Create a comprehensive view of the data ecosystem. - Combine “internally owned” structured data with both internal and externally sourced semi-structured and unstructured data, market data, telemetry data, etc. - Seek out opportunities to enhance the core business or develop new products and services. - Put in place a data infrastructure that can provide the necessary foundation to enable the organization to unlock the value of data assets. - Continue to evaluate core capabilities to determine what suits the particular situation best.

Investment in continuous improvement Cataloging & mapping existing data Developing insight adjacencies Combining structured & unstructured data

Building data infrastructure

Source: Strategy&




Most financial-services companies still don’t think of their data as an asset they can monetize if they build the right capabilities. They’re overlooking an opportunity in plain sight. Not only can they make better use of their data to enhance products and services for current clients, but new technologies now allow them to take the next step and combine their data with external data. This marriage will create powerful new insights into the customer and supply base and could transform the enterprise, unleashing a whole new generation of highmargin products and services for current customers and customers in adjacent markets. The enormous revenue potential will not go ignored by others. If financial-services firms don’t embark on data-led transformations to seize this opportunity, more nimble nonfinancial players surely will.



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This report was originally published by Booz & Company in 2013.
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