Fast-Track Data Monetization With Strategic Data Assets

For years, using more data to make better decisions has been the holy grail for global companies, and most of them aim to treat data as a strategic asset. But new research from the MIT Center for Information Systems Research (CISR) has found that future-ready companies have greater ambition regarding their data. These organizations strive to maximize their data monetization outcomes by pervasively improving processes to do things better, cheaper, and faster; wrapping products with analytics features and experiences; and selling new, innovative information solutions.1

To monetize data, companies must first transform it so that it can be reused and recombined to enable new value creation. The easier the reuse and recombination, the higher the data’s liquidity, which we define as “the ease of data asset reuse and recombination.”

Preparing Strategic Data Assets for Reuse and Recombination

Data liquidity is a continuum, not a binary condition. It is a function of the ability to convert data for use, which means that a particular data asset may be more liquid or less liquid than another. Many companies’ data has low liquidity — it may be trapped in local business processes, locked in closed platforms, or replicated in multiple locations, for example — or it may be inaccessible simply because it’s incomplete, inaccurate, or poorly classified or defined.

Much managerial attention focuses on liberating data from silos and applying it to a new, specific use, such as calculating customer churn or spotting supply chain breaks. This is a good exercise, but not a strategic one. Sure, an initiative on customer churn or supply chain will realize new value for the company. But companies that continue to pursue only a linear value creation cycle are leaving money on the table.

It’s crucial to recognize that data does not have to be treated like traditional company assets. Heavy equipment, office furniture, land, and even cash will deteriorate or be depleted over time. Data is different and can be reused and recombined freely without degradation. Data assets are born to be liquid, but while data is inherently reusable and can be recombined, the organization must deliberately activate these characteristics.

There is, of course, a cost to liquidity, so the organization also must deliberately select which data assets to liquify. A good place to start is with strategic data assets — data that holds potential for future value creation and appropriation. Only some data assets, then, are strategic. These strategic assets have myriad possible uses across the enterprise; some uses are known, and others will emerge over time. Strategic data assets typically include customer data, digital channel behavior data, product data, and other data that informs business performance and customer needs and is relevant enterprisewide.

To advance from simply using data to building liquid strategic data assets, companies need to decontextualize data from a designated purpose and prepare each asset to become accurate, complete, current, standardized, searchable, and understandable across the enterprise. This process may entail implementing practices such as master data management, metadata management, data integration, data quality management, and taxonomy/ontology development. The five data monetization capabilities MIT CISR identified in its research — this data asset capability combined with data platform, data science, customer understanding, and acceptable data use capabilities — can drive an increase in data’s liquidity.2

As more of a company’s strategic data assets become more highly liquid, data is made increasingly available for conversion to value, accelerating the company’s data monetization.

Building Highly Liquid Strategic Data Assets at Fidelity

Fidelity Investments, a Boston-based financial services company, is pursuing highly liquid strategic data assets for enterprisewide use through a multiyear data replatforming initiative. It is working to advance its data monetization capabilities through state-of-the-art technology, thoughtful architecture, clear lines of accountability, and local-global coordination.

Fidelity employs more than 47,000 associates and has multiple business units, including asset management, retail brokerage, clearing and custody, 401(k) and employer record-keeping services, and capital markets. In 2020, the company reported a record $21 billion in revenue. Enterprise head of data architecture and engineering Mihir Shah attributes this continued success in part to “the interconnection between all of Fidelity’s businesses.”

In 2019, Fidelity clustered sets of common strategic initiatives into executive-led “neighborhoods” that group similarly themed projects across business units. Of seven neighborhoods, five are business-centric — focused, for example, on acquiring new customers; the other two neighborhoods are enablers of platforms, including APIs, data, and the cloud — Shah’s focus areas.

As Shah explained, “We want to create long-term data assets for creating value — not only immediately, but also for use cases that are yet to be identified.” Leading a data-specific neighborhood, he initiated a four-year program that aimed to rationalize 100-plus data warehouses and analytics stores into a common analytics platform. The primary objective was to organize, elevate, and curate the data concerning priority subject areas — such as customers, employees, and investible security products — into strategic data assets that are integrated and easily consumable across the entire company.

Fidelity first implemented a set of key foundational structures:

• Universal IDs, with a common identifier assigned for each major data entity universal across the company, such as customer information.
• Single-customer profiles, to provide a 360-degree view of each customer to simplify account management across all interaction channels.
• A single advanced, cloud-based analytics platform to house and serve data products at a large scale.
• A central taxonomy and catalog to organize shared Fidelity terminology and the definitions of more than 3,000 company data elements.
• A strong governance function to ensure strict enforcement of privacy, legal, contractual, and ethical policies.

The operating model chosen for the new platform was an internal marketplace. Local data owners contribute to Fidelity’s strategic data assets by structuring data to conform with the enterprise taxonomy; a nonnegotiable rule dictates that data and definitions align with the central catalog. Central repository technologies enable enterprise search and cataloging. As long as core platform rules are followed, users can take data from multiple producers, combine the data, and build for local business area requirements.

Built using cloud-based technology, the platform allows for the sharing, access, and application of data without the data having to be moved. Instead of being transferred, the data is maintained in place, and the platform allows owners to control access. When employees request access, they initiate a simple workflow that confirms that their access is authorized; security technology allows tokenization of the data fields, rendering them undecipherable if they fall into the wrong hands.

In 2021, Fidelity is well into the program’s execution. Each quarter, leaders communicate anticipated value creation outcomes. Some successes are already materializing: more time spent by data scientists on business problems and models than on data gathering and cleaning, a significant (60%-80%) reduction in the data-gathering effort required to onboard new analytics use cases, and an improved ability to enrich Fidelity’s data with external data faster and more efficiently than ever. Further, company leaders have begun to identify opportunities for data use that were never before possible — and are pursuing data monetization use cases that add value for customers, boost revenue, and increase efficiency.

Growing Data Liquidity in Strategic Digital Initiatives

While exploring how companies create competitive advantage using data assets, MIT CISR researchers identified 73 strategic initiatives.3 A notable finding from this exploration was how organizations are using strategic digital initiatives to build data liquidity in high-profile ways, such as Fidelity’s data replatforming effort.

French banking group BNP Paribas, for example, created a proprietary environmental, social, and governance (ESG) scoring framework, fueled by underlying strategic ESG data assets, to quantify a company’s performance on material ESG issues relative to that of peers.4 BNP Paribas now uses this exclusive ESG performance score to inform a host of investment decisions.

The Australian Taxation Office (ATO) deploys data in another way, using advanced analytics to pre-populate tax forms and evaluate in-process claims.5 Drawing on strategic data assets produced by a dedicated program called Smarter Data, launched to increase the agency’s data analytics capabilities, this initiative furthers ATO’s progress toward its mission of reinventing the citizen tax experience.

Pegasystems (Pega) adapted its commercial B2C AI-based tool for its own use in order to streamline and rationalize the company’s B2B customer outreach.6 The AI model draws on strategic data assets about Pega contacts to automatically serve up relevant content to leads as they move through the Pega sales cycle.

The beauty of each of these organizations’ strategic digital initiatives lies not in a single use of data but in the recurring reuse and recombination of carefully curated strategic data assets. As companies transform into future-ready entities, they need to recognize their strategic digital initiatives not simply as ways to exploit digital possibilities, but also as opportunities for reshaping their data into highly liquid strategic data assets. How far is your organization from growing exponential value through data monetization?