Like financial assets, data assets can have different levels of liquidity. Certificates of deposit tie up money for a certain length of time, preventing other use of the funds. Siloed business applications tie up data, which makes it difficult, even impossible, to use that data in other ways across an organization.
A recent research briefing, “Build Data Liquidity to Accelerate Data Monetization,” defines data liquidity as “ease of data asset reuse and recombination.” The briefing was written by Barbara Wixom, principal research scientist at the MIT Center for Information Systems Research (CISR) and Gabriele Piccoli of Louisiana State University and the University of Pavia.
Unlike physical capital assets listed on a corporate balance sheet, such as buildings and equipment, data does not deteriorate over time. In fact, it can become more valuable as it is used in different ways.
“While data is inherently reusable and recombinable, an organization must activate these characteristics by creating strategic data assets and building out its data monetization capabilities,” the authors explain.
Typically, companies use data in “linear value-creation cycles,” where data is trapped in silos and local business processes. Over time, the data becomes incomplete, inaccurate, and poorly classified or defined.
To increase data liquidity, organizations need to decontextualize the data, divorcing it from a specific condition or context. The authors suggest using best practices in data management — including metadata management, data integration and taxonomy/ontology — to ensure each data asset is accurate, complete, current, standardized, searchable, and understandable throughout the enterprise.
Such data management practices build key enterprise capabilities like data platform, data science, acceptable data use, and customer understanding, which increases data’s monetization potential.
“As a company’s strategic data assets become more highly liquid and their number grows, data is made increasingly available for conversion to value, and the company’s data monetization accelerates,” write the authors.
Fidelity Investments leads the way
In explaining how an organization can create highly liquid data assets for use across an enterprise, the authors cite the example of Fidelity Investments, a Boston-based financial services company.
The firm is combining more than 100 data warehouses and analytics stores into one common analytics platform, built upon five foundational structures:
- Universal IDs: Common identifiers for each data entity.
- Single customer profiles: 360-degree views of customers that span all channels of interaction.
- Analytics platform: A single, advanced cloud-based analytics platform to house and serve data on a large scale.
- Central taxonomy and catalog: A repository of common terms and definitions of more than 3,000 data elements.
- Governance: Strong oversight to enforce privacy, legal, contractual, and ethical policies.
Fidelity’s goal is to organize the data around a common set of priorities — such as customer, employee, and investible security. The result will be strategic data assets that are integrated and easily consumable. “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,” says Mihir Shah, Fidelity’s enterprise head of data, in the briefing.
As long as Fidelity’s internal data consumers follow core rules, they can combine data from different sources and build for specific requirements. Not only has Fidelity already created valuable data assets through this platform, it has begun to “identify value-add opportunities using data that were never before possible —activities that would add value for customers, revenue, and efficiency,” according to the authors.
Once data is highly liquid, “future ready” companies can use it to produce value in three ways, according to MIT CISR research:
- Pervasively improve processes, continuously doing things better, cheaper, and faster.
- “Wrap” products with analytics features.
- Sell innovative solutions based on data use.
Fidelity is one of more than 70 strategic data asset initiatives that MIT CISR researchers uncovered in the course of interviews with its member organizations. The projects illustrate how “the beauty lies not in a single use of data, but in the recurring reuse and recombination of the carefully curated underlying strategic data assets,” the authors write.
“As companies transform into future-ready entities, they need to view their strategic digital initiatives not simply as a way to exploit digital possibilities, but also as opportunities for reshaping their data into highly liquid strategic data assets,” they conclude.