Data Is a ‘Tangible’ Asset

There are compelling reasons to start thinking deeply about how to value a company’s data assets for accounting purposes.
Dr. Henna A. KarnaMay 16, 2018
Data Is a ‘Tangible’ Asset

Much talk is swirling around the need to value a company’s data as a business asset on its balance sheet. The idea is compelling. Data, in the right hands, is often as valuable as land, buildings, and equipment.

If an insurance company, for example, can make better underwriting decisions than its competitors because of an enhanced ability to acquire brilliant insights from its data, investors and Wall Street would want to know that for valuation purposes.

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But that information is generally nowhere to be found on the balance sheet. Investors are in the dark. What’s more, many organizations know very little about the value of their own data. As Doug Laney, vice president at Gartner, stated, “Even as we are in the midst of the information age, information simply is not valued by those in the valuation business.”

Despite the obstacles, there are compelling reasons to start thinking deeply about how to value a company’s data for accounting purposes.

Mining the Gold

In information-rich businesses like financial services, data is the primary asset — the feedstock for pricing products. Until recently, for example, few insurers fully appreciated that fact.

Ten years ago, the industry had no chief data officers or chief digital officers. Even today, only a few large insurance companies have hired data scientists and data engineers to manage their data assets at the enterprise level.

Henna Karna

Dr. Henna A. Karna

The few insurers that have made those investments, though, are at the threshold of real digital transformation. They are converting their data into insights and generating better underwriting, claims, loss reserving, and risk retention/risk transfer decisions.

The change is remarkable because insurance has always been focused on historical data.

Past claims activity has directed decisions on pricing products and loss-reserving. Thanks to predictive data analytics, machine learning, and cognitive computing technologies like natural language processing, the opportunity is at hand for insurers to analyze a vast array of structured and unstructured data. Doing so will enable them to create new commercial insurance products and customized coverages addressing specific risk-transfer needs.

By integrating historical information with real-time data produced by sensors powered by the internet of things (IoT), those capabilities will expand. Off-the-shelf commodity products will give way to an array of unique insurance coverages customized to a client’s specific demands — a one-of-a-kind cybersecurity insurance policy, for instance, could absorb a specific cyber risk for a single day or a single hour.

Another reason for treating data as an asset is its potential usefulness as a separate revenue stream. Data and analytics can be a new product line and source of income for many companies. Insurers, for example, can provide benchmarking information on workers’ compensation claims across different occupations in disparate geographies. That data can be sold to third parties or supplied to customers.

Adding Value

Our company’s ongoing digital transformation is based on a simple reality — the world of data is changing fast. Not only are the kinds of data changing for analytical purposes (historical and real-time structured and unstructured data), but the characteristics around that data, such as metadata and semantic layers, are also changing.

Semantic data is crucial to tomorrow’s business decisions, as it represents data in business terms. In other words, users can access the data they need by using familiar business words like “product,” “customer,” or “revenue” to obtain a unified, consolidated view of data across the enterprise.

At XL Group, we’re focusing our digital transformation on what we call “dense data,” which we define as big data with context and connectivity.  Relying on big data alone increases the chances we will miss something, while giving us the illusion we know everything. It can also introduce quantification bias — the unconscious belief of valuing the measurable over the immeasurable. Quantifying is addictive.

Dense data gives us the context – answers “why” something is happening, provides the human insights. Using dense data, we will be able to continuously enhance the value of our products to customers and extend the lifespans of them. While actively looking to monetize our growing volumes of data, therefore, we are also continuing to enrich our data assets with context.

Taking Charge

Figuring out how to gauge the value of different types of structured and unstructured data is a challenge. While the “plumbing” is available to flow data from across the enterprise into a data lake, the harder part is developing the algorithms to unearth hidden insights. Without that capability, board directors, analysts, and investors are hindered in easily and dependably valuing one company’s data compared with another’s.

To assess the business value, CFOs should consider leveraging insights from data scientists and engineers about the organization’s information assets, including where those assets reside and how they produce business benefits.

At XL Group, the enterprise data team is entrusted, in partnership with the heads of finance and operations, to begin the process of valuing our data.

Each time a data element is used in one part of the organization (say claims) to produce a particular insight, and the same element is subsequently reused by another part (like underwriting), the reuse capability not only brings down the marginal cost of data, it also creates a metric for monetary return.

How often this particular data element is reused can be a means of calculating its value as an asset. As time goes by and the data element is reused less and less, its value will correspondingly decrease.

Accurately valuing data is a tough uphill climb, but it’s not impossible: Dealmakers in the M&A environment are constantly putting a value on information assets, particularly when the target acquisition is a data-rich business — such as when Microsoft acquired LinkedIn.

Being at the beginning of a major industry’s transformation is scary and exciting.

Insurance has always been a numbers game; now the numbers have more meaning than ever before. It will take time for insurance (and many other industries) get the most out of their data and to compute its value. That’s okay. With data touted as the “new oil,” it makes sense that “exploration” should be the first step.

Dr. Henna A. Karna is chief data officer and managing director at Bermuda-based global insurance and reinsurance company XL Group.

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