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.
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.
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.
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.
Our company’s ongoing digital transformation is based on a simple reality — the world of data is changing fast.
