We’re constantly told data is the most valuable asset in our digital economy. Yet in the worlds of accounting and insurance, data isn’t a formal asset on the books. It is not an object with tangible value, such as a server or a building.
That status feels unusual as people and companies buy and sell data products, and there’s a hyper-reliance on data-driven capabilities, such as artificial intelligence (AI) and advanced analytics. Technology and business professionals must treat data as an indispensable and tangible asset of varying value, even if it isn’t on the books.
Also: Want to become a successful data professional? Do these 5 things
“Why is it that an intangible asset like data is not in the company’s balance sheet — a statement of the assets, liabilities, and capital of a business at a particular point in time?” said Prashanth Southekal, managing principal of DBP-Institute in a post at CFO.University.
Southekal said determining the fair market value of data is a challenge. Organizations “struggle to put a dollar figure both on the cost of data management in the data lifecycle — from origination to consumption — and the benefits that data brings to the organization.” Other factors include uncertain depreciation and compliance questions.
As data assets are off the books, insurance companies don’t consider these assets as actual “property” for which enterprises can be compensated, said Doug Laney, innovation fellow at West Monroe, former Gartner analyst, and author of Infonomics and Data Juice.
Also: Want to be a data scientist? Do these 4 things, according to business leaders
However, the value of data assets as property has been brought before courts for decades, Laney explained in a recent keynote address at Data Summit in Boston. “The courts are confused,” he said. “Some courts have rules that data should be considered property, because they’re represented by bubbles on an optical disk, other courts have said that data shouldn’t be recognized as property, because electrons have negligible mass.”
The value of data in accounting is based on rules formulated in the 1930s when it was stored on tangible pieces of paper. Insurers “are not going to recognize data as a mass because there is nothing about the value of your data on our balance sheets. Basically, the keepers of what constitutes property and what constitutes an asset doubled down on their antiquated notions that data is neither.”
But with modern organizations running on data — and drawing income from data and losing income if data is destroyed or stolen — these perceptions might change. Laney said companies are even collateralizing data assets to back up financial deals.
“Increasingly, companies that are data rich and cash poor are finding they can get loans for their data assets. A company that we partnered with has a fund that will issue that loan and has a valuation model that will establish the level of collateral. They have technology that will sit on your systems and actually escrow the data on a daily basis into a secure cloud environment.”
Also: 5 ways CIOs can manage the business demand for generative AI
Laney said the ability to gain the full potential from data tends to be held back by several myths or misconceptions. Here are some hard truths about data monetization:
- Data monetization isn’t just overtly selling files of database records – Rather, it’s an approach that addresses how data is managed, measured, and used to deliver new sources of value and revenue.
- Data is not the “new oil” – Unlike oil, which is a commodity item that is “consumed a drop at a time, data is very different, a non-rivalrous, non-depleting and pro-generative asset — meaning it can be used simultaneously and continuously in multiple ways,” Laney said.
- Latent data is a prime candidate for monetization – Laney said data that seems outdated or spent may still have value.
- Data monetization goes by different names – Organizations may refer to the approach as “data enablement or data commercialization or data product development, whatever’s comfortable,” said Laney.
- External data can be monetized – “You can monetize data that comes from external sources as well,” Laney said. “We should be looking at external data to supplement our own and generate more value.”
The onus now is on professionals and managers who handle and store data to understand the new dynamics data monetization adds to their jobs. “It should be an ongoing or periodic process,” Laney said.
“Managing and measuring data assets go together. They say you can’t manage what you can’t measure. And you can’t monetize what you’re not managing. We’re all doing a lot with digital data, but organizations are not measuring the impact of that. We’re not connecting the dots between the data that we use, and the outcomes — enhanced business process and performance. Just as with any other asset that your organization has.”
Also: The real-time revolution is here, but it’s unevenly distributed
Executives must advance data monetization efforts, from working with the business to generating and prioritizing ideas. Testing these ideas also needs to be part of the process. Finally, sales and marketing teams need to be told about the value of data assets. Laney said there are two flavors of data monetization, indirect and direct:
Indirect data monetization:
- Improving process performance or effectiveness
- Reducing risk/improving compliance
- Developing new products or markets
- Building and solidifying partner relationships
- Assetizing data on the balance sheet via special corporate structures
- Publishing branded indices to promote data products/services
Direct data monetization:
- Bartering/trading with data for non-cash commercial considerations
- Enhancing products or services with data
- Licensing raw data through brokers or data markets
- Selling insights, analysis, and reports
- Inverted data monetization via referral/reseller arrangements
- Collateralizing data to secure loans
For anyone who doubts the benfits of leveraging data assets, look at the values of data-driven companies. “Companies that have enterprise data governance programs, chief data analytics, and AI officers, advanced AI and analytics programs are favored by two to one over the others,” Laney explained.
“And companies that are data products companies, that make a living selling data or licensing data or data derivatives of some kind, have market-to-book values that are three times higher. Of course, they’re making more value from an asset that is not on the books.”
Disclosure: I was a speaker at Data Summit, an event mentioned in this article.
+ There are no comments
Add yours