Bring your data accounting up to speed

Data accounting looks for ways how the value of your data could be reflected on the balance sheet. To objectify the value of your data there are a couple of ways to look at this:

Objectifying the value of data

• You can develop a business case to estimate increased income or cost savings.
• You can use historical KPI dashboards as a way to quickly show interested acquirers of your company you have your ducks in a row in effect increasing the multiples at which you can sell.
• Or you can figure out a way to integrate the value of the data itself in the balance of the organization: data accounting.

Gartner predicted a couple years ago that by 2022 companies will be valued on their information portfolios: "Anyone properly valuing a business in today's increasingly digital world must make note of its data and analytics capabilities, including then volume, variety and quality of its information assets."

Gartner continues by advising to use its Information Valuation Method to value information assets much like other enterprise assets. This method was developed by Douglas Laney during his time at Gartner. In Laney's model he lists 3 foundational measures that have to do with improving information management discipline, and 3 financial measures that have to do with improving information's economic benefits. Reasons to engage in this type of data accounting would be to increase data literacy, plan/justify data management investments, budget for data security, assess data monetization ideas, claim M&A premiums or assess acquisitions, and even to collateralize data assets.

Foundational measures

The foundational measures are focused on improving information management discipline:

• Intrinsic value of information: How correct, complete and exclusive is the data?
• Business value of information: How good and relevant is this data for this specific purpose?
• Performance value of information: How does this data affect key business drivers?

Financial measures

The Financial measures are focused on improving information's economic benefits:

• Cost value of information: What would it cost us if we lost this data?
• Market value of information: What good could we get from selling or trading this data?
• Economic value of information: How does this data contribute to our bottom line?

Once you have answered these questions the next step in putting Data Assets on the books would be to conduct an annual Data Assets Impairment Assessment, like it’s done with all the other Intangible Assets.

Conclusion

We expect to see more of this type of data accounting in the near future, but companies will still need to establish the means to rapidly identify, accumulate and evaluate data to apply the valuation methodology accordingly. This kind of valuation of information will only increase in importance when accounting regulations will change to require listing data as a tangible asset on the balance sheet.

We also have to consider how to keep the balance between more regulated standards to value data on the one hand and limitations this may put on innovations and speed of development on the other. As for accounting standards, simply reporting on data's value on the balance sheet wouldn't interfere with innovation, but it would create a transparency allowing one's competitors (not just investors) to somewhat see what you're up to with data.

Those organizations who get their house in order now will have a competitive advantage in the future.