Companies must understand how to value their data to be able to monetize it accurately. The issue is, there are currently no common standard models for data valuation, and generally accepted accounting principles do not yet recognize data as an asset. Data valuation is complex, as the value of data can depend on several factors, and even the same data can have a different value for different users. This is a serious challenge for both potential investors and the company itself.

From the investors’ perspective, business valuation is complicated by the fact that data is not an asset shown on the balance sheet. For example, in many cases when you look at start-up valuation, the physical infrastructure and equipment of a start-up may only be a small part of the net worth of the company, whereas intangible assets, such as data and intellectual capital, may form the foundation of the company value.

In this case, how is it possible to know which start-ups are going to survive, let alone succeed and which ones are not? The company’s estimated value range can be broad, and the external factors increasing cash flow uncertainty, such as price elasticity and demand are increasingly difficult to predict in advance in a digitalized and hyper-connected society.

From your company’s perspective, it’s essential to have a clear understanding of the value of your data assets because that information is important when potential opportunities, urgent changes, or M&As must be considered.

Factors affecting the value of a particular type of data can be measured as Objective and Subjective Data Quality Metrics. Objective metrics include the relevance, validity, completeness, precision, uniqueness, timeliness, and accessibility of data. Subjective Data, on the other hand, are more context-dependent. They include the existence of data, its scarcity on the market, its relevancy in the market and the broader ecosystem, and interpretability, believability, and objectivity of the data.

Some of these qualities are directly considered when valuing information using different valuation methods, such as Business Value of Information, where the relevance, validity, completeness, and timeliness of data increase its value for the business. However, the Business Value of Information is only one of the numerous data valuation mechanisms.

Different data valuation methods

As there is no commonly agreed method for data valuation, multiple methods have emerged. They can be divided into foundational methods and financial valuation methods. Foundational methods include Intrinsic value of information, Business value of information, and Performance value of information, and they are focused on improving information management discipline.

Foundational methods for intangible asset valuation

Financial valuation methods, on the other hand, are focused on improving the information’s economic value.

Financial methods for intangible asset valuation

How to apply the chosen data valuation method is up to you. Once you know the value of your data assets, you can

• Prioritize and fund information management initiatives that have high business value
• Monetize your data
• Gauge how improving data quality metrics affects KPIs
• Drive innovation and digitalization by identifying information that would have potential business relevance, which could be used for improving economic benefits

Summary

It’s hard to estimate the company’s business value and future potential accurately. This is especially true if neither the investor nor the company properly understand the potential future value of the company’s data assets. However, what makes data valuation difficult is that data is an asset not yet recognized by generally accepted accounting practices.

The future potential of your business can hide in your business data. Therefore, you will surely benefit from having a strategic plan acknowledging the actions that can raise your information’s value. These actions can be done either internally or partnering up with others in the broader  ecosystem . A good  data operating model  is crucial to  unlocking the real value of your data .

Author: Michael Hanf, Executive Partner at Taival Advisory Oy