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Business and technology executives are nearly unanimous in their recognition of data as one of their most strategic assets. For many, data is the raw material of business. It’s an asset from which tangible value can be created. This conviction is backed by a decade of unprecedented investment in data and data technologies, which has had a profound impact on every aspect of the digital economy.

It’s natural then for one to ask: How much is my data worth? Unfortunately, there isn’t an accepted financial model to answer this question, but there are familiar and less formal approaches, both financial and non-financial, for valuing data assets. By examining them, businesses can become more deliberate and purposeful in how best to use their data.

Be exclusive, granular

A basic approach to understanding the relative value of data is to think about it in terms of exclusivity and specificity. The more exclusive it is, the more valuable. Insights mined from private data collected from your customers is certain to be more valuable than the data from public sources. Likewise, granular data is more valuable than less specific data because it allows for more options in slicing and dicing information and combining it with other data sets. The addition of more specific personal information (e.g., income, family size, affinities) to a customer profile warrants fresh prospects for analytical exploration.

Another approach to estimating value is to use traditional methods for valuing intangible assets, such as intellectual capital and brand equity. These include cost-based (cost to create the asset), market-based (price of comparable goods) and income-based (value assigned by projected cash flows from the asset) accounting methods. The characteristics of data make these methods complicated to execute. However, determining consistency of the features that make data valuable offers a structure for measuring that value.

Understanding why the different characteristics of data make it so difficult to quantify in absolute terms actually helps in understanding its intrinsic value. An analysis of a data valuation chain by Chloe Mawer, a data scientist at Silicon Valley Data Science, illustrates this. As data passes through stages of transformation from raw data to analysis, insight, action and value, its worth increases. The valuation chain must be completed to realize value, but there are other potential outcomes, both good and bad. For example, multiple value chains can originate from the same raw data when the same aggregated GPS data is used by both a retailer to choose a new store location and a city government for better road planning. In other cases the valuation chain can be completed only to result in no value. Overall, the value chain method is beneficial in considering data’s specific uses, increases or decreases in value and other factors in buying and selling data.

Why learn the value of data?

Organizations should first identify the objective for valuing information assets. Is the purpose to improve your information management discipline, or to leverage the information’s economic benefits? Gartner offers a more robust model in its approach. It prescribes a set of six measures for valuing information, including intrinsic value, based on the quality of information assets, and economic value, measuring the contribution to an organization’s bottom line. It’s easy to imagine an organization employing multiple measures, depending on the type of data being valued.

Quantifying the value of data can be complicated, but it’s important. It’s unlikely that data will appear as an asset on a balance sheet anytime soon, but that doesn’t mean it doesn’t have value. Businesses must be deliberate and purposeful in how they consider its value and adopt appropriate measures for doing so. Data is a strategic asset and is growing in importance. As the old adage goes, “You can’t manage what you don’t measure.” Organizations need to start inventorying and measuring their information assets.

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