Valuing Data as an Asset

Valuing data is a rather abstract process and standardized methods have yet to emerge. While many types of data already have a market price (such as information that can be purchased about an individual) or a zero price-point, data that is created as a by-product of a company’s normal operations is far more intangible.


There are clear benefits for understanding and valuing data, including the ability for a company to more accurately value itself in a merger or acquisition. This is particularly relevant for companies in highly acquisitive application markets.

Companies should also measure the change in the value of their data over time. The question every CEO should be asking his or her team is: are we constantly increasing the value of our data? And the follow-up question is: how do we measure that?

Not all data is created or treated equal. Nor will it be captured, measured or accounted for the same.

For example, how should Amazon classify and value the data collected about eBook purchases and readership through their direct publishing platform? Or, when Uber goes public or is acquired, how should the market put a value on the data and social insights Uber has gleaned by analyzing years worth of transportation and socioeconomic data?

While the focus of this discussion is on for-profit companies, these concepts and questions also apply to measuring social returns realized from using data to address problems such as climate change and reducing human trafficking.

These are tough questions, and just scratch the surface of what conversations need to happen as we enter the new data age.

Coming to a data valuation process requires us to understand and assess which types of data have financial value and in what context financial returns can be measured. To do this, we need a common framework and language to develop our economic models.

In the coming posts, I’ll be proposing a new data-ecosystem framework in an attempt to establish common models and language when thinking about emerging data. I welcome your input and ideas. Email me direct, or please comment below.

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