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    Adopting an Approach to Data Valuation that Actually Works

    In every field, you can get a sense of trending topics by attending a few industry conferences. I’ve just been to another conference where I heard a lot of talk about the evolving topic of data valuation. This topic is one of my favorites, but I find it frustrating that there is so little specific and useful information available.

    Instead, I hear a lot of what sounds like hype, and little if any discussion of exactly how people are proposing to value the data. I’ve spent a lot of time thinking about this issue, and believe strongly that the field is ripe for development. Although there is not yet a solid solution on the market, it’s just a matter of time. In fact, I’m betting that most of the people who will be leading in this area aren’t even players in the game — at least not in any direct sense.

    Putting a price tag on the intangible

    Right now, as an innovator, my view is that no one has data valuation perfectly figured out yet. As an entrepreneur, my vision is to be the first firm to create a reliable method for valuing data as an intangible asset.

    At the conference I mentioned, I heard representatives from two respected industry analyst firms address the issue of data valuation. They were speaking at what seemed to me to be a fairly superficial level, talking about how companies could learn to turn data into dollars. Ten years ago, that insight might have been enough to turn some heads and get people thinking about data in a new way. But today, you’re late to the game if you’re not thinking about exactly how you’re going to do it, rather than merely talk about it.

    In addition, to the extent that these analysts make concrete recommendations, they often over-index on the technology aspect — and specifically, on the “gold standard” technology organizations that are perennial stars on Gartner’s Magic Quadrant. However, if your primary goal is to value data as an intangible asset, these organizations can only do pieces and parts of the work that is required.

    In short, I humbly suggest that in this case, the emperors have no clothes. Here’s why I say this: in order to account for data assets, you must account for all three categories — physical, logical and conceptual — and some of the major analysts are so focused on the technical aspects of data that they address only the physical and a little bit of the logical.

    Where is data valuation headed?

    There are some organizations that are taking a more robust approach to data valuation, however. The most exciting example I can think of this is the company Palantir, which is doing a lot of work in the area of physical and technical metadata discovery and research.

    From my perspective, although Palantir is not quite as strongly positioned in the conceptual aspect of metadata, their work is opening new frontiers in data and metadata management, and has amazing potential. For example, their technology is already giving federal law enforcement organizations the ability to conduct truly surgical searches for information on persons of interest.

    Palantir was cofounded by Peter Thiel, who also cofounded PayPal — and I’m betting he has a team exploring the field of data valuation, even as I write. His company’s advanced work in this area, combined with its strong financial position, tells me that they could spark a new gold rush in the area of data valuation, and my organization hopes to be there waiting.

    I think we’re on the cusp of a major evolutionary cycle involving data, and that one of the key obstacles will be to get past the conundrum of data valuation. It can be done — in fact, it must be done — but it’s a far more involved process than just buying an application. On the other hand, the payoff can be enormous.


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