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    4 Major Benefits of Treating Data as an Asset

    I often hear colleagues in the data industry talk about how valuable data is, but that’s where they generally stop. They rarely, if ever, take the point to its logical conclusion. My view is that if you regard data as an asset that is actually valuable, that also means it’s an asset that should, or at least could, be accounted for.

    If data was accounted for, you can be sure that it would be evaluated, and if necessary, remediated and cleansed to maximize its value.

    Of course, as it stands, one of the standard responses is that there’s no real quantitative way to value data as an asset. In another blog post, I’ve suggested that there are indeed ways one can do so. But now I want to tackle it from another perspective — the reasons one should want to do so. When I put on my CFO hat, there are four key benefits that immediately come to mind.

    1. Imagine several years from now, when everyone recognizes data as an asset with real monetary value (despite the fact that it’s intangible). Data-centric companies might start accounting for their data value in their annual reports to shareholders — much like the McDonald’s arch, even though it’s essentially an intangible piece of intellectual property, is still part of that corporation’s collective value. With data calculated, valued, and reported as an intangible asset, an organization could conceivably even go to the bank and take a loan out against its value.

    There could be additional ways to leverage data value as well. In reports to shareholders or to Wall Street, an organization could talk about the valuation of its data. Quantifying how much of your business the data is driving could also come as good news to shareholders, and even move the needle on your share prices. (Of course, this is a two-edged sword: if your data is costing your organization more to maintain than it adds to your bottom line, you may not want to talk about it at all.)

    2. A second benefit has to do with the role of valuation in Mergers & Acquisitions (M&A) activities. If your company is about to be acquired, your total book value is driven by your EBDITA multiple (earnings before interest, taxes, depreciation and amortization). If you could make the case to the acquirer that you’re really worth seven times your multiple, that could make a huge difference for your shareholders.

    There’s no secret to this line of reasoning — it all starts with your ability to quantify data value. Imagine, for example, that you could show a correlation of X% between your data valuation and your business model, and that you’re scheduled to increase data volume by Y% over the next six quarters.

    To a venture capital or private equity firm, this argument provides for easy back-of-the-napkin math: for example, your earnings and revenue will grow by five times over the next three years. That’s the kind of math that deals get done on – and it’s all about multiples, projected earnings, and revenue. Granted, this financial argument is probably most appropriate for medium to large organizations that are collecting a lot of data as part of their ongoing operations.

    3. A third benefit also relates to the M&A space. When you hear that one company is acquiring another, you may also hear speculation about what’s driving the acquisition. In my experience, there are three chief reasons why M&As happen:
    a) the acquirer wants to get a competitor off the market;
    b) regulatory forces are requiring market consolidation (such as what we’re seeing in the healthcare space); or
    c) the acquirer wants to gain operational synergy.

    This third scenario — operational synergy — may be the most commonly cited rationale for M&A activities. But let’s be honest: what this often means is that the buyer recognizes it has a major weakness in some core activity, so buying another company that does the activity better is a nice, painless solution. We’ll just buy the right process/tools/people (or so the thinking goes), and then we’ll migrate our whole company over to the new system.

    But the devil is in the details. Based on my experience working at organizations that were engaged in M&A activities, such a combining of organizations can come at a significant cost. In my experience, in most cases the process went too rapidly, and there was never a true migration to a single platform.

    Rather, there was often conflicting and confusing data all over the place: an operational nightmare. A company may have hoped that an acquisition would pay off in operational synergy — however, because the two data sets didn’t mesh, the acquirer may soon be worse off, in some ways, than it was prior to the acquisition.

    On the other hand, if you treat data as an asset, you might approach the whole due diligence process differently. Instead, you could start by running a data certification model on the company you’re considering buying, comparing the balance sheets of data for both firms. This would let you compare apples to apples — and see if there really will be operational gains.

    4. The fourth benefit is really a subject for another day — but suffice it to say, it’s all about the “El Dorado” of data valuation: the ability to monetize data. If you truly treat data as an asset, you’re already well on your way — because you know the financial value of your data to your own organization, you have a much better perspective on pricing it to sell to others.

    But is it all worth the trouble?

    So, those are four compelling reasons to take a new look at the data you already have to determine its financial value.

    One thing you probably can count on is at least a little resistance, because most organizations and data professionals still only pay lip service to treating data as an asset. But I can assure you that taking this perspective will not only result in a more realistic view of your own budget priorities regarding data — it could also open the door to some very significant gains in both financial and strategic areas.

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