Data Is an Asset: Say It Like You Know What It Means

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EDITOR’S NOTE: The following blog is based on Trevor Hicks’s presentation at the 2019 Houston Professional Petroleum Data Expo. The annual conference is sponsored by the Professional Petroleum Data Management Association (PPDM). PPDM is the global, not-for-profit society within the oil and gas industry that provides leadership for the professionalization of data management in the oil and gas industry.

Those of us on the IT side of the house have asserted for years that data is an asset. But is that true? On one level, we are certain that data has an intrinsic value in the sense that it can be leveraged for strategic business advantage—say, for reporting and advanced analytics. That said, and as someone who has used the phrase “data is an asset” throughout my career, I submit the real reason we say it is because we believe we can capture the attention of the those on the business side of the house if we use their language. Business managers invest in managing assets, our reasoning goes, and we data management experts want them to invest in managing data. Thus, data must be an asset.

In short, we’ve made – and continue to make – a conscious choice to use the word asset, and more specifically the language of finance, to signify the value of data, and by extension the value of our work to our organizations. The rub is that, to the finance professionals we are trying to convince, the term asset has a precise meaning. An asset is an owned resource that is expected to produce future cash flow. However, when we data managers say data is an asset, we are speaking colloquially to say data is a valuable thing. That’s not the same thing. Or as Inigo Montoya says in The Princess Bride: “You keep using that word. I do not think it means what you think it means.”

The point is, our “data is an asset” cliché doesn’t pass the credibility test with financial folks. So, we find ourselves using a business term incorrectly in hopes of convincing business people to invest in IT, to invest in us. And while I believe this disconnect represents a problem for both sides of the house, it’s a more serious problem for us—because we lose credibility when we use their language incorrectly. And in business as in life, credibility is everything.

If we’re going to keep telling the business side that data is an asset, we really ought to be more precise with our language. And perhaps we should first try to understand what terms like asset and cash flow mean to them. With cash flow, there’s a wide range of possibilities. Perhaps the least tangible are assets like goodwill, which basically is the expected benefit of synergies for an acquisition. At the other end of spectrum are bonds, which are assets with very well-defined future cash flows. Further, cash flows can be a future sale of an asset, or rental or leasing income, or the provision of some advantage in the marketplace. But that point is, the fuzzier or more ill-defined the future cash flow, the more pushback accountants and auditors give on booking something as an asset.

Given the variability, can we really say that data is an asset? I’m not aware of any operator that explicitly books data as an asset. And the operative word here is explicitly. But the stubborn fact remains that data’s future cash flows are ill-defined. As such, we’d have trouble getting data assets past an auditor in a SOX world. We’d need to develop an industry standard data valuation model, which is easier said than done, and which won’t help us move the credibility needle in the short term.

But perhaps there is a way to re-imagine data as an asset. What if data is implicitly represented today on an operator’s financial statements? And if it is, and I am convinced that it is, then how can we use that fact to explicitly express data’s true value and restate the “data is an asset” idea in a meaningful way?

Producing wells are assets and every operator capitalizes drilling costs into the well, including data acquisition activities such as MWD/LWD and mudlogging, open hole wireline, testing, coring, surveying, micro-seismic, and so on. Given the extent of these embedded data acquisition activities, it’s certainly reasonable to estimate that every well drilled in North American has tens of thousands to even millions of dollars of pure data cost capitalized into each well asset. By that measure, a large operator with 20,000 net wells might have data assets on their books embedded in well assets measured in hundreds of millions or even billions of dollars. These are numbers large enough to get any CFO’s attention.

The challenge for us in IT is, how do we leverage data’s implicit book valuation to motivate business investment in data management? I believe the answer goes back to communication and to using the language of finance—but using it more correctly, more strategically.

As O&G professionals, we know that core assets such as wells have asset integrity programs. For example, there’s well integrity, which includes activities like performing CBL logs and physical inspections, and in midstream pipeline integrity like pigging, corrosion management, physical inspections, compression management, and tubular trace-ability. No finance person would question these programs or their value for ensuring these assets are operating safely and efficiently. And on the data side, we should build on the implicit value of data—which is embedded in the valuation of a well—and appropriate the integrity management language by proposing data integrity programs. This term would encompass common IT terms like data management and data governance.

Framing data management in asset integrity terms has a clear advantage. Business and finance are comfortable investing in asset integrity and they understand the terms. And the framework and operational principles of our data integrity programs can also reflect the language of business:

  • Develop STANDARDS
    • Governance
      • Data Owners
      • Business Glossary
      • Golden Record Definition
      • Work Processes/Mapping
      • Data Quality
      • Rules
        • Exception Tolerances
        • Disaster Recovery/Business Continuity
      • Cybersecurity
      • DQ Dashboards
      • Exception Notifications
    • Establish REMEDIATION
      • Data Ownership
      • Data Stewards

What I’m suggesting is that success in any oil and gas organization depends on communication, on the words we choose to use—which is why we need to sell the need for data integrity by using the same language, the same thinking, the same techniques business and finance employ to inform the idea of the asset integrity.

Here’s an outline for constructing our data integrity message:

  1. Develop an estimate of data already implicitly booked on the balance sheet.
    1. Frame the cost of the proposed program in terms of percentage of this asset.
  2. Emphasize corporate risk management and the costs of failure.
  3. Factor in regulatory compliance.
  4. Stress data integrity’s criticality to operational success.

To be clear, my intention is not to add data explicitly on the balance sheet. Data cash flows are too fuzzy and there is no generally accepted data valuation model.

That said, it is possible to demonstrate that data is already on the books in a big way, just embedded in other assets, and we can use that logic to prove the value of data integrity management as a corporate competency. The takeaway here is understanding how much of the value of existing financial assets is derived from data, and then using data’s implicit value to attract an optimal level of investment in enterprise data management.

About the author
Trevor Hicks is a managing director at Stonebridge. Before joining the company in 2015, Hicks held senior management roles at Noah Consulting, Baker Hughes, DNV GL and Schlumberger. He currently serves on the PPDM Board of Directors and is a former multi-term PPDM Board Chairman. Trevor holds a bachelor’s degree in math from the University of Tulsa, a master’s degree in computer science from the University of Texas at Austin, and a master’s degree in business administration from the Fuqua School of Business at Duke University. He also has achieved the designation of Certified Petroleum Data Analyst (CPDA) With Distinction.

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