What Is Data Worth?

INDUSTRY - Oil and Gas, SOLUTION - Information Management

Most exploration and production (E&P) companies struggle with defining the value of data within their organizations. For data managers, this impacts their ability to seek funding for projects and show measurable value to shareholders. Although most agree that data does have value, there remains much debate over how to define that value. Therefore, the value of data remains elusive. But, what if there was a way?

In the paper presented at the 15th Annual PNEC International Conference on Petroleum Data Integration, Data and Information Management titled, “Quantitative Value of Data and Data Management,” Paul Haines of Noah Consulting and Mark Wiseman of Hess Corporation make a case that data is an asset and demonstrate how an E&P company can value data through a repeatable method of calculation. Making some general assumptions, they start by defining the “base cost” and then factor in the effects of good or bad data management.

Base-cost is derived of factors such as cost, value, and risk. The authors use a simple comparison to make the point: “Your car and your house are important assets. Your assets have a cost associated with them, and you get more benefit from them than they cost you. Taking care of and managing your assets makes them more valuable, while not taking care of your assets can deteriorate them until a point where they literally fall apart. You insure your assets because there are risks associated with assets that you want to minimize, alleviate, or transfer.”

Most costs are fairly straightforward, such as the cost of acquiring, using, maintaining, and replacing data. Slightly more difficult to define, but as important, is the cost of decisions based on the data, such as when and where to drill. The authors do note that clearly all of the credit for such decisions should not go to the data, but show how to use a percentage to calculate the value of data in those decisions.

The authors call the next factor “value,” however, it could just as easily be labeled “time.” They demonstrate this point using productivity, integration, and timely decision making examples. These examples include how readily available data saves time that would otherwise be spent looking for the data. The time saved can bring value to the company through increased productivity. Perhaps the simplest example is how the ability to make a good decision based on good data brings enormous value when it is timely – specifically, drilling in the best possible location or grabbing a lease at a bargain because no one else has seen its potential yet. Conversely, data can have little or no present value if a lease increase or price per barrel/mmbtu decrease makes an opportunity unprofitable. You would want to know that before drilling!

Undoubtedly, the most difficult factor to value is risk. The authors provide three examples of why risk is an important factor that should not be overlooked: regulatory, HSE (health, safety, and environment), and bad decisions. Most companies potentially pay hefty regulatory fines for late or missing reports; merely reducing those costs by a small percentage can equate to big savings. The advantage of good data and wise decisions around HSE is priceless – when trusted and used correctly, data can avoid catastrophic environmental damage and loss of life. But in the interest of valuation, the authors took into account the cost of mitigating the risk as a primary influence. The last example is the risk of a bad decision. This is really just the cost of not having the right data at the right time to make the best decision, such as losing a potential lease to a competitor because they were able to value a play and move more quickly. No one has to dig too deep to come up with a list of examples for this one.

Data managers know that accounting for the cost, value, and risk of data is only part of the picture. Data has to be managed, and there is a cost associated with that, as well. This can either contribute or lessen the overall value based on its quality. The authors do not attempt to go into detail defining good data management; their simple definition for the purposes of valuation is, “the right data at the right time in the right format.” Naturally, the definition of bad data management is the opposite.

In the end, they want the reader to understand that data is worth more than it costs. It is possible to develop and implement a methodology to quantify the factors to come up with a “number” as a data asset net worth, and data management has an effect on the value of the data asset. If data is treated as an asset and not strictly a cost, then data can be added to a company value in a similar manner as reserves.

The authors include several graphics and statistics that will start any E&P company on a path to valuation of its data. The outcome of a data valuation can be not only important to a balance sheet, but also be an indicator of the health of the company. Noah Consulting works with clients to evaluate attributes of oil and gas data whether it is acquired/purchased data or in-house data. In turn, Noah’s evaluation of this data and data systems has enabled businesses to minimize, alleviate, or transfer the risk associated with decisions for the business.

The full paper, available here, has been called one of the most memorable of the conference and is cited in a book by Steve Hawtin, The Management of Oil Industry Exploration & Production Data.

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