Quantitative Value of Data and Data Management
INDUSTRY - Natural Resources, INDUSTRY - Oil and Gas, INDUSTRY - Power Services, INDUSTRY - Retail Utilities, INDUSTRY - Supply and Trading, SOLUTION - Information Management, SOLUTION - Strategic Services
Paul Haines, Senior Principal - Noah Consulting
Mark Wiseman, Senior Manager - Hess Corporation
Data is at the heart of all exploration and production workflows and is always a pivotal factor in any business outcome (be it good or bad!). The E&P business customer consistently acknowledges that data availability, integrity, and readiness are crucial to the success of their business. Yet formally accepting data as a corporate asset, both in terms of "book value" and workforce behavior, remains elusive and undermines any strategic approach and long-term investment in data management programs. In IM conferences, meetings, and discussions we, as IM professionals, continually hear about the difficulty or inability to create a robust business case for IM projects and/or value for data as a corporate asset in dollars and cents. We often only think about trying to calculate a value for data as a sub-task (activity) of “due diligence” when acquiring new E&P assets, divesting E&P assets, etc. Having valid and repeatable methods to calculate the value of data would be a great benefit for many reasons in an E&P company. Additionally, even if you have a good and valid number for the value of data, it is more difficult to get a valid and repeatable method for calculating the value of data management on top of that.
This paper will discuss these challenges and provide a repeatable method to calculate the value of data to an E&P company – a true value in terms that business sponsors will stand behind and defend when funding approval, business support, and business participation reaches critical decision points. The method will analyze data from a cost standpoint, a value standpoint, and a risk standpoint. Once established, we will use this number as the “basis” for analyzing the value of data management; bad data management practices will detract from the base number and good data management will add to the base value of the data asset. Finally, we will use historical data to demonstrate the validity of our approach.