Software advances in recent years have changed the way E&P's use and manipulate data. These new solutions are "data hungry" and able to consume more kinds of data from diverse sources. Because of the variability of data, technology, data capture methods and data accuracy levels collected over the long operating history of a well's life cycle, the challenge in the E&P industry remains how can you trust the data?
Experience shows that the real roadblock to integrating specialized upstream data is the quality and integrity of the data itself, not the systems used to access it. Data quality is the foundation and prerequisite for standardization and ultimately the integration of diverse data repositories into value added knowledge management systems.
This paper provides a roadmap for enhancing business performance through examples of E&P companies who understood the data quality problem, successfully measured the business impact associated with poor data quality, and developed solutions that delivered benefits throughout their organizations. "Second generation" data profiling, cleansing and migration tools are introduced with an emphasis on how these automated tools helped E&P's define, and deliver a compelling business benefits with data quality.
Gartner Group put a sharp point to the problem of data quality when it released a startling statistic: "More than 25% of critical data used in large corporations is flawed, due to human data-entry error, customer profile changes and a lack of proper corporate data standards."1 What's more, through 2007 they forecast that "more than 50% of data warehousing projects will experience limited acceptance if not outright failure, because they will not proactively address data quality issues". 2
Nowhere has this problem of data quality and standardization been more acute than in the upstream Oil and Gas sector, which relies on quick access to a broad array of timely information to make critical investment and drilling decisions. Access to timely and good quality exploration and operational data has become one of the most significant constraints to the effectiveness of finding new or exploiting hydrocarbons. G&G data, well drilling data, and production data is at the heart of the exploration and exploitation processes and is the key to any operating decision on where to drill for hydrocarbons. Bad data also has a direct impact on other areas of E&P business risk, increasing the risk of noncompliance in financial, regulatory and contractual areas as well.
Much has been written of the promise of data quality and industry based standards; however, the management challenge in the mature E&P industry remains because of the variability of data, variability in technology, data capture methods and data accuracy levels collected over the long operating history of a well's life cycle. Unlike other industries, the energy industry must also contend with the complication of aligning large volumes of geophysical, operating metadata and financial data to geospatial references, in a manner that facilitates ready access across diverse crossfunctional project teams.
