Data as a Strategic Asset: Improving Results Through a Systematic Data Governance Framework
Authors: Huff Elena , Lee John 
Published: July 2020

Summary
This paper presents a systematic proven and effective nine-step data governance framework that helps to enable oil and gas organizations to improve results by treating data as a strategic asset. The world is becoming increasingly data driven and technologically disrupted, and this momentum is expected to continue. According to the World Economic Forum (Kirk Bresniker 2018), "every two years we create more data than we've created in all of history. Our ambitions are growing faster than our computers can improve." Our industry's success will depend on timely development and appropriate application of machine learning, artificial intelligence (AI), and other advanced analytics. However, these tools may be ineffective and even destructive if the data used is compromised.
In our study, we identified the factors that contribute to the problem of compromised data. Technical data and business landscapes are highly complex, segmented and largely disjointed. Most organizations are fragmented, working in silos rather than collaboratively. Production data owners in the field are traditionally far removed from data users at the office. Often "raw" data owners in the field have limited appreciation of the importance of "trusted data," while data users at the office spend 80% of their time looking for and cleansing data, and only 20% of their time transforming data into actionable insights to drive informed decisions (Gabernet and Limburn 2017). The disconnect between departments often results in significant "hard-dollar" loss, which contributes to unknown potential value loss.
Our data governance framework (Fig. 1) is a nine-step, methodical procedure that helps address these problems and positions the company to be more agile moving forward.
Determine organizational priorities and define the scope Invest in organizational change management – key for adoption and sustainability Establish the data governance organization, demonstrating comprehensive leadership support Connect and align teams through a fit-for-purpose data catalog Establish data governance policies to support priorities Define new operating model by transforming policies into new ways of working Design enabling technologies to support the data governance objectives Implement ongoing data quality and availability monitoring Facilitate sustainability via periodic audits based on pre-defined key performance indicators (KPIs)
Figure 1 Our tested and effective nine-step data governance framework. Data governance isn't solved in any one business unit of an organization; it is a partnership and collaboration between all functional domains.
We developed and applied this systematic procedure to create trusted data in a world-leading natural resources company. Quality production data clearly enabled better informed and more strategic decisions and helped protect the company against potential litigations. Specific benefits were quantified to the following annual recurring economic value (in the context of sub-USD 60/bbl oil price environment) for 1,700 wells in the U.S. shale:
Improved information and decision quality (USD 3.7 million) Reduced cost and cycle time (USD 4.4 million) Increased production (USD 3.2 million)
Trusted production data converted into robust, actionable insights improved performance in the following functions:
Well performance Predictive maintenance Operational intervention Revenue and joint venture accounting Regulatory reporting Reservoir analysis
In this paper we detail our tested and effective nine-step data governance framework, developed based on years of experience in the oil and gas industry, ranging from operations, engineering, and production volumes allocations to information technology and continuous improvement. Key to the importance of this work is treating data as a strategic asset and recognizing that, without trustworthy data, planning and performance analysis, whether performed by human colleague or robotics/advanced analytics, is of limited and misleading value.