The oil and gas industry, especially those companies operating in the Exploration and Production (E&P) segment, has unfortunately not found much success with its data management systems and analytics. There are several reasons for this, such as:business users not being adequately engaged in analytics and their general distrust of the data in use, traditional databases under-delivering, relatively long gestation of data management projects, inefficient use of metrics, and the business leadership view that investments in data management projects do not deliver the expected ROI.
Many a time, accessing the data itself proves difficult, resulting in loss of productivity. Inconsistency in the data captured across databases, lack of a unified system to track, maintain and govern data, and the data itself being siloed in different corporate locations add to the users’ antipathy towards the usage of analytics for decision making.
Data management initiatives in oil and gas companies are also constrained by the management and project teams not sharing enough information on analytics with the business users. Consequently, the users tend to view data management and analytics as IT functions in which they have no direct role. They are also circumspect about basing their business decisions on data sets that they do not trust fully. The common view therefore is that the pain of change is greater than the gain of change.
However, with today’s rising competitive pressures and new complexities in the global oil and gas sector, it is important that all key players sharpen the focus on analytics to obtain fresh insights on the emerging challenges in business. It is important for oil and gas companies to visualize the business information to stay ahead of the growth curve.
Given the challenges, companies would do well to undertake a deep assessment of the symptoms and root causes of common upstream data management problems. My article Exploring New Frontiers of Growth, emphasizes that it is important for business leadership to take ownership of the exercise, and the data management initiative should focus upon highly visible data that most stakeholders see, use, or build on for their workflows. This will attract the interest and participation of all business units in the initiative.
It is equally important for the organization to develop high-level data architecture with standard common applications and integrated workflows. The use of open architecture and common standards will help companies develop solutions that can be modified as per changing business needs. A sharp governance and architectural vision will help firms in converting the data assets into powerful insights for building business strategies.
How have you tackled complex data management issues in your industry? Share your experience with us.