The Making of Digital Oil and Gas Fields: Enhancing Productivity with Robust Data Governance

Corporate governance – an oft used phrase, is in effect an extension of directed and controlled corporate management. The same way, data governance refers to a set of defined processes that enable controlled enterprise-wide data management. The goal of data governance is to realize and promote the value of data assets by ensuring availability, usability, integrity, and security of enterprise data.

Data Governance: Looking at the Oil and Gas Industry

“Technology makes it possible for people to gain control over everything, except over technology” – John Tudor

Maybe this is one of the reasons why we see oil and gas (O&G) companies often struggling to sustain data quality due to large data volumes being generated and complex data processes. The O&G industry, one of the backbones of global economies, is facing multiple disruptions these days from dynamic economic changes, tighter regulations, and emergence of digital technologies. Ensuring sustainable and predictable business outcomes therefore, is becoming increasingly challenging. A recent PwC survey revealed that while economic and market factors are beyond an organizations’ control, 80% of the c-suite executives in O&G companies are turning to digital technologies to analyze the wealth of their data and boost efficiencies.

As Émile Lahud has said “Democracy, good governance and modernity cannot be imported or imposed from outside a country”. Similarly, investing in good data governance and management practices should come from within an organization. Applied correctly, it can yield substantial benefits for the business of O&G exploration as sound understanding of the subsurface information is critical to ensure a sustainable revenue model for the industry.

What are the Challenges in Data Governance in a Dynamic O&G Market

O&G companies have three distinct business lines – upstream exploration and production (E&P’s), midstream (storage and transportation), and downstream (refining and distribution). Being major business processes, each of these lines have their own core data domains that require management, standardization, and governance.

“Wisdom and good governance require more than the consistent application of abstract principles” Anthony Daniels and this is absolutely true, and very relevant for O&G data. There has been a lot of abstract governance principles preached by experts for data governance in O&G, but seldom practiced due to several practical challenges it faced. Some of these challenges are highlighted below:

#1 Lack of enterprise data management (DM) strategy: In the absence of a clear enterprise DM strategy, most DM solutions in O&G firms are driven by specific ad-hoc corporate needs or proposed by external consultants, thereby cluttering the landscape with multiple disparate sub-optimal solutions across the organization.

#2 Multiple disparate data sources: Despite the digital world taking over every imaginable industry, it is still not unusual for oil companies to have critical data distributed across mixed databases comprising raw and structured data, local hard drives, or unmanaged data repositories.

#3 Legacy data storage techniques: Lack of a centralized data repository is one of the biggest challenges impeding data governance in O&G companies.

#4 Exploding data volume: It is no secret that for an industry as diverse as O&G, the digital disruption is leading to an explosion in data volumes. Add to that, multiple proprietary data formats that pose a big challenge when it comes to data integration and sharing across platforms.

Robust Data Governance is What the Evolving O&G Landscape Needs

Integrating data governance with business processes to ensure seamless synergy and secure long term growth is the way forward for the O&G industry. Accustomed to traditional ways of working, most O&G firms govern their data and processes separately in silos and that’s the root of disconnect. According to Forrester research, synchronizing business process management (BPM) and master data management (MDM) strategies to eliminate complexity and gain ‘one version of the truth’ is a critical enabler in ensuring efficient data governance and business process transformation.

Implementing the Right Data Governance Framework

The right data governance framework rests on two pillars – safety and profitability and it should be able to satisfy long term business objectives as well as meet demands for short-term data needs. The key tenets of a good data governance model include:

• Resources: Dearth of competent upstream exploration and production (E&P) data managers having sound understanding and expertise in managing new age DM challenges is one of the biggest hurdles today. The O&G industry being a specialized segment, it is not possible for a routine analyst with no knowledge of E&P procedures to take on the new role. Industry experts feel the future will see the role of a subsurface data manager evolving into a data scientist – one who can understand the core procedures and has the capabilities to mine data to derive meaningful insights.
• Ownership: Lack of clear ownership can hold back data governance issues despite your best efforts. Having a sound enterprise DM strategy will make every individual accountable for their technical data, ensuring that data issues don’t fall through the gaps and are not left in the hands of a few data management practitioners.
• Means of implementation: The right data governance model should be able to efficiently bridge the gap between the E&P business and IT i.e. it should be embedded in the organization wide data strategy rather than just be restricted to IT.

The Business Case for Data Governance

Strengthening an organization’s commitment to data governance depends on the value stakeholders see in doing so. The onus is on DM practitioners to proactively demonstrate the tangible benefits of managing information and data assets such as improved operational efficiencies, risk mitigation, and OpEx optimization to the management.