Enable a pathway to excellence with a fit-for-purpose E&P data governance model
What is E&P data governance? To start off on the right foot, an energy company should consider researching definitions and selecting one that best fits what lines up with the organization’s objectives.
Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. It encompasses people, processes and technology required to manage and protect data assets and its sources. —CIO.com
This definition does not mention the influencers in an organization, the people who may not be an official part of the governance model, but have a large amount of influence in an organization. E&P data governance should be mindful of those influencers and make sure that they are engaged in the process.
Data governance is important and cannot be covered in one article, so where should we start?
Executive Strategy and Support
Senior management support and understanding the business strategy are critical factors to an oil and gas company beginning their data governance journey. Some questions to consider are:
- What is the company trying to excel at?
- What business problems have top-priority?
- How and where does data governance fit?
- Does the organization want to lead, follow or find a sweet spot in between?
- Can better data governance improve the gaps that are identified and accelerate the journey to excellence, or will it just get in the way?
- Are you organizationally ready?
- Does your organization recognize the value of data?
In other words, data governance absolutely has to be fit-for-purpose.
E&P companies can find an abundance of good governance material either in the public domain or through think tanks, such as Gartner. Data governance documents published by the National Association of State CIOs (NASCIO) are not specific to petroleum or upstream, but they are very good for reference. The first two fairly accepted basic steps are around gaining executive support and understanding this aspect of your business and what that looks like.
This data governance maturity model is a great place to start. The goal for an organization would be to move from the lower left to the top right, from an undisciplined governance model to something that is very disciplined and governed. From the bottom left quadrant to the top right quadrant, a company is reducing the risks around their data and providing more reward as they go forward.
Down in the lower left quadrant, E&P data governance practices are undisciplined, meaning the IT group is driving projects, data is inconsistent and there is not a lot of ability to adapt to business changes. As the data governance program moves forward, management will start to see the business influence more of the projects and more collaboration. As an organization continues to move to the right of the chart, business groups are collaborating and data is viewed as a corporate asset.
The goal is to reach that nirvana around governance, where the business is proactively driving all of the IT projects rather than technology.
Oil and gas companies need to think about data governance in this way, but they also need to recognize that sometimes, specific data governance around business processes will not move the needle in the business strategy. Some of the most obvious broken processes, for example, in the business might be authorization for expenditure (AFE) approval. Addressing the AFE approval issues will not make as much of an impact on the bottom line as managing asset performance. It will make a difference and an improvement but it may not be as impactful as other business priorities.
A good approach to strategic governance may involve a deliberate decision to not have a mature governance model across every single business process, simply because it does take a lot of work and resources. It is crucial to understand the E&P business strategy and focus on governing processes that are most important to the business.
Oftentimes, E&P companies are either going through a phase of innovation or a phase of standardization. It is important to understand which side of the innovation scale the businesses is positioned (or if the business wants to be balanced). The approach to data governance needs to acknowledge the goals of the business.
Management may be more focused on driving consistent outcomes, controlling innovation, being driven by key performance indicators (KPI’s) with strict boundaries, typically because they are trying to work in a low cost environment. The company is trying to drive efficiency and avoid spending a lot of money. The best way to drive efficiency is with consistent outcomes and strict boundaries.
In this case, data governance projects will be bound by others’ decisions and innovation is controlled because management wants consistent outcomes. As different businesses and assets compete for capital funding, the measurement of the value that those projects bring back needs to be consistent.
Sometimes an oil and gas business is in more of an innovative mode with solutions that are fit for purpose. Perhaps the organization wants to be the “best in class” at a particular set of business processes. They allow people to be empowered to make decisions, and innovation is encouraged. That can mean that assets are between silos, groups stay focused within their specific roles because they are innovative and experts in their field.
The result of having no strict boundary may be a possible loss of efficiency. The KPI’s may not be normalized, but the business decided to live with that decision because they are driving innovation.
As the data governance model is established, understanding which side of the scale the business is on makes a difference in how the governance norms are developed.
As an example, a set of typical business processes that are common across the E&P value chain can include anything from asset evaluation, all the way down to financial planning analysis. If the business wants to be a low cost operator, they focus on operational excellence by trying to drive down costs and increase the performance. For a mature asset, asset performance monitoring and production operations are very important. Asset evaluation and opportunity identification may not be high priority because you have to evaluate where the asset is at a given period of time.
In a particular period of time, for example, the company may have come through a cycle where they have a huge portfolio of billable opportunities. They may have 600 wells that are that are identified as “on the books” for proven undeveloped reserves that are ready to be drilled. But the drilling program is ratcheted down so far that it will take two years to get through all of those drillable opportunities. The E&P company has already done all of the licensing or the lease agreements that they need to put in place, so asset evaluations are not huge at this particular point in time.
As a governance models is set up, it is important to understand where the pain points will be on all of these items. These pain points will need the most attention, another reason to understand the business strategy before launching off into governance.
Data Governance Challenges
Some other challenges around establishing data governance can be organizational readiness. For example, over the last 20 years or so, E&P organizations have become more siloed in the way they function. Drilling superintendents and drilling engineers report to drilling managers. Reservoir engineers report to the reservoir manager. Geology and geophysics are in the geoscience group.
Silos do provide a better focus on expertise, which is why they have become so popular. But it also introduces gaps as new business processes move data from one silo to another. Sometimes, it feels like the silo mentality may be getting out of hand and can be challenging to get that integration/communication to work.
Data governance can help address some of the challenges of these siloed environments, especially when assets are acquired. Integrating assets in a siloed organization can be very difficult without proper data governance.
Anytime downsizing occurs, companies lose key people that may have been providing manual fixes or working around broken business processes where there were gaps. Without these people, data management could become inconsistent or non-existent. Data governance will ensure your organization is process driven, and not people driven, protecting your data assets.
Restructuring organizations in general can impact morale and productivity. Employees that have a defined job doing a certain set of activities may not close the gaps in some workflows and data flows if there is no data governance in place.
Corporate-driven initiatives can be disruptive, especially if they do not have good change management. If during all of this, the organization is also trying to implement a big SAP project, for example, but does not have good change management or governance around that project, then some level of governance body needs to stand in its place.
Silo Example: Well Location
Well location is something that all of these different silos need.
- Exploration and Development: plan the wells, and those planning wells, plan on a map.
- Regulatory: has to provide the permitting and include the location, so they may need a different format.
- Drilling: need to know where to go drill the hole.
- Production Operations: are planning their daily operations around their routes for their multi-site operators (MSO’s).
All of these silos are dependent in some way or another on well location but which one is the source of the truth? In many cases, each one is using a different reference model. For example, one uses the NAD 27 and the other NAD 83. Someone needs to standardize on a certain coordinate reference system (CRS) so that each group is looking at exactly the same point on the ground.
If you look at how an asset is structured in a siloed organization, the only person that has final say over all of these different siloed asset managers is the CEO. Who wants to go to the CEO to ask if they should use WGST or NAD 83? Sometimes, the role of a chief data officer (CDO) or chief data manager is established to make decisions like which CRS will be used consistently across all assets. This role helps alleviate some of the pressure for a governance body, but the CDO may also want to have a governance board to use as a tool.
Technology Trends and Business Impact
If an oil and gas company is leaning towards more innovation autonomy, there tends to be more fragmentation of business processes. It becomes very important to address governance in this case. If people are not aligned on doing the same thing, using the same tools and understanding data the same way, then the result of that can be inefficient, costly transactional processes. If many different systems operate across different platforms, an organization needs to integrate within those systems and move data along the E&P value chain.
Sometimes the right information is not always available in order to move on to the next piece of the value chain. If data is unreliable due to a lack of integration and there are no standards, the result is slow, onerous reporting and data consolidation. The C-suite wants to see consistent reports around how the business is doing. Without normalizing things, the reporting takes a lot of data consolidation, people and processes.
The widespread use of spreadsheets to perform key business functions limits opportunities to automate and makes it impossible to integrate data. Without business governance in place for standardizing work processes and related applications, the prioritization process can be ineffective. The people that are supporting the systems architecture and bloodstream need to identify which systems need the most attention, which processes are broken and which data issues need to be addressed first. Without business governance, the company tends to be governed by the squeaky wheel.
If you would like to learn more about E&P data governance, contact us and a member of our consulting team can help you with the process.
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