Within the industry, digitalization is a key enabler for accelerating business outcomes. It allows businesses to leverage data insights and makes it possible for decisions to be made in a correct and timely manner. But, despite the recent strategic focus placed on digitalization and the digital oil field, sustained digitalization success stories are rarely seen in the industry. While there are several critical reasons for this, the most important is that recent digitalization efforts have been over-focused on technology and software solutions. While digital-strategy implementation is at least superficially reliant on technical and architectural advances and vendor solutions, the narrow focus on technology has come at the expense of a data-centric digitalization approach. As Nicolas Wykes recognized in a 2017 LinkedIn post, a data-driven approach to digitalization is just as relevant today as it was in the early days of the digital revolution. In other words, data centricity is, and will always be, critical for driving digitalization success.
Reflecting on my own career, I have found this to be true. In the early 1990s, digitalization focused on digitizing data—for example, digitalizing paper well logs and providing customers digital automated data access and visualization. It essentially provided the customer easy access to reliable well and log integrated data, which helped geoscientists develop valuable insights. The digital user interface was simple and used behind-the-scenes dynamic queries that integrated multisource data. Although much has changed since then—including the advent of a myriad of tools and faster, better technologies such as cloud platforms—we have also significantly increased the volumes of data captured or acquired for usage in the upstream business and industrywide. Just as it was in the 1990s, data is still central to achieving digital aspirations and for achieving success. In fact, across the industry, there has been an increase in (1) business demand for end-to-end data integration across the value chain, (2) the need for quick access of data from different discipline source systems, and (3) usage of various formats of data in achieving collated data insights. Additionally, there is significant focus on data competencies and knowhow.
Recent technical discussions within the SPE Digital Energy Technical Section have indicated that digitalization needs to adopt change-management discipline to achieve effective digital transformation. But this misses the point. It is critical that data practitioners and digital leads highlight the fact that digital success hinges on establishing a data-centric culture and mindset. While it is easy to launch a new point solution, this can sideline painstaking understanding of the underpinning data. Digitalization should start with data at the center. Before initiating technical solutions, practitioners should improve data reliability and accessibility, including by identifying what data is critical, its current health, and mapping data end to end for the entire digital value chain. These are the foundational underpinnings of data health that cannot be ignored.
Another issue impeding digitalization success is the over-focus of individuals and disciplines on their specific data needs. Because disciplines tend to be comfortable within their business process arena, they often fail to view the entire data landscape with an enterprising mindset. This can result in a limiting of data value within a discipline’s context (silos), and individual activities can easily impede overarching success. The sense of urgency to accelerate outcomes within a discipline can result in narrow solutions that leave blind spots for standardization, simplicity, and scalability.
Supporting data centricity requires
- Establishing data strategies that address the most-critical business data first
- Establishing a governance and accountability structure that breaks discipline and data silos and drives data integration that is less focused on individual discipline systems or processes
- Shifting to scalable standardized solutions vs. point solutions
- Driving accessibility and reliability (quality) of data, mitigating gaps using data authority leaders and heightened transparency
- Developing enterprise data architecture and consistent data definitions across the enterprise that enable cross-functional continuity and seamless flow of data
- Extracting data value generation
Decision are only as good as the data is. So, knowing what critical data supports business decision-making should drive data analytics and value extraction. One needs to understand which analytics are used to support decision-making related to interventions or actions, ensuring usage of trusted data right from the onset.
What are your views on the following questions:
- Is a data-centric approach attainable, and what are the top elements to consider?
- Does the entire organization need to shift its ways of working and thinking, including lines of business that may restrict data within silos or functions?
- Is having clear, well-defined data roles (data executive, data definition owner, data owner, chief data officer) necessary to accelerate digitalization journey outcomes?
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