The UMPTeenth reason for agile data management
October 28, 2021
Upstream companies were forced to rethink their operating models in 2014, once fundamental market shifts were set in motion. Many gravitated toward a “Market Pull” model that enables them to flex production, dynamically deploy resources across assets and adjust investment plans, as needed, based on economic conditions.1
There are several pillars of these companies’ upstream market pull transformations (UMPTs):
- Remote operating centers (ROCs) that break down functional silos, improve operational excellence, drive inspection and maintenance process efficiency, improve productivity and increase safety2.
- Digital technologies that simplify workflows, accelerate decision making and drive end-to-end accountability3.
- The OSDUTM Data Platform4, which removes data silos, enables transformational workflows and better decision making, and accelerates innovation and the deployment of emerging digital solutions (see Overcoming OSDU Data Platform Challenges5).
- New and agile ways of working that create operational and commercial flexibility, improve cross-functional coordination and ecosystem collaboration and accelerate performance improvements6.
The missing link
There’s another element of UMPT that gets less attention: agile data management. One reason agile data management is overlooked as a stand-alone pillar of transformation is that it enables each of the other pillars described above. Reaping the benefits from ROCs, new technologies, the OSDU Data Platform and agile work practices requires agile data management and access to reliable, high-quality data across the value stream. Companies aren’t ignoring agile data management. They’re just not giving it its due.
There are five things upstream companies need to consider when building data capabilities that can fuel their transformation efforts.
1. Not all data is created equal
Given resource constraints, upstream organizations need to focus on identifying and providing the high-priority data that will best support remote operations and decision making. For example, ROCs can add tremendous value by automating routine tasks, optimizing asset utilization by deploying self-diagnostics and coordinating inspections and maintenance activities. But to do these things, organizational leaders need to assign data management and governance team members who know the business and can identify the most pressing data needs and opportunities. They need to deploy data teams to high-value opportunities, such as supporting workflows and analytics. And they need to engage their ecosystem partners to execute core data management activities and provide the reliable, high-quality data a given opportunity requires.
2. Data management is a team sport
As UMPTs progress, the amount of data created, curated and consumed grows exponentially—especially when asset self-optimization is implemented using industrial Internet of Things (IIoT). Because of that data growth, all team members involved in the transformation, especially ROC team members, need to be engaged in data management and governance. Different roles have different responsibilities.
3. Data is a product to be consumed. Manage it as such.
To enable field operations and ROCs to harness the value of data, UMPT data teams need to focus less on where the data is stored and more on how data is consumed. This requires a customer- focused data product mindset and new data products described in a data catalog. Equipment master data and raw and curated performance data to support improved equipment operations and management by exception are examples. Data scientists can search the catalog to quickly find and access the data products they need to develop analytics and insights with which they can make predictions, increase safety, reduce failures and resolve issues. A customer-focused approach to data management also calls for new data features such as plug and play capabilities7, self-service infrastructures, and marketplaces that provide consumers easy access to trusted data.
4. Agile operations require agile data management
Successful UMPTs use ROCs to break down functional silos and establish real-time, cross-functional coordination and decision making. To support such operational agility from a data perspective, data management teams can do three things. The first is simplifying data governance. We’ve found that current data governance structures have grown to encompass a complex set of frameworks, processes and rules with multiple hierarchies. These structures need to be reworked with guardrails and guiding principles that provide team members the independence they need to make data decisions aligned to customer needs. The key objective is to allow leadership teams to focus on “what” and allow the data agile team members to focus on “how.”
The second is using Scaled Agile Framework® (SAFe®) for collaboration and decision making. To achieve UMPT data goals and objectives, companies will need to streamline data management complexity, reduce risk and speed up delivery. The SAFe Lean Portfolio Management competency—which can be used to facilitate cross-function interactions and decision making, lower risks, resolve issues and accelerate delivery of solutions—should be the cornerstone of the data management and governance transformation.
The third thing data management teams can do is execute as a single team through DevOps for Data (DataOps) pods. We believe that execution with agility is key to breaking down the walls of conflict that exist between silos. Using DataOps, which provides a framework for collaboration, can help ROC leaders achieve a “one team” mindset around high-priority data (e.g., equipment operations, inspection and maintenance data). DataOps also provides a structure to help pods focus on the right things and increase the speed with which they address operational needs.
5. Inclusivity in the workplace extends to data
For a UMPT to be successful, operations and ROC teams need to establish an inclusive, data-driven culture. Doing so requires the organization to transition data management practices from being tech-/app-centric to outcome-focused. It calls for organizations to enhance their data management skills, agile delivery capabilities and data literacy. And it demands a strong change management program to mobilize the workforce.
Data first. Data forever.
Given that upstream industry players are at the beginning of their UMPT journeys, establishing a data-driven culture can seem daunting due to the complexity and cultural changes required. However, we have seen large organizations in other industries successfully employ the approaches outlined above to deliver tangible and meaningful results. For example, a traditional manufacturing company using the above approaches was able to utilize and reuse previous under-utilized data to make critical business decisions, produce highly relevant insights and increase productivity8. Ultimately, the data transformation journey described above can help Upstream Market Pull Transformations achieve long-term, tangible value. This journey focuses more on the technical aspects of change. It builds new agile data management capabilities and a true “data-first” mindset.
Disclaimer: This content is provided for general information purposes and is not intended to be used in place of consultation with our professional advisors. This document refers to marks owned by third parties. All such third-party marks are the property of their respective owners. No sponsorship, endorsement or approval of this content by the owners of such marks is intended, expressed or implied.
Sources:
1The race to agility: Upstream plans its next big move
2The “moonshot” for offshore oil and gas
3The race to agility: Upstream plans its next big move
4OSDU: All things considered
5OSDU: All things considered
6The race to agility: Upstream plans its next big move
7How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh
8Accenture analysis
