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Breaking down silos: turning a data strategy into action for a global energy leader
In this client case, discover how BrightWolves supported an international energy player to transform their corporate Data Strategy from theory to reality. Our mission? Break down silos between departments and branches of the client’s organization, simplify complex processes, and lay the foundation for seamless data governance and collaboration across the different entities.
Challenge
Our client’s core business spanned many diverse functions, from network operations to energy distribution and market interactions. Despite having a comprehensive Data Strategy at the corporate level, the organization struggled to put it into practice due to fragmented efforts across departments and geographical regions. Finding synergies between teams was challenging, even within the same department across countries.
Other challenges included:
Complex processes with unclear data ownership and a lack of data lineage, making it difficult to identify and resolve data quality issues in a timely manner.
Misalignment between business and IT teams on how to manage the company’s data and who is responsible for its quality, accuracy, …
Resistance to change due to varying departmental priorities.
50+
10
1
Stakeholders engaged
Data domains identified with their respective Data Owners
Common framework for multidisciplinary Product Teams
Approach
Our tailored approach followed a structured, iterative process:
1. Understanding the starting point: Our team began by interviewing the sponsors of the initiative to identify strategic use-cases. These served as examples to understand pain points. This led us to conduct extensive stakeholder workshops involving both business and IT teams from the five use-cases selected as critical.
From these, we mapped out the current state of data governance practices and identified key issues, including gaps in collaboration between business and IT, as well as unclear responsibilities.
2. Defining a governance framework: Drawing inspiration from the industry’s best practices, coupled with our own practical, knowing-what-works experiences, we addressed accountability issues by defining data governance roles — including Data Stewards, Data Owners, and Product Owners — with defined expectations. Furthermore, we developed standardized definitions for data products and data domains to ensure a common language across the organization. This step was critical, as defining data domains and the role of Data Owner required alignment with the managers who would we assume those roles.
Additionally, we defined new procedures and artefacts to foster collaboration with the newly created roles and develop a future way of working. For example, a data classification guide was developed to help Data Owners and Data Stewards assess the confidentiality and criticality of their data and guide them on the appropriate way to handle it.
3. Creating an implementation roadmap: To start the implementation phase, we defined and aligned a set of data domains across the company’s entities. Data Owners were also identified for each domain to clarify ownership and streamline issue resolution. This domain map was created through collaborative sessions, ensuring they were not too granular or too broad. Based on this exercise, we designed a phased implementation roadmap aligned with organizational goals and priorities, focusing on a selection of domains where we could start implementing the roles, procedures and artefacts previously identified. Early efforts focused on quick wins, such as introducing data contracts between data producers and consumers, to build momentum and demonstrate tangible benefits.
4. Driving change: Through this data governance initiative, we helped foster collaboration between business and IT teams, enabling the company to further strengthen the multidisciplinary product teams by ensuring business knowledge is accurately translated into data transformations. By addressing cultural barriers, emphasizing shared goals, and showcasing tangible impacts through quick wins, the organization positioned itself to adopt a data governance model shared across its different entities. During the roll-out, targeted training sessions were delivered to educate teams about the new roles in place and the methods and tools required for successful implementation.
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Impact
Through the use-cases tackled, this project transformed a fragmented data governance into a cohesive and scalable framework. By establishing clear roles like Data Stewards and Data Owners, and defining data domains, the organization clarified accountability and ownership. A phased roadmap provided structure for operationalizing data governance, while fostering seamless business-IT collaboration set the stage for ongoing success. These multidisciplinary teams ensured that business expertise would effectively inform data transformations.
All of these efforts contributed to reducing the burden associated with maintaining current operations, so that more resources could be allocated to building a resilient energy network for the future.
