Bank of Lithuania

Edita Lukaseviciute

Edita Lukaseviciute has extensive experience in implementing data governance frameworks. She specializes in regulatory compliance, data strategy and promotion of advanced analytics tools to drive the bank's digital transformation and ensure data integrity and transparency across operations.

Insights about Professional Journey and Current Responsibilities

I work at the Bank of Lithuania, the central bank and supervisory authority in Lithuania, in the Data Governance Division of the Data and Statistics Department. My role centers on driving effective data governance throughout the organization by developing and implementing robust standards, processes and procedures that empower employees to work with data efficiently and consistently. My team consists of a data architect who ensures that the organization’s data architecture aligns with these same standards and several data experts who manage the data catalog, supervise data quality, and track data processes to ensure that all aspects of data governance and management are executed efficiently and in compliance with established protocols.

New Approaches in European Banking Data Governance

The latest trend in data governance is shifting towards a more decentralized approach and moving away from the traditional centralized model where all data-related functions and teams were consolidated in one place.

This decentralized approach is often referred to as a ‘data mesh’ or a federated governance model. The transition is driven by the need to enhance data expertise directly within business units. For example, in the case of the Bank of Lithuania, the goal is to empower business units with stronger data competencies while maintaining support from departments like Data and Statistics or IT.

The core idea is to create cross-functional data teams that blend business, data and IT representatives. These teams collaborate to develop what are now called ‘data products,’ rather than merely focusing on data tables or datasets. A data product is viewed as a valuable asset that generates value for its creator and for others, allowing it to be reused for multiple purposes across the organization.

"Data governance isn't about building walls around information; it's about constructing bridges that connect teams, enhance collaboration, and unlock the full potential of data as a shared asset"

This represents a shift from traditional, siloed data management to a more holistic and collaborative approach that enhances both the creation and consumption of data across various business functions.

Navigating the Data Governance Maze

One of the key challenges in data governance is data literacy, which has two distinct aspects. On one hand, business professionals often lack familiarity with advanced data tools and need to upskill, moving beyond their reliance on Excel. Many business people still prefer Excel and are resistant to adopting more progressive tools. On the other hand, data experts may not fully understand the business context, which limits their ability to appreciate the economic value of the data they handle. A potential solution to bridge this gap is the formation of data product teams that combine both business and data professionals. This allows for collaboration, where data experts and business professionals can learn from each other and better align their understanding of both tools and business objectives.

Staying Ahead with Personal Data Governance Tactics

For data governance professionals, the Data Management Body of Knowledge remains the cornerstone guide—a trusted resource that shapes our strategies and decisions. However, we also turn to a variety of other frameworks.

One example is the CMMI Data Management Maturity Model, which, though retired now, played a pivotal role in the early stages of our data governance efforts. In addition organizations like the EDM Council offer valuable resources, including their own data management maturity model, which can serve as a helpful guide.

That said, in my experience, nothing compares to the value of peer collaboration. When we meet with other central banks and exchange ideas—whether it’s discussing challenges, sharing lessons learned or celebrating achievements—we gain insights that no book or consultant could ever provide. These real-world conversations help us navigate the complexities of data governance far more effectively than any theoretical model or textbook.

The Role of Data Governance in Digital Transformation

Data governance plays a crucial role in ensuring standardization by establishing the necessary rules and processes. This is particularly valuable during organizational transformations, as it provides clear instructions and guidance on where to access the information needed. Data governance supports this by offering tools like data catalogs, which help organizations locate required data and maintain detailed procedures. For example, new employees can follow established protocols for accessing certain types of data or creating data products. Without data governance, even the best technology can lead to chaos. No matter how advanced your tools are, without knowledge and standardization, you risk inefficiency and confusion. Data governance bridges this gap, ensuring that transformation efforts are not just about adopting new technology but also about embedding the processes and standards needed for long-term success.

Advice for Senior Leaders and Upcoming Professionals

A crucial piece of advice is to prioritize open communication with internal stakeholders. Actively seek to understand their needs, preferences and challenges, and explore how they envision using the data. This collaborative effort will unlock meaningful insights and foster stronger alignment. Connect with your peers to exchange ideas and learn from their experiences as it can spark fresh perspectives and drive positive change. People don't have to reinvent the wheel—most innovative solutions are already out there. Focus on adapting proven best practices to fit your organization’s distinct context, enhancing your journey without unnecessary complexity.