Bank Of Lithuania

Edita Lukaševičiūtė is a seasoned data governance leader at the Bank of Lithuania, specializing in establishing robust data management frameworks, promoting transparency, and integrating cross-functional collaboration. She spearheads initiatives to transform metadata into strategic assets for smarter public sector decision-making

In an exclusive interview with CIO Applications Europe she shared invaluable insights on strong data governance, clear ownership and cross-functional collaboration are essential to transforming the Bank of Lithuania into a transparent, data-driven institution capable of faster, smarter public sector decision-making.

In line with this, we have crafted a set of questions that will guide our conversation. This will be featured as a Q&A piece in our magazine. It will offer valuable perspectives for data leaders and digital transformation strategists, exploring how strong governance frameworks, metadata management, and cross-functional collaboration can elevate the value and reusability of data assets across an organization.

• Can you walk us briefly through your leadership role as Head of Data Governance Division? What are your key responsibilities and areas of focus?

My journey in data governance began when I took on the role of managing a data governance project. The primary objective was to establish a comprehensive data governance framework within the organization. We adopted a hybrid approach—centralizing certain data management functions such as data collection and dissemination, while retaining data ownership within individual business departments.

Through this process, we recognized that data governance itself is a distinct function. This insight led me to initiate the development of a dedicated data governance division. Within my team, we focus on key areas including data architecture, metadata management, data quality, data processes, and data literacy.

My core responsibility is to ensure the effective implementation of data governance. By adhering to best practices and data management principles, our goal is to transform the organization into a truly data-centric and data-driven organisation.

What are some of the core pillars or principles behind your Data Governance Policy and Management Strategy?

We treat data as a strategic asset managed to be reusable, explicit, high-quality, timely, and secure. Our goal is to become a data-centric organization by making data discoverable and accessible, collecting granular data only once, ensuring a single source of truth, and enabling open data ecosystem.

Our data strategy focuses on:

• Enhancing analytical capabilities and adopting advanced analytics tools for informed decision-making.

• Promoting a culture of knowledge sharing and self-service.

• Implementing the "only once" principle in data collection.

• Fostering collaboration with strategic partners through open data exchange and modern technologies.

• Ensuring timely, relevant dissemination of high-quality data.

• How do you ensure data availability, quality, and reusability across different departments within a central bank environment?

Our data governance approach is built on Organizational and Technological Foundations of Data Governance. From an organizational perspective, we foster collaboration and accountability through regular Data Governance Group Meetings. These sessions serve as a forum to discuss emerging data needs, track the progress of data product development, and assess data quality within specific domains. On the technological side, we leverage tools such as a data catalogue and data quality dashboards. These platforms enhance data discoverability, transparency, and monitoring, enabling teams to better understand, trust, and utilize the data available to them.

• Data is no longer just support its strategy. When managed with governance, transparency, and purpose, it becomes the backbone of smarter, faster, and more trusted public decisions

• Metadata is often the unsung hero of data quality. How is your team leveraging metadata to strengthen governance and unlock greater value from data assets?

One of the key responsibilities in the data product creation process is ensuring comprehensive metadata documentation. Metadata provides essential context describing the structure, meaning, and usage of data which is critical for both discoverability and governance.

To support this, we utilize a data catalogue, which enhances metadata visibility across the organization. Beyond basic descriptions, the catalogue allows for the application of custom tags, such as confidentiality levels or privacy classifications. These tags play a vital role in downstream data management processes, helping ensure compliance, security, and appropriate data usage.

• What advice would you give to other data leaders or public institutions that are just beginning their journey toward structured data governance?

Data governance is not solely the responsibility of the Data Department or Data Office. It is a horizontal function that spans the entire organization. For it to be truly effective, it’s essential to involve business units from the very beginning ensuring their needs, insights, and ownership are embedded in the process.

Equally important is communication. Successful implementation of data governance relies on continuous, clear, and consistent communication. Whether it's aligning stakeholders, clarifying roles, or sharing progress, the routine remains: communicate, communicate, and communicate.