A New Data Governance Model for the Bank of Finland
Paananen, Jukka-Pekka (2020)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2020120125484
https://urn.fi/URN:NBN:fi:amk-2020120125484
Tiivistelmä
A common problem in the enterprise environment is that data is fragmented and in silos. The case company of this thesis, the Bank of Finland has a classical data governance model that suffers from similar problems. The solution proposed in this study is to modernize the data governance model.
Both qualitative and quantitative research approach is used in this study. Formal and informal discussions were used to the analyze the current state. Data collection was used to identify patterns and create the proposal. The study started with identifying the business challenge. The challenge is how to best utilize data assets and remove the usage barriers. The study was conducted by investigating the current state based on discussions with stake-holders, literature study and participation in conferences to find best practices. A proposal was formed based on the identified best practices.
The proposal includes four pillars for data governance. These pillars are roles and responsibilities (people), processes, policies, methodology (framework) and technologies. First, Data as a Service is seen as a modern way to access data. It provides a centralized access to relevant data assets in a single location. Data factory architecture provides such a common data service model. Second, the people part includes the organizational structure and roles. Modern data governance includes data engineers, data stewards, data consumers, and data governance council members, who are the data owners. Third, for data governance process, data definition, structure and semantics need to be agreed as a common language. Data quality and integrity is a major topic to ensure reliable data usage. Change management is key to building an agile data governance process. Fourth, technology for data governance includes enterprise data management architecture, data modelling and integration, master and reference data management, and data catalogue and data portal.
Both qualitative and quantitative research approach is used in this study. Formal and informal discussions were used to the analyze the current state. Data collection was used to identify patterns and create the proposal. The study started with identifying the business challenge. The challenge is how to best utilize data assets and remove the usage barriers. The study was conducted by investigating the current state based on discussions with stake-holders, literature study and participation in conferences to find best practices. A proposal was formed based on the identified best practices.
The proposal includes four pillars for data governance. These pillars are roles and responsibilities (people), processes, policies, methodology (framework) and technologies. First, Data as a Service is seen as a modern way to access data. It provides a centralized access to relevant data assets in a single location. Data factory architecture provides such a common data service model. Second, the people part includes the organizational structure and roles. Modern data governance includes data engineers, data stewards, data consumers, and data governance council members, who are the data owners. Third, for data governance process, data definition, structure and semantics need to be agreed as a common language. Data quality and integrity is a major topic to ensure reliable data usage. Change management is key to building an agile data governance process. Fourth, technology for data governance includes enterprise data management architecture, data modelling and integration, master and reference data management, and data catalogue and data portal.