Proposed Data Governance Framework for Small and Medium Scale Enterprises (SMEs)

Data governance is not a one size fits all, instead, it should be an evolutionary process that can be started small and measurable along the way. This research aims at proposing a data governance framework by ensuring data management processes, data security and control are compliant with laws and policies. This article also presents the first results of a comparative analysis between three data privacy laws and outlines five components which together form a data governance framework for SMEs. The data governance model documents data quality roles and their type of interaction with data quality management activities exploring how data is perceived and applicable to SMEs providing best practices for proper data management which includes roles and responsibilities about the use of data for automated decision making, privacy, compliance to data laws, the intersection of data governance and data science in the digital era

Operationalizing data governance via multi-level metadata management.

Today’s rapidly changing and highly regulated business environments demand that organizations are agile in their decision making and data handling. At the same time, transparency in the decision making processes and in how they are adjusted is of critical importance as well. Our research focusses on obtaining transparency by not only documenting but also enforcing data governance policies and their resultant business and data rules by using a multi-level metadata approach. The multi-level approach makes a separation between different concerns: policy formulation, rule specification and enforcement. This separation does not only give more agility but also allows many different implementation architectures. The main types are described and evaluated

Operationalizing data governance via multi-level metadata management.

Part 3: Big and Open DataInternational audienceToday’s rapidly changing and highly regulated business environments demand that organizations are agile in their decision making and data handling. At the same time, transparency in the decision making processes and in how they are adjusted is of critical importance as well. Our research focusses on obtaining transparency by not only documenting but also enforcing data governance policies and their resultant business and data rules by using a multi-level metadata approach. The multi-level approach makes a separation between different concerns: policy formulation, rule specification and enforcement. This separation does not only give more agility but also allows many different implementation architectures. The main types are described and evaluated