The Relevance of Data Governance: Building the Foundation for Digital Success
Data governance is not a project, it is a philosophy. It requires the establishment of rules and policies for all corporate data and compliance by every data user. Data governance lays the foundation for digital sustainability and drives business profit and cost efficiency. This blog post explains the importance, relevance, and opportunities of data governance.
What is Data Governance?
Data governance is the framework for data quality management (DQM) and defines which roles take on the tasks of DQM with which responsibilities. General and specific standards for data handling and data management can be defined in the form of a data governance policy that should be followed by all employees.
Data governance as a framework for DQM includes three basic design elements
Definition of necessary tasks within DQM (e.g. development of a data quality strategy and definition of data maintenance processes)
Identifying the roles (responsibilities to be assumed by people) in performing these tasks (e.g., data stewards who implement data quality standards)
Define the responsibilities of each role in performing the DQM tasks.
Thus, data governance defines the scope of all DQM activities, while separating itself from the operational implementation of DQM activities. innoscale_Data Governance Infographic
Importance of Data Governance
High quality data is the basic prerequisite for an organization to meet customer demands: In an era of advanced digitalization and increasingly individual customer requirements, companies often have to adapt their business processes. Data governance lays the groundwork for improving and securing data quality in the enterprise, which in the medium term means that data can be used more efficiently.
The Importance of Data Governance
High-quality data is the foundation of a company's ability to meet customer demands: In an era of advanced digitalization and increasingly individual customer requirements, companies often need to adapt their business processes. Data governance lays the groundwork for improving and securing data quality in the enterprise, which in the medium term means that data can be used more efficiently.
Examples that illustrate the importance of quality data include
Customer management:
All customer data must be available for high customer satisfaction and good customer service. This often requires extracting data from different information systems (e.g., CRM and data warehouses). Customer data integration can only be successful if the data quality in all systems is equally high.
Business Management:
Critical business decisions require consistent and reliable information. Poor data quality can lead to wrong information and wrong decisions. To avoid this, companies need DQM that goes beyond the boundaries of a single system.
Regulatory and legal requirements:
There are an increasing number of regulations and guidelines that companies must comply with. To meet the associated burden of proof, companies must be able to provide the necessary data.
High data quality across the enterprise is critical to meeting business objectives. In other words, poor data quality creates costs and slows down business success.
