Data governance and data management are two distinct concepts that are often confused. Data governance is a broad set of policies that are implemented throughout the organization, while data management is the act of executing the specific processes that support the data governance policy. Data governance establishes policies and procedures around data, while data management enacts those policies and procedures to compile and use that data for decision-making. Data governance acts as a model for building a new building, while data management is the act of building.
Data governance is one of 11 areas of knowledge about data management. It has a relationship with all of these areas, such as data quality, data security, metadata, reference data, etc. An effective data management strategy is essential to maintain regulatory compliance, minimize risks, improve data security and create accountability for an organization's data. Using a data management strategy helps ensure that data is managed in accordance with general company policies throughout its lifecycle, from the time it is created until it is retired.
This involves everything from defining rules for storing data to configuring access rights and controls. Quickly access and load data into your cloud data warehouse (Snowflake, Redshift, Synapse, Databricks, BigQuery) to accelerate your analysis. Harness the power of the cloud with Matillion solutions specifically designed to work with major cloud data storage environments: Amazon Redshift, Google BigQuery, Microsoft Azure Synapse and Snowflake. Matillion Data Loader makes it easy to move your data to the cloud data stores of your choice, allowing you to create a single source of truth for your data. AWS recently announced the launch of new serverless offerings to help organizations simplify the management of their data and analysis environments. Data governance and data management are both essential for successful operations.
Data governance provides a blueprint for how to manage data, while data management is about execution, implementing the governance framework, and influencing the organization's business objectives. Understanding and improving data quality will allow you to access reliable data, the kind that will allow you to make truly information-based business decisions. Any professional working in the field of data should easily understand the difference between these two concepts. Learn practical steps and best practices, and use the templates provided to build and implement your data management program from scratch or improve the one you already have. With an effective combination of data governance and management, organizations can ensure their data is secure and reliable.
