In organizations, where data is prioritized to be a valuable asset, it needs proper management. Data management includes creation and execution of frameworks, policies, and standards that manage the comprehensive data lifecycle essential to an organization. Data Governance involves defining policies and standards associated with data, while data management complies with the established policies and standards for data collection, storage, usage, and analysis for decision-making.

Data management common elements

Data management includes different data projects including data governance. The common data management elements include –

• Data preparation – It is a process to clean and transform raw data into usable data for accurate analysis. Some companies rush or miss this process for reporting and make incorrect decisions because of poor quality data.
• Data pipelines – Data is transferred automatically from system to system.
• Data ETL [extract, transform, load] – Data is transformed to a load in the data warehouse. It is an automated process and when built will need preparation and pipeline process.
• Data catalogs – Metadata is managed, so data becomes easy to locate and track. Users gain a clear picture of their data.
• Data warehouse – All the data sources are fused, so the data analyst gains a clear route.
• Data governance – Defines the policies and standards to maintain compliance and security of the data.
• Data architecture – A formal structure designed got data flow management.
• Data security – It is a process intended to protect data from corruption and unauthorized access.

Organizations take help from data governance services like EW Solutions to enhance their data management and governance skills. The consultants recommend a strategic solution to impact the company bottom-line optimistically, while simultaneously lessen the risks.

What’s data governance?

It is a crucial element of data management, basically guidelines on how to process and manage data across the company. It answers the following questions –

• Who can access which kind of data?
• Who has data ownership?
• How much data is the data compliant with new regulations?
• What protective measures are taken for data privacy and data security?
• Which data sources have been approved for usage?

Data governance practices and models differ from one company to another, but the following are crucial elements of the process.

• Data quality – It is the foundation of data-source management. Without quality data, your solid data governance program is a total failure. With complete, consistent, reliable, and accurate data every data-driven organization can make prudent business decisions confidently.
• Data compliance & security – It is a practice of outlining and cataloging the data resources based on their risk level. A secure access point is created with a balance between security and user interaction.
• Data stewardship – It is a role allocated to the right member with a responsibility to monitor how the team members use data sources as well as comply with the regulatory policies and standards.
• Data transparency – Every segment of the process and procedures undertaken must work within the transparency model. Users and analysts need to find the origin of the data to identify if there is any special consideration.

The key difference between data management and data governance is that the latter designs a blueprint for a new project, while the former is an act to implement the project. A project without a blueprint is prone to fail down the road, so it is wise to have a solid data governance strategy planned and implemented.