# Data Governance

<mark style="background-color:green;">Data governance refers to the overall</mark> <mark style="background-color:green;"></mark><mark style="background-color:green;">**management of the availability, usability, integrity, and security of the data used within an organization**</mark><mark style="background-color:green;">. It is a framework of policies, procedures, and standards that ensure that data is reliable, accurate, and consistent across the enterprise.</mark>

In the context of data engineering, data governance refers to the processes and practices that ensure that the data used by an organization is of high quality and meets the needs of the business. This includes defining data quality standards, ensuring that data is properly documented and labeled, and establishing processes for data access and usage.

For example, let's consider a scenario where a company wants to implement data governance practices for its customer data. The first step would be to define what data quality means for this organization. This might include ensuring that customer data is complete, accurate, and up-to-date.

Next, **the company would establish processes for ensuring data quality.** This might include setting up automated data validation checks, implementing data entry standards, and providing training to employees who handle customer data.

**The company would also need to establish policies and procedures for data access and usage.** For example, they might set up data access controls to ensure that only authorized employees can access customer data, and establish protocols for how the data can be used and shared.

Overall, data governance is a critical aspect of data engineering as it helps ensure that the data used by an organization is reliable, accurate, and secure.

## Categories

1. Data Strategy
2. Data Management
3. Data Quality
4. Data Operations
5. Data Platforms
6. Supporting Process


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