What are the steps to create data governance policies?

1 Assess data needs and goals

The first step is to identify the data needs and goals of the organization and its various business units, functions, and roles. This involves assessing the current state of data, the data sources and systems, the data flows and dependencies, the data users and consumers, and the data challenges and opportunities. This step also helps to define the scope, vision, and objectives of data governance, as well as the key performance indicators (KPIs) and metrics to measure its success.

2 Establish data governance roles and responsibilities

The second step is to establish the data governance roles and responsibilities, such as data owners, data stewards, data custodians, data analysts, data consumers, and data governance committee members. These roles should have clear definitions, expectations, and accountabilities for data quality, security, compliance, and usage. They should also have the authority, resources, and support to perform their tasks and collaborate with other data stakeholders.

3 Define data governance principles and standards

The third step is to define the data governance principles and standards that will guide the data policies and practices. These principles and standards should align with the data needs and goals, as well as the organizational culture and values. They should also cover the key aspects of data governance, such as data quality, data security, data privacy, data ethics, data lifecycle, data architecture, data lineage, data catalog, data dictionary, data classification, data access, data sharing, data integration, data reporting, and data analysis.

4 Develop data governance policies and procedures

The fourth step is to develop the data governance policies and procedures that will implement the data governance principles and standards. These policies and procedures should be specific, measurable, achievable, relevant, and timely (SMART). They should also be documented, communicated, and enforced across the organization. Some examples of data governance policies and procedures are data quality policy, data security policy, data privacy policy, data ethics policy, data retention policy, data deletion policy, data access policy, data sharing policy, data integration policy, data reporting policy, and data analysis policy.

5 Monitor and evaluate data governance performance

The fifth step is to monitor and evaluate the data governance performance using the KPIs and metrics defined in the first step. This involves collecting, analyzing, and reporting on the data quality, security, compliance, and value indicators, as well as the data governance maturity and effectiveness indicators. This step also helps to identify the data governance gaps, issues, risks, and opportunities, as well as the best practices and lessons learned.

6 Review and update data governance policies

The sixth step is to review and update the data governance policies based on the feedback and insights from the previous step. This involves revising, adding, or removing the data governance policies and procedures as needed to address the changing data needs and goals, as well as the evolving data landscape and regulations. This step also helps to ensure the continuous improvement and sustainability of data governance in the organization.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?