Understanding Data Governance for Data Analysts

Data governance is complex

Many organizations are still under the impression that data governance can be achieved by implementing a data governance tool.

This is false. 

Tools support broader initiatives but cannot provide an organization with a successful data governance framework or the aspects required to successfully manage governance on an ongoing basis. A tool may support overall processes and procedures but cannot be used as a standalone option to manage the governance process. This is similar to the way in which human intervention is required to fix complex data quality issues. A tool may identify the issue and flag it, but human intervention is required to ensure data quality is managed and maintained over time. This blog post provides a starting point for organizations when evaluating what is needed to initiate a data governance program.

Many organizations realize that building data pipelines become more complicated as organizations become more mature with their analytics initiatives. Over time, simply managing integration activities aren't enough as data projects, increasing storage and access requirements, and analytics complexities create the need for broader data management to ensure reliability and security across all moving pieces.

Here's where understanding data governance is important. 

Defined by Google Cloud as "Data governance means setting internal standards—data policies—that apply to how data is gathered, stored, processed, and disposed of. It governs who can access what kinds of data and what kinds of data are under governance." Data governance helps ensure that organizations maintain their data assets and make sure policies are aligned to data initiatives to ensure collaboration between those who work with data and those who own data assets. 

Creating a data governance framework from scratch is a lot of work and requires support from business stakeholders as well as technologists. At the same time, organizations can simplify the process by setting realistic expectations and understanding that developing the framework supports longer-term data management success.

The following data governance blog provides an overview of how an organization should start looking at data governance and breaking it down into different steps to ensure policies are aligned to an overall data governance framework that can be maintained and supported over time. 

The Importance of Data Governance for Data Analysts

Data analysts rely on high-quality data to derive meaningful insights. Here are some reasons why data governance is particularly important for them:

• Data Quality Assurance: Data governance establishes standards and practices that ensure data accuracy, completeness, and consistency. High-quality data is critical for reliable analytics outcomes.

• Enhanced Security: With increasing concerns around data breaches and privacy regulations, robust data governance frameworks help protect sensitive information through measures like encryption and access controls.

• Regulatory Compliance: Data governance ensures that organizations comply with legal standards such as GDPR and CCPA, thus avoiding potential fines and reputational damage.

• Streamlined Data Access: Effective governance facilitates better integration and accessibility of data across departments, enabling analysts to access the necessary information quickly.

Challenges in Data Governance

Despite its importance, implementing effective data governance can be challenging due to:

• Siloed Data Environments: Data may be isolated within departments, making it difficult to manage consistently across the organization. Breaking down these silos is essential for effective governance.

• Defining Roles and Responsibilities: In larger organizations, clearly defining who is responsible for what can be complex but is critical for accountability in managing data.

• Maintaining Data Quality: Ensuring ongoing accuracy and consistency requires continuous monitoring and validation processes which can be resource-intensive.

Best Practices for Implementing Data Governance

For successful implementation of a data governance framework, consider the following best practices:

• Establish Clear Policies: Develop comprehensive policies regarding data access, usage, storage, and sharing to create a structured environment for managing data.

• Utilize Governance Tools: Leverage software solutions for effective management of data assets. Tools can help automate compliance checks and streamline workflows.

• Foster a Data Governance Culture: Encourage ongoing training and awareness programs to instill a culture of responsibility towards data management among all employees.

• Regularly Review and Adapt: Continuously assess the effectiveness of your governance framework and adapt it as necessary to address emerging challenges or changes in business strategy.

Things You Should Know About Data Governance

1. Business processes and ownership

Understanding business processes and how data flows to support those processes are important when developing a data governance initiative. In essence, organizations should align how data moves through systems with business processes that are used for operations/transactional outcomes or analytics and make sure that there is an understanding of the people responsible for those processes. Processes and data assets should be catalogued to ensure that the organization understands how data sources interact and overlap. Key stakeholders that may be aligned to business domain should take responsibility for the data assets to ensure that policies are aligned to business needs.

2. Roles and responsibility

A cross-functional team should be created that reflects how data is used across functions within the organization. How that data is gathered, stored and processed for analytics and operations becomes key in identifying stakeholders. Additionally, identifying responsibility creates accountability and also lets people know who they need to reach out to if there are questions or roadblocks to data access or analytics success.

3. Privacy, security and compliance

Many organizations also look at managing their data assets to ensure security and enable better privacy. This is because there are regulations surrounding data storage and usage. Global regulations and industry standards add to the complexities involved. In addition to selecting solutions that address and meet regulatory compliance like GDPR and overall privacy, organizations need to ensure that data can be masked when needed and that only people who require access actually see specific data. Governance initiatives overlap because the policies and standards created will lay out how compliance and security are managed, how data is stored, security and privacy measures, and when and how data is disposed of or purged. For example, leveraging automated ETL tools help to comply with data integration governance.

4. Policies and framework

Policy development requires involvement from key stakeholders across the organization. People may own business assets and business rules and need to be involved in the governance process to develop the policies that are put in place to manage data more broadly. Policies will cover the standards required to manage data across domains, how data is handled, and who has access to what data across the organization. The overall framework requires regular involvement to ensure policies are up to date and are linked to new data pipeline initiatives and the company's overall data management strategy. 

Bottom Line

This blog just scratches the surface of what organizations need to get started on their data governance journey. But it also provides a basis to understand key components of program creation and the complexities involved and dedication to ensure overall data management success through data governance. As organizations' data environments increase in complexity, data governance becomes essential to mitigate risks and increase actionable information insights. 

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