How does data governance support analytics?

Agree with all previous responses, yet I would also call the "closed-loop" instead of "open-loop" interactions between the two areas. Encoding the right rules requires contextual knowledge, that often analysts and data scientists derive faster and continuously by exploiting curated data. Even the best AI tools can fall short in proposing business-accepted definitions of "wrong data". Same can happen in the data governance team, despite they should be working a lot closer to data owners and data stewards. Experience tells me that only the best business managers have deep understanding of the data emanating from the processes they manage.

Data governance will enable developers to create secure, quality, and reliable data to support analytics and downstream users.

Also auditing: who is accessing "your" data, when, how?

I  agree with the previous responses. Data governance is incredibly important for supporting analytics because it provides us with the rules, tools and framework necessary to effectively manage, protect, and utilize data. By establishing standards and guidelines for data collection, storage, and maintenance, data governance ensures that the data used for analytics is of high quality. This results in analytics that offer reliable insights, enabling us to make informed and optimal decisions based on data.

Data governance plays a crucial role in supporting and enabling effective data analytics by ensuring data quality, consistency, accessibility, and trustworthiness.

A governance program would have at least the following components:

Quality Assurance

Data governance establishes policies, standards, and processes to maintain data quality. This includes data validation, cleansing, and monitoring to ensure accuracy, completeness, and integrity of data used for analytics. Data considered to be quality is essential for reliable and meaningful analytical insights.

Data Definition

Business glossaries and data dictionaries allow organizations to establish common definitions and semantics for data elements across the enterprise. This consistency enables integration an analysis from disparate sources and reconciles positions of analysts.

Managed Assets

By establishing data ownership, stewardship, and accountability, data governance instills trust in the data assets used for analytics. Users are more likely to rely on analytical insights derived from well-governed data.

Data governance lays the foundation for reliable, trustworthy, and accessible data, enabling organizations to fully leverage data analytics for informed decision-making.