So, what exactly is data analytics? Simply put, it's the science of analyzing data to find trends, answer questions, and draw conclusions for decision-making. However, like most processes, it's influenced by the inputs it receives — in this case, the quality, structure, and context of the data being used.

One of the significant challenges facing data analytics today is the subjectivity of data interpretation. Here's the reality I encountered myself over and over again: two data analysts can be handed the same question but arrive at two vastly different answers. How? By sourcing their data from various places, by interpreting ambiguous values in different ways, by cleaning and prepping the data using varying techniques. What we have then is a veritable Pandora's box of uncertainty, muddying the waters of what should be crystal-clear, data-driven decisions.

In this context, what could possibly be the guiding light? Enter data governance — the unsung hero of the data analytics world. Data governance is the holistic approach to managing, improving, and leveraging data to create business value while ensuring compliance and security.

So here are some points how data governance directly influences data analytics:

1. Data Quality Management:

Data governance upholds data quality, crucial for accurate analytics and avoiding misleading results.

2. Data Security:

Governance policies guard sensitive data, influencing the use and context of data analytics.

3. Data Accessibility:

Governance dictates data access, impacting an analyst's ability to obtain necessary data.

4. Data Compliance:

Governance ensures adherence to data-related laws and regulations, defining the scope of permissible analytics.

5. Metadata Management:

Governance oversees metadata management, providing necessary context, origin, and structure for data analysis.

6. Consistent Data Understanding:

Governance fosters a shared understanding of data, promoting reliability in analytics across the organization.

7. Data Integration:

Governance aids in merging data from diverse sources, a common need in comprehensive data analytics.

8. Data Auditability:

Governance establishes data lineage and manipulation history, vital for understanding complex analytics processes.

9. Trust in Data:

Governance bolsters data quality, security, and compliance, thereby enhancing trust and the acceptance of analytics outcomes.

10. Analytics Culture:

Robust governance cultivates a data-driven culture through transparency, accountability, and literacy, leading to more effective use of data analytics.

Data governance might not be as fancy as using AI or doing some fancy analytics with python, but without it, these ‘rockstars’ will never develop their full value. So, here's to data governance - the understated, unsung hero of data, quietly working behind the scenes to make sure your data is ready for its big performance.

If you are interested in learning more about data governance or data management, feel free to reach out.

Best regards,

Yannick