Welcome to Data Governance & Quality! You've mastered advanced techniques and ethics. Now let's explore the foundation that makes all analytics trustworthy: ensuring your data is reliable, traceable, and well-managed.

Poor data quality can make even the most sophisticated analysis worthless.

Engagement Message

What is a data quality issue you faced and how did it impact your analysis?

Data governance isn't just about rules - it's about creating systems that ensure data remains accurate, consistent, and useful over time. Think of it as building infrastructure for reliable analytics.

Without governance, data degrades like an unmaintained road, becoming increasingly unreliable.

Engagement Message

What is a data source you trust completely and why?

Let's explore data quality dimensions. Accuracy means data reflects reality. Completeness means no critical gaps. Consistency means the same data looks the same everywhere. Timeliness means data is current enough for decisions.

Each dimension matters differently depending on your analytical needs.

Engagement Message

Which quality dimension - accuracy, completeness, consistency, or timeliness - most affects your current work?

Here's why data lineage matters: when you find suspicious numbers, you need to trace them back to their source. Was it a system error? A calculation mistake? A data entry problem?

Data lineage maps the journey from raw data to final insights, making debugging possible.

Engagement Message

Can you describe a time when you couldn't trace where problematic data came from?

Data governance scales with organizational maturity. Level 1: individuals check their own data. Level 2: teams establish shared standards. Level 3: organization-wide systems ensure quality automatically.

Most organizations operate at Level 1, creating inconsistent analytical foundations.