data quality | analytics governance | data ownership | metric definitions | data trust | reporting | dashboards | BPMN
What You Will Learn:
- Define a metric precisely enough that two departments calculating it get the same number
- Tell validity from reliability, and know which one your dataset is failing
- Trace a number back to the process that produced it and the system that stored it
- Design the process description that makes data collection consistent instead of improvised
- Run key driver analysis and a regression without overclaiming what the correlation shows
- Assign data ownership using the three lines of defence model rather than assuming IT owns it
- Apply ISO 31000 to data risk, including the biases inside your own estimates
- Build a reporting layer people trust enough to make decisions from
- Learn alongside Mike’s 1.6 million students from 185 countries
- Get the author’s experience from Preply, Wargaming, iDeals and Alfa-Bank
The Reality of “Garbage In, Garbage Out”: An Honest Take
If you’ve spent more than five minutes in a high-stakes meeting, you’ve probably witnessed the “battle of the spreadsheets.” Marketing has one number for CAC, Finance has another, and the Data Team is sitting in the corner trying to explain that both are technically correct but semantically different. It’s a nightmare. Mike’s course, Data Quality and Analytics Governance: Trust Your Data, is essentially a survival guide for anyone tired of defending reporting that nobody trusts.
Most career growth paths in data focus heavily on the “sexy” stuff—building complex neural networks or mastering the latest cloud warehouse. But we rarely talk about the plumbing. What I appreciated most about Mike’s approach is that he doesn’t treat data quality as a side-quest. Drawing from his real-world projects at heavy hitters like Wargaming and iDeals, he frames data governance as a core business function. This isn’t a course about writing better SQL; it’s about building a culture where the data actually means something. It’s about moving from being a “ticket-taker” to a strategic partner who ensures data trust across the entire organization.
Prerequisites
You don’t need a PhD in Statistics to get value here, but you should have some skin in the game. If you’ve never felt the pain of a broken dashboard or a stakeholder questioning your numbers, the lessons might feel a bit abstract. I’d recommend:
- A basic understanding of the data lifecycle (how data moves from a system to a report).
- Familiarity with business metrics (Revenue, Churn, Active Users).
- Zero fear of “process.” This course leans heavily into the BPMN and organizational side of tech.
- Intermediate Excel or SQL knowledge is helpful but not strictly required for the governance frameworks.
Skills & Tools
Mike does a great job of bridging the gap between theoretical frameworks and job-ready skills. You aren’t just reading slides; you’re learning how to architect a reporting layer that scales. Key takeaways include:
- Governance Frameworks: Implementing the three lines of defence model to stop blaming IT for data errors.
- Risk Management: Applying ISO 31000 specifically to data assets and identifying the biases that creep into your analytics governance.
- Process Modeling: Using BPMN to map out how data is actually generated, ensuring data collection is consistent rather than improvised.
- Statistical Sanity: Running key driver analysis and regression without falling into the “correlation equals causation” trap.
- Documentation: Creating metric definitions that are precise enough to survive a departmental audit.
Career Benefits & Job Roles
This course is certification prep for the real world. If you’re looking to transition from a junior analyst to a Data Governance Manager or a Data Quality Engineer, this is your roadmap. It provides the vocabulary needed to talk to C-suite executives about data ownership and risk—conversations that usually lead to promotions.
Common roles that benefit:
- Analytics Managers: To build teams that produce reliable insights.
- Data Architects: To ensure the structures they build aren’t filled with junk data.
- Business Intelligence Developers: To move beyond just building “pretty” dashboards and start building “accurate” ones.
- Product Managers: To understand why their “A/B test” results might be fundamentally flawed due to poor data capture.
Pros
- Battle-Tested Experience: Mike’s background at Alfa-Bank and Preply shines through. You get the sense he’s been in the trenches and has seen these systems fail in every way possible.
- Process Over Syntax: It focuses on the “why” and “how” of data ownership, which is often ignored in hands-on labs that only focus on coding.
- Global Perspective: Learning alongside 1.6 million students creates a sense of community, and the industry-standard tools discussed are applicable globally.
- Framework-Driven: Using ISO 31000 provides a professional rigor that elevates your work from “best guess” to “standardized practice.”
Cons
- Not a “Coding” Course: If you are looking for 10 hours of Python or R exercises, you’ll be disappointed. This is about the governance and structural side of data, not the tactical execution of code. It requires a mindset shift that some purely technical folks might find “slow” at first.