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
Overview
If you’ve spent any time working with data, you know the drill: conflicting reports, metrics that mysteriously shift, and a pervasive sense that nobody quite trusts the numbers. That’s precisely the messy problem ‘Data Quality and Analytics Governance: Trust Your Data [EN]’ aims to tackle head-on. This isn’t just another dry lecture series on data definitions; it’s a pragmatic deep dive into building an organizational culture where data isn’t just collected, but genuinely trusted and actionable.
Mike, with his impressive track record spanning 1.6 million students and experience at heavyweights like Wargaming and Alfa-Bank, cuts through the noise. He understands that robust data quality isn’t a luxury; it’s the bedrock of any sound enterprise data strategy. The course shifts the paradigm from reactive data firefighting to proactive analytics governance, offering a holistic approach that bridges the gap between technical implementation and business utility. Itβs about more than just cleaning data β it’s about establishing clear data ownership, designing resilient processes, and ultimately, fostering true data integrity across the entire data lifecycle management spectrum. Frankly, if your organization struggles with inconsistent reporting or decision paralysis due to unreliable data, this course offers a vital roadmap.
Prerequisites
Let’s be real: while the course is comprehensive, it’s not for someone who’s never seen a spreadsheet. You don’t need to be a Python wizard or an SQL guru, but a basic understanding of what data is, how it’s used in business, and the general challenges of reporting will serve you well. Think of it less as a technical coding bootcamp and more as a strategic framework course. If you’re a data analyst frustrated by bad inputs, a BI developer struggling with conflicting requirements, or a business stakeholder who constantly questions the numbers, you’re perfectly positioned. This course is beginner-friendly for the *concepts* of governance, but assumes you’re already familiar with the *need* for it. It’s an excellent step for anyone looking to upskill from purely technical roles to more strategic ones, understanding the broader context of data governance best practices.
Skills & Tools
This course equips you with a powerful arsenal of conceptual frameworks and practical methodologies, designed to provide immediate job-ready skills. You’ll master the art of defining metrics with surgical precision, ensuring consistent calculations across departments β a surprisingly rare and valuable skill. The course dives into distinguishing validity from reliability, crucial for truly understanding your datasets’ shortcomings. You’ll learn how to trace data lineage, connecting a report’s number back to its originating process and system, essential for root cause analysis in data incident management.
A significant takeaway is the application of BPMN (Business Process Model and Notation) to design robust data collection processes, moving beyond improvisation to consistent, repeatable execution. For those delving into analytics, Mike teaches how to run key driver analysis and basic regression, critically emphasizing the pitfalls of overclaiming correlation. Crucially, youβll learn to apply the three lines of defence model for assigning realistic data ownership, dispelling the myth that IT “owns” all data. Furthermore, applying ISO 31000 to data risk, including understanding inherent biases in your own estimates, provides an invaluable framework for managing organizational data risk effectively. These are not just theoretical ideas; they are industry-standard tools for strategic data management.
Career Benefits & Job Roles
The insights gained from this course are a significant booster for your career growth, particularly in an increasingly data-driven world. By understanding the intricacies of data quality management and analytics governance, you become an invaluable asset. This curriculum is perfect for aspiring and current Data Governance Specialists, Data Stewards, BI Managers, Analytics Managers, Data Architects, and even Project Managers overseeing data-heavy initiatives. It arms data analysts and scientists with the knowledge to advocate for better data inputs, improving the reliability of their models and reports. Business leaders will gain a deeper understanding of what it truly takes to build a trustworthy reporting layer, fostering data-informed decisions. This isn’t just about technical skills; it’s about developing a strategic mindset that elevates your role, making you a key player in shaping your organization’s data culture and driving true business value through reliable information.
Pros
- Holistic & Comprehensive Approach: The course masterfully blends the technical aspects of data quality with the organizational challenges of governance, covering processes, people, and technology. It provides a complete picture, unlike many courses that focus solely on one facet.
- Actionable & Practical Methodologies: Mike doesn’t just theorize; he provides concrete frameworks and step-by-step guidance. From using BPMN for process design to applying ISO 31000 for risk, the content is immediately applicable to real-world projects, making it excellent for developing job-ready skills.
- Expert Instruction with Real-World Examples: Mike’s extensive experience across diverse industries shines through. His ability to draw on practical scenarios, combined with his track record teaching millions, makes complex concepts accessible and relatable. You feel like you’re learning from someone who’s “been in the trenches.”
- Focus on Building Data Trust: The core objective of enabling people to *trust* their data is consistently reinforced. This focus on reliability and confidence in reporting is paramount for effective decision-making and is often overlooked in purely technical data courses.
Cons
- Limited Hands-on Tool-Specific Labs: While the course is rich in strategic frameworks and methodologies, it doesn’t dive deep into specific commercial data governance or data quality software (e.g., Collibra, Informatica, Alteryx). Learners seeking extensive hands-on labs with particular `industry-standard tools` for data cleaning or cataloging might find this less focused on direct software interaction, instead emphasizing the underlying principles and processes applicable across any toolset.