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Data Quality and Analytics Governance: Trust Your Data
1,132 students
Data Quality and Analytics Governance: Trust Your Data
data quality | analytics governance | data ownership | metric definitions | data trust | reporting | dashboards | BPMN
Last updated 9/2026
English
English [Auto],
What you'll 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
Explore related topics
Course content
16 sections • 41 lectures • 20h 43m total length
Requirements
- Data somebody makes decisions from, and doubts about it
- No statistics background needed. Correlation and regression are built up from zero
- Comfort with a spreadsheet, since the analysis is run in one
- English at intermediate level or above, since all lessons are in English
- Access to at least one real report or dashboard you can take apart
Description
This course contains the use of artificial intelligence.
The moment that tells you an analytics function is not governed is when two departments present the same metric with different numbers and both are technically correct.
Nobody was careless. They used different definitions, and nobody owned the definition.
Where trust in data actually breaks
The metric was never defined in writing, so each team calculates it in the way that makes sense from where they sit. The process behind it was never described, so the data is entered differently by different people on different days. Two systems both hold a version of the truth and neither is authoritative. The dashboard is beautiful and nobody can trace a number back to where it came from. And when a figure looks wrong, the person who notices does not raise it, because raising it means becoming responsible for it.
That last one is a culture problem, and it is the reason most data quality initiatives stall after the tooling is bought.
What the course covers
Thirty-nine lessons following the chain from definition to trust. Definitions first, six lessons on metrics: what each family measures, where the numbers come from, how a single indicator like turnover produces different answers for different teams, and what has to be written down before anyone calculates anything.
Then analysis, ten lessons of it — segmentation, lifetime value, journey and funnel analytics, attrition analysis and its standard mistakes, key driver analysis, correlations in Excel, multiple regression with R-square and variance, forecasting and controlled experiments. This is where you learn what a dataset can and cannot honestly support.
Systems, processes and the ownership question
Then the systems your data actually lives in: core records, recruitment platforms, goal and review tools, learning systems, collaboration platforms, chatbots and AI. Seven lessons on what each one produces, how they integrate, and where the same fact ends up stored twice.
Then the processes that create the data in the first place. Describing them in BPMN, running a description project properly, and the point most people miss — inconsistent data is almost always an undescribed process rather than a system fault.
And finally governance itself, ten lessons on risk: ISO 31000, the risk hierarchy, escalation, the three lines of defence model that answers who actually owns a number, the cognitive biases sitting inside your own estimates, and the culture question of whether a bad figure can travel upward without punishing the person carrying it.
Who is teaching this
Mike, the number one HR instructor on Udemy. More than 1.6 million course enrolments, over 150,000 professionals trained, PHRi and SHRM-CP certified, HRCI representative in more than 10 countries. I built the people function of the unicorn Preply and worked at Wargaming, Alfa-Bank and iDeals. The analytics examples run on workforce data, which is the messiest data in most companies and the best possible practice ground.
What is included
Lifetime access to all course materials
Active instructor support in the Q&A section
Udemy Certificate of Completion
Practical assignments and real business cases
A section with additional courses, tools and resources
The first thing to check
Take one number your company reports regularly and ask two people from different teams how it is calculated. If the answers differ in any detail, you have found where the governance is missing, and it is not in the tooling. Enrol now and start the first lesson today.
Who this course is for:
- Analysts whose numbers get questioned every time they are presented
- Data and BI specialists building definitions and ownership for the first time
- Operations and HR leaders who own reporting and inherited the metrics from someone else
- Managers who receive dashboards and quietly do not believe them
- Consultants auditing an analytics function
- Anyone whose organisation has two systems producing two different answers to the same question
- Professionals moving into a data governance or analytics ownership role