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.