[100% Off] Operations Analytics: Kpis, Dashboards And Forecasts [En]

operations analytics | operational metrics | dashboards and reports | okr | people analytics | scorecards | forecasts

Added on September 10, 2026 Business 4 min read

Requirements

• Access to any operational data export, however messy
• A spreadsheet, and an AI assistant for the analysis lessons
• No statistics background required beyond averages and percentages
• English at intermediate level or above, since all lessons are in English
• Willingness to retire metrics that nobody acts on

Description

This course contains the use of artificial intelligence.

Most companies have metrics and make decisions without them. The dashboard exists, the report goes out on Monday, the figures update on schedule, and nothing changes.

Nobody agreed in advance what a given number would make you do

That is the whole failure, and it is almost never a tooling problem or a data quality problem. A metric that has no decision attached to it becomes a ritual: people look at it, discuss it briefly, and carry on exactly as before. Worse, it crowds out attention — a weekly review spent reading twenty numbers that change nothing is a weekly review not spent on the one that would. So the first skill in operational analytics is not building a chart. It is telling apart a metric that has an owner and a consequence from a metric that merely exists, and retiring the second kind.

What this course covers

Thirty-eight lessons. The operating frame first: why an operations function appears past fifty people, mapping processes without over-documenting them, input against output against outcome indicators, leading and lagging, metric owners and accountability, avoiding analytics for its own sake, where a week actually disappears into queues and context switching, operating rhythms that keep meetings from becoming formalities, and when a spreadsheet beats a platform. Then the metric catalogue: where metrics come from, types of analysis, the stages of reading one, funnel indicators and service levels, engagement measures tied to business performance, market data and competitiveness, budget calculations, development indicators, churn by segment, and finding real causes through regression. Then the path from a raw export to a board deck: cleaning and validating data, one dictionary of metrics so terms stop drifting, a descriptive portrait in fifteen minutes, pivots built through a model rather than by hand, diagnosis by slicing across manager, unit, level and tenure, pay gap detection, an explainable risk score, a quarterly forecast with no black box, and a presentation that speaks in money rather than in metrics. Then decisions instead of impressions: cognitive bias, the halo effect, the cost of a wrong decision, behavioural indicators, a five-point anchored scale, RACI, and calibration between assessors. Then connecting metrics to goals: the planning cycle, the format a goal must be written in, synchronisation between levels, running and closing a cycle, five company cases including Preply and iDeals, tooling, and the contentious link between goals and pay. Finally how a company built on data actually decides: algorithms in selection, structured interviews, committee consensus, errors of the first and second kind, autonomy against control, an internal marketplace for roles, twenty per cent time, and forecasting with machine learning.

Five of the six blocks use workforce data, and there is a reason

The examples are headcount, churn, cost per hire and pay gaps rather than throughput and defect rates. The machinery does not care: a metric owner, the input-output-outcome split, data cleaning, anomaly hunting across slices, an explainable risk score, an anchored scale and assessor calibration are data work rather than an industry method. There is also a practical advantage, and it is why the analysis block is built on this data specifically — workforce datasets are small and complete, so a single course can walk the entire path from a raw export to a board presentation without drowning in preparation. With most operational data that walk-through would take three times as long and teach less. The operations block is about the function itself and carries no industry.

Who is teaching this

I am Mike Pritula. I built the people system at Preply as it became a unicorn, and I have worked at Wargaming, iDeals and Alfa-Bank. More than 1.6 million students have enrolled in my courses across 185 countries, and over 150,000 specialists have gone through my programmes. I hold PHRi and SHRM-CP certifications and represent HRCI in more than ten countries.

What is included

• Lifetime access to all 38 lessons

• Active instructor support in the Q&A section

• A Udemy Certificate of Completion

• Working material: the input-output-outcome metric set, the metric dictionary, the data cleaning sequence, the question-sub-query-slice pattern, the explainable risk score, the anchored five-point scale, the RACI for decisions, and the goal-setting format

• The whole path from a raw export to a board deck, shown end to end

Where to start

Open your own dashboard and pick one number. Write down what you would do differently if it moved ten per cent in either direction. If the answer is blank for most of them, that is the finding. Enrol now and start today.

Author(s): PapaHR ★ 170K students: Courses in Human Resources, HR, SHRM, AI Talent Analytics, HRMS, HRIS, CIPD, Claude, HRCI, PHR, Rewards