Data Analytics for HR
People analytics: turnover, hiring, engagement and pay fairness — with privacy protected.
What this track covers
For HR practitioners who want to answer questions about people with data rather than gut feel. Calculating turnover properly, measuring the hiring funnel, reading engagement surveys, checking pay and promotion fairness, and using AI in people decisions responsibly. Privacy and bias are part of the method, not an appendix. No coding.
New to AI? Every track builds on AI & Data Fundamentals.
What you will be able to do
- Calculate turnover, time to hire and promotion rates correctly
- Read engagement survey results without over-reading them
- Check pay fairness with raw and adjusted gaps
- Handle employee data in line with data-protection law and anonymity rules
Units and bites
- Unit 15 bitesAbout 15 min
People analytics basics
Using data for decisions about people — starting from the right question.
- 3 min
What people analytics is for
Using data about work and workers to make better, fairer people decisions.
- 3 min
HR data you already have
HRIS, payroll, recruiting and surveys: what's in them and how far to trust it.
- 3 min
Headcount and FTE
Counting employees properly, including part-time and contract staff.
- 3 min
Rates per period
Why "12 people left" means nothing without a denominator and a period.
- 3 min
Asking a good people question
From a manager's complaint to a question data can answer.
- Unit 26 bitesAbout 18 min
Turnover and retention
Who leaves, when and why — without guesswork.
- 3 min
Attrition rate
The standard turnover formula and the usual mistakes in calculating it.
- 3 min
Voluntary vs involuntary
Resignations and dismissals tell two very different stories.
- 3 min
Regretted attrition
Not every leaver is equal: focus on the people you wanted to keep.
- 3 min
Tenure curves
In which month people most often leave, and what that means.
- 3 min
Exit data
Reading leaving reasons honestly, including the unspoken ones.
- 3 min
Predicting attrition, carefully
What a "flight risk" model can and cannot do, and its ethical risks.
- Unit 35 bitesAbout 15 min
The hiring funnel
From applicant to employee: measuring every step of hiring.
- 3 min
Stages and pass-through rates
What share of applicants reach each stage, and where it gets stuck.
- 3 min
Time to hire
Measuring hiring speed, and why the best candidates don't wait long.
- 3 min
Source of hire
Which channels produce hires who stay, not just the most applicants.
- 3 min
Quality of hire
Defining a "good hire" in a way you can measure.
- 3 min
Offer acceptance
Why candidates say no, and what the numbers can teach you.
- Unit 45 bitesAbout 15 min
Engagement surveys
Designing, reading and acting on employee surveys properly.
- 3 min
Designing questions
Questions that are clear, neutral and ask one thing at a time.
- 3 min
Response rates
Who did not answer can change the whole result.
- 3 min
Reading scores and eNPS
Averages, distributions and how eNPS is calculated.
- 3 min
Comparing teams fairly
Small teams, small gaps, and when a score difference means nothing.
- 3 min
Acting on results
A survey with no follow-up makes the next one worse.
- Unit 55 bitesAbout 15 min
Pay and fairness
Reading pay and promotion data without misleading — and without exposing individuals.
- 3 min
Pay bands and compa-ratio
Where someone's pay sits within the range for their role.
- 3 min
Raw vs adjusted pay gap
Two pay-gap numbers, two different questions, and why both matter.
- 3 min
Promotion rates
Comparing promotion chances across groups fairly.
- 3 min
The small-group problem
When a group is too small to conclude from — or to report safely.
- 3 min
Reporting fairly
Presenting sensitive findings to leadership clearly and responsibly.
- Unit 64 bitesAbout 12 min
Ethics and privacy
Employee data, anonymity, and using AI in people decisions responsibly.
- 3 min
Employee data privacy
What Indonesia’s data-protection law means for data HR collects and analyses.
- 3 min
Anonymity thresholds
Minimum group sizes so that nobody can be identified.
- 3 min
Bias in HR algorithms
How CV-screening models can copy past unfairness.
- 3 min
AI in hiring, responsibly
Where AI may help with hiring, and where the decision must stay human.
Questions about this track
Learning
How long does Data Analytics for HR take?
About 90 minutes in all: 6 units made of 30 bites of about three minutes each, and every bite ends with a quick check. You can do one bite a day or several in a row.
Do I need to know how to code?
No. Every Datamew track is written for working professionals without a technical background. Where there is arithmetic, it is the kind you can do on a phone or in a spreadsheet.
Where should I start?
Everyone begins with AI & Data Fundamentals, which covers how AI works, how to write a good prompt and how to read data honestly. Then take Data Analytics for HR for your role.
When can I start learning?
Datamew is coming to iOS and Android soon. There is no release date or price announced yet: join the waitlist and we will tell you when the app is out.
Is it available in Bahasa Indonesia?
Yes. Datamew is written in English and Bahasa Indonesia.
More tracks
- AI & Data FundamentalsHow AI works, how to instruct it, and how to read numbers honestly.
- AI for MarketingResearch, content, personalisation and campaign analysis with AI — with your judgement still in charge.
- Data Analytics for MarketingMetrics, funnels, A/B tests, attribution and cohorts — the numbers behind marketing decisions.
- Forecasting for Retail & E-commerceForecast demand, plan stock, and be ready for Ramadan, 11.11 and 12.12.
- Forecasting for MarketingForecast traffic, leads and campaign results, and defend the numbers in the budget meeting.
- Forecasting for FinanceBudgets, revenue and cash forecasts, rolling forecasts and scenarios, with spreadsheet arithmetic.
- Product Recommendation for E-commerceHow "you might also like" works, the data behind it, bundles, and how to measure the impact.
- Product Recommendation for Retail & MediaLoyalty programmes, personal vouchers and "watch next": recommendations beyond the online shop.
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