Build an HR Analytics and Workforce Plan

Proof required

Submit: (a) a workforce data audit summarising current headcount by function and level, last 12-month attrition rates by function, average time-to-fill by role type, and any identified skill gaps, (b) a planning scope document defining the horizon and the business objectives driving workforce demand over that period, and (c) confirmation that the data is from a real organisation (name of organisation and your relationship to it — current employee, consultant, or HR data access with permission).

What gets checked

• Workforce data is from a real organisation — not hypothetical
• Attrition rates are broken out by voluntary and involuntary
• Skill gaps are specific — 'shortage of data engineering skills in the analytics function' not 'skill gaps exist'

Common mistakes

• Using assumed workforce data rather than real organisational data
• Planning horizon not linked to specific business objectives — workforce planning exists to serve business needs, not as an independent exercise

Resources

What a verifier looks for

• Verify that the data is from a real organisation — the submitter should be able to name the organisation and explain their access to the data.
• Challenge the skill gap identification: 'You identified a shortage of data engineering skills — but how was this identified? Through manager assessment, recruitment difficulty, or a formal skills inventory?'

Proof required

Submit a workforce plan (minimum 1,500 words) covering: (a) demand model showing projected headcount requirements by function and level for the planning horizon with assumptions, (b) supply model showing projected available headcount after attrition and internal mobility, (c) supply-demand gap analysis by function and role type, and (d) a sourcing strategy recommendation for the top three gaps using the hire/build/buy/borrow framework.

What gets checked

• Demand model is linked to specific business drivers — revenue growth, product launches, geographic expansion — not arbitrary headcount additions
• Supply model uses actual attrition rates from the data audit, not industry averages
• Sourcing strategy is specific — 'hire externally for data engineers at P4–P5 level on a 6-month timeline, build internally for junior analysts through a structured rotation programme'

Common mistakes

• Demand model adds headcount as a fixed percentage of revenue without linking to specific business drivers
• Sourcing strategy recommends 'hiring' for all gaps without considering build or borrow alternatives

What a verifier looks for

• Challenge the sourcing strategy: 'You recommended external hire for data engineers — but your own attrition data shows you lose data engineers faster than you hire them. What changes to retention would you make before scaling hiring?'

Proof required

Submit: (a) Q&A log from the review session (minimum 250 words with at least two challenges and responses), and (b) attendance record with reviewer name and HR credentials.

What gets checked

• Reviewer has HR or people analytics expertise
• Q&A log shows challenges to modelling assumptions or strategic recommendations
• Responses address challenges with analytical reasoning

Common mistakes

• Review with someone without HR or people analytics expertise
• Challenges are met with 'the data shows X' without acknowledging the limitations of the data

What a verifier looks for

• Challenge the demand model: 'Your demand model assumes the same productivity ratio as the current year — but your business is growing into a new market. How might the headcount-per-revenue unit change as you scale into that market?'
• Provide written confirmation (minimum 150 words) of your HR credentials and the challenges raised.