The Fastest-Growing HR Function
LinkedIn's 2023 Workforce Report identifies people analytics as the fastest-growing HR specialty, with job postings up 147% since 2020. The function has graduated from "nice to have" to "strategic necessity" — driven by the same forces making workforce analytics mainstream.
But most organizations are starting from zero. They know they need people analytics. They are less sure about what to build first, who to hire, and how to prove value quickly. Here is the playbook.
First Hires and Team Structure
Do not start by hiring a data scientist. Start by hiring a translator — someone who understands both data and human resources deeply enough to ask the right questions and communicate findings in business terms.
The ideal first hire is a people analytics lead with three skills:
- Data fluency: Comfortable with SQL, visualization tools, and basic statistical analysis
- HR domain knowledge: Understands the business context of workforce decisions
- Communication: Can translate analytical findings into executive-ready insights
As the function grows, add specialists: a data engineer for pipeline management, an analyst for deep-dive research, and eventually a data scientist for predictive modeling. But the translator comes first.
Organizations that hire a data scientist first often produce brilliant analysis that nobody uses — because nobody translated it into actionable HR language. Hire for translation, then for technical depth.
Quick Wins That Build Credibility
A new people analytics function needs early wins to justify its existence. Focus on questions the organization is already asking:
Win 1: Turnover analysis. Why are people leaving? Connect exit data with engagement scores, tenure, manager effectiveness, and work pattern data. Most organizations have this data — they just have not connected it.
Win 2: Meeting culture audit. Analyze meeting frequency, duration, and participation patterns. Every organization suspects they have too many meetings. Give them the data to prove it and optimize.
Win 3: Hiring funnel analysis. Where are candidates dropping out? How do time-to-fill and quality-of-hire vary by source, role, and team? Recruiting teams are usually data-rich and insight-poor.
Win 4: Workload visibility. Use workforce analytics tools like Teambridg to show workload distribution across teams. Surfacing overloaded teams and individuals generates immediate action and demonstrates the function's value.
Tools and Positioning
Your initial tool stack should be simple:
- Workforce analytics platform (like Teambridg) for work pattern data
- HRIS system for employee demographics, tenure, and organizational structure
- Visualization tool (Tableau, Power BI, or even advanced spreadsheets) for custom analysis
- Survey platform for qualitative data to complement quantitative patterns
Positioning matters as much as tooling. The people analytics function should report to the CHRO and have a dotted line to the CFO. This dual reporting ensures both talent and financial perspectives inform the work.
Present findings in business language, not analytics jargon. "Our Q2 data shows a 0.73 correlation between focus time and sprint velocity" means nothing to executives. "Teams with more uninterrupted work time deliver projects 25% faster" changes decisions.
People analytics is not a support function — it is a strategic function. Position it that way from day one, and the investment will repay itself many times over.
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