People Analytics

From HR Promise to Measurable Enterprise Value ... “People are our greatest asset” – we’ve all heard it. But unless HR proves it with data that CFOs and investors rely on, it remains a slogan. Too often, HR impact remains abstract, encompassing engagement surveys, leadership programs, and culture scores. What’s missing? A translation layer that turns HR strategy into process economics and numbers that sit confidently next to the financials. Four shifts unlock measurable #value: 1️⃣ From activity to outcome – Recruiting, onboarding, learning, performance, exits: track them as end-to-end processes with metrics like time-to-productivity, cost-per-outcome, compliance exposure. 2️⃣ From narrative to portfolio – Attrition, capability gaps, and leadership depth must be modeled as a people risk portfolio, quantified and reported like financial risks. 3️⃣ From silo to system – Link HR data with finance and operations so leaders see direct cause-and-effect on revenue, margin, and resilience. 4️⃣ From soft KPIs to investor metrics – Private equity and CFOs expect transparency. People ROI dashboards that connect process efficiency to EBIT, market entry speed, and enterprise risk change the game. The #impact: -Faster pipelines = faster market entry -Lower attrition = EBIT protection -Aligned culture = fewer failed transformations -Transparent compliance = reduced hidden liabilities Why it #matters: -70% of transformations fail because the people engine breaks down. Companies with strong people analytics report up to 30% higher revenue per employee. -Treating people risk like financial risk directly strengthens enterprise resilience. The #bottom line: HR doesn’t create value by speaking business language alone. It creates value by designing scalable, auditable, ROI-linked processes that investors can trust. Imagine a board meeting where HR presents a People ROI dashboard alongside the financials! That’s when HR stops being a “partner” – and becomes a market-making function.

People keep asking me what People Analytics is. And what a People Analytics Partner actually does. Totally fair. The title sounds like I either design dashboards or officiate HR weddings. So here’s an answer: People Analytics is the practice of using data to make better decisions about people at work. Things like: who to hire, why people leave, what actually makes a team effective, how employees feel about the company. Basically: figuring out what matters, what’s noise, and how not to embarrass yourself in front of an exec with a weird pie chart. The role exists because a lot of people decisions still come down to gut feel. We help ground those decisions in evidence. We work a lot with data from HR systems (most common punching bag: Workday), plus tools like applicant tracking systems, learning platforms, and engagement survey tools. (I have a lot of experience and a *lot* of gripes about that last one.) As a People Analytics Partner, I sit between the business and the nerds. I don’t mean that in a bad way. I am one of the nerds. But I speak fluent stakeholder. So I spend my days translating things like: Stakeholder: This team feels off. Me: Let’s test that. Stakeholder: We need a dashboard. Me: You need a therapist. (Kidding. Kind of.) I partner with researchers, engineers, and data scientists to figure out which questions are worth asking, and how to get real answers that actually drive change. And sometimes I also build the dashboard. Because, well, life comes at you fast. People sometimes dismiss this work as just reporting numbers. But when we’re doing it right, we’re asking better questions and helping leaders act on what the data’s really saying. That’s my little slice of People Analytics. It’s a broad field. And yes, I also get asked to fix HR systems I’ve never seen before. It’s part of the charm. -- I'm 🏴☠️Bill and yes it's a real job stop asking me

We have spent years measuring activity and outputs. But now we have such an amazing opportunity to do the real work of measuring outcomes/impact... the crown jewel of project management. That’s exactly why we put together this Hacking HR Guide to People Analytics: Definitions, Leading and Lagging Indicators... It is a practical framework to help HR leaders move from reporting numbers to understanding what actually drives performance, culture, and business outcomes. A few key ideas behind the guide: 1️⃣ Not all metrics are equal Lagging indicators (like turnover or cost per hire) tell you what already happened. Leading indicators (like engagement signals, training participation, or early turnover) tell you what is about to happen. Both matter — but only one helps you act before problems explode. 2️⃣ HR metrics are business metrics Turnover, engagement, quality of hire, and revenue per employee aren’t “HR topics.” They influence productivity, innovation, customer satisfaction, and long-term profitability. People analytics is not about HR dashboards. It’s about business performance. 3️⃣ Context matters more than the number itself Every metric in the guide includes common pitfalls. For example: • High retention isn’t always good if it signals stagnation. • High overtime can signal burnout, not dedication. • High salaries alone won’t retain talent without growth and culture. Numbers without interpretation create bad decisions. 4️⃣ Metrics must connect into a system Hiring → onboarding → performance → development → retention → productivity. The power of people analytics comes from connecting these signals, not looking at them in isolation. 5️⃣ The future of HR is evidence-based In the age of AI and increasing organizational complexity, HR leaders will be expected to explain decisions with data, not intuition alone. People analytics is becoming the language of strategic HR. This guide walks through dozens of key indicators, from turnover and engagement to skills gaps, workforce capacity, and human capital ROI, and how they connect to real business outcomes. If you work in HR, leadership, or workforce strategy, one question is worth asking: Are you measuring HR activity… or are you measuring human impact on the business?

Forget about all the vague talks about "AI in HR" - after trying to integrate AI in people analytics as much as I can for the last year, here are the 3 highest ROI areas: 🚀 Skills Intelligence & Career Pathing: LLMs can now parse through internal job architectures, project documentation, and external market data to create dynamic skill graphs. We've mapped 150+ emerging tech skills and their relationships across 1000+ roles, enabling us to spot capability gaps months before they impact delivery. Most importantly: it updates automatically as new skills emerge in our industry. 🚀 Attrition Pattern Detection: Modern AI analyzes multi-modal signals - from collaboration patterns to communication sentiment - to provide contextual understanding of retention risks. The key isn't just predicting who might leave, but understanding why. We're now catching specific team dynamics and workload imbalances that traditional metrics missed entirely. 🚀 Natural Language Feedback: Analysis Beyond basic sentiment scoring, AI now identifies specific, actionable management behaviors from unstructured feedback. The breakthrough? Connecting these insights directly to team performance metrics, showing us exactly which leadership practices drive results in different contexts. 💡 Key learning: AI's real value is beyond higher efficiency, for it's revealing patterns and connections in our people data that used to be hard to get to. The new possibilities and use cases are genuinely exciting. #peopleanalytics #ai

The first time I presented a data-driven HR strategy to the board… They didn’t ask about culture. They didn’t ask about performance reviews. They asked: “How does this move the business?” That moment shifted my mindset forever. As HR leaders, we often talk about engagement, inclusion, and retention. But unless we connect people to performance, it’s all just noise. That’s where HR metrics come in. Not dashboards for vanity. Not numbers for compliance. But people data that drives real business decisions. Here are the 10 essential HR metrics every strategic HR leader must watch: ✅ Headcount – Are we staffed to meet strategic goals? ✅ Turnover – Are we leaking talent, and what’s it costing us? ✅ Diversity – Are we building inclusive teams that attract top talent? ✅ Total Cost of Workforce – Are we balancing efficiency with value? ✅ Compensation – Are we aligned with market realities and internal equity? ✅ Spans & Layers – Are we structured for agility or buried in hierarchy? ✅ Engagement – Are our people emotionally invested in our mission? ✅ Talent Acquisition – Are we hiring right—or just hiring fast? ✅ Learning – Are we preparing for the skills of tomorrow? ✅ Workforce Planning – Are we ready for what’s next? I’ve used these metrics to launch cultural transformations, align HR with corporate governance, and deliver real ROI—not just HR wins, but business wins. Because here’s what I’ve learned: 👉 You can’t improve what you don’t measure. 👉 You can’t lead without insight. 👉 And you can’t expect impact without alignment. If HR wants a seat at the strategy table, we need to speak the language of metrics. Because in today’s world, the most human organizations… are the ones who understand their people through data. #PeopleAnalytics #HRStrategy #DataDrivenHR #HRMetrics #FutureOfWork #BusinessImpact

🎬 Episode 10 - 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗣𝗿𝗼𝗺𝗽𝘁: 𝗠𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 "𝗛𝘂𝗺𝗮𝗻 𝗥𝗢𝗜" We spent the last two episodes diagnosing the trust crisis AI is creating. Now, it's time to fix the dashboards. 🛠️ Last week in Episode 9, we uncovered the dark side of forcing AI adoption: a massive spike in "Cultural Debt," where 80% of workers fear their peers are simply faking productivity. The problem isn't just the technology; it's our analytics. If your HR dashboards are only tracking "tools logged into" or "prompts per week," you are measuring machine efficiency, not human impact. Activity does not equal value. In today’s episode, we are looking at the antidote. To rebuild trust, People Analytics teams 📊 need to pivot from tracking usage to measuring the "Human ROI" of AI: 1️⃣ 𝗕𝘂𝗿𝗻𝗼𝘂𝘁 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: Is AI actually reducing after-hours work and burnout, or is it just cramming 12 hours of output into an 8-hour pressure cooker? Measure well-being, not just output. 2️⃣ 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: Are people still talking to each other? Use Organizational Network Analysis (ONA) to ensure teams aren't retreating into isolated 'AI silos' and breaking human-to-human collaboration.  3️⃣ 𝗨𝗽𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: Are we measuring how our employees' critical thinking is improving, or just their tool proficiency? We need to track the growth of uniquely human skills." AI was supposed to take the robot out of the human. But if we don't update our People Analytics to measure trust, psychological safety, and connection, we are just turning our humans into faster robots. The true ROI of AI isn't found in a server room, it's found in a thriving, trusting human workforce. 👇 HR and Analytics leaders: Are your current dashboards measuring human well-being, or just AI usage?  Dave Ulrich #PeopleAnalytics #FutureOfWork

HR loves data. But can we make sense of it? Most of the time, no. People analytics is so pivotal for HR. But collecting data isn’t the same as using it. A dashboard full of numbers won’t fix retention, engagement, or culture. Unless HR knows what to do with it. Before diving in, ask yourself: - Does leadership actually care? Approving a budget isn’t enough. They need to act on the data. - Are we tracking what matters? If it doesn’t tie back to business goals, it’s just noise. - Do we understand the ‘why’ behind the numbers? Turnover is up? Why? Engagement is low? Why? Data shows symptoms, not solutions. - Do employees trust us with their data? Without transparency, expect resistance. People need to know how their data is used, and why it helps them. - Are we actually acting on insights? A report means nothing if nothing changes. And one more thing, do we have one source of truth? When data is scattered across ten different systems, no one knows what’s real. A single source of truth is the only way to make sense of it. Because data is only useful if you use it. HR’s job is really to turn data into action. That means: ↳ Asking the right questions before drowning in the wrong data. ↳ Training managers to use insights, not just read reports. ↳ Breaking down silos so HR, finance, and leadership see the full picture. ↳ Communicating data in a way that actually drives change. People analytics can transform HR. But only if you do something with it. PS: How is your team using data to make better decisions?

I’ve been wanting a space to write honestly about the real work behind analytics...so I built one. It’s called The People Data Lab. It’s where I’m documenting what it actually takes to build People Analytics inside a real organization: the technical work, the constraints, the decisions, and the gaps between what analytics should look like and what’s realistically possible. My first article discusses a cycle I’ve witnessed play out: Team builds sophisticated analytics → Stakeholders don't use it → Team assumes it's a training problem → More training happens → Nothing changes My first article unpacks this pattern. We often diagnose our challenges as skills gaps when the real issue is strategic. Dashboards, training, and documentation only go so far when the organization isn't structured to actually use analytics in decision-making. If you work in People Analytics, HR, or data strategy, this will probably resonate. Link to my first piece below. More to come as I keep building and learning!

Most People Analytics leaders are already doing a second job. But no one is talking about it. They aren’t just analysts. They are architects of the entire workforce data stack. One Model maps this evolution clearly. People Analytics leaders don’t stay in one lane. They evolve through engineering, tech, ops, and strategy because no one else owns the whole picture. They start by fixing data quality. Then they rebuild the plumbing. Then they align systems. Then they connect insights to strategy. The title stays the same. But the job changes completely. These leaders stitch together broken data from siloed platforms. They translate insights into action across HR, IT, and Finance. They don’t get headlines. But they carry the technical weight of bold HR ambition. Without them, strategy doesn’t scale. No transformation lasts. They build trust in data. They surface real-time insights. They make systems talk to each other. And they do all of it quietly. It’s time to recognize them. AI adoption. Skills intelligence. Performance analytics. None of these work without People Analytics at the center. If you want transformation to stick, invest in the people behind the dashboards. People Analytics is no longer just a function. It’s the foundation. Are you giving these leaders the credit they deserve?

Annual surveys are dead and ABN AMRO realized it the hard way —by watching engagement data arrive months too late, after the damage was already done. ABN AMRO replaced their once-a-year surveys with a 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐥𝐢𝐬𝐭𝐞𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥.  Every month, they ask a representative group of employees one core question: Would you recommend this place to work? Plus—open-ended feedback on what’s working and what’s not. Over 𝟏,𝟎𝟎𝟎 𝐜𝐨𝐦𝐦𝐞𝐧𝐭𝐬 𝐩𝐞𝐫 𝐦𝐨𝐧𝐭𝐡 are analyzed using NLP models like TF-IDF, Word2Vec, and SVM. That means 150+ themes clustered and tracked—𝐢𝐧 𝐫𝐞𝐚𝐥 𝐭𝐢𝐦𝐞. And the impact: 1. Spot issues before they spiral 2. Build trust through transparency 3. Align HR insights with quarterly leadership decisions They didn’t just collect data. They turned feedback into fuel—for culture, strategy, and trust. 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐰𝐡𝐚𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐬𝐡𝐨𝐮𝐥𝐝 𝐥𝐨𝐨𝐤 𝐥𝐢𝐤𝐞. Fast, actionable, employee-led. Not a dashboard no one opens, 10 months too late. When employees feel heard and see change—HR becomes a driver of transformation, not just measurement. #PeopleAnalytics #EmployeeEngagement #HRTech #Leadership #ContinuousListening #FutureOfWork