How to Address Challenges in People Analytics

Analysts often sacrifice a good data foundation for speed. Met with a people analyst who was still using Excel to run ALL of their #peopleanalytics analyses. Even though they have the skills to build in a BI tool, write SQL... they are actively choosing not to. Their CHRO has access to the Excel files... even HRBPS and recruiters. Everyone is working off these complex, manually manipulated, and perfectly curated Excel files that are acting as the company dashboards. Thought it was interesting and proved key theories: -- speed often wins over structure -- -- stakeholders care about results, not the tech stack -- At some point though, scalability and reliability start to matter. The question shifts from: "How fast can you get the answer?" to "How repeatable (read: scalable) and trustworthy is it?" The shift happens quickly... and is mostly correlated to the company growth. So this analyst is able to get away with it for now because their company's growth has flatlined. ....a high growth company could never get away with using Excel for this long. People analytics needs to invest in the shift from SPEED to FOUNDATION. Here's the playbook I've used in the past: 1/ Solidify the KEY pain points Show where speed-first analytics leads to inconsistent results, duplicated work, or lack of trust in the data. 2/ Quick wins This will always be in any playbook I write - you CANNOT slow down everything overnight. Start by automating the "low-hanging fruit" or the most painful manual processes. Example would be replacing an often-used Excel file with a basic dashboard. 3/ Build for adoption Too many times PA teams adopt an "if we build it they will come" attitude. This will fail most of the time. The best data stack is useless if it doesn't produce meaningful results. Practically speaking this could mean including certain key datasources or thinking about historical data from the start. 4/ Data governance Whenever PA teams mention scaling or moving to a modern data stack, privacy and security become immediate stakeholder concerns. Analysts NEED freedom, but without sacrificing data governance. Creating a centralized source of truth (data warehouse) with self-service tools and a concrete data governance plan will help everyone be happy. 5/ Rip off the damn bandaid You'll never improve if you're always stuck in the past. The common concern I hear always is "our current processes work, so why mess with them?"... but do they really? Three quick questions to ask as a sanity check: ✅ Can the team run any analysis the company needs? ✅ Can you share the data easily—with appropriate permissions? ✅ Is the data automatically up-to-date? If any of these are a NO, it's time to upgrade. ---------------- The goal of a PA team going through this exercise should be, keep the speed but make it repeatable, scalable, and trusted.

🚨 Spoiler: The "People Analytics maturity ladder" is broken. In an AI-first world, climbing one rung at a time is a luxury we can't afford--and your CFO won't fund. Here's how to jump the queue ⬇️ 1️⃣ Start from the future, not from wherever your dashboards sit today. Only 10% of companies systematically connect people data to business results...despite decades of BI spend. Why? Because they’re still polishing metrics instead of tackling P&L problems head-on. (The Josh Bersin Company) 2️⃣ Pick the business wound that bleeds cash. Example: attrition. Replacing one skilled employee can cost 0.5 – 4× their salary (and up to $40k per frontline hire). (WTW) Yet many orgs treat a rising turnover rate as a “bad vibes” indicator—never modeling the hit to revenue, productivity, or morale. 3️⃣ Frame a hypothesis your P&L cares about. “Attrition above 8% in Market A cuts same-store sales by Y% within two quarters.” Then map the data you’ll need: payroll, POS, scheduling, engagement, exit surveys, even weather if it matters. 4️⃣ Let modern ML surface what humans can’t see. New systemic-analytics platforms can ingest messy HR, finance, and ops feeds and spotlight the stores, shifts, or managers most likely to trigger a turnover spiral...well before Finance feels it during their quarterly scramble. 5️⃣ Close the loop with action. McKinsey & Company finds companies that truly focus on people performance beat peers by 30% revenue growth and suffer 5pp lower attrition. When your CHRO walks into the ELT with dollars saved instead of charts displayed, the “maturity ladder” debate disappears. 🔄 Your move: Which P&L-grade problem will you attack first: attrition, absenteeism, productivity, schedule gaps, skills shortages? Drop a comment and let’s crowdsource the questions that matter. #PeopleAnalytics #HRTech #AI #FutureOfWork

𝐀 $𝟓𝐌 𝐚𝐭𝐭𝐫𝐢𝐭𝐢𝐨𝐧 𝐩𝐫𝐨𝐛𝐥𝐞𝐦—𝐬𝐨𝐥𝐯𝐞𝐝 𝐢𝐧 𝐐𝟐. Not through a new retention program. Not through exit interviews. But through people analytics. One of our enterprise clients noticed an unexpected spike in high-performer exits—specifically in two product teams. Instead of guessing, their HRBP used early warning signals from internal mobility, engagement dips, and compensation mismatches. They uncovered a pattern: Mid-level engineers weren’t leaving for money—they were leaving for 𝐠𝐫𝐨𝐰𝐭𝐡. And this need an fix → A rapid redesign of career pathing and peer mentorship across engineering. Three months later: → Voluntary attrition dropped by 𝟑𝟔% → Internal mobility rose by 𝟐𝟐% → Estimated cost avoidance? $𝟓𝐌+ This isn’t a one-off. According to Deloitte, companies using advanced people analytics are 𝐭𝐰𝐢𝐜𝐞 𝐚𝐬 𝐥𝐢𝐤𝐞𝐥𝐲 𝐭𝐨 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐥𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 and 𝐭𝐡𝐫𝐞𝐞 𝐭𝐢𝐦𝐞𝐬 𝐦𝐨𝐫𝐞 𝐥𝐢𝐤𝐞𝐥𝐲 𝐭𝐨 𝐨𝐮𝐭𝐩𝐞𝐫𝐟𝐨𝐫𝐦 𝐩𝐞𝐞𝐫𝐬 𝐟𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥𝐥𝐲. But what really matters: 𝐘𝐨𝐮 𝐜𝐚𝐧’𝐭 𝐟𝐢𝐱 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐜𝐚𝐧’𝐭 𝐬𝐞𝐞. And too many leaders are still leading blind. Data isn’t just about efficiency. It’s about 𝐩𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐢𝐧 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐦𝐚𝐤𝐢𝐧𝐠—especially when your people are your biggest investment. #CHRO #HR #Dataanalytics #Datainsights #LeadershipPipelines