When organizations migrate from one HR or analytics platform to another, the focus is usually on features, cost, and rollout timelines. But there’s a hidden risk in the transition: the numbers themselves start to drift.
The same metric — headcount, turnover, time-to-hire — can produce different values in the old system and the new one. Leadership loses confidence, debates begin, and before long, every dashboard is suspect.
This is the story of how I used a DMAIC framework (Define, Measure, Analyze, Improve, Control) to keep turnover data trustworthy during a platform transition.
Define: The Problem Behind “Another Position”
In both the old and new systems, the leading termination reason was “Another Position.”
It accounted for nearly 19% of all exits — the single largest driver of turnover. Combined with three other categories (“Unhappy,” “More Money,” “Hours”), almost 60% of exits fell into just four buckets.
On paper, the story seemed simple: employees were moving on. But “Another Position” was so broad it risked hiding the real push factors. Were people leaving because they found better pay? Or were they exiting to avoid poor management or mismatched roles?
Measure: Locking the Metric Contract
To prevent drift between systems, I established metric contracts so the math was consistent:
- Headcount EOP: Active employees as of date d.
- Terminated (Period): Exits with termination date inside the window.
- Avg Headcount (Period): Mean of monthly EOP values.
- Turnover Rate: Terminated ÷ Avg Headcount.
- “Another Position” Rate: Exits with that code ÷ All exits.
I also normalized:
- Dates → ISO format.
- Pay rates → numeric fields.
- Gender & categories → harmonized values.
With this, both systems aligned on the same math. Confidence was restored.
Analyze: Beyond the Surface
A Pareto chart confirmed the 80/20 principle: four reasons explained most of the exits. But digging deeper revealed the real story.
High-level takeaway:
- Many “Another Position” exits were not truly voluntary.
- Early attrition and performance management issues were hiding inside a neutral label.
- Some managers had concentrated spikes in turnover.
Technical appendix (what the slice showed):
Breaking “Another Position” by performance score and tenure revealed:
- Fully Meets (75%) — average tenure ~27 months. Productive employees leaving.
- Exceeds (12%) — average tenure ~30 months. High performers lost.
- Needs Improvement (9%) — average tenure ~8.6 months. New hires leaving quickly.
- PIPs (4%) — average tenure ~51 months. Long-timers exiting late in the process.
👉 This wasn’t just people chasing greener pastures. It was a mix of productive attrition, early misfit, and late-stage performance exits.
Improve: Interventions That Matter
Armed with this, we piloted changes in three departments:
- Pre-PIP coaching window — structured support before formal discipline.
- Manager enablement — training where turnover was clustered.
- Transparent role previews — recruiters clarified shifts, pay, expectations upfront.
- Exit interviews redesigned — force-ranked cause list plus free-text.
Control: Sustaining Trust
To ensure improvements stuck, we could add BI monitoring:
- AP Rate by Department (monthly).
- Early attrition ≤ 6 months (leading indicator).
- AP Rate by Performance Band (watch for NI/PIP declines).
- Data health KPIs (% missing fields, invalid dates).
The Bigger Lesson
Platform migrations are inevitable. But new software alone doesn’t solve data problems. In fact, transitions are when data fidelity is most at risk.
The DMAIC framework gave us discipline:
- Define the problem precisely.
- Measure with contracts that survive system shifts.
- Analyze by segmenting beyond surface labels.
- Improve with targeted, high-impact actions.
- Control with dashboards that keep governance alive.
The result wasn’t just cleaner data. It was restored trust, actionable insights, and a retention roadmap that leadership could stand behind.
👉 Have you seen your metrics drift during a system migration? How did you bring leadership back to confidence?
All data was sourced from Kaggle.com
The raw text file can be downloaded HR Data.txt
#PeopleAnalytics #DMAIC #HRTech #Workday #UKG #PowerBI