Short answer
People analytics software becomes useful after purchase when a team can move from a defined workforce question to reviewed evidence, an owned action, and a return date. Build one workflow before expanding dashboards or data connections. Keep the source, data gap, human reviewer, and decision limit visible so that a generated insight cannot become an unsupported verdict.
This is not another tool comparison
Use a people analytics tools comparison to choose a product and test a vendor. This guide begins after that decision: it helps the operating team set up the first repeatable path from data to review to action.
The first workflow should answer one question that a named owner can act on. For example: "What is making new supervisors uncertain during their first rota-planning month, and should we change the handover material?"
Map the workflow before adding data
| Stage | Owner | Record to keep |
|---|---|---|
| Question | Operations and people lead | Decision, population, and review date |
| Data input | Data owner | Definition, source, coverage, refresh date, and gap |
| Work context | Conversation or research owner | Question boundary, participation explanation, and examples to examine |
| Review | Authorised HR and operational reviewer | Evidence, disagreement, limitation, and alternative explanation |
| Action | Named accountable person | Chosen action, scope, and communication plan |
| Return | Decision owner | Date, observed result, and whether to continue, adapt, or stop |
CIPD's people-analytics guidance describes a process that runs from planning and data collection through reporting and evaluation. The table makes that process concrete for a first software workflow.
Check the source before you trust the output
For each measure or theme, ask:
- What is the original source and who owns its definition?
- Which people, time period, and locations does it cover?
- What is missing, late, duplicated, or recorded differently?
- Can a reviewer inspect the source behind a summary or chart?
- What would challenge the current explanation?
ONS data-quality policy covers the full lifecycle from inputs to outputs. In practice, this means a dashboard refresh is not the same thing as a quality check. If a location changed its role codes last month, a trend across the change may need explanation before it drives an action.
Add employee context carefully
When records reveal a question but not an explanation, ask a focused question of the relevant people and let them provide examples voluntarily. Keep their account separate from the system interpretation and the eventual decision.
Fictional example: a hospitality business sees new managers transfer more often after a scheduling-policy change. The records establish the timing, not the reason. A clearly explained conversation surfaces mixed accounts: some managers cite late rota changes, others cite role clarity. HR checks the policy and local handover practice before the operations lead decides whether to test one revised briefing. No account proves the cause, and no individual is labelled by the software.
Run the first review meeting
Bring the decision owner, data owner, and relevant people lead together. The agenda should be short:
- Re-state the question and evidence boundary.
- Check source quality and coverage.
- Read the context and competing explanations.
- Choose one proportionate action or decide that more evidence is needed.
- Record the owner, communication, and return date.
The output is not a scorecard. It is an action that people can explain and revisit.
Where Lontra can fit
If the workflow needs current employee context, explore Lontra’s employee conversations and manager briefs. Start with a focused work question and inspect whether the material helps the designated reviewer prepare a human discussion. Confirm integrations, exports, and implementation requirements directly before making them part of the workflow.
Sources
- CIPD: People analytics, for planning-to-evaluation as a people analytics process.
- ONS: Data Quality Management Policy, for quality across the input-to-output lifecycle.
