Climbing the People Analytics Staircase – Step 1 Deep Dive

Building Reliable Data from Day One: A Technical Guide to People Data Collection

Expanded guidance based on Step 1 of Climbing the Staircase of People Analytics: Why Every Step Matters

Introduction

In Step 1 of the People Analytics Staircase, we discussed the importance of intentional, structured data collection as the foundation of any successful analytics journey. But what does it actually take to do this well? What systems, processes, and practices must be in place to move from “we have an HRIS” to “our data is reliable, consistent, and actionable”?

This article takes a deeper dive into the operational and technical details behind great data collection—highlighting key success factors, common pitfalls, and best-practice recommendations that every HR and People Analytics team should follow.

1. Design with the End in Mind: Reverse-Engineer Your Data Model

Successful data collection starts not with the system, but with the questions you want to answer. Before you configure a single field or workflow, identify the business questions your People Analytics function needs to support.

Do you want to track internal mobility trends? Then you’ll need consistent data on position changes, effective dates, and org hierarchy.
Do you want to predict early attrition? Then you’ll need timely onboarding records, performance milestones, and exit data.

From there, map the data requirements backward. What fields are needed? Where will they come from? Who owns them? This exercise should define your data architecture—a living blueprint for all future people data decisions.

2. Prioritize Structure Over Flexibility

Many HR systems allow for flexibility in data entry—open text fields, user-defined categories, custom tags. But in People Analytics, flexibility without structure leads to chaos.

Where possible, avoid free-text fields. Use drop-downs, predefined lists, and controlled vocabularies. For example:

• Job titles should come from a master job catalog, not be typed in manually.
• Locations should align with standard business entities or cost centers.
• Departments should be linked to financial or operational hierarchies.

If people can enter the same thing five different ways, they will. Structure enables consistency—and consistency enables analysis.

3. Centralize Ownership, Distribute Accountability

One of the most overlooked aspects of data collection is process accountability. It’s critical to define who is responsible for entering or updating which types of data—and at what point in the employee lifecycle.

Here’s a model that works well:

• HR Operations own core employee data (e.g., legal name, job code, employment type).
• Talent Acquisition owns pre-hire and onboarding data (e.g., source of hire, recruiter).
• People Managers update role changes, location shifts, and team assignments.
• Employees may own self-service fields such as personal contact information or preferred name.

The key is ensuring that each stakeholder knows their role—and that your systems are configured to support those workflows seamlessly. Don’t rely on emails or spreadsheets. Embed accountability into automated tasks and approvals.

4. Build for Integration from Day One

Even if you’re starting small, your HR data will eventually need to connect across platforms. That’s why you should build with integration in mind from day one.

Use consistent identifiers (e.g., Employee ID) across all systems. Avoid manual reconciliation. Where possible, configure APIs or middleware (like Workato, Boomi, or MuleSoft) to sync core fields between platforms such as HRIS, ATS, payroll, and learning systems.

Integration ensures that data collected in one place is usable in another—without delay or rework. And it reduces the likelihood of human error when re-entering or copying data.

5. Implement Real-Time Data Validation

Validation rules are your first line of defense against bad data.

Examples of validation rules that should be non-negotiable:

• Start dates cannot be after termination dates.
• All active employees must have a current manager assigned.
• A person cannot be both on leave and active at the same time.

Depending on your system, you can also configure:

• Drop-down dependencies (e.g., job family limits the list of job titles)
• Format rules (e.g., email must end with company domain)
• Required fields (e.g., no record can be saved without compensation data)

Real-time validation helps prevent data errors before they enter your analytics pipeline.

6. Version Control and Metadata Matter More Than You Think

Many teams neglect to track when fields were created, updated, or deprecated. But that metadata—when a title changed, who approved a new level, what naming convention was applied—can be critical to interpreting patterns down the line.

Build version control into your data collection process. Maintain a data dictionary that defines each field, its source, purpose, and allowed values. Document every field change and store it in a versioned repository (e.g., Notion, Confluence, or a shared Google Doc with changelog).

This provides traceability—and helps future team members understand the "why" behind the structure.

7. Audit Early, Audit Often

The best way to ensure that data collection is working is to regularly check it.

Run monthly audits to look for:

• Duplicate records
• Orphaned employees without a manager
• Inconsistent job titles
• Empty or null required fields

Use scripts or scheduled reports in your HRIS, or connect to a BI tool like Power BI or Tableau for visual anomaly detection. Even a basic Excel audit with filters and conditional formatting can reveal major quality issues.

The earlier you catch inconsistencies, the easier (and cheaper) they are to fix.

Final Thoughts: Start Strong, Stay Grounded

It’s easy to overlook data collection. It feels simple, even obvious. But as we’ve seen throughout this series, it is the most foundational—and the most fragile—step of the entire staircase.

Without reliable, structured, well-governed data entry, everything that follows will wobble. Reports will be questioned. Dashboards will be distrusted. Models will produce noise instead of clarity.

But when you build this step with intention—from your data architecture to your validation rules, integrations, audit loops, and ownership structures—you create a system that supports not just analytics, but transformation.

If you want People Analytics to influence business decisions, you have to start by influencing how data is collected.

That’s where strategy begins.

New to the Staircase? Start from the overview → Climbing the Staircase of People Analytics: Why Every Step Matters
Continue exploring the series: Step 2 – Building Data Quality and Ownership