Data Modeling for People Analytics: The Beginner-Friendly Deep Dive HR Teams Actually Need

MARIA NOLAZCO MASSON

11 Nov 2025 • 9 min read

Most HR and People Analytics problems look like reporting problems. In reality, they’re structure problems. If your underlying data isn’t modeled well—if your systems don’t agree on who someone is, which department they’re in, or when a job change became effective—no amount of dashboard magic will save you. Your charts will look pretty but won’t match Finance, IT, or the CEO’s spreadsheet. That destroys trust.

This guide explains data modeling for People Analytics in simple terms first, then goes deeper. We’ll use friendly language, lots of examples, and repeat the big ideas so they truly land. The goal: help you build a backbone that makes your HR data consistent, explainable, and trusted, even if you don’t see yourself as “technical.”

1) What is data modeling… really?

Before we talk about tables, keys, or warehouses, we need to agree on what “data modeling” even means. A lot of people hear the term and think it’s something only engineers should care about. In reality, it’s something HR and People teams live with every day—because it’s the reason numbers do or don’t match.

So… what is it?

Short version: Data modeling is deciding how your HR data is organized and how the pieces connect.

Slightly deeper:
It’s making choices like:

What counts as an “Employee” vs. “Contractor”?
What is a “Department” vs. “Business Unit”?
How do we show who manages whom?
Where do we store job changes, promotions, and pay updates—and how do we keep the history?

Why it matters:
If two systems say different things about the same person, your model must reconcile that. When your model is clear, everyone can answer questions like “How many people did we have last month?” the same way, every time.

Beginner tip:
Think of data modeling as agreeing on a glossary + wiring diagram. The glossary defines terms. The wiring diagram shows how the terms connect.

2) The three layers: conceptual, logical, physical (zoom in gradually)

Before worrying about specific tools (Snowflake, BigQuery, spreadsheets, etc.), it helps to know that there are different “zoom levels” when we describe data. We can talk about data at a very high level, at a detailed design level, or at a technical level. All three matter, especially when HR, People Analytics, and IT need to work together.

Conceptual, logical, physical — what’s the difference?

Conceptual (big picture):
List your core “things” (we call them entities): Employee, Job, Department, Location, Manager, Performance Review. Draw lines to show how they relate (Employee belongs to Department; Employee reports to Manager). No columns yet—just relationships.

Logical (blueprint):
Now add attributes to each entity (Employee has EmployeeID, HireDate, EmploymentStatus, ManagerID, etc.). Still not picking specific database types—just structure and rules.

Physical (implementation):
Create the actual tables, columns, data types, indexes, and partitions in Snowflake/BigQuery/Redshift/etc. This is where performance tuning lives.

Beginner tip:
Don’t jump straight to the physical layer. If the conceptual and logical layers are fuzzy, the build will crumble later.

3) Entities, attributes, keys, and relationships (the starter pack)

Before we build anything concrete, it helps to learn the basic vocabulary. These are words you’ll hear from data people all the time. Once you understand them, conversations with engineers become much easier and less intimidating.

The building blocks

Entity = thing you track (Employee, Department, Job, Location).
Attribute = detail about the thing (Employee’s hire date, job level, manager ID).
Primary key (PK) = unique ID of a record (e.g., EmployeeID or EmployeeSK).
Foreign key (FK) = link to another table’s PK (e.g., DepartmentID inside the Employee table).
Relationship = how things connect (one manager to many employees; many employees on many projects).

Beginner tip:
If you’re lost, start with three tables: Employee, Department, Job. Connect Employee to Department and Job. Add ManagerID on Employee that points to another Employee.

4) Normalization vs. denormalization (accuracy vs. speed)

At some point, you’ll hear people say, “We need to normalize the data,” or, “We denormalized that table for performance.” This can sound very technical, but the idea is simple: it’s about whether you store something once or copy it in convenient places.

Two strategies for organizing data

Normalization (cleaning up duplicates):
Store each fact once. Example: Don’t repeat “Marketing” on every employee row; store DepartmentID on Employee and keep the department name in a Department table.

Why normalize?

Fewer inconsistencies (“HR”, “People”, “Human Resources” mixing everywhere).
Easier updates (rename once → updates everywhere).
Smaller, tidier core data.

Denormalization (making analytics fast & simple):
Copy a few handy fields into your analytics tables (e.g., include DepartmentName and ManagerName in your headcount snapshot) to avoid slow joins in dashboards.

Rule of thumb:
Normalize the core to keep data correct. Denormalize the analytics layer to keep users happy and dashboards fast.

5) OLTP vs. OLAP (why your HRIS isn’t your warehouse)

Most HR teams work with different systems without realizing they’re designed for different purposes. Some systems are built to record changes; others are built to analyze trends. If you mix those roles, you get pain.

Two types of systems you deal with

OLTP = transactional systems (HRIS, ATS, Payroll). They record day-to-day changes: hires, promotions, terminations. They’re great at writing data fast and correctly.
OLAP = analytics layer/warehouse. It summarizes, aggregates, trends, and slices data. It’s great at reading and analyzing lots of data.

Beginner tip:
Don’t try to do all analytics directly in the HRIS. You need a separate analytics model designed for reporting and history.

6) Dimensions and facts (two kinds of warehouse tables)

Once you’re in the analytics world, you’ll see two main types of tables: dimensions and facts. This is a simple but powerful idea. Understanding it unlocks most data models.

Nouns and numbers

Dimensions = the nouns (who/what/where): Employee, Job, Department, Location, Manager, Date.
Facts = the numbers or events (how much/what happened):
- f_headcount_snapshot (state at month-end)
- f_hire, f_termination, f_promotion (events with dates)
- f_comp_transaction (pay changes)
- f_performance_rating (ratings per cycle)

Beginner tip:
If you can’t tell whether something is a dimension or a fact, ask: “Is this a thing that describes context (dimension), or an event/measurement we analyze (fact)?”

7) Slowly Changing Dimensions (SCD): how to keep the history

In HR, nothing stays still. People get promoted, change departments, move locations, or report to new managers. If you don’t model this properly, your historical charts will flicker and change when new data arrives.

Tracking change over time

You must decide how to track change:

Type 1 (overwrite): Fix data, no history (e.g., correct a misspelled name).
Type 2 (new row, date ranges): Keep old and new versions with EffectiveFrom, EffectiveTo, and IsCurrent. This is crucial for “what did HC look like then?” questions.

Beginner tip:
For org, manager, job, and location, use Type 2. It’s the difference between “trustable trend lines” and “why did our numbers shift again?”

8) Surrogate vs. natural keys (IDs that survive system changes)

If you’ve ever migrated HR systems or integrated multiple tools, you know that “Employee ID” is not always the same everywhere. If your joins depend on those changing IDs, your data falls apart.

Two types of IDs

Natural key: the real ID from a source system (e.g., Workday Employee ID).
Surrogate key: a warehouse-generated ID (EmployeeSK) that never changes.

Why use surrogate keys?
Because natural keys can change across systems or after migrations. Surrogate keys give you one stable join key for everything.

Beginner tip:
Keep both: use the surrogate key to join, and store all source IDs as attributes for auditing.

9) Time modeling: snapshots vs. events (photo vs. movie)

Most leadership questions aren’t just about “now.” They’re about trends: How has headcount changed? What is our attrition over time? To answer that, the model must understand time, not just current state.

Two ways to represent time

Snapshots (photo): one row per employee per snapshot date (usually month-end). Great for headcount and org views.
Events (movie scenes): one row per event (hire, termination, promotion) with exact dates. Great for attrition, mobility, and hiring pipeline analytics.

Beginner tip:
You need both. Snapshots answer “who existed on 2025-06-30?” Events answer “what happened on 2025-06-17?”

10) Star vs. snowflake schemas (how you arrange your tables)

Once you’ve got facts and dimensions, you still have choices about how they’re arranged. Some designs are simple and flat; others are more structured and layered. This is where star and snowflake schemas come in.

Two common patterns

Star schema: one central fact + denormalized dimensions. Simple, fast, perfect for BI tools and HR leaders.
Snowflake schema: dimensions are further normalized (e.g., location → city → country). Tidy and governed, but more joins.

Practical pattern:

Core layer: more snowflaked (clean governance).
Presentation layer: star schemas (easy, speedy dashboards).

11) Data integrity and quality (make bad data obvious)

Even with the best design, things go wrong: missing data, invalid dates, or fields that don’t line up. Instead of pretending it won’t happen, good data modeling assumes it will and builds defenses.

Guardrails that help

Constraints (FKs, not-nulls).
Validation rules (hire date < termination date; FTE between 0 and 1).
Reference tables (valid job families, levels, statuses).
Data quality checks (duplicate people, missing managers, overlapping assignments).
Clear definitions (“Who counts as headcount?” “What’s a contractor?”).

Beginner tip:
Put quality checks on a dashboard for HR Ops. Make data issues visible and fixable.

12) Refactoring a messy model (when you inherit chaos)

Many People Analytics teams don’t start from a blank page. They inherit spreadsheets, exports, and half-broken data pipelines. It’s normal. The question is: how do you improve things without stopping the business?

A realistic clean-up path

Inventory what you have (systems, tables, pain points).
Define the business rules (employee vs. contractor, headcount rules).
Introduce surrogate keys (EmployeeSK, DepartmentSK, JobSK).
Separate domains (identity, org assignment, comp, events).
Normalize the core; denormalize the analytics layer.
ETL to clean, dedupe, and load; add automated tests.
Document and assign ownership (who fixes what).

Beginner tip:
Small consistent improvements beat a giant “big bang” rebuild that never ships.

13) Performance at scale (when data gets big)

At the beginning, everything feels fast because you only have a few thousand rows. As you start storing several years of data for thousands or tens of thousands of employees, performance questions show up. Suddenly, what used to run in seconds takes minutes.

Making large HR data usable

Partition large facts by date (e.g., SnapshotMonth).
Use columnar warehouses (Snowflake/BigQuery).
Pre-aggregate frequent queries (HC by month & department).
Denormalize high-value attributes into facts (department/manager names).
Index common join/filter keys (employee, manager, snapshot date, status).

Beginner tip:
If dashboards slow down, profile the join count and filters first. Often a tiny denormalization wins big.

14) Data Vault (for complex HR landscapes)

Some organizations have a simple HR system landscape. Others have multiple HRIS instances, regional systems, historical data from M&A, and lots of overlap. For those cases, you need something more flexible and audit-friendly. That’s where Data Vault comes in.

What Data Vault does for you

When you have multiple HR systems, acquisitions, and long history, Data Vault helps you integrate and audit cleanly:

Hubs = core entities (Employee, Job, OrgUnit).
Links = relationships (Employee–Manager, Employee–Department).
Satellites = attributes and history (e.g., employee demographics over time).

Typical approach: Use Data Vault as the integration layer, then build star schemas on top for analytics. You get flexibility plus easy reporting.

15) Hierarchies, JSON, NoSQL, and real-time data

Not all data is flat and simple. Some of it is hierarchical (like org charts), flexible (like surveys), or very fast-moving (like real-time support tickets or shifts). Good data modeling has patterns for each of these.

15.1 Hierarchies (manager chains)

Self-reference in dim_employee (ManagerEmployeeSK → EmployeeSK).
Or a hierarchy bridge (precompute all manager paths for fast span-of-control).
Or parent–child structure in dim_org_unit.

15.2 Semi-structured data (surveys, feedback in JSON)

Store raw JSON to preserve detail.
Also extract key fields (date, overall score) into columns for speed.
For very flexible surveys, consider EAV (Entity–Attribute–Value).

15.3 NoSQL and streaming

Useful for unstructured or high-velocity data (support tickets, check-ins, shift events).
Design around access patterns (“show me all feedback for this employee”).
Enforce governance and permissions—people data is sensitive.

Beginner tip:
If JSON scares you, start simple: keep the raw JSON column, plus 3–5 lifted columns your reports actually use.

16) A practical blueprint you can copy

At this point, you might be thinking, “Okay, but what does all of this look like as a concrete design?” This section gives you a simple blueprint you can adapt to your own company.

Suggested model

Dimensions (nouns):

dim_employee (Type 2 for org/manager/job/location)
dim_job (code, family, level)
dim_department (dept, BU)
dim_location (site, country, region)
dim_manager (or self-join to dim_employee)
dim_date (calendar helpers)

Facts (events/states):

f_headcount_snapshot (one row per employee per snapshot date)
f_hire, f_termination, f_promotion (dated events)
f_comp_transaction (salary, bonus, equity changes)
f_performance_rating (cycle ratings)

Keys:

Surrogate keys for all dims (e.g., EmployeeSK).
Keep business keys (source IDs) as attributes.

Governance:

Data dictionary (plain-language definitions).
Quality dashboard (alerts to HR Ops).
Ownership matrix (who fixes what).

17) Common pitfalls (and easy escapes)

Finally, it helps to know the usual traps so you can spot them early. These problems show up again and again in People Analytics. The good news: they all have known solutions.

Patterns you’ll probably see

Pitfall: Headcount doesn’t match month to month.
Escape: Use snapshot facts and SCD Type 2 for org/manager/job.
Pitfall: Joining everything feels slow and brittle.
Escape: Denormalize key attributes into facts; create star schemas for the BI layer.
Pitfall: Employees appear twice.
Escape: Mastering + deduplication rules (match on name + DOB + official ID/email).
Pitfall: Different systems disagree on IDs.
Escape: Surrogate keys in the warehouse; keep all source IDs as attributes.
Pitfall: No one agrees who counts as headcount.
Escape: Write definitions down. Publish them. Enforce them.

Final Thoughts: Start clear, stay consistent, grow carefully

You don’t need to be a data engineer to care about data modeling. If you work in HR or People Analytics, you live with the consequences of good or bad models every single day.

If you remember only three things, let them be these:

Clarity beats complexity.
Start with plain-language definitions: What is an employee? What is headcount? What is a department? Make sure your data model reflects those definitions.
History is gold.
Use SCD Type 2 and snapshots to preserve how your workforce looked at different points in time. It’s impossible to have serious People Analytics without reliable history.
Design for humans, not just machines.
Normalize the core for correctness, but give your users simple, fast, denormalized views for analysis. Build a model that people can actually use without getting lost.

When your model is thoughtful and well-structured, something shifts. The conversations move away from “Why doesn’t this number match?” and toward “What story is this data telling, and what should we do next?”

That’s when People Analytics goes from reactive reporting to strategic impact—powered quietly, but decisively, by solid data modeling.