9 Criteria to Evaluate People Analytics and Workforce Intelligence Solutions

Most people analytics vendor evaluations begin the same way: a features spreadsheet, vendor demos, and a pricing negotiation. Months later, the organization is live on a new solution and still can’t answer the question that started the whole process, which could be anything from “why are our best people leaving us” to “what business impact does opening up an office in a new location have?”

This isn’t necessarily a procurement failure, but rather a framing one. The people analytics category is harder to evaluate than most enterprise software because vendors use the same language to describe fundamentally different architectures. And most evaluation processes don’t account for that, or the various teams that may use the solution (HR, IT, Managers, etc.). Moreover, they optimize for today’s problem and aren’t considering long-term viability.

What’s more, the pace of AI is fundamentally speeding up the time to value for tech stacks, putting increased pressure on PA and IT teams to deliver value quickly.

The organizations getting the most value from the analytics investments right now are mostly the ones that selected a vendor for where they are going, not just where they once were.

It’s answering questions like: How do I scenario plan if an entire employee demographic ceases to exist in five years? Or, based on current business plans, what skills gaps do my managers need to focus on so we can remain competitive in the market?

If you’re an HR tech leader evaluating a new solution, getting the right vantage point for choosing a people analytics solution is critical to business success. The right partner can evolve and change with workforce transformation. 

The people analytics vendor landscape is more fragmented than it looks

The first thing worth understanding is that "people analytics" covers more ground than most buyers expect. The same label gets applied to purpose-built analytics platforms, HCM reporting modules, internal BI builds, and AI-powered skills tools—architectures that have almost nothing in common and are designed to solve fundamentally different problems.

The more important distinction is between solutions built for reporting and those built for decision-making. Reporting tells you what happened. The more sophisticated platforms go further, surfacing actionable workflows that inform strategic workforce planning, org design, and scenario modeling. That difference in ambition requires a fundamentally different architecture underneath.

Conflating different solution types is where most evaluations go wrong, and it tends to happen early.

1. Purpose-built people analytics platforms are designed from the ground up for workforce data. They come with standardized data models, pre-built HR content, and benchmarking datasets aggregated across their customer base.

2. HCM-native analytics, built into systems like Workday, SAP SuccessFactors, and Oracle, covers both reporting and analytical use cases well within their own ecosystems but can struggle when analysis requires data from outside them.

3. Internal builds, using tools like PowerBI or Tableau on top of data warehouses like Snowflake and Databricks, offer flexibility and look inexpensive on paper, but place the full burden of building and maintaining them on your team.

4. A growing category of AI and skills intelligence vendors, such as Eightfold AI or Phenom, focuses on talent matching and inference, useful for specific use cases, but not a substitute for a core analytics platform.

The common approach is to compare these on a feature matrix and pick the winner. A better question cuts to the real tradeoff: how much of the work of building insight does the vendor’s solution do for you, versus how much falls on your team to construct from scratch? The answer to this question should drive everything else.

9 Criteria to Evaluate People Analytics and Workforce Intelligence Solutions

✅ Strong 〰️ Partial or build dependent ❌ Limited or none

4 criteria that actually differentiate people analytics vendors from the rest 

From our experience, we see specific criteria that get overlooked or underweighted in almost every RFP, and for people analytics and workforce intelligence solutions, they are the ones that arguably matter most.

1. Time-aware data modeling

Workforce data is temporal by nature. Employees change managers mid-quarter, cost centers get restructured, and a platform that can’t natively track what was true at any given point in time will produce reports that are technically accurate and operationally misleading. 

The Vendor Ask: How do you handle slowly changing dimensions? Can historical data from a legacy system be blended with current data into a single trend line? 

Why It Matters: It's a basic question, but it's a quick way to separate platforms with genuine time-aware data modeling from ones that just claim it. A vendor that handles this well has built infrastructure to account for this: who someone's manager was last quarter, what cost center they sat in before the reorg, what their job grade was before a promotion. One that counters or pivots to a workaround probably hasn't.

2. Pre-built HR content versus the blank slate

There is a real difference between vendors that give you a data warehouse and one that gives you answers. Purpose-built platforms come with thousands of pre-calculated metrics and agreed-upon definition concepts like voluntary turnover, time-to-fill, and compa-ratio. Generic BI tools arrive empty, which can ultimately lead to wasted time in build and internal misalignment on what the metrics really mean. 

The Vendor Ask: “Can you walk me through your metrics library and definitions?” Use this to spot-check against your own definitions and metrics internally to understand the level of customization your organization might require. 

Why it Matters: Consider this: Before a single dashboard goes live, HR, Finance, and IT have to sit down and agree on what “headcount” means. Does it include contractors? Employees on leave? 

That discussion, repeated across dozens of metrics, is where analytics projects stall, especially when looking at larger, complex enterprise matrices. The less obvious risk is downstream, where inconsistent definitions make it nearly impossible to build reliably on top of your data later, including with AI.

3. Data model maturity and AI readiness

We’ll call it here: this criterion is the one most likely to determine whether your vendor decision ages well. A standardized, well-structured data model is the prerequisite for any AI capability to work reliably on workforce data. Without it, even a well-designed AI assistant has nothing consistent to reason from. 

Again, think of the downstream effects of this. While an answer may seem fine on the surface, if it doesn’t take into consideration all of the context within your business, it could lead to a bad decision. It could be as simple as a solution that understands your organization doesn’t include contractors in the definition of ‘headcount,’ which would lead to inaccurate data outputs.

The Vendor Ask: Don’t just ask them, “Do you have AI capabilities?” Literally everyone will confidently say yes. That’s table stakes now. The real question to ask is how their underlying data model and infrastructure supports AI capabilities. 

Why it Matters: A general-purpose language model layered on top of inconsistent or incomplete workforce data will produce confident-sounding errors. A domain-grounded AI, built on a model that understands how your organization defines its workforce concepts, is a meaningfully different capability and one that compounds in value as AI tooling matures. 

This is where Visier Workforce AI can support you most through our workforce context engine. It’s purpose-built to handle constant work change by unifying, enriching, governing, and delivering accurate workforce context to any tool, AI agent, or human that’s asking a workforce-related question. 

See why context matters in AI capabilities. 

4. Security model and data democratization

Getting data into the hands of managers and HRBPs, not just the central analytics team, is one of the main things people analytics is supposed to accomplish. 

Whether it’s achievable without introducing compliance risk comes down to how the platform manages permissions, which is why finding a system that emphasizes data democratization can be key to increasing usage and adoption of a new platform. 

Systems that tie access to the formal org chart break down the moment your data needs don't match your hierarchy. Giving a manager visibility into a cross-functional team or dotted-line report often means granting broader permissions than intended, because the system can't be more precise. 

Cell-level security fixes this by operating one layer below standard row and column controls, defining permissions down to individual data points. For example, a manager can see a direct report’s salary while simultaneously viewing the average salary of that report's peer group without exposing any individual data within that group.

The Vendor Ask: Walk me through your permissioning and role-based access permissioning. Can you give me some examples of how front-line managers in various departments could access information and interact with the platform?

Why it Matters: You want to trust that every individual can only see what they're supposed to see, even when the org chart changes mid-quarter. Building it manually often means delayed updates, security risks, and maintenance of brittle access rules without the granular control of cell-level security.

Internal Build vs. Buy: What’s the real cost?

When a capable IT team is in the room, the internal build argument will come up. The software is “free.” You already own the PowerBI licenses and the data warehouse is on Snowflake. Why pay for something you can build?

The license cost comparison happens often, and we’ll admit, it normally looks good on the surface. 

But it’s important to note that this is never looking at the total cost of ownership comparison. This can be because the actual costs are distributed across departments that make the build look cheaper than it is, rather than being a discrete line item. 

Most evaluations fail to consider what happens post-implementation of a non-purpose-built people analytics and workforce intelligence solution.

An internal build requires a custom integration for every data source, and someone has to fix it every time a source system vendor pushes an API update. Purpose-built platforms ship with pre-built connectors and maintain them as part of the product. For any substantial build, that ongoing burden typically requires at least one dedicated engineering role just to keep the lights on.

Fully built out, that headcount cost tends to exceed the cost of a purpose-built platform well before the two-year mark. This is the actual computing power that data warehouses and data lakes charge for, and because their self-built solutions aren't optimized for people data, their data queries are often slower and more expensive to calculate.

Databricks and Snowflake compute charges compound this further, since their self-built solutions aren’t optimized for people data. It’s variable, hard to forecast, and prone to growing faster than expected as query volume scales.

When you choose a purpose-built platform that integrates seamlessly with your data lake or data warehouse, which is what our partnership with Databricks does, you get the benefit of centralized storage and governance—without the build, maintenance, and computing costs of managing people data infrastructure yourself. 

That means your team spends less time and resources wrangling workforce data and more time acting on it. 

See how Visier and Databricks work together.

Benchmarking is key to strategic decision-making

There’s another missing piece when you look at an internal build: Benchmarking. The reason is simple here. Your data warehouse contains only your data, and building a benchmarking capability as a single company is neither legally nor technically feasible. 

Purpose-built platforms like Visier address this by aggregating anonymized data across their customer base—more than 17 million employee records—producing benchmarks from actual transactions rather than surveys. 

For organizations trying to make a credible case to the C-suite, that external context is often the difference between a recommendation and a guess.

This discussion deserves time and energy, rather than being a checkbox exercise. Our team has seen internal builds fail over time because of the lack of maintenance or ongoing changes needed. A purpose-built solution like Visier is focused solely on this upkeep to ensure things run smoothly for our users.

The crystal ball: The question most evaluations don’t ask

Most organizations come to a vendor evaluation knowing they have a gap. The question they ask is: what do I need to close it? That may be the right question, but it’s not the most important one.

The more uncomfortable question is: are we solving for the analytics function we have today, or the one we’re going to need in the future? Will our solution stand up to the shifting needs of workforce transformation?

Organizations that treat this as a reporting problem tend to buy tools that solve for reporting. Two or three years later, when the expectations of the function have grown, and the platform can’t keep up, they run that evaluation again at a significantly higher switching cost.

If the goal is operational reporting, headcount lists, leave summaries, and compliance outputs, an HCM-native tool or a maintained internal build may be sufficient. But it’s worth being clear about what that actually means: Does this platform give us visibility into more than just what happened? Does it give insight into why, and help build the foundation for what comes next?

If your true ambition is to create a “crystal ball” of understanding how to move from insights to impact and how that will take shape in your future workforce planning, the architecture has to match that ambition from the start. 

And part of that is asking whether the approach can grow and scale with you. You might not need advanced capabilities like predictive attrition modeling or org design on day one, but if accessing those capabilities later requires another vendor or a different build, the switching cost compounds. 

Getting the architecture right the first time is worth more than any feature on a scorecard.

If you want to see how these criteria hold up in a real evaluation, Visier’s team can walk you through where purpose-built infrastructure tends to make the biggest practical difference, today, and over time.