Many HR teams can report on – and trust – common metrics like attrition. But when it comes to performance data and the productivity metrics, our recent CHRO survey confirmed that confidence fades.
That’s the blind spot in People Analytics: we know who left, but not how well the people who stay are actually performing. In our survey, only about one in three CHROs said they feel highly confident in productivity data — a sharp contrast to the 70% who trust their attrition metrics.
That gap reflects a broader trend across the industry: according to Gartner’s 2024 HR Trends and Priorities Report, only 36% of HR leaders say they are confident in their organization’s ability to measure productivity effectively. And without credible performance analysis, leaders risk missing what’s truly driving results.
1. What Is Performance Data?
Performance data is information about how work gets done and the value it creates. Unlike turnover or headcount, it doesn’t come from one source or fit neatly into a single chart. It’s scattered across systems and often measured in conflicting ways: sales quotas, training hours, project completions, or subjective manager ratings.
With no universal definition of performance, and the required manual data wrangling across multiple systems, productivity metrics are historically tricky to trust. In One Model's Q4 2025 CHRO survey, 42% of CHROs said fragmented systems make it difficult to produce reliable performance reports — their number-one barrier to confidence in productivity data.
As one CHRO put it, “Most of the data is based on non-quantitative information, as our systems currently lack the capabilities to analyze productivity or performance at the macro level.” — John, Senior AVP and Chief Human Resource Officer (Higher Education)
Another shared the same frustration: “Not very confident in performance areas or areas that we have to assess risk. We have very basic data available, and it is not very insightful.” — Carl, Chief Human Resources Officer (Manufacturing)
That finding echoes a 2024 study where 40% of HR leaders said their HR tech stack is too fragmented to enable reliable analytics (Deloitte’s 2024 Global Human Capital Trends Report).
2. What Productivity Data Should You Measure?
This is where the debates begin. Should productivity be measured in emails sent, hours logged into a laptop, or the number of Slack messages posted? Those are easy to track, but they tell you very little about actual contribution. Counting activity is not the same as measuring value.
More meaningful measures are tied to outcomes — deals closed, projects delivered, customers retained, campaigns launched, software shipped. These reflect the impact of an employee or a team, but they’re much harder to standardize. Nearly half (47%) of CHROs in our survey admitted they often “trust dashboards even when incomplete.”
That’s the trap: metrics that are convenient and visible end up standing in for real performance, even when everyone knows they fall short. But measuring activity volume is like measuring how many steps a world-class chef takes instead of how good the meal tastes. We need to measure the meal — the outcome that impacts the business.
3. Where Performance Data Lives
So how do we go about calculating impact? First, we have to find the data. Performance data hides in the systems where work actually happens:
- CRM — sales effectiveness, pipeline health, conversion rates
- LMS — training completions, learning outcomes, certification rates
- Performance reviews — manager evaluations, promotion readiness, peer feedback
- Project tools — Jira, Asana, GitHub, Trello — evidence of projects delivered and timelines met
- Engagement surveys — discretionary effort, motivation, and cultural impact
- Time & attendance — context for presence and patterns, though rarely the full story
On their own, none of these sources explain performance. Not only does this fragmentation make your job harder; it erodes confidence. As one CHRO told us, “I know I have the data but can't readily access it, so I have to dig deeper or ask my team to analyze.” When data requires extra steps, human cleanup, or delays, confidence drops dramatically — a challenge cited by more than half of our survey respondents.
To build reliable metrics, you must connect your systems — not just report from them. You’ll need:
- The Context (HRIS/HCM): Provides the who, where, and when, such as performance review scores, tenure, manager, and location (WFH vs. Office).
- The Input (LMS): Tracks the investment, like training completions, competency scores, and learning path progress.
- The Outcome (CRM/Goals): Delivers the business result, including sales pipeline velocity, quota attainment, OKR completion, or service ticket resolution time.
Together, they create the real story — but only if you can connect them.
4. Correlation Metrics You Can Actually Defend
When you connect these systems, performance metrics become more than a dashboard. They become a way to answer meaningful questions and test hypotheses. Leaders move beyond reporting numbers to telling stories executives actually care about. They stop hedging and start leading.
You can ask (and get answers to) questions like:
Do high performers maintain their performance whether they work from home or in the office?
According to Gartner, nearly 60% of leaders say they still rely on perception, not data, when evaluating remote performance — a blind spot that analytics can close. For example, by correlating work location (from your HRIS) with true business outcomes (like CRM attainment or engineering velocity), you shift the debate from sentiment to data-backed strategy.
Do employees who complete relevant L&D courses for their role improve year over year in performance reviews?
McKinsey research has shown that organizations that effectively measure and align employee performance with business goals outperform peers by up to 20% in productivity per employee. Linking LMS completions (the input) to subsequent annual performance review scores and promotions (the outcome) reveals whether your learning investments translate into measurable performance gains.
Are manager ratings consistent and are calibration processes effective across departments?
Harvard Business Review noted that self-ratings and supervisor ratings overlap by only about 4%, underscoring how inconsistent most evaluation systems are. However, by analyzing the distribution of performance ratings across your organization, you can identify managers who use only the top 10% or bottom 10% of the rating scale. This is a critical tool for ensuring equity and addressing management issues before they turn into retention problems.
What is revenue per FTE and how does it vary across roles and geographies?
By linking financial data to your HRIS, you can calculate revenue per full-time equivalent and see how productivity varies across roles, regions, or business units. Revenue per FTE is just one example, but it is a powerful way to examine how the workforce is generating business value. Combining this with other external data can help you identify relationships with economic, or other competitive factors, and looking at your trends will provide insight into seasonality and company growth.
What is the ratio between direct revenue-generating employees and indirect revenue-generating employees?
Connecting workforce composition to financial output reveals how many employees contribute directly to revenue versus those in supporting or enabling roles. Both groups are essential, but the ratio provides a lens on organizational design and efficiency. A high proportion of direct revenue-generating employees may signal a lean structure with a strong commercial focus, but it can also risk underinvestment in the supporting systems, culture, and innovation needed to sustain growth. However, too many indirect employees may create organizational drag and raise questions about whether resources are optimally aligned to business priorities.
By tracking this ratio, CHROs can lead conversations on workforce strategy that balances today’s revenue pressures with tomorrow’s capacity to innovate, scale, and thrive.
Does AI adoption speed up time-to-productivity?
Some organizations are now measuring how quickly employees reach full impact once they begin using AI tools. A study by the Federal Reserve Bank of St. Louis found workers using generative AI saved 5.4% of their work hours in the previous week, which suggests a 1.1% increase in productivity for the entire workforce. (Federal Reserve Bank of St. Louis)
That shows time savings can be real, but not automatic: benefits tend to depend on usage intensity, role, and how well the AI is embedded into work. In fact, research shows a J-curve effect (a.k.a. the productivity paradox): productivity sometimes drops initially after new AI tools are adopted, then rises as processes, skills and systems align. (MIT Sloan+1)
These examples illustrate that when you have integrated performance data, you can move from assuming to proving — and from reactive reporting to proactive insight.
5. The One Model Integration Advantage
The only way to get this level of insight into productivity is through full HR data integration. Performance data will always come from multiple places; the real challenge is turning it into a story leaders can trust.
This is where One Model shines.
Our Promise: A Single, Defensible Model
We don’t ask you to trust a black box. We help you build a system where every performance metric is transparent, traceable, and easy to defend:
- We build the integrations, pulling your scattered data sources into one place.
- We connect the data, creating an auditable model where the logic behind every metric is clear.
- We give you the tools to move from reactive reporting to proactive storytelling.
It boils down to this: Defensible data = Confident CHRO.
Tired of your performance data being a blind spot in your org? Talk to our team.
