Overview
.svg){kind=small}
One AI
Data Mesh
.svg){kind=icon}
One AI Assistant
Data Stories
Enterprise AI
Employee Engagement
.svg){kind=small}
Talent Retention
-1.svg){kind=small}
HR Dashboards
Succession Planning
.svg){kind=small}
Talent Acquisition
Workforce Analytics
Why One Model
-2.svg){kind=small}
Whitepapers
Blog
-2.svg){kind=icon}
Events & Webinars
.svg){kind=icon}
Product Innovation Blog
-1.svg){kind=small}
Roles in People Analytics
.svg){kind=small}
Contact Us
-3.svg){kind=small}
Partners
About US
-3.svg){kind=icon}
Careers
-1.svg){kind=icon}
News
Paul Burch has spent over two decades spanning software, data leadership, and building and transforming businesses and business functions. He has experience working with One Model's people analytics platforms from a client perspective, giving him a practical understanding of how workforce analytics is applied in real organizational contexts. Paul has an MBA from La Trobe University and is the Founder and Principal at Inevitably Good, a data-driven business performance consultancy.
Organizations are increasingly expected to use people data to better understand and shape workforce outcomes, including capability, risk, and future needs. As part of this shift, more leaders from strategy and enterprise data backgrounds are taking on responsibility for workforce analytics. Yet as they step into the workforce data domain, many discover something unexpected:
Workforce data behaves very differently from most other enterprise datasets.
Sometimes this shift is driven by structural decisions, with workforce analytics sitting within broader enterprise data functions. In other cases, it reflects a recognition that stronger data capability is needed alongside deep HR expertise.
In reality, it requires both. Workforce analytics depends on an understanding of organizational context and people dynamics, as well as the ability to design, model, and scale data effectively - something many organizations are still working through.
For many leaders, this is one of the first times they are working in a domain where data engineering, organizational design, governance, and human behavior all intersect so closely. One Model’s Amy Hammond sat down with strategy and data leader Paul Burch to explore six key realizations leaders often have when they take on workforce analytics.
1. The Data Is Both Human and Organizational
One of the earliest insights data leaders describe is that workforce data is fundamentally different because of what it represents. As Burch explained:
“The complexity comes from the fact that the data are people… Our people data is made up of 1’s - and each of those 1’s is a human being.”
This introduces a dimension that is rarely present in other data domains. The data is not abstract. It represents individuals, relationships, and decisions.
In practical terms, the unit of analysis is not a transaction or event, but a person evolving over time. Employees move through a lifecycle: joining, changing roles, being promoted, transferring, taking leave, exiting, and sometimes returning again.
Capturing that journey means working with longitudinal data, not static snapshots. It also means reconciling records across multiple systems, each with different identifiers and structures, into a coherent view of a single employee.
Leaders often find themselves balancing two things at once: simplifying data so it can be analyzed, while ensuring it still reflects real human experience.
2. The Organization Is a Moving Target
Another realization is recognizing how dynamic the underlying system is. “Organizations are constantly changing shape, people are constantly evolving, and the needs of leaders are evolving just as quickly,” Burch says.
Departments merge. Teams restructure. Leaders move roles. Reporting lines shift. A level of change that is less common in many other data domains.
Even answering a seemingly simple question like: ‘What was our headcount last July?’ requires reconstructing the organization as it existed at that point in time.
That means understanding not just who was employed, but how teams were structured, who reported to whom, and what roles people were in at that moment.
This temporal modeling requirement introduces complexity that many data leaders encounter for the first time in workforce analytics. Without the ability to model historical states accurately, analysis of trends, attrition, leadership impact, or workforce composition quickly becomes unreliable.
3. Complexity Is Layered, and Often Hidden
Leaders know to expect technical complexity when it comes to data. But with people data, complexity arises from other things, like data structure, policy, and organizational context.
Leave data is a great practical example of this. Seems simple to track at face value. A record of time away from work. In practice, it can reveal a great deal about people, organizational needs, and organizational health. And it’s actually much trickier than it looks, due to the data modeling required. As Paul Burch explained: “There are so many things to learn from leave data… but before you get to that, it must be modeled with knowledge of legislative context, organizational policy, and then there’s the cultural overlay.”
This is often where leaders first see the hidden complexity in people data. Leave is not just a field or transaction. It sits within legislation, internal policy, payroll treatment, local practice, and organizational culture. Without understanding those layers, analysis can be technically correct but practically misleading.
The same applies to common workforce metrics. Measures such as headcount or attrition may sound straightforward, but they rarely have universal definitions. They depend on organizational choices about what to include, how to classify movement, and how to interpret outcomes.
For many leaders, this is a turning point.
4. Without Organizational Context, the Data Doesn’t Make Sense
Leaders from strong data backgrounds often begin with the dataset, which seems intuitive. But in workforce analytics, you have to look beyond the data. As per Burch’s recommendation:
“Don’t start with the data - start by getting out into the organization and understanding the pain points of people, teams, and leaders.”
The challenge is often knowing where to focus. That comes from understanding the organization first. “Be wary of over-promising… people data is complex and can take time to clean, model, and present in a way that delivers real insight,” Burch says.
5. The Same Data Must Serve Multiple Perspectives
In many analytics domains, insights emerge directly from patterns in the data. In workforce analytics, patterns require more interpretation. Context is critical. This becomes especially clear when you look at how workforce insights may be used across an organization:
- Line managers use insights to drive outcomes in the realm of performance, engagement, and retention.
- HR Business Partners may provide context, guidance, and an understanding of organizational dynamics, policy, and risk.
- Analytics teams may focus on deeper analysis, modeling, and identifying patterns across the organization.
It’s the same underlying data, but used very differently. Managers need clarity. HR needs interpretation. Analytics teams need depth.
This creates an additional layer of complexity. Workforce data is not designed for a single user or purpose. It has to support multiple perspectives at once, each requiring different levels of detail and context.
The same dataset must support operational decisions, strategic insight, and human judgement at the same time.
6. You Have to Win the Trust of the HR Team
One of the more surprising lessons for leaders, as emphasized by Burch, is how important trust is: “You can’t solve problems with data until you’ve developed the trust of the HR team.”
HR operates in a sensitive, confidential, and high-stakes environment. They also bring a deep, often implicit understanding of governance, organizational context, and the consequences of getting people data wrong.
This means trust between HR and data leaders cannot be assumed. It has to be built. Trust in how the data is modeled. Trust in how metrics are defined. Trust in how context has been considered and applied.
No matter where workforce analytics sits structurally, it depends on HR having confidence in the outputs. Without that, access may be limited, insights may be challenged, and adoption will be low. But when HR is brought on the journey, and that trust is established, the impact is very different. Analytics becomes something that is not just technically sound, but credible and usable within the organization.
Building that trust takes time, and it sits outside the technical work.
What Data Leaders Bring And Why It Matters
Despite the complexity, leaders from strategy and enterprise data backgrounds bring important strengths. They bring systems thinking, architectural perspective, and the ability to connect data to organizational outcomes. They also bring experience in enterprise data modeling, analytics methodologies, and designing scalable platforms, along with an understanding of identity, access frameworks and how data is governed and consumed across an organization.
As Burch noted: “Being able to see the organization at different levels of abstraction from a systems perspective is incredibly useful.”
From a strategy perspective, leaders often bring the ability to define and architect the data needed to measure strategic outcomes, with a clear understanding of how data should inform and guide decision-making.
They also bring a broader lens. Workforce analytics sits at the intersection of organizational design, talent strategy, behavioral insight, labour economics, and data engineering. That combination is not always easy to navigate, but it is where the real value sits.
“Blending leadership perspectives can accelerate development.”
Workforce analytics does not require choosing between HR and data. It requires bringing them together effectively.
The Leader’s Conclusion
For many leaders, the transition into workforce analytics changes how they think about data. At first, the focus is on getting the data right. Over time, the question becomes something else.
Is the data actually representing how the organization works?
Workforce data reflects decisions, relationships, and behaviors that are constantly evolving. Capturing that in a way that remains useful across different audiences is not straightforward.
The challenge is no longer just technical accuracy. It is ensuring the data faithfully reflects how the organization actually operates in practice. And that is what makes workforce data and analytics both complex and uniquely valuable.
See One Model in Action
