The Tree of Value

In the ever-evolving world of HR and workforce strategy, sometimes we become distracted by the latest trends, tools, and frameworks promising revolutionary insights. It’s easy to get caught up in the noise of new HR trends in an effort to stay current. Yet, despite the ever-changing landscape, four fundamental disciplines have consistently had the potential to deliver value to HR and businesses: people analytics, workforce planning, talent intelligence, and behavioral science. These four disciplines, despite originating independently from one another, actually have the same roots: the value of human capital labor data. Although they are branches, they are all from the same tree.

I call the relationships between these four concepts the Tree of Value. Just as a tree draws strength from its interconnected branches and deep roots, these disciplines—despite their differences—are ultimately working toward the same goals, and their fates are intertwined within the roots of data from which they all draw upon.

While practitioners from these fields may come from diverse academic and professional backgrounds, use different methodologies, and apply distinct lenses to problem-solving, the data being analyzed is largely the same and the problems tackled largely overlap. By recognizing our similarities rather than our differences, we can better navigate the rapid changes in today’s workforce landscape and elevate the strategic value of HR decision-making, and, most importantly, how to utilize the tree to create value and competitive advantage for your organization.

The Tree of Value represents HR’s opportunity to be leaders driving decision making for the business. If we work together, leveraging insights across our fields, we can drive greater impact, demonstrate our work is essential to business success, and lead organizations into the future.

The Origins of the Branches

Each of the four primary branches of the Tree of Value has distinct origin stories, shaped by unique histories and challenges. Understanding these origins helps us appreciate how they complement one another and why their integration is so critical today, and why their roots in the value of human capital labor data are so intertwined.

First, let’s establish our terms. 

People Analytics: The scientific approach of collecting and analyzing internal employee data to understand workforce patterns, predict trends, and inform strategic HR decisions.

Workforce Planning: The strategic process of anticipating an organization's future talent needs by aligning human capital requirements with business objectives and operational goals.

Talent Intelligence: The practice of gathering and interpreting external labor market insights to understand talent availability and demand, location strategy, competitive positioning, and the overall skill landscape across an organization’s industry and talent competitors.

Behavioral Science: The systematic study of human behavior. In a workplace context, this involves providing evidence-based frameworks to understand motivation, decision-making, and organizational dynamics.

People Analytics

People analytics emerged from the digitization of HR functions, gaining traction through advancements in HR technology, recruiting optimization, and workforce reporting. The field was further enriched by behavioral science, data science, and AI, which introduced predictive capabilities and deeper workforce insights. Over time, people analytics evolved from basic reporting to strategic decision-making, enabling organizations to measure, predict, and influence workforce trends using employee data (i.e., the roots of the Tree of Value). 

People analytics emerged from multiple origins, independently but around the same time. While no single moment defines its birth, through both formal research and informal conversations, a few key sources stand out as pivotal in shaping the field:

• HR Technology – As HR functions transitioned from paper to digital systems (e.g., HCMs, ATSs, and LMSs), the first wave of HR technology emerged. This digitization made vast amounts of employee data accessible, and with it came the need to justify the cost and effectiveness of these systems. The term human capital gained traction, signaling a shift toward leveraging data for strategic workforce decisions.

• Reporting – The rise of HR technology made employee data more accessible, but extracting insights required specialized expertise that many HR teams lacked. Organizations began hiring analysts to generate reports. Over time, some of these analysts sought to do more than just pull data—they wanted to leverage it for real organizational change.

• Recruiting – Early people analytics efforts originated within recruiting teams seeking to optimize applicant funnels—tracking who applied, who was hired, and when. Simple counts evolved into sophisticated analytics on efficiency, quality, and hiring effectiveness, setting the stage for broader applications.

• Data Science – Early maturity models of people analytics often described prediction as the field’s ultimate goal. As disciplines like data science, machine learning, and AI advanced, these methodologies began to permeate HR. The rise of “big data” enabled organizations to identify hidden patterns and extract novel insights from HR datasets. Tech companies were the first to heavily invest in these approaches, but the trend quickly spread to most large enterprises. 

Lightcast was a pioneer in this space over two decades ago, among the first to collect information from job postings posted online. This allowed for definitive, quantifiable information on what skills were in demand and how the labor market was changing over time, so that enterprises, educators, and the public sector could be better informed about their workforce and make better decisions as a result.

This is not a full list of the fields that contributed to people analytics, these are just a few of the most prominent. But even this small sample shows how people analytics has always been an evolving discipline, shaped by diverse influences rather than a single defining moment. One of the most significant of these precursors was workforce planning, a field that laid much of the groundwork that people analytics has built upon.

People Analytics Job Postings Over Time:

Workforce Planning

Workforce planning predates people analytics, originating in military planning, government labor programs, and operational workforce management. It focuses on aligning workforce supply with demand, ensuring organizations have the right people, in the right place, at the right time, at the right cost. While often associated with headcount planning, its true power lies in strategic workforce forecasting and scenario modeling. Workforce planning provides the forward-looking strategy that people analytics needs to drive action. By combining these disciplines, organizations can move beyond reactive reporting to proactive workforce shaping.

So, what exactly is workforce planning? At its core, workforce planning is about matching the supply of workers with the demand for workers in a given company, organization, or industry. The field can be categorized into three key approaches:

• Strategic workforce planning (SWP): Focused on long-term planning, typically looking 3–5 years ahead to align workforce strategy with business objectives.

• Operational workforce planning: Near-term planning, usually covering a 90- to 180-day horizon, often used for high-volume hiring environments.

• Headcount Planning: The most tactical form of workforce planning, focused primarily on how many people need to be hired within the next fiscal year.

These approaches have much in common. Whether you’re making hiring decisions based on five weeks, five months, or five years, you need to understand the context in which you’re operating. That might mean using salary data from job postings to know how your competitors are hiring, a broader profile of the local workforce and their educational attainment, or a deep dive on demographics with government data to know how many people you can anticipate joining the local workforce in the coming years.

Much like people analytics, workforce planning has evolved from multiple disciplines, often independently but in parallel. Below are some of its key origin stories:

• Public Sector – The US government has played an outsized role in developing and formalizing workforce planning. Dating back to World War II, the armed forces pioneered workforce planning methodologies, which later expanded into civil service, the Office of Personnel Management (OPM), and broader government workforce initiatives. These efforts laid the foundation for modern workforce planning through tools like O*NET, government labor surveys (e.g., Current Population Survey), the Bureau of Labor Statistics, and Census workforce tracking.

  The government’s emphasis on workforce readiness, job analysis, and forecasting labor supply and demand makes it the undisputed leader in workforce planning history. Government statistics provide a foundation for other data suppliers to build on; for instance, Lightcast supplements and refines publicly available datasets with profiles and job postings data for a more detailed look at supply and demand of workers and contribute to the Build, Buy, Borrow, Bot strategies of workforce planning. Each of these datasets enriches the others, providing greater context together than they could apart. A combined view of proprietary and publicly-available data is greater than the sum of its parts.

• Recruiting – While the government was building structured workforce planning frameworks, corporate HR teams—particularly talent acquisition—were unintentionally developing their own version. As recruiting functions became more specialized, they took on headcount planning, analyzing turnover rates, retirements, and business growth or contraction, as well as components of talent intelligence such as computing external talent availability and competitor hiring trends. Over time, recruiting leaders realized that effective hiring couldn’t be done in isolation—it required workforce planning and people analytics integration, as well as talent intelligence.

• Financial Planning & Analysis (FP&A) – The Finance function has always been closely intertwined with workforce planning, though it has traditionally approached it from a budgeting perspective rather than a talent strategy perspective. FP&A teams often dictate how much headcount an organization can afford, making workforce planning a necessary counterpart to annual financial forecasting. In many organizations, this leads to a sometimes tense relationship between workforce planning and FP&A, as Finance holds the purse strings while HR attempts to forecast and plan talent needs—but on the other hand, when workforce planning can use data to prove ROI, Finance tends to respond well, and can become a valuable ally.

• Operations – In high-volume hiring industries (e.g., call centers, distribution centers, fast food, retail), operational workforce planning emerged as a business necessity rather than an HR initiative. These industries quickly realized that if they couldn’t forecast and optimize hiring throughput of workers, their business and operational continuity would suffer. While HR functions eventually took notice, operations teams were the true pioneers of workforce planning in these environments. Interestingly, I/O psychologists played a major role in developing validated pre-hire assessments to optimize high-volume hiring, and their primary stakeholders were often business leaders, not HR.

• Talent Management – Talent management, which focuses on attracting, developing, and retaining top employees, has much in common with workforce planning. As HR Centers of Excellence (COEs) evolved in the 1980s and 1990s, creating specialized teams focused on optimization and best practices within HR teams, workforce planning became a subject that talent management functions began to address. Concepts such as critical roles, succession planning, single points of failure, and skills inventories became common in boardroom discussions. While the government operationalized workforce planning, talent management elevated it to a C-suite priority.

Other subjects contributed to the development of workforce planning—including performance management, organizational design, and compensation structures—but even among the fields considered, one key takeaway is clear: workforce planning evolved both inside and outside of traditional HR functions. Headcount and staffing decisions are nuanced and complex, and HR departments don’t always have the internal information they need to create an effective strategy for their workforce. That’s where talent intelligence comes in.

Workforce Planning Job Postings Over Time:

Talent Intelligence

Unlike people analytics and workforce planning, which oftentimes focus on internal data, talent intelligence integrates external labor market insights to solve business problems for organizations. It is the newest discipline among the three, but its relevance in positioning HR as a truly strategic function cannot be overstated. It evolved from talent sourcing, executive search, HR technology vendors, and corporate strategy, providing businesses with a competitive edge in hiring, skills analysis, and location strategy; and includes overlap with concepts such as job architecture, labor market analytics, and the shift toward skills-based organizations. Talent intelligence helps organizations understand macroeconomic labor trends, benchmark against competitors, and anticipate talent shortages. When paired with workforce planning and people analytics, it ensures that internal workforce strategies are informed by broader market realities.

Talent intelligence lacks a clear, universally accepted definition—in part because many HR technology vendors have co-opted the term to market their products. While this has fueled its visibility, it has also led to a fragmented understanding of what talent intelligence truly encompasses. At its core, talent intelligence is about extracting insights from internal and external data related to an organization’s human capital, with a strong emphasis on labor market analytics. It can operate on both macro and micro levels:

• Macro-level talent intelligence examines labor market supply and demand across cities, countries, or regions to inform workforce strategies.

• Micro-level talent intelligence, similar to sourcing intelligence, focuses on sourcing hard-to-find skills and individuals for specific roles or niche functions within an organization.

  Much like people analytics and workforce planning, talent intelligence has evolved from multiple origins. Below are some of the key influences that shaped its development:

• Executive Recruiting & Head-Hunting – Not long ago, executive search firms were the gatekeepers of elite talent intelligence. A top headhunter’s rolodex, their ability to reach exclusive senior candidates, and their deep insights into prevailing wages, employer reputation, and competitive hiring strategies were invaluable to corporations. These firms pioneered early executive recruiting databases, which are still used today to track high-potential executives and inform C-suite hiring strategies.
  

• HR Technology Vendors & Consultants – While the US Bureau of Labor Statistics (BLS) and other government agencies have provided rich labor market data in the past, their reports are often complex and difficult to interpret. Enter labor market intelligence providers such as Lightcast: In the early 2000s, these firms began aggregating and simplifying labor market data to provide benchmarks, job profiles, and insights into talent supply and demand. Early players like Saratoga (HR benchmarks) and Lightcast (labor market insights, job profiles, & job postings) filled this gap, though adoption was slow. However, after the 2008 financial crisis, organizations rapidly embraced labor market analytics to inform workforce planning and recruiting. By the 2020s, the demand for talent intelligence skyrocketed, driven by skills shortages, demographic shifts, and changing immigration policies, as highlighted in Lightcast’s The Rising Storm research.
  

• Strategy, Mergers & Acquisitions (M&A) – Business, much like war, is rarely a fair fight. Corporate strategy teams have long understood that human capital is a competitive advantage, even if it isn’t always acknowledged explicitly. In M&A, private equity, and venture capital, the value of a company isn’t just in its financials—it’s in its people. If a competitor employs the only 20 engineers in the world with expertise in a breakthrough technology, acquiring that company outright might be more effective than trying to outcompete them. Likewise, understanding why one company consistently outperforms another in EBITDA, margin, or stock price often boils down to hidden human capital insights. Insiders know this—and if you want to operate at a strategic level, so should you.
  

• Corporate Real Estate – Unlike other talent intelligence origin stories, corporate real estate has historically been considered an operational function, not a strategic one. In the early 2000s, however, business leaders began recognizing location strategy as a critical workforce variable. Trends such as corporate headquarters relocations (e.g., to lower-cost states), globalization (e.g., outsourcing to developing nations), and skill-based geographic expansions (e.g., opening R&D centers in tech hubs) reshaped corporate real estate decision-making. The impact was so significant that when one company moved, competitors often followed—as seen in the highly publicized Amazon HQ2 selection process, which was driven almost entirely by talent intelligence data.
  

• Talent Acquisition Sourcing - Talent Intelligence emerged as a transformative evolution from traditional Talent Acquisition sourcing, born from the recognition that requisition-level sourcing insights could be strategically scaled to address upstream talent challenges. What began as tactical candidate identification expanded into a sophisticated discipline delivering actionable, market-wide intelligence through tools like competitor or DEI battlecards, enabling organizations to make proactive, data-driven workforce decisions rather than reactive hiring moves. This elevation from transactional sourcing to strategic intelligence has fundamentally reshaped how organizations understand and engage with talent markets, positioning Talent Intelligence as a critical business function that informs not just hiring strategies, but broader organizational planning and competitive positioning affecting upstream decisions rather than mitigation issues downstream.

Talent Intelligence Use Cases

Talent intelligence was not born out of HR: It was born out of necessity. Organizations realized that understanding talent at a macro and micro level was a competitive differentiator, not just an HR function. In today’s business landscape, talent intelligence is no longer optional—it is a critical input for workforce strategy, hiring, and business decisions. Companies that fail to integrate labor market insights into their planning risk falling behind competitors who leverage talent intelligence to identify new opportunities, mitigate risks, and optimize their workforce. 

As we move forward, talent intelligence will continue to evolve, blending elements of AI, people analytics, workforce planning, and business strategy, and those who master it will not only shape the future of HR but also influence the strategic direction of their entire organization. But that requires understanding the organization’s talent clearly—which means understanding how people behave.

Behavioral Science

Behavioral science is the odd one out of the four. Behavioral science has a history and applications beyond HR and enterprises, but it’s woven throughout all three of the other fields, enhancing the ability to interpret data and drive meaningful change. Rooted in over 100 years of industrial-organizational (I/O) psychology research—and, more recently, behavioral economics and decision science have entered the equation—behavioral science provides the frameworks necessary to understand human motivation, decision-making, and behavior in the workplace, and bring evidence-based practices to the HR function. 

People analytics leverages behavioral science to conduct predictive studies that model human behavior patterns. Workforce planning incorporates change management interventions that influence human behavior change and as a consequence business effectiveness.  Talent intelligence benefits from behavioral science’s insights into labor market behaviors such as trying to understand the wants and needs of top talent you’re trying to recruit. 

Without behavioral science, the data-driven insights from other disciplines risk being mechanical rather than human-centric. People in the workplace are not interchangeable units, and behavior science and I/O psychology ensure that organizations incorporate this factor into their practices while driving organizational effectiveness.

The Value of Human Capital Labor Data: The Roots of the Tree

While these branches form the intellectual framework of the Tree of Value, the real opportunity for the value of human capital labor data lies in the roots of the tree. This is where the collaborative opportunity resides. Whether human capital labor data comes from Human Capital Management systems, external labor market providers like Lightcast, or new advanced AI applications, the value of these four disciplines doesn’t exist without the right data collection, integration, and analysis across a variety of data sources and technologies.

• People Analytics relies on HR systems and operational data to accurately aggregate, model, and visualize workforce trends.

• Workforce Planning uses human capital data and forecasting to model workforce supply and demand.

• Talent Intelligence draws on external labor market data, such as Lightcast, to inform hiring and skills strategies.

• Behavioral Science utilizes data from psychological assessments, employee sentiment and surveys, and analyzing employee behavior patterns to understand people at work.

The roots of the Tree of Value are made up of data from many sources, but when combined together this data enables real value creation for enterprises. However, data alone is not enough—success depends on how well organizations integrate insights across the Tree of Value to drive business impact. We must work together. 

For Organizations Just Starting Out:

Your primary focus should be data. Invest in robust, integrated data collection systems that can capture:

• Internal workforce metrics

• External labor market trends

• Skills inventories

• Key performance indicators

• Behavioral assessment data

Quality data is the foundation that will enable you to gradually build capabilities across all four disciplines. Start by ensuring your data is comprehensive, clean, and capable of being analyzed from multiple perspectives. The ultimate goal is not only mastery of any single discipline, but creating a flexible, insights-driven approach to understanding and managing human capital for your organization.

For More Mature Organizations:

Your organization has likely invested in entire functions dedicated to people analytics, talent intelligence, workforce planning at this point, and have embedded advanced behavioral science techniques throughout your organization. Where you must invest in the future is not just having data, but having the right, highest quality data possible and a breadth of data as well. This will prepare you for the Tree of Value, and also the GenAI applications to come.

Whether it's using behavioral science to improve employee engagement, leveraging talent intelligence for better hiring decisions, applying workforce planning techniques to create a skills-based organization, or utilizing people analytics to drive strategic business outcomes, every branch of this tree offers actionable insights. The key is to begin by understanding and investing in human capital labor data – the Tree of Value – which will provide ROI for each of these four fields—individually and together.

The Future of HR Leadership: The Strength of the Tree

HR functions that operate in silos struggle to adapt to the rapid changes shaping the workforce—and as Generative AI spreads throughout the function, the speed of change is only going to increase. HR should embrace the interconnectedness of people analytics, workforce planning, talent intelligence, and behavioral science to unlock new levels of strategic influence as the function undergoes this shift. Ultimately, the Tree of Value is about recognizing that we are stronger together than apart. If we cultivate collaboration, draw on the wisdom of all our fields, integrate our insights effectively, and combine our data sources, we can redefine the role of HR leadership and lead organizations in a future where AI is a more ever-present shaping force of the workforce strategies now and into the future.