Overview
.svg){kind=small}
One AI
Data Mesh
.svg){kind=icon}
One AI Assistant
Data Stories
Enterprise AI
One AI Data Intelligence
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
Gathering information about the workforce is as vital to an HR team as air and water. The most important information that teams will gather comes from active, attentive employee listening. This is arguably one of the most critical skills for HR professionals.
To listen to a member of the workforce is to give them respect, time, and attention, and to hear what is going on. It’s the oldest way we learn. We’ve seen listening programs, supported by companies like OrgAcuity, grow from those roots into programs that survey the full company and beyond.
People analytics could be described as the art of "listening at scale." But workforce systems, like your HRIS, ATS, etc, are not always in the mix where employee listening is considered. In this article, I advocate that they should be. To paint the full picture and ensure employees are seen and heard, we need an integrated framework that captures workforce information from the three channels where it lives: conversations, surveys, and workforce systems.
The 3 Channels of Workforce Listening
Margaret Mead, an anthropologist, best captured the complexity of working with humans with her quote: “What people say, what people do, and what people say they do are entirely different things."
While humorous, I believe this quote can also act as the foundation to inspire an integrated framework for workforce listening. Mead's quote effectively outlines three “information channels” for gathering information about the workforce: conversations, surveys, and systems. I’ve rearranged them slightly for the purposes of this blog:
- “What people say” = Conversations: People having conversations in the workplace
- “What people say they do” = Surveys: Respondents assessing themselves and their ideas through surveys
- “What people do” = Systems: What people actually do in the workplace which can be tracked within HRIS or collaboration technologies (HR Tech / Work Tech / Collaboration tools)
Conversations, surveys, and systems are where workforce information is generated. Data and insights then flow from those channels to central storytellers and decision-makers. This is an end-to-end view of the HR decision-making process.
This is different from how we usually discuss data in HR. We often hear data described by its topic (e.g. Recruiting data, L&D data, or Comp data), source system (e.g. Workday data, Greenhouse data) or its application (e.g. descriptive, predictive, prescriptive data). This channel view seeks to depict the supply chain of information.
Let’s delve deeper into this framework to create a more comprehensive understanding of the workforce. I believe this holistic approach to listening will allow HR professionals to make better-informed workforce decisions that positively impact the organization.
1. Conversations
Speaking to your workforce and acting on that information is how HR first emerged as a profession. Conversations refer to the 1:1 interactions, observations, and ethnographic tools that HR employs to understand the workforce. These are very human tools and can be a powerful method for sense-making and storytelling within an organization.
When conducted effectively, conversations allow HR personnel, managers, and leaders to gain a nuanced understanding of their workforce that technology struggles to replicate. For instance, it will be a long time before computers can comprehend how grief impacts performance, the unsettling chaos of a reorganization, or the pride of a promotion. Despite recent advances, empathy, connection, and meaning-making will remain distinctly human domains for some time.
In the move towards data-driven decision-making, I believe we have underestimated the impact that these conversations can have on decision-making. The anthropological sensemaking that occurs when an experienced HRBP listens to the workforce is unmatched when it comes to quickly understanding cultural dynamics and understanding the core of workforce issues.
Bias and human error in this channel of conversations is a well-documented concern and there are dangers in relying solely on conversations to inform the HR decision-making process. These are issues that must be thoughtfully planned for and mitigated, both in how this method is employed, but also the use of other channels to validate, verify, and correct for bias in information gathered from this channel. However, that does not mean that those other channels will replace conversations and conversation still has an important place in decision-making.
Conversations types include:
- Formal Conversations: These include regular 1:1s, performance reviews, and formal checkpoints that ensure the workforce is heard, managed, and supported. These conversations not only help managers and HR leaders evaluate their employees' performance but also provide an opportunity for information gathering for the organization and for understanding the employee experience.
- Informal Conversations: This refers to the casual conversations that take place around the “watercooler” (in person or remote), where employees can share what's really going on. These conversations can lead to surprising insights about the workplace, culture, and organization. For instance, employees might discuss work-related challenges, share ideas for improvement, or provide feedback on a topic that you wouldn’t expect. Such conversations can help managers and HR leaders identify potential issues before they become problems, and can be a channel for business context that is not otherwise captured.
- Ethnographic research: The most formalized version of conversation-based information gathering would be ethnographic research. This refers to the scientific and qualitative research techniques such as observation, participation, and immersion in the workplace to gain cultural and organizational understanding. Ethnographic research can provide a validated and scientifically sound understanding of employee behavior and attitudes, and can also uncover hidden dynamics and cultural norms that might not be apparent through formal or informal conversations alone. By conducting ethnographic research, organizations can gain a deeper understanding of their workforce and tailor their strategies and policies accordingly.
2. Surveys
Surveys can provide a structured, valid, and reliable method to collect information about workforce attitudes, opinions, behaviors, and demographics. This channel represents whenever a form is completed to capture novel data that is otherwise not captured by a system passively. This includes engagement surveys and other forms such as filling out performance reviews or feedback forms after trainings.
Surveys let you gather information from a large amount of people quickly. I could spend 30 minutes speaking to 80 people (a full back-to-back week for me and a 30-minute disruption for every person I speak to) or I could design and send a survey that everyone completes on their own time.
Survey types include:
- Structured survey questions: Questions about the environment, factors in the workplace, and information that the creator wishes to assess. Ideally structured and evidence-based. Questions could include items like "How satisfied are you with your current role?" and "Do you feel valued by your employer?" followed by a distinct multiple-choice scale.
- Open-ended survey questions: Open-ended survey questions provide a prompt with a text box for a respondent to complete. These questions could include a variety of open-ended topics like “please tell us about your onboarding” or “Are there tools you need to perform your role that you cannot acquire?”. The volume and variety of data that is brought back through open-ended surveys is much higher than structured surveys and these require further coding or understanding before they can be used in decision-making.
- Psychometric surveys: Psychometric survey questions could be either structured or open-ended, so this is a bit of a false breakout, but it is important to call out as it is a unique type of information gathered about the psychology of the employee in the workplace. Psychometric surveys gather information about employees' attitudes and sentiments which can be helpful in understanding variations in trends such as attrition.
3. Workforce Systems
The third internal information channel in this framework is systems. The workforce’s interactions with technology generate a wealth of data about people, processes, and work habits. Skilled data engineers, analysts, and data scientists can process this data to extract valuable insights about the workforce.
Your HR systems are a goldmine of information. Unfortunately, the data inside them can be disorganized, siloed, and contradictory across systems. This makes it difficult to analyze and act on. Before it can be used, it has to be intelligently extracted, modeled, governed, defined and unified in a pristine data foundation. Without that step, workforce system data can't be used, by you or your Enterprise AI tool. So, the key advantage of systems data is its potential readiness for use... While unifying these fragmented systems requires is not easy and requires a data foundation, the insights once unified makes it valuable.
Once all that has been accomplished through a tool like One Model, your Systems data can offer a broader perspective of the organization as a whole. Conversation and surveys gather information from each employee from their personal viewpoint, but their perspective may not be broad enough to see organization-wide issues. The view of what is going on end-to-end, which is needed for workforce planning, workforce readiness, or skills gaps analysis, can be generated from this systems channel.
Want to see how One Model gets your systems data ready for analysis?
Systems data is also valuable because it is largely a passive data source, produced as a byproduct of work conducted through technology. That doesn't mean using it for analysis is inherently easy, but its generation is easy at least. Overall, it is less subject to biases and limitations of human perception, memory, or interpretation.
However, systems data often lack the nuanced information density of business context provided by conversation and survey methods. Additionally, the bias it does have is often embedded in the software design choices which can often be harder to detect and understand. Choices made by programmers regarding UX, data capture, native reports, and interactions available can introduce potential areas for bias in the extracted information.
Systems data sources include:
- HR tech: This is the traditional tech stack managed by HR tech teams. Systems handling HR-related processes and programs (e.g., Core HRIS, ATS, Performance Management, LMS). For example, when a worker is hired, the applicant tracking system (ATS) captures data about their demographics, prior experiences, and the interviewing team's assessment.
- Collaboration Tech: Systems capturing collaboration (e.g., Slack, Microsoft Teams). These tools (Slack, Teams, Zoom, Google Docs, etc.) produce information about teams, interactions, and how work gets done within an organization. Techniques like organizational network analysis can reveal how information flows through an organization or identify influential individuals.
- Work Tech: Technology capturing broad work data outside of HR tech (e.g., procurement systems, code tracking, or attendance). Systems like intranets, timekeeping, expense systems, and ticketing systems. These work tech systems also produce data that can be used to recreate, model, and analyze the flow or work in the workplace. By associating these systems with HR tech systems, we can build powerful stories connecting HR data to work outcomes.
The Tradeoffs of Each Information Channels
Selecting the right channels for a given decision is vital for success. To do so, it helps to understand the limitations and tradeoffs in the realm of trust, effort, and information density.
Trust
Trust is a key factor in how we interpret information that comes from the various channels.
Conversations: Information from conversations can be difficult to trust, particularly when not everyone involved is present or when they are not recorded, transcribed, or made public. If I talk to my manager about a coworker, my manager will need to verify their side of the story. Even when conversations are recorded, they can still be misleading.
Surveys: Surveys are generally more trusted than conversations due to the structured way that they are delivered. Surveys can have academic ties on their design and are typically more consistent, reliable, and objective than conversations. That said, it can still be difficult to know what someone was thinking when they read a question on their own. Employees may also have an incentive to game a survey or mislead the survey, which can lead to reduced trust.
Systems: The systems channel is often seen as the most objective source of information because it is generated as a passive byproduct of work and remains unchanged from the moment of entry. Unlike conversations or surveys, systems data doesn’t rely on human memory or subjective context; instead, it simply tracks the concrete actions that have occurred. However, this trust is entirely dependent on data quality and alignment. While raw transactional data is objective, fragmented data across disparate HR, payroll, and financial systems can lead to conflicting definitions and metrics. When an organization establishes a unified, standardized foundation for this systems data, it removes that friction—eliminating spreadsheet chaos and giving leaders a single source of truth they can trust implicitly.
Effort
The effort required to transform raw inputs from each channel into actionable workforce intelligence is another critical consideration.
Conversations: This channel is exceptionally high-effort to scale. Conversation data is rarely converted into structured, tabular formats that you can interact with in a spreadsheet; instead, it must be manually synthesized and interpreted by the individuals hosting the discussions. Having—and making sense of—hundreds of individual conversations requires massive organizational time.
Surveys: Survey responses are much easier to transform into insights due to the standardization and upfront planning involved in creating the tool. The data generated from closed-ended and psychometric surveys can be instantly analyzed in tabular formats. For open-ended text fields, many of the high-effort qualitative challenges of the conversation channel return, though on a more contained scale.
Systems: The systems channel produces data that is inherently built for scale. While ingestion, extraction, and creating a unified data model require a strategic upfront effort, that investment pays off exponentially. Once a standardized foundation is in place, the ongoing effort to deliver automated insights is significantly lower than manually trying to parse language or run ad-hoc studies in the other channels. This scalability is precisely what makes systems data the engine of modern people analytics teams.
Information Density
Density refers to the richness of information each channel provides. Each channel has a certain density of core information, but some channels layer on personal and business nuance, context, and depth.
Conversations: This is where the conversation channel shines. Interactions between people are incredibly dense with human nuance, layering core content with critical emotional context derived from voice pitch, body language, and facial expressions.
Surveys: Open-ended surveys attempt to capture some of this qualitative nuance at a larger scale, but they naturally lag behind live conversations when it comes to capturing real human depth and immediate context.
Systems: Systems data is easily mistaken as a comparatively flatter information source. This is not really the case. It actually carries the entire institutional and business context (the business rules, calculations, and standardized metrics) that things like enterprise AI need to function. It lacks emotional nuance, but it holds massive structural nuance.
Beat the Tradeoffs by Combining the Sources
One way to mitigate the strengths and weaknesses of these channels is to pull them into a narrative together. For example, systems data can provide a high-level overview of the situation and help frame the story. Survey data can be used to capture precise additional information needed for a study. Conversations can provide a much deeper understanding of the context of the problems at hand.
While combining HRIS output with surveys and conversations can be challenging, translating all three into workforce information is what allows us to pull them both into a coherent narrative.
Example #1:
We start to see high turnover rate among a specific demographic in an organization. Relying solely on systems data ("what people do"), we may jump to the conclusion that this demographic is not a good fit for the company. However, by listening to "what people say they do" in survey data, we may discover that this demographic is leaving because of a lack of training or career advancement opportunities. If we listen to "what people say" in follow-up conversations between HRBPs and employees, we may identify that there is a particular manager that is not allowing teams to attend trainings. All three channels together create a more comprehensive story.
Example #2:
A software development firm leverages information from the systems channel to identify patterns of late-night work activity among its employees. By approaching the data with empathy and understanding, they initiate conversations with affected employees and discover that tight deadlines and unrealistic expectations are causing stress and burnout. As a result, the firm adjusts its project management approach to prioritize employee well-being and work-life balance. They perform a quarterly survey on these topics going forward which finds that the changes they implemented have led to a healthier and more sustainable work environment.
There are instances when each channel should also be used independently. An employee relations professional may depend solely on conversations, bypassing surveys or systems. Surveys can offer feedback on large-scale events not covered by systems and where conversations are not feasible. Systems data may be all that is needed for a first-pass analysis or exploratory pass at understanding the organization.
Visualizing the Framework
In the following graphic, I’ve laid out the supply chain from each information channel and how it is converted to information. Once it is synthesized and analyzed, it becomes stories which inform decisions.
This graphic also factors in the talent strategy of a business (how they want to create strategic advantages with talent) and the experience of the decision maker, which both inform stories. Those two areas have unique influence and in turn are influenced by decisions made by a decision maker.
Note: While this article was focused on internal channels of information, additional external channels of information include external labor market data (information about the context that a workforce sits within) or evidence-based practices (academically validated information).
This flow from information generation to how we inform decisions with stories should be top of mind for any team working in employee listening, people analytics, or HR. We ground ourselves when we are reminded that our goal is to support the HR decision-making process to drive business results.
The Benefits of Using Systems Data as a Listening Tool
Now that we've explored the value of combining conversations, surveys, and systems data for a comprehensive understanding of the workforce, let's focus specifically on what it means to bring the systems channel into the conversation on listening.
There are five key benefits that an HR team achieves when blending systems data into the conversation on listening along with a fictional story detailing how this could work in practice:
1. Engages HRBPs
By embracing systems data as a form of listening, we make analytics more accessible to HR professionals who may be more comfortable with traditional listening methods. HRBPs are good at listening and this is another way to do what they are good at. By viewing systems as another way to listen, we can reduce initial fears and skepticism that someone feels when they hear “HR Analytics” or “People Analytics” which will help bring HR into the fold, tapping into their strengths.
For Example - An HR business partner at a retail organization believes that a new schedule that has been set for employees could be causing work-life balance issues. They have had conversations with a few employees which prompted the investigation and after sending out a work-life balance survey they confirmed the issue. However, leadership was still not convinced, so the HR business partner listened to the data from the time-management system to analyze patterns of absenteeism and tardiness among employees before and after the shifts were changed and they found significant increases in each, which they brought into their story. The HRBP took this story which was informed by conversations, survey, and systems data to the leadership team and it convinced them to make a change to the shift schedule, resulting in improved attendance and employee satisfaction.
2. Supports Integrated Storytelling
This framework creates a more integrated approach to analytics, where we can combine the insights gained from systems data with other channels of information to create a more complete picture of the situation at hand. This integrated approach to methods will lead to better workforce decisions as more information can be brought to bear.
For Example: A healthcare organization seeks to improve diversity and inclusion within its workforce. By combining data from employee demographic systems, engagement surveys, and focus group conversations, they created a comprehensive narrative that revealed disparities in career development opportunities for underrepresented groups. As a result, the organization implemented targeted mentorship programs and inclusive leadership training, which fostered a more diverse and inclusive workplace.
3. Strengthens Employee Trust
Organizations can demonstrate that they value their employees by actively listening to them through various channels, including systems data. By framing systems data as a form of listening and bringing empathy to bear on that, teams can communicate to employees why they are performing analysis and reduce mistrust related to the analysis of systems data.
For Example: A financial services firm transparently communicates their use of systems data to track employee work patterns in order to optimize team productivity. By sharing this information with employees, explaining how data is protected, and explaining how the data would be used to inform the HR decision-making process, employees felt more involved in the process and trust the company's intentions, which leads to increased engagement and commitment.
4. Reduces Debate
Recognizing that all three information channels — conversations, surveys, and systems—are necessary to tell complete human stories fosters a collaborative environment between different teams and functions. This encourages analytics teams, listening teams, and HR business partners to work together to create a comprehensive narrative, rather than focusing on just one aspect of data collection.
For Example: In a manufacturing company, there is disagreement between HR and operations teams about the most effective way to allocate resources for employee training. By incorporating data from all three channels—systems data on employee performance, survey feedback on training preferences, and conversations with both employees and managers—they are able to reach a consensus that ultimately leads to more efficient training and improved workforce capabilities.
5. Human-Centered Analytics:
Framing systems data as listening to the workforce emphasizes empathy and understanding. We should always remember that behind every data point in HR is a human who has a livelihood, friends, family, and a world outside of work. Approaching systems data as listening to employees reminds analytics teams to respect the human behind the data and ensures that the focus remains on the human aspect, rather than treating employees as data points, which ultimately leads to better workforce decisions.
Final Thoughts
We must recognize the unique value of each channel in capturing the complexity and richness of the workforce experience. But to make this framework work, we have to talk about the systems channel. It is the sleeper hit of workforce listening. While conversations and surveys give us critical human nuance, the systems channel holds the vast, objective reality of how work actually gets done across the entire enterprise.
However, unlocking the systems channel requires a real commitment. We cannot just point to a messy stack of siloed HR, finance, and operational tech and claim we are "listening." To use systems data correctly, organizations must make a deliberate commitment to extracting, cleansing, and preparing it into a unified data foundation built on consistent business metrics and definitions. Without this up-front investment, the sleeper star stays asleep, buried under fragmented definitions and spreadsheet chaos.
When we do commit to building that trusted foundation, everything changes. It provides the essential operational context that allows our analytics teams—and our modern intelligence tools—to deliver insights leaders can act on with absolute confidence.
And, a final note: For all three systems, transparency is key. Employees deserve to know what information is being gathered, how it is used, and who can see or share that information. Proper data privacy controls, data governance, and agreements between company and employee must be established. Without empathy and these protections, all information channels will break down.
As we move forward in an increasingly data-driven world, it is crucial that we remain grounded in empathy and the human aspect of decision-making. Understanding and supporting the individuals that make up our organizations is core to who we are in HR. By actively seeking input from the workforce through all available data channels and embracing a comprehensive listening approach, we will be better equipped to drive meaningful change, foster employee trust, and ensure the long-term success of our organizations.
Margaret Mead hit a point of truth when she said, “What people say, what people do, and what people say they do are entirely different things.", but we’ll end on another quote from Mead which I’ll pass on to you as you think about the work required to get these three channels speaking together instead of apart at your organization:
“Never doubt that a small group of thoughtful, committed, citizens can change the world. Indeed, it is the only thing that ever has.”― Margaret Mead
Many thanks to Mike Merritt, Kyle Davidson, Keith Kellersohn, Peter Ward, Beverly Tarulli, Ethan Burris, Shahfar Shaari, Allen Kamin, Anna Tavis, Al Adamsen, Lyndon Llanes and many others for wonderful conversations on this topic and your feedback! I am grateful and reminded daily of what an incredible community we have in the people analytics world.
Interested in talking to my team to learn more?
Fill out the form below.
