Understanding Organizational Network Analysis (ONA): A Guide for HR Leaders

By K. Arthur O’Dea | Business Consulting | People Analytics

Organizational Network Analysis (ONA) is the discipline of mapping and measuring the informal relationships — information flows, collaboration patterns, and influence pathways — that actually drive how work gets done inside an organization. Unlike org charts, which capture reporting lines, ONA captures who talks to whom, who unblocks problems, and where knowledge gets bottlenecked. For senior HR leaders, People Operations teams, and CHROs at mid-to-large organizations, mastering ONA is increasingly a prerequisite for making workforce decisions with the same rigor applied to financial or operational data.

What Is Organizational Network Analysis and Why Does It Matter for HR?

The Org Chart Is a Map of Authority, Not a Map of Work

Every organization has two structures running simultaneously. The first is the formal hierarchy: reporting lines, job titles, functional divisions. The second is the informal network: the relationships through which decisions actually get made, knowledge actually gets shared, and innovation actually emerges. Org charts capture the first structure with precision. Organizational Network Analysis reveals the second.

ONA draws from social network theory — specifically the mathematics of graph theory — to represent organizations as a network of nodes (people) and edges (relationships or interactions). Each connection in that network can be weighted by frequency, direction, and type (information sharing, decision-making, emotional support, expertise referral). The resulting map surfaces patterns invisible to any org chart: a mid-level analyst who informally brokers communication between two siloed divisions, an overloaded team leader who has become an unavoidable bottleneck, a high-potential employee who has zero meaningful connections outside her immediate team.

How ONA Differs From Traditional HR Analytics

Conventional HR analytics — headcount reports, attrition rates, engagement scores, performance ratings — describe individual employees in aggregate. ONA describes the space between employees: the quality and structure of the relationships that determine whether strategy actually executes.

Consider a practical contrast. An engagement survey might reveal that a business unit scores low on “collaboration.” ONA tells you why: perhaps two teams that should work together share only a single connector — one person through whom all cross-team communication flows. If that person leaves or burns out, collaboration collapses entirely. Engagement surveys cannot show you that structural fragility. ONA can.

Research by organizational network scholar Rob Cross and colleagues at the Connected Commons has documented this gap repeatedly across large enterprises. Their studies found that roughly 20% to 35% of value-added collaborations within organizations come from only 3% to 5% of employees — a concentration that creates both enormous leverage and enormous risk that traditional HR data never captures.

Why HR Leaders Should Care Now

Three converging forces make ONA a strategic priority for HR in the current environment:

Hybrid and distributed work has broken informal networks. Remote and hybrid models disrupted the hallway conversations, spontaneous cross-team lunches, and ambient awareness that once kept informal networks alive. Microsoft’s 2021 Work Trend Index found that employees’ professional networks had become more siloed since the shift to remote work — with time spent on cross-group collaboration declining significantly. ONA provides the instrumentation to detect and address that damage.

Organizational changes move faster than org charts. Mergers, restructurings, and rapid headcount scaling outpace the ability of formal reporting structures to reflect how people actually collaborate. ONA gives HR a real-time or near-real-time signal of where collaboration is breaking down during transitions.

Workforce analytics has matured enough to demand it. CHROs are increasingly expected to present workforce insights at the board level. Reporting headcount and attrition is no longer sufficient. Boards want to understand organizational resilience, knowledge transfer risk, and the human architecture underlying business performance — all of which ONA can address directly.

What Is the Difference Between Active and Passive ONA?

Two Fundamentally Different Approaches to the Same Question

Both active and passive ONA seek to answer the same question: who is connected to whom, and how? They differ profoundly in how they gather evidence to answer it. The choice between them — or the decision to combine them — carries significant consequences for data quality, employee trust, and legal compliance.

Active ONA: Survey-Based Methods

Active ONA collects network data by asking employees directly. A purpose-built network survey presents each respondent with a roster of colleagues and prompts them to identify, and often rate, specific types of relationships.

Typical active ONA survey prompts include:

“Who do you go to for the information or expertise you need to do your job?”
“Which colleagues do you rely on when a decision needs to get made?”
“Who do you turn to when you face a difficult problem at work?”
“Who energizes you when you interact with them? Who drains you?”

Each prompt captures a distinct type of tie — information exchange, decision influence, and energy relationships — producing separate network maps that together reveal organizational dynamics with nuance unavailable from any single data source.

Advantages of active ONA:

Perceptual precision. Because respondents self-report relationships that feel meaningful to them, the resulting network reflects the relationships that actually matter behaviorally — not just the ones that generate digital traces. A weekly email thread may be perfunctory; the colleague a developer calls on Slack when truly stuck may not appear in calendar data at all.
Relationship typology. Active surveys can disaggregate information ties, decision ties, energy ties, and trust relationships — distinctions that passive data cannot easily make.
Low technical overhead. Active ONA requires a well-designed survey instrument and a way to collect and visualize the results. It does not require integration with enterprise communication systems.
Explicit consent by design. Employees choose to answer, which simplifies privacy compliance.

Limitations of active ONA:

Response bias. Employees may name who they should work with rather than who they do. Survey fatigue can lower response rates, and partial response skews the resulting network map.
Snapshots, not streams. Active ONA captures a moment in time. Running it repeatedly to track change is resource-intensive and risks respondent fatigue.
Limited scale. In organizations above roughly 2,000 to 3,000 employees, full-roster active ONA surveys become logistically unwieldy. Survey designers often need to sample subpopulations rather than map the full enterprise.

Passive ONA: Metadata and Communication-Data-Based Methods

Passive ONA infers network connections from digital behavioral data — the metadata traces left by how employees actually communicate and collaborate, rather than how they say they do. The data sources typically include:

Email metadata: Sender, recipient, timestamp, thread length (but not message content)
Calendar metadata: Meeting invitations, attendee lists, meeting frequency and duration
Collaboration platform data: Channel membership, @-mentions, co-authorship, reaction patterns in tools such as Microsoft Teams, Slack, or Google Workspace
Badge or physical proximity data: In environments where these are deployed

Microsoft Viva Insights (formerly Workplace Analytics), Worklytics, Humanyze, and Cognitive Talent Solutions are among the vendors that have built platforms specifically to ingest this metadata and produce ONA-style visualizations without surfacing individual message content.

Advantages of passive ONA:

Objectivity and continuity. Because passive data reflects what employees actually do — not what they report — it captures authentic collaboration patterns, including patterns employees themselves may not be conscious of. And because the data flows continuously, HR can detect network changes in near real time rather than relying on periodic surveys.
Scale. Passive ONA can map an entire global enterprise without survey logistics, translation costs, or response rate anxiety.
Longitudinal tracking. Passive methods enable before-and-after comparisons — measuring how a reorganization, an office closure, or a leadership change affects collaboration patterns over weeks or months.

Limitations of passive ONA:

Relationship type ambiguity. Metadata cannot distinguish between a high-frequency, high-trust collaboration and a high-frequency but low-value one. An email thread between two employees may represent deep strategic partnership or routine administrative coordination — passive data alone cannot tell.
Digital bias. Passive ONA only captures what flows through the monitored channels. Employees who rely heavily on in-person conversation, phone calls, or unmonitored messaging tools will appear underconnected in the resulting network — not because they are underconnected, but because their connections are invisible to the data pipeline.
Implementation complexity. Integrating with enterprise communication systems, establishing data pipelines, and validating data quality requires IT partnership and meaningful upfront investment.

How Do GDPR, Data Minimization and Employee Trust Affect Passive ONA?

Privacy is not a secondary consideration in passive ONA — it is a design constraint that must be addressed before any data collection begins.

GDPR and similar regulations create concrete obligations. Under Article 5 of the General Data Protection Regulation, personal data must be collected for specified, explicit, and legitimate purposes; limited to what is necessary for those purposes (the data minimization principle); and stored no longer than necessary. Passive ONA that aggregates email metadata involving EU-resident employees almost certainly constitutes personal data processing, requiring a documented lawful basis — most commonly legitimate interests, which must be balanced against employee rights.

Practical privacy safeguards for passive ONA include:

Aggregate-only reporting. Surface insights at the team or cluster level rather than profiling individual employees. Most enterprise ONA platforms enforce minimum group sizes (typically five or more people) before displaying any metric.
Content exclusion. Process only metadata — sender, recipient, timestamp, meeting duration — never message content, subject lines, or attachment names.
Role-based access controls. Restrict access to ONA outputs to those with a legitimate need to act on them. A team manager has no need to see cross-divisional network maps.
Transparent communication. Inform all employees that passive data collection is occurring, why it is occurring, and what will be done with the results. Employees who understand the purpose and see aggregate (not individual) results are far more likely to trust the process.
Data retention limits. Define and enforce a retention window — most practitioners use rolling 90-day to 12-month windows — and purge data beyond it.

Choose active ONA when:

Relationship quality and type precision matter more than scale.
The organization has fewer than 2,000 employees and survey response rates can realistically reach 70% or above.
The analysis focuses on a specific subpopulation — a leadership team, a critical project community, a newly acquired business unit.
Privacy sensitivity is particularly high, such as in highly regulated industries or regions.

Choose passive ONA when:

Scale, continuity, and near-real-time tracking are priorities.
The enterprise already uses Microsoft 365 or Google Workspace, enabling relatively straightforward metadata access.
The goal is longitudinal measurement — tracking how a restructuring affects collaboration over six to 12 months.
Sufficient privacy infrastructure and transparent employee communication can be established before deployment.

Many mature people analytics functions combine both: passive ONA for continuous network monitoring and macro-level structural insights, active ONA for periodic deep dives into specific populations or relationship types that passive data cannot distinguish.

Which ONA Metrics Should HR Leaders Track?

Why Metrics Matter: Translating Network Maps Into HR Decisions

A network visualization is only as useful as the questions it answers. Without a shared vocabulary of network metrics, ONA outputs risk becoming visually compelling but analytically inert. The four metrics below are the workhorses of applied ONA in HR contexts. Understanding what each measures — and what high or low values imply for team health — is essential for translating analysis into action.

Centrality: Who Is Most Connected, and in What Way?

Centrality measures the relative importance of a node (a person) within the network. Critically, “importance” means different things depending on which centrality metric is applied — each illuminates a different dimension of influence or position.

Degree centrality is the simplest form: the number of direct connections a person has. High degree centrality indicates someone with many direct relationships — a natural hub. Low degree centrality indicates someone who is isolated or peripheral.

HR implication: An employee with very high degree centrality may be indispensable, or may be overloaded. An employee with very low degree centrality relative to peers at the same level may be disengaged, excluded, or simply working in a silo.

Betweenness centrality measures how often a given person lies on the shortest path between two other people in the network. Someone with high betweenness centrality is a critical broker or connector — information flowing between different parts of the organization tends to pass through them.

HR implication: High betweenness centrality often correlates with influence disproportionate to formal authority. These individuals are informal leaders. They are also collaboration bottlenecks and high-attrition risks: their departure can fragment the network.

Eigenvector centrality (the mathematical basis of Google’s PageRank algorithm) measures not just how many connections a person has, but how well-connected those connections are. High eigenvector centrality means a person is connected to other highly connected people.

HR implication: Eigenvector centrality is a useful proxy for strategic influence. Employees with high eigenvector centrality are positioned near the organization’s most active collaboration clusters — and their inclusion in or exclusion from key initiatives carries outsized organizational consequences.

Closeness centrality measures how quickly a given person can reach all other nodes in the network. High closeness centrality means a person can disseminate or receive information across the organization rapidly.

HR implication: High closeness centrality indicates someone well-positioned for roles requiring rapid information synthesis — crisis management, change management, or knowledge transfer during onboarding.

Bridge Roles: The Informal Connectors That Hold Organizations Together

A bridge role — sometimes called a network broker — is an individual whose connections span what would otherwise be separate or weakly linked groups. Remove the bridge, and two clusters that previously collaborated become isolated from each other.

Bridge roles are not always visible in org charts. A senior analyst may bridge the commercial team and the engineering team, not because her title requires it but because she happens to have worked in both departments at different points in her career. She translates priorities, vocabulary, and context across the divide. ONA makes her visible. A traditional reporting structure does not.

What high bridge centrality indicates:

Organizational agility — the team or cluster can access diverse knowledge and resources.
Knowledge transfer capability across functional silos.
A potential single point of failure if the bridge is not redundant.

What it indicates when bridges are absent:

Persistent cross-functional friction — two teams nominally required to work together but with no informal relational infrastructure to make it happen.
Innovation deficits — research on structural holes (see below) consistently shows that ideas generated at the boundary between distinct groups are more novel than those generated within a homogeneous cluster.
Merger and acquisition integration risk — ONA conducted in the early months after an acquisition frequently reveals that the combined organization is two separate networks with no meaningful bridges between the legacy organizations.

Structural Holes: Where Opportunity and Risk Concentrate

Sociologist Ronald Burt introduced the concept of structural holes in the early 1990s: a structural hole exists when two groups or clusters are disconnected, or only weakly connected, within the broader network. Individuals who span structural holes — Burt’s “brokers” — occupy a position of strategic advantage, able to access information from multiple distinct clusters and synthesize it in ways unavailable to those embedded within a single cluster.

The organizational implications are significant:

Structural holes as innovation opportunity. Burt’s research found that managers with networks that bridged structural holes generated ideas rated as more novel and valuable than managers embedded within dense, homogeneous networks. The diversity of exposure drives creative synthesis.

Structural holes as efficiency risk. A structural hole between two teams that should be closely coordinated — a product team and a sales team, for example — means coordination cost rises, miscommunication rates rise, and alignment on shared objectives weakens. ONA can quantify both the location and severity of structural holes, allowing HR and organizational design leaders to intervene deliberately.

Structural holes and inclusion. Research increasingly links structural holes to diversity and inclusion outcomes. Employees from underrepresented groups often sit on the periphery of organizational networks — disconnected from the informal information flows that surface opportunities, sponsors, and stretch assignments. Measuring structural holes around underrepresented populations can surface inclusion failures invisible to engagement surveys.

Network Density: Is Your Organization Over- or Under-Connected?

Network density measures the proportion of possible connections within a network that actually exist. A network where everyone is connected to everyone else has a density of 1.0; a network where no connections exist has a density of 0.

High network density within a team indicates tight cohesion, rapid information spread, and strong collective identity. It also can indicate groupthink, echo-chamber dynamics, and resistance to outside input.

Low network density indicates a more loosely coupled network. Information spreads slowly, and diverse perspectives are less likely to self-organize. Very low density within a team that requires close coordination is a warning signal — it may indicate fragmented culture, trust deficits, or a team that exists on paper but not in practice.

Optimal density is context-dependent:

Team Type	Network Density Implication
Creative / innovation team	Moderate density preferred — enough connection to share ideas, enough openness to outside perspectives to avoid groupthink
Crisis response or operational team	High density often beneficial — rapid, reliable information flow is paramount
Cross-functional project team	Low density may indicate silos that need bridging; target moderate density with deliberate bridging roles
Leadership team	Both very high and very low density warrant scrutiny — the former may signal an insular leadership culture, the latter insufficient coordination at the top

Taken together, centrality, bridge roles, structural holes, and network density provide HR leaders with a diagnostic vocabulary specific to organizational architecture — distinct from, and complementary to, the language of engagement, performance, and competencies.

How Do HR Teams Implement ONA Step by Step?

Implementing ONA inside an organization is a change management exercise as much as a technical one. The framework below reflects the sequencing that consistently reduces resistance, produces higher-quality data, and converts network insights into actionable decisions.

Step 1: Scope the Analysis

Begin by defining three things with precision: the question you are trying to answer, the population you want to study, and the time horizon relevant to your question.

Clarify the business question. ONA is not a general-purpose HR diagnostic tool. Its value lies in answering specific questions about structure, collaboration, and network risk. Example questions that motivate ONA well include:

“We’re planning a reorganization. Where are the informal connectors we must not disrupt?”
“Our innovation output has declined. Is our product team’s network too insular?”
“Attrition in our middle management layer is high. Are those managers overloaded as network hubs?”
“We acquired a company 18 months ago. How well have the two organizations actually integrated?”

Vague mandates — “tell us about our collaboration” — produce vague results. A sharp question produces actionable findings.

Define the population boundary. ONA can be run on a team of 50, a business unit of 500, or an enterprise of 50,000. The population boundary should match the question. Cross-boundary ONA requires careful coordination of consent and data governance across divisions.

Choose the time window. For passive ONA, a 90-day lookback period captures a meaningful collaboration pattern without conflating pre- and post-change behavior. For active ONA, ensure the survey is fielded during a representative period — not during a holiday period or immediately after a major disruption.

Step 2: Select the Right Method and Tools

Based on the scoping decisions from Step 1, select your approach:

Active ONA tooling: Platforms such as Cognitive Talent Solutions, TrustSphere (now part of Qualtrics XM), and Aon’s ONA assessment products offer purpose-built survey design, roster management, and network visualization. In organizations where a commercial platform is not justified, a well-designed survey in a standard research tool (Qualtrics, SurveyMonkey) can collect raw relational data, with visualization done in Gephi, NodeXL, or Python’s NetworkX library.

Passive ONA tooling: Microsoft Viva Insights (native to Microsoft 365 tenants) provides collaboration analytics derived from email and calendar metadata with privacy controls built in. Worklytics and Humanyze offer similar capabilities across broader tool sets. These platforms handle data ingestion, anonymization, and visualization, significantly reducing the technical burden on internal teams.

Combination approach: Use passive ONA to map the macro structure — who is collaborating with whom at scale — and then deploy a targeted active ONA survey within a subpopulation of interest to understand the quality and type of those relationships in depth.

Step 3: Communicate to Employees Before Any Data Collection Begins

Employee communication is not a box to check before launching ONA. It is a determinant of data quality and organizational trust. Passive ONA conducted without clear communication breeds surveillance anxiety. Active ONA conducted without explaining why the data will be used generates suspicious or socially desirable responses.

The employee communication should cover:

What is being measured. Describe the specific data types being collected. For passive ONA: “We are analyzing email and calendar metadata — who meets with whom, how often, and in what group configurations. We are not reading message content.”
Why it is being measured. Connect the analysis to a concrete business question employees can understand and care about. “We want to understand whether our recent reorganization has improved collaboration between the product and commercial teams.”
How results will be used. Explain the aggregate, team-level nature of the outputs. Clarify that no individual profiles will be shared with managers or used in performance assessments.
Who has access to the data. Name the function (typically People Analytics or a senior HR leader) responsible for the analysis and who will see the results.
How employees can raise questions or concerns. Provide a specific point of contact.

For active ONA, add an explanation of survey anonymization: ideally, results should be aggregated such that no individual’s responses are traceable back to them.

Step 4: Run the ONA

For active ONA:

Design the survey with a maximum of three to five network questions. More questions reduce completion rates sharply.
Send the survey from a trusted internal sender — the CHRO or a respected HR business partner — not an unfamiliar platform address.
Set a two-week response window with a single reminder at the one-week mark.
Aim for a minimum 70% response rate within each subgroup you want to analyze. Below this threshold, the resulting network is too incomplete to be reliable.
As responses come in, clean the data: standardize employee identifiers, handle self-nominations (which some surveys exclude), and flag unusually high or low self-reported connectivity as potential response bias.

For passive ONA:

Validate the data pipeline before generating insights. Spot-check that the collaboration patterns appearing in the data match what HR business partners or managers know anecdotally about their teams.
Establish a baseline period — the first four to eight weeks of data collection — before drawing conclusions. Early anomalies (holidays, quarterly close periods, all-hands events) can distort the network picture.
Run network calculations using your platform’s built-in algorithms or export to a graph analysis tool. For large networks (above 500 nodes), ensure your tooling can handle the computational load.

Step 5: Interpret the Results With Domain Context

Raw ONA metrics mean nothing without context. Interpreting results requires combining the network data with the organizational knowledge HR business partners carry.

Build your interpretation around the business question, not the metrics. The question is not “what does this centrality score mean?” but “does this centrality pattern explain the collaboration problem we are trying to solve?”

Common ONA patterns and their organizational interpretations:

A small number of individuals with extremely high betweenness centrality: This is the bottleneck signature. It often correlates with high workload, slow decision-making, and elevated attrition risk at those nodes. Intervention: rebalance their portfolios, develop parallel connectors, or restructure team dependencies to reduce the load on single brokers.
Two large clusters with minimal connections between them: This is the silo signature. Cross-cluster bridges are absent or fragile. Intervention: create structural mechanisms for cross-cluster collaboration — joint working groups, shared OKRs, rotational assignments, or social programming designed to build cross-cluster relationships.
A peripheral cluster loosely connected to the main network: This may indicate a team experiencing cultural exclusion, geographic isolation, or post-acquisition disconnection. Intervention: deliberate integration programming, leadership bridging, or physical co-location where feasible.
Unusually low network density in a senior leadership team: Leadership teams with weak internal connections struggle to build organizational alignment. Intervention: structured leadership team development focused on building interpersonal trust and information-sharing norms.
A high-potential employee cluster isolated from senior leadership: Sponsored connectivity — actively introducing high-potential employees to senior leaders — is a targeted intervention ONA can justify with data.

Step 6: Turn Insights Into Action — and Close the Loop

ONA insights that do not produce organizational change are worse than no ONA at all. They consume organizational goodwill without delivering value — and make future ONA initiatives harder to launch.

Translate findings into a small number of prioritized interventions. Three to five targeted actions, well-executed, outperform 20 diffuse recommendations.

Assign accountability. Each intervention should have a named owner, a target population, and a measurable outcome indicator — whether that is a change in collaboration patterns, a reduction in a specific leader’s centrality load, or an increase in cross-team meeting frequency.

Re-run the ONA at an appropriate interval to measure change. For passive ONA, a 90-day re-analysis can detect whether the intervention shifted network patterns. For active ONA, a six-to-12-month cycle is typically appropriate given survey fatigue constraints.

Communicate results back to employees. Closing the loop — telling employees what you found, what you are doing about it, and what changed — is not optional. It is the foundation for maintaining consent and trust for future ONA work.

Frequently Asked Questions About Organizational Network Analysis

What is the difference between organizational network analysis and social network analysis?

Social network analysis (SNA) is the broad academic discipline studying network structures in any context — sociological, biological, digital. Organizational network analysis applies social network analysis methods specifically to organizations, focusing on the professional and informational relationships within and between work groups. ONA draws from SNA’s mathematical foundations but is tailored to HR and organizational design applications.

Does ONA require specialized technology to implement?

Not necessarily. A carefully designed survey, a spreadsheet to capture the relational data, and a free visualization tool such as Gephi can produce a meaningful active ONA for a team or small business unit. That said, enterprise-grade passive ONA — particularly at scale and with appropriate privacy safeguards — benefits significantly from purpose-built platforms such as Microsoft Viva Insights or Worklytics that handle data ingestion, anonymization, and continuous analysis without manual overhead.

How is ONA different from employee engagement surveys?

Engagement surveys measure how employees feel — their sense of belonging, motivation, satisfaction, and psychological safety. ONA measures how employees relate — the structural patterns of who interacts with whom and how. The two are complementary: engagement surveys can tell you that a team has a problem; ONA can tell you where in the relational architecture the problem originates. Used together, they produce a more complete picture than either provides alone.

How do you handle privacy concerns when running passive ONA?

Privacy-by-design practices include collecting only metadata (not content), enforcing minimum group sizes for any reported metric, role-limiting access to outputs, and clearly communicating to employees what is being collected and why. In GDPR-regulated environments, a lawful basis for processing must be documented, and data minimization — collecting only what is necessary for the stated purpose — must be respected throughout. Transparency and aggregate reporting are the two most effective tools for maintaining employee trust.

What is a good starting network size for a first ONA project?

A pilot population of 100 to 500 employees in a single business unit or function is a practical starting point. This size is large enough to surface meaningful network patterns and small enough to manage the communication, data quality, and interpretation work with a small team. Successful pilots build internal credibility and demonstrate the method’s value before scaling to the full enterprise.

How often should organizations run ONA?

The appropriate cadence depends on the method and the organizational context. Passive ONA, because it runs continuously, can be reviewed quarterly or whenever a significant organizational event — a restructuring, an acquisition, a leadership change — warrants a network diagnostic. Active ONA, given the survey burden on respondents, is typically appropriate on an annual or biannual basis, with targeted pulse surveys in between when a specific question arises.

Conclusion: Why Consulting Expertise Accelerates ONA Adoption

Organizational Network Analysis has cleared the proof-of-concept stage. The methods are well-established, the tooling has matured, and the business case for relational analytics is no longer theoretical. The gap most organizations face is not access to ONA — it is the capability to design analyses that answer the right questions, interpret network data with the organizational context it requires, and convert findings into interventions that stick.

Experienced consultants who specialize in people analytics and organizational design accelerate every stage of that journey: shaping the right question before any data is collected, navigating the privacy architecture that makes passive ONA legally defensible, reading network patterns with the diagnostic fluency that distinguishes a bottleneck from a hub, and translating insights into the kind of concrete, accountable action plans that executive sponsors will fund and managers will execute.

For HR leaders ready to move beyond engagement scores and headcount dashboards, ONA represents the next analytical frontier. Getting it right from the start — with the right method, the right governance, and the right interpretation — makes the difference between analysis that changes how an organization is designed and analysis that sits in a presentation deck.

K. Arthur O’Dea is a solution consultant and B2B strategist at karthurodea.com, specializing in data-driven workforce analytics, organizational design, and people strategy for mid-to-large enterprises. To explore how ONA might apply to your organization, contact K. Arthur O’Dea directly.