Large companies with geographically dispersed workforces, an ongoing need for the best talent, and the desire to have an adaptive collaborative organization are finding a decades-old academic instrument – organizational network analysis (ONA) – has fresh relevance. ONA, a technique originated by sociologists, maps the informal networks that play just as crucial a role as formal organizational charts in shaping employee experiences, delivering business benefits, and improving operational efficiencies.

Analyzing how people work together, the networks they form to get work done, make decisions, and act on them, reveals how organizations really operate. And it’s typically not the command-and-control structures that leaders have drawn up.

For decades, companies have relied on static organizational charts to clarify roles, improve communication, and streamline decision-making. “Julius Caesar organized his army into commanders and lieutenants – a hierarchy that is basically how employees work today,” says Maya Bodan, leader of the organization strategy and design talent group for Deloitte LLP in San Francisco, California.

ONA is a tool for creating visual representations of how communications, information, and decisions flow through an organization using data from employee surveys, enterprise systems, and other sources. Unlike an organizational chart’s breakdown structure, ONA provides “an x-ray” into how a company actually works by “mapping patterns of connectivity and collaboration inside the organization,” says Rob Cross, an expert on ONA and SVP of research at the Institute for Corporate Productivity.

By making strategically important individuals visible, the analysis sheds light on informal relationships and invisible patterns, providing deep insight into how information flows, the identity of key influencers, and the existence of silos that can lead to bottlenecks. This has never been more important – or more possible – than in today’s geographically dispersed employees working together using virtual collaboration tools.

Experts describe how the results of an organizational analysis highlight different types of individual contributors. Cross says ONA can reveal, for example, who in the company is a central connector, someone whom other members of the network consult frequently for information and hold the network together; knowledge brokers, people who connect different groups or teams within a network, and peripheral, those who have limited connections with the rest of the network.

These characterizations can reveal who the major influencers and actors are in an organization, as well as those who could benefit from more engagement. (As you will learn in this article, there are additional employee archetypes.)

Consider this hypothetical. Mark serves as a central connector in a large pharmaceutical company. As an entry-level data administrator, Mark is low on the corporate hierarchy, but he is critical in that he connects human resources, sales, and marketing colleagues by serving as a conduit for the exchange of ideas and information. Mia, on the other hand, is an expert coder who is great at developing apps, but she is peripheral – disconnected from others in the employee network who could assist and collaborate with her. On paper, Mia’s programmer title may be higher in the organizational hierarchy. However, she is unable to make the connections she needs to progress and be selected for high-value projects.

These are the kinds of situations and relationships that exist in organizations of any substantial size. Understanding them in depth can help leaders foster more collaboration, enhance employee engagement, and promote talent development.

But these benefits rely on good data. Organizations need to allay fears about data privacy, communicate the advantages of ONA to employees, and remain clear-eyed about the technology’s limitations.

Visibility into far-flung workforces

Organizational network analysis emerged in the 1930s as a framework for sociologists to study the social interactions of prison inmates and reform school girls. Then, the work was done through observation and paper diagrams. In recent years, ONA has found a foothold in the corporate world for two reasons: the changing nature of work and the ability to analyze communications and other data.

Over the past few years, organizations of all kinds have gone to great lengths to set up distributed, geographically dispersed, flexible teams. The workplace changes that occurred during the COVID-19 pandemic only reinforce the idea that employees need to be able to respond quickly to change and have the necessary space to pivot and experiment.

However, companies’ work to make this shift has led to a new challenge: gaps of knowledge and awareness that prevent leaders from knowing who’s working effectively with whom on a wide range of projects.

This is where the data comes in. Never before have organizations had access to vast volumes of passive data – information that exists within the company, such as e-mail, chat, calendar, and collaboration-software data. Unlike active data, gathered from time-consuming surveys typically administered by human resources teams, passive data provides a more objective, less biased view of workplace dynamics without requiring direct input from employees.

ONA applications are also becoming easier to use, notes Bodan of Deloitte. “In the past, you needed a data science degree [to understand ONA],” says Bodan. “But there are many user-friendly platforms now that allow non-technical users like HR professionals to easily interpret the data and drive insights.”

Together, the rise of remote work, passive data, and easier-to-use ONA tools are encouraging organizations to make ONA a key component of their talent strategy, experts say.

Revealing the most valuable (network) players

As its name suggests, ONA is about analyzing the networks among people who work together. Its value lies in helping companies identify important connections and potential obstacles to information flow and collaboration. ONA can serve like a tide that washes away the sand to reveal what’s hidden underneath. The results of an analysis can pinpoint different employee archetypes that don’t always show up in organizational charts. For example:

Hidden-value creators

One important node in an organizational network is hidden-value creators – employees who contribute significantly to an organization yet are easily overlooked due to factors, such as geographic location, communication style, or corporate rank.

“A very small set of people have a disproportionate impact on how work gets done,” says Cross of i4cp. He notes that, based on his work with more than 300 organizations, he has found that, on average, 2% to 3% of employees account for 20% to 35% of an organization’s key collaborative work. The problem, he says, is “if these workers are overlooked, they’re far more likely to leave the organization and take their networks with them.”

ONA can cast a light on these unsung heroes, giving leaders the opportunity to offer incentives for them to stay and keep valuable networks intact.

Energizers

Energizers are positive thinkers who are quick to support coworkers’ ideas and “leave people feeling better after an interaction,” says Peter Gray, an ONA expert and professor of commerce at the University of Virginia’s McIntire School of Commerce in Charlottesville, Virginia.

Gray’s research highlights the role human behavior can play in building a network. The most effective networks include energizers, says Gray. A single energizer in a network can greatly reduce the risk of attrition among team members while leading high performance.

Organizational network analysis can help identify these such contributors, Gray says, by asking employees in a survey to identify the people in the company who make them feel good about themselves.

Fast movers

Employees who make a concerted effort to discover coworkers’ needs are also more likely to establish mutually beneficial and meaningful connections with coworkers. For example, while researching employee relationships, Cross discovered that 10% to 15% of employees are “fast movers” who become well-connected in less than a year after hiring (most workers require three to five years to become well-connected.) These fast movers, Cross says, share an interesting trait: When asked to disclose information about themselves, “fast movers are much more likely to pivot in that moment and say, ‘I can, but can you first give me a sense of what your priorities are? What are you excited about? What keeps you up at night?’”

By investing in coworkers’ needs, Cross says fast movers are not only able to infiltrate powerful networks at rapid speed, but they are also more likely to be tapped for high-value projects.

Weak nodes

As one might expect, the analysis can also highlight employees who are not well-connected to their networks. These weak nodes represent a situation that can “undercut an organization’s ability to deliver enterprise value,” Cross says. Underutilized and at higher risk of departure, he says these siloed workers can create “breakdowns in connectivity that are likely to hurt a company in terms of revenue potential, innovation, scale, or efficiencies – quantifiable points of impact.”

In this scenario, knowledge is power. By identifying disconnected employees, organizations can stage team-building exercises and launch cross-functional projects that connect these peripheral players to central connectors and knowledge brokers for a more solid network.

The good news is organizations are finding ways to bridge the gaps of knowledge created by issues related to scalability, engagement, and context. These best practices are not only helping organizations identify key influencers and isolated figures within a network, but also gaining a better understanding of how people work in today’s modern world and why some succeed – and others don’t.

When insight leads to new organizational structures

Retaining hidden talent and connecting valuable outliers aren’t the only business benefits of using ONA. Organizations can also realize substantial operational gains by visualizing the informal relationships that exist within an organization.

Deloitte’s Bodan describes one client who had 14 different functions, including legal, finance, and accounts payable, all working within a single business unit. Desperate to break down these functional silos, Bodan says the company turned to ONA. Upon analyzing the company’s passive and active data, “we discovered that the natural communities that existed, based on how people operate on a day-to-day basis, was actually four,” she says. Moreover, although the company’s organizational chart dictated that employees collaborate according to function, networks had formed naturally around customer segments.

In response, Bodan says the company whittled down its 14 functional silos into six key collaborative groups, each one dedicated to a particular customer segment. Creating these more meaningful and connected groups not only provided employees “with much more of an end-to-end view of how they’re working,” says Bodan, but also helped to increase speed to market, improve customer experience, and streamline decision-making.

That’s a case when the analysis leads to organizational change. However, realizing such a result requires overcoming a set of challenges.

Addressing challenges to implementing ONA

To achieve the benefits of ONA, companies will need to address technological, cultural, and organizational challenges to effectively apply it.

Confronting privacy concerns

As with any data-crunching tool, privacy concerns can limit the value organizations derive from ONA. Although employees can opt out of participating in a survey, passive data is generated from e-mail, texting, calendars, and collaboration applications without any employee involvement – or expressed consent.

“Privacy has to be handled incredibly carefully in terms of how and what you're disclosing and to whom,” says Cross.

But while establishing privacy practices related to disclosure can help protect employees, most ONA tools are already limited in terms of the data they’re able to gather, according to Gray, the University of Virginia professor.

For example, Gray says, “Passive network data collection software programs that mine e-mail and meeting data are excellent at stripping out any information that could get a company in trouble.” That’s not to suggest, however, that passive data isn’t worth parsing. Rather, by examining the nature of e-mail exchanges, not the communication itself, organizations can discover patterns of collaboration, including which employees are most likely to respond to messages after work hours, and from whom – telltale signs of important relationships.

Managing scalability limitations

Another obstacle to effective ONA is the nature of work itself – especially when the organization itself is very large. Multinational corporations have hundreds of thousands of employees, making it impractical to chart every single worker and their respective networks. Many employees simply don’t have the time to respond to lengthy surveys, which can prevent companies from achieving the response rates needed for accuracy. Passive data in isolation can fail to provide the necessary context for truly understanding the ties that bind employees.

For example, is a particularly intense rally of text messages about a business-critical project or Sunday brunch plans? And what exactly constitutes a positive link between two employees? There are simply no standards for what a good network should look like.

Leaders need to account for these potential weaknesses – just as they adjust their sights when evaluating outputs from business analytics systems with the knowledge that the results reflect incomplete data.

Securing employee acceptance

Even the most clearly marked targets can be missed if employees aren’t on board with ONA. “There is sometimes a concern that ONA will be used in regressive ways – that it will be used to fire people,” says Gray. For this reason, he recommends that managers earn employee trust by assuring them that the insights generated by ONA will be used in ways that will benefit them directly, such as improving performance, pinpointing places to build strong connections, and uncovering hidden factors for success.

Leaders must also take steps to present the discoveries made by ONA as compelling information rather than intractable truths. Consider, for example, the damage that could be done if a veteran manager learns that his energizer score is a fraction of the department’s latest hire. To avoid conflict and bruised egos, Gray says employees should be offered training on how to become more of an energizer, because “there are actual behaviors they can adopt to change their energizing score. You have to help them see that this is a function of behaviors that are malleable and change over time. No one is doomed.”

Adopting a data-first method to organizational thinking

Adopting ONA will require leaders to use a data-first method to measure and illustrate the collaboration patterns among employees, says Bodan. Moreover, she adds, an “adaptable mindset” is crucial, because ONA’s analysis of the strength, frequency, and nature of employee interactions may reveal some counterintuitive truths about which employees really wield influence in the organization and which ones are simply coasting on their titles.

Either way, says Bodan, the modern workplace “is moving away from functional fiefdoms to a much more collaborative environment”– one in which ONA can serve as a valuable tool and the hierarchy of Caeser’s legions really is history.