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Prevent Collaboration Overload Using Organizational Network Analysis

Highly collaborative employees are often quiet drivers of business performance. Discover how to help your helpers with Organizational Network Analysis (ONA).

Every organization needs them — those “go-to” people who are at the hub of collaboration.

Whether it’s a talented coder who is always sharing best practices with junior colleagues or a finance professional who is a wizard with spreadsheet formulas, an employee who spends a large portion of his time sharing knowledge and responding to requests for help can be a major performance catalyst.

In fact, one global technology firm found that key players increase team member productivity by over 60%. Other research confirms that one person who goes that extra mile can influence team effectiveness more than all the other team members combined.  

While their impact can be exponential, these star employees can be hard to spot. Collaborative employees may not necessarily rank high on the org chart, and they often focus on helping others at the expense of their own productivity. As HR expert John Boudreau states, “even sophisticated talent management systems tend to overlook about half of these central players.”

In other words, highly collaborative employees are often quiet drivers of business performance. And this can lead to major problems.

The Business Impact of Collaborative Overload

As discussed in an HBR post, research conducted by Rob Cross and other academics shows that employees who are “seen as the best sources of information and in highest demand as collaborators in their companies — have the lowest engagement and career satisfaction scores…”

A highly connected employee can easily become overloaded with urgent, ad-hoc requests, undermining her sense of control. Because she may not also be getting the management recognition she feels she deserves, she can end up feeling underappreciated. At the end of the day, she can suffer from “collaborative overload.”

This is the classic recipe for burnout, as identified by the leading experts in the area. It explains why an eager programmer who starts out going “above and beyond” for his team, for example, may eventually head down a path towards exhaustion, detachment, and a general sense of ineffectiveness.

Beyond concerns for individual employees, there are also major business impacts to consider. Collaborative overload results in sufferers doing two things, according to Cross et al. The first is “staying and spreading their growing apathy to their colleagues.” The second is leaving the organization entirely.

This can make a serious dent in the balance sheet. When the direct costs of replacing an employee, interim reduction in labor costs, and costs of lost productivity are all taken into account, the total cost of voluntary turnover is $109,676 per exiting employee for an average US organization, according to Bersin by Deloitte research. An increase, then, of turnover by just 1% in a company of 30,000 employees can cost $32.9 million per year.

Now imagine what happens when employees start leaving in rapid succession after a well-connected employee departs. That’s called turnover contagion, a term experts use to describe what happens when people quit their jobs simply because other people are talking about leaving, job searching, or actually jumping ship.

When several well-connected people leave, it can have a multiplier effect. Think of a virus: the more contact an infected person has with other people, the more it spreads. Every organization has the ability to withstand the loss of talent, but that resiliency has a limit before there are serious business impacts.

Help Your Helpers With Organizational Network Analysis

If you are concerned about “helper burnout” within your organization, getting your executive team to support initiatives to address it may be a challenge. You need to show that this is about more than demonstrating concern for employee well-being — it’s also about reducing potential bottom line impacts and maintaining productivity. One way to demonstrate this is by quantifying the impacts of turnover.

To do this, you need to develop a hypothesis (such as “we are experiencing turnover amongst people who are key to business performance due to collaboration overload”), test it, and frame any findings in financial terms. This can’t be accomplished with a couple of data sources. To ensure you are collecting all the relevant data, follow these main steps:

Step 1. Identify the Quiet Influencers

A growing number of companies are using Organizational Network Analysis (ONA), which goes beyond the traditional org chart to identify the true influencers in a business through the analysis of social interactions.

A proper ONA study incorporates data coming from multiple sources, such as calendar, email, instant messaging, sociometric badges, and knowledge sharing applications. This provides a realistic view of how work actually gets done, and which people are the main conduits of productivity (of course, data privacy also needs to be properly addressed).

Be aware that digital streams can provide false positives and negatives. For example, people use instant messaging differently — some write long paragraphs, and others use one-line phrases. An analysis which simply counts the number of sends without considering the volume of words may under or over-state the level to which individual employee communication differs. To properly control for these, you will also need to leverage employee survey or observation data. Consider working with a vendor who specializes in this area, such as Trustsphere, to ensure you are gaining real insights to test your hypothesis.

2. Uncover Turnover Trends

Once you have identified the hidden stars in your organization, it is time to determine whether they are experiencing higher levels of turnover than their teammates and the rest of your employee population. To do this, you will need to calculate and compare resignation rates by pulling data from your HRIS.

If you find that there is indeed a higher level of turnover amongst people with many connections, then you can conclude that collaborative overload is a problem. From here, you can dig deeper into your data to uncover more granular insights, such as whether certain employee recognition programs make a difference in terms of retention.

Of course, the best approach is to nip the problem in the bud in the first place. One early sign that burnout is happening is absenteeism: people who are on the road to burnout are prone to illness or may feel entitled to take unscheduled days off. Predictive analytics can also help you identify those collaborators who pose a flight risk.

Data visualization showing breakdown of headcount by risk of exit
Data visualization showing breakdown of headcount by risk of exit

3. Quantify the Dollar Impact of the Problem

Before approaching your executive team to garner support for an HR program, it’s important to quantify the dollar impact of the problem and make projections about how it might worsen in the future. Executives will want to know how investing in the proposed solution — whether it involves increasing rewards for previously unidentified influencers or making a repository of resources more readily available to teams — will avoid costs or drive up revenue in the long run.

Instead of saying turnover is at 19% within a particular group, for example, demonstrate that the upcoming increase in turnover will likely impact 3% of revenue, or $3 million. If you lead with that kind of information, you will have a captive audience that is more receptive to hearing about your proposed solution.

Data visualization showing turnover rate vs revenue per store
Data visualization showing turnover rate vs revenue per store

Shifting the Burden of Collaboration

Organizational Network Analysis has emerged as one of the hottest topics in HR — and for good reason. Without it, the people who truly deserve to be recognized within an organization can be overlooked. When a solid ONA investigation premise is combined with multi-dimensional analysis that combines data from several systems, you can gain powerful and actionable insights that will help you design the best interventions and get the support of relevant stakeholders. In this way, you can alleviate the burden of collaboration — and keep your best stars shining bright.

Author Photo
Ian Cook |
Curious about the differences between gaussian and pareto distribution? Ask Ian. Want to know what it’s like to kite ski North of the Arctic Circle? Ask Ian. Not only is he an expert in statistical analysis and HR metrics, he’s also an avid cyclist, skier and runner. At Visier, Ian helps customers drive organizational change through linking workforce analysis to business outcomes. He is responsible for the workforce domain expertise within the Visier solutions.