Visualising Relationships at Work

Owen Analytics ONA

We know that successful team dynamics is critical in building organisations, however many of our HR and people management processes are still designed around the individual.

We have embedded ‘institutionalised silos’ such as performance management, employee engagement, induction and  training, that are all geared up to individual performance, but focus much less so on the impact on their team or organisation.

According to Deloitte, 88% of respondents of a global HR survey, believe building the organisation of the future is an important issue.  This is sweet music to my ears, after years of fumbling around the employee engagement survey wilderness, we can start looking at the bigger picture and organisation structures, and ask questions like:

  • How does team cohesion impact performance?
  • Is there a relationship between team collaboration and attrition?
  • Does increased collaboration impact output?
  • Does a hierarchical structure impact retention?
  • Who are the most influential people in our organisation?
  • Can we see what good leadership looks like?
  • Can we detect bottlenecks in an organisation?
  • Which individual are at risk of leaving the organisation?
  • Can we detect ‘silos’ in our company? (the answer is always yes to that one!)

 

What is very surprising is how little analysis and research is done into how our teams operate.  One way to gain a better understanding on these questions is Organisational Network Analysis (ONA).  One method is for employees to complete quick pulse surveys which combine “ME” questions (My opinions count) and “WE” questions (I would like to appreciate the following individuals for helping me in my day-to-day work).  Open feedback questions are also interspersed to understand sentiment and key issues.

The end result is a visual representation of your team dynamics – the example in the image above, is an ONA diagram from OWEN Analytics and was used to understand team dynamics in a pharmaceutical organisation.

In my article on the use of wearables and emerging technologies in the workplace, I highlighted that The Quantified Workplace will be introduced, but only at the speed of employee trust.  Looking at relationship patterns might also give insights into understanding trust.

This type of approach throws up some interesting insights.

Research by Rob Cross, a leading researcher in ONA, found that highly connected people are among the least engaged in a company. So your most valued staff, those go-to people, are often hidden, underappreciated and sometimes over-worked.

Mark Bolino of the University of Oklahoma points to a hidden cost of collaboration. Some employees are such enthusiastic collaborators that they are asked to weigh in on every issue. But it does not take long for top collaborators to become bottlenecks: nothing happens until they have had their say—and they have their say on lots of subjects that are outside their competence.

In most cases, 20% to 35% of value-added collaborations come from only 3% to 5% of employees according to research by Rob Cross, Reb Rebele and Adam M.Grant, covered in their article in Harvard Business Review, “Collaborative Overload”.

If we go back to the questions on what causes collaboration, effective teams and higher productivity, then ONA can play a big part in helping us understand what is going on in our organisations. 

Our people management practices are rapidly changing as we move to a world with collaborative teams working with different employment terms in different countries. ONA is a technique that people analysts can add to their tool-kit and help us to uncover the hidden dynamics of team effectiveness rather than the obsession with individual achievements. 

By being able to visualise teams relationships we can begin to build a strong foundation for organisations of the future based on a deeper understanding of effective teams. I am looking forward to sharing more case-studies and success stories at PA World over the coming years.  

(This was a guest article on the Tucana Blog)

Make sure you don’t miss out by signing up for our articles direct to your inbox.

Share this post
(click on the flashing icon)

Managing Attrition – Are You Looking Backwards or Forwards?

Glass Bead Consulting - Attrition

How do you manage attrition?

Most companies will review last month’s attrition figures, long after the star employees have had their farewell leaving do. This is the equivalent to looking behind in the rear-view mirror, whilst travelling at speed – you may know what’s behind you but it’s too late to do anything about it.   Or do you look forward, anticipating trends of employee flight risk and making small adjustments as you travel down the road?  After all, if you can see the possible obstacle ahead you have a better chance to avoid it.

As wages continue to rise, we see more employees dipping their toes in the welcoming water of the job market.  Keeping our best employees with us on our journey is going to be hard and managing the cost of unwanted employee turnover is going to be even harder.

Marc Andreessen, co-founder of Netscape and early Facebook investor, has said,

“Five great programmers can completely outperform 1,000 mediocre programmers.”

So how do we quantify the cost of losing our best employee? 

The calculation for the cost of losing an employee varies from one organisation to another, but typically includes hiring, on-boarding, training and ramp-up time to peak productivity. Other costs that need to be factored are loss of morale due to high turnover, higher business error rates, and a possible impact on a company’s culture and customer reputation.

Deloitte estimate the cost of losing an employee can range up to two times the employee’s annual salary.  Given the significant financial impact, it is surprising that 40.7% of UK organisations do not measure the cost of attrition, according to XpertHR.

This prompts some basic questions organisations should be asking about attrition such as:

  • Do you have a good idea of what your attrition levels will be over the next few quarters?
  • Do you calculate the probability and the impact of losing an employee?
  • Do you know the actual cost of attrition in your business?
  • Can you prove which factors cause unwanted attrition in your organisation?
  • Do you know which interventions are more likely to keep the higher performers (the five great programmers) and let the laggards leave?

Looking at attrition in the rear-view mirror

Many HR teams measure who has left the company in the last period, in which division, and what type of role as a way of broadly measuring attrition.  However, looking in the rear-view mirror only describes what’s behind us, it doesn’t tell us what is coming up, which makes it harder to prepare for unexpected change.   By the time we have realised it is too late.

Looking forward using predictive analytics

What we really need is to manage attrition more proactively by understanding who is more likely to leave and what the impact of them leaving would be on the business.  In a smallish company this is straightforward, but where you have larger teams, spans of control and distributed teams this becomes much more difficult.

Credit Suisse found that a one-point reduction in regretted attrition saved the bank $75 million to $100 million a year.  So building an attrition prediction model is one way for HR to make a substantial impact on the bottom-line.  See “The Algorithm that tells the boss who might quit”.

For those interested in more People Analytic case studies, including attrition, go to David Green’s excellent summary “20 People Analytics Case Studies

Using an evidence-based approach, we need to critically assess different sources of evidence.  Building your own predictive model is one way of building up a reasonably strong source of organisational evidence.  It is also worth reviewing the scientific research as another source of evidence, see this meta-analysis for example and reference below.  

Tej Mehta from Owen Analytics, explains the benefits of using predictive analytics,

“A typical approach will brainstorm all the potential factors that might cause an individual to leave. These are then used as inputs into machine learning algorithms that can predict flight risk with a high degree of accuracy which is often over 80%.”

The attrition landscape needs to be revisited if organisations are to remain competitive as they make their respective journeys.  Predictive analytics can be a step change for the HR community, at the very least providing some useful dashboard controls to enable better decision making.

I hope this article has given you some useful ideas and maybe some inspiration.  As always I would be interested in hearing about your examples using predictive analytics to better manage retention and attrition.    In response to our clients’ request to provide this service, are delighted to announce that we have launched a new service “Managing Attrition using Predictive Analytics”.

Some other useful resources to improve attrition management

Why Do Workers Quit? The Factors That Predict Employee Turnover (19 page PDF from Glassdoor)

Turnover: Predicting Attrition – A great free training resources from University of Pennsylvania | Coursera

Whether your company has 500 or 120,000 employees, there are many things you can do to improve retention and manage attrition, see Managing attrition using simple analytics

Meta-Analytic Review of Employee Turnover as a Predictor of Firm Performance (2011)    Julie I. Hancock, David G. Allen, Frank A. Bosco, Karen R. McDaniel, Charles A. Pierce

 

Make sure you don’t miss out by signing up for our articles direct to your inbox.

Share this post
(click on the flashing icon)

Consulting Tools and Resources
Get in Touch with Glass Bead Consulting
About The BLog


Featured in Alltop