Centre for Performance in HR - talent and networks

QuantumBlack ©2009-2012 Page 0

Centre for Performance-Led HR

For discussion 17 October 2012

mark.mitcheson@quantumblack .com

www.quantumblack.com

@quantumblack

Data Science for HR Lessons from Formula One on collaboration, talent and effectiveness


We help our clients gain better intelligence from data, craft strategic responses to this intelligence and shape an organization’s capability to deliver this response. We work with some of the worlds most interesting organizations to gain edge through data. BNP Paribas | Boeing | Citibank | Lotus Renault GP | McKinsey & Co | Mercedes GP | Microsoft | Read Elsevier | Rolls-Royce | Standard & Poors | Thomson Reuters | Williams F1


“Using data-driven business insights to identify solutions… …has the strongest effect on HR’s strategic impact.” --CLC: Engineering the Strategic HR-Line Conversation

But not all data gives the same level of insight - or impact.

HR Needs Data

Anecdotes

Reactive Checks

Ongoing Reports

Benchmarks Correlations Causality

Weak Powerful

Potential Business Impact

Simulations and Forecasting


100

Impact of Effectiveness at Collaboration on Revenue Targets

Indexed

Business Units Ineffective at Collaboration

Business Units Effective at Collaboration

Impact of Effectiveness at Collaboration on Employee Engagement

Indexed

100

Business Units Ineffective at Collaboration

Business Units Effective at Collaboration

136 138

Collaboration Gives Your Business An Advantage

Effective collaborators outperform the ineffective on revenue targets and employee engagement

Source: CLC Business Case for Social Media Collaboration 2011


Percentage of Organizations Reporting Improving Collaboration

as High Priority

Percentage of Organizations Reporting Effectiveness at

Collaboration

Source: CLC Business Case for Social Media Collaboration 2011

Collaboration is Important, and Difficult.


“CEOs with seats on other company boards have connections that significantly increase their chance of successfully transforming their organisation” Abebe, Angriawan and Ruth. Journal of Leadership and Organisation Studies 2012

“It’s more important to have different networks that execute quickly… …combining their expertise with other groups in the company. Having this ability to solve big problems quickly is a big deal.” From Cross and Katzenbach, The Right Role for Top Teams, Strategy + Business 2012

“In a recent IBM survey, high performing organisations were 57% more likely to provide global teams with collaborative and networking tools.” CHRO Report – Working Beyond Borders 2010

“Organisations could get more from their investments in talent if they focused on collaboration. Look at employees from both individual performance and network effectiveness perspectives to identify valuable pockets of hidden talent.” Schweer, Assimakopoulos, Cross and Thomas . MIT Sloan Review 2012

Does Talent Need A Network?


Typically a 20% improvement


A five year analysis of project team performance in Formula 1 design teams was initiated to understand:

1.  Whether certain methods of organizing work translate into a sustained organizational competitive advantage

2.  If information about collaboration foretells anything about individual and organizational productivity, or about the projects on which they work.

The analysis included many thousands of projects and 2300 individuals in five different organisations.

Analysing collaboration revealed sources of productivity improvement, and led to actionable insights leading to enhanced team organisation and talent management.

In addition, the ability to predict team effectiveness resulted in significant enhancements in resource allocation, as low performing project could be discontinued early and resources re-deployed.

Below are some brief highlights of the research.

Lessons from Formula One


Performance over time varies significantly across teams

8

Differences are not a function of resource levels, which are largely the same across organisations and determined by regulatory limits.


9

Collaboration profiles vary significantly across teams

Examples from two groups (each of 7 engineers) working on the same projects classes in the same organization


Projects that have a sustained level of communication (over time) have a 32% higher success rate, but a lower average return (even accounting for success) than projects with the most volatile collaboration intensity signatures.

Communication intensity strongly relates to topological features of the associated social network, which vary greatly based on organizational and project attributes.

Comparison of the structural differences in collaboration between two project classes reveals significant variety, illustrated below.

Class A Class B

Some Conclusions About Project Communication and Effectiveness


Communication between teams is a function of their interdependence. And team effectiveness is a function of communication intensity. Levels of communication traffic can be sub-optimal because the interfaces or channels between interdependent nodes are too easy or too difficult to use.

What is the Optimum Level of Communication Between Teams ?


Modifying these interfaces – for example by merging highly interdependent groups, or co-locating others – results in a more appropriate and effective level of communications.

What is the Optimum Level of Communication Between Teams?


1-25% 25-50% 50-75% 75-100%

Bottom 5% 33 30 5 32

5-25% 24 27 8 41

25-50% 16 26 12 46

50-75% 14 22 17 46

75-95% 10 20 14 56

Top 95% 6 17 17 60

1. Individual’s productivity

ranking in any given project

2. Individual’s probable ranking in next project

Individual & team performance levels are variable, but not uniformly so

Individuals who communicate least tend to have the most variable performance levels , but also highest mean (!)


Tracking Collaboration Data Enables Performance Predictions

The ability to predict outcomes enabled 3 racing teams manage their project portfolio better, by early termination of projects predicted to be unsuccessful.

This increased project yield by 12% 15% and 18% respectively for those teams.


So what? Some thoughts

  Talent needs to be connected to perform optimally. Some talents will already be excellent at this. Others will have the potential, not the connectivity to realise it. Network analysis will reveal who is who.

  The ‘stars’ performance levels vary most, Can this be managed better , to reduce variability? Or is it more realistic to expect it and react accordingly?

  There is an optimal churn rate among team members, and you can identify this. In aerospace we found this to be 4 years, but 18 months in Formula One.

  Tracking team collaboration, and applying advanced data science, enables the prediction of performance outcomes, enabling early intervention – or termination of the project.

  The data needed to do this kind of work exists today in all organisations.