Building Messy Teams for an Analytics Age

In the complex environment that we’re asking business teams to operate in today, we need leaders to have a more innovative response to this complexity. Teams faced with challenges such as mass industry disruption from start-ups, economic instability or political uncertainty must leverage diversity of thinking to ask the right questions. Here is where organizations will be able to unlock the potential of advanced analytics and Artificial Intelligence - such capabilities are going to seperate winning from losing organizations across every industry in the future.

A paradigm shift in team performance

Leadership and development departments and leadership training are dominated by programs such as effective team-working and high-performing teams, but this theory has been born of a more stable environment and is not suitable for the complexity that we now face.

Effective teamwork and high-performing teams are, of course, still relevant, but these are table stakes and are not where teams will find a competitive advantage in the information age. Traditional thinking in teams will have to pivot from one way of thinking and act swiftly.

“Carefully crafting an aim or end state 6-12 months away, channels team thinking and does not support discovery driven leadership, critical for the information age.”

Teams need to build a set of practices and rituals, checklists and challenging techniques that will help them make data-driven decisions.

These resources should not be available only to the top management team, but rather to teams throughout the organization, especially those closer to the front line and the customer.

Messy teams are committed to establishing the following habits and behaviours through everyday interactions:

  • Openness and willingness to change minds by not ‘falling in love with the plan’
  • Feel comfortable working with uncertainty
  • Use frameworks and checklists to explore different perspectives from both domain and analytics expertise
  • Challenge assumptions in everyday interactions
  • Never stop at the first good idea
  • Use competing hypotheses rather than seeking a data report to support a preferred theory

The winning skill of teams moving on to the information age will be the ability to balance these thinking skills for any particular scenario. They will be able to avoid an over-reliance on analytical thinking and employ the right techniques to trigger lateral thinking to gain new insights and situational awareness. Here is where we get to the right questions!

Teams need to attract other members from the organization with a particular thinking style as an advantage point to their discussions and give them a clear remit to challenge, challenge, challenge. This alternative thinking style will be of critical importance when dealing complex situations.


"The biggest risk and opportunity for Big Data lies in the team."

Get these everyday rituals right and teams will be in a strong position to embrace analytical thought. Teams also need to be aware of and consciously manage the wrong group dynamics for the data age. With these techniques, teams should try to bridge the divide between analytical and domain expertise.

The Experienced Domain Expert:

  • Operates under constant time pressure, reacting to other events from the wider team
  • Vulnerable to bias and heuristics
  • May use valuable situational information unconsciously built over time – ‘grey hair’ experience
  • Comfortable with unstructured problems and business complexity; familiar with politics
  • Large capacity for parallel thinking

The Highly Skilled Analytics Expert

  • Useful when accuracy and evidence are needed and information is available
  • Less influenced by emotion
  • May fall victim to silo thinking
  • Uses tools and techniques that have been developed in the tradition of objectivity; less familiar with politics
  • Tendency towards linear thinking
Connecting these two types of thinking effectively will provide the competitive advantage for (messy) teams across businesses in the future.


Everyday interactions and behaviour change through a messy team paradigm is where we will build team fits for the future of Big Data.

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