detective work

For the past couple of months I have been engaged in a couple of programs that focus on organizational performance improvement. In the Performance-based Learning program we ask — What is the performance gap and what are the influencing factors? This is part of the Performance Detective role. In the Emerging Stronger Masterclass, one of the key questions is — What is your hypothesis? — and then you have to confirm it based on data, observations, and especially experiments.

But how does one ‘think like a detective‘? Let’s observe what makes a good detective.

“As a homicide detective, I began to notice how my more skilled colleagues were different from the others. It wasn’t apparent at first. They never spoke loudly nor did they frown at how obvious things were. They didn’t voice their opinion any more than others; they didn’t jump to conclusions. Rather, they observed, asked questions, and calmly kept on digging. This detached involvement and the ability to keep digging are the main attributes that set expert detectives apart from the rest of the crowd. Hence, not making a decision is the best decision a good investigator can make. For some of us, it will be hard, and it might take some practice. It seems counterintuitive to walk away from a problem you want to solve. Forcing your mind to take a step back is not easy.”

Ivar Fahsing goes on to provide some key lessons that are applicable to any type of detective, including performance detectives.

  1. Assume nothing and find out what you really know
  2. Identify all the possible explanations
  3. Test the alternative explanations and narrow your investigation

With increasing complexity and interconnectedness, we all need to be better detectives in order to make sense and understand our world [my emphasis].

“Managing a major investigation or in fact any modern project today is fundamentally different than it was 30 years ago. According to the management scholars Gökçe Sargut and Rita Gunther McGrath, complexity has gone from something found mainly in large systems, such as cities, to something that affects almost everything we do: the life we live, the jobs we have, and the projects or organisations we run. As a consequence, the gap between our first idea and reality has almost exploded. Most of this increase stems from the information-technology revolution of the past few decades. Phenomena that used to be hidden, constant or separate are now tangible, interconnected and interdependent. Complex systems interact in unexpected ways. New patterns form, and the outlier is often more significant than the average. Making matters even worse, our analytical tools haven’t kept up with these developments. Collectively, we know a good deal about how to navigate complexity but this knowledge hasn’t been transformed into effective tools. Some predict that artificial intelligence might be our salvation, while others see it as our downfall.”

In the Tulser 4I model, the performance detective is primarily involved in the Identify phase as shown below.

4I model by Tulser

4I Model by Tulser

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