Why do you think so? a guide for checking your thinking
We constantly make decision. Most decisions are simple: What to eat, when to get up, what to wear. Other decisions are more complicated (where to travel to, where to live, should we accept a job offer).
Then there are organizational decisions: Decisions managers make. These have additional layers of complexity: organizational politics, a diverse range of stakeholders, lack of decision aids, incomplete information. Importantly, decisions that managers make do not only impact their work and their life, but have an impact on other people. Like a row of dominoes, one decision has a ripple effect through a company and the people who work for it.
Thrown to this mix of complexity are the biases humans, also managers, have when trying to solve a problem. Biases can appear right at the beginning: What problem to solve. Managers might solve the wrong problem after reading or hearing about a shiny new management tool. They might have listen to one stakeholder group and plan to solve their problem, inadvertently neglecting other stakeholders.
Data to overcome bias
I’m not advocating for a tech solution to replace managers making decisions. Managing is a human centered task and the person should be flexible enough to accommodate outliers, people who are different and don’t fit in a box or cluster. The entity, person or machine, should be able to deal with events that are new and therefore not yet part of the routine.
I’m suggesting a greater use of data when making people related decisions. Commonly departments have marketing analytics teams. Finance is full of analyst. But often the people function is less data focused or the data HR people need for decision making is scattered in different tools. This is making it hard to for people managers to get a complete insight into their workforce.
However, one trouble with suggesting data to overcome bias is that people need to be able to find the right data to solve the right problem and develop the right solution. This means the process of collecting data is prone to be biased. For this reason, if you know about your biases or at least about strategies to point out where you biases are, you can develop more accurate business challenges and collect more suitable data.
A thinking model to help you find your biases
The first step is to accept that yes, you have biases and these biases impact all of your decisions. While economist like to think that humans are rational, most of us are not rational. We find it hard to look at problems and solution without emotions or discounting recent experiences we had and stereotypes we picked up throughout our life.
When making a decision, You need to discover your biases when looking for problems and solutions. This is easiest done when working with someone who is slightly different than you. People think different, have different preferences, different values etc. Working with someone who is not like you is crucial if you want to learn to overcome biases.
The thinking model I’m presenting here is based on work by Eric Barends and Denise M. Rousseau. The authors present a process from how to define your problem to evaluating the solution. I’m going to discuss what I see is the key element from this process: What is your chain of reasoning? It’s nothing more than spelling out the cause-and-effect relationship you think exists and govern your world.
In the most simplest form, its a sequence of if-then statements. It’s up to you to decide how complex you want it to be.
What is a chain of reasoning?
A chain of reasoning is a sequence of events or actions that influence each other. Like a chain, it is tightly knit, every link connected to the next link. This is important, as these little links between a cause and an effect, show you your biases. Take these two statements:
Statement 1: Innovation requires people to collaborate.
Statement 2: Remote work is bad for companies.Both sentences are statements that contain assumptions. Once you know the assumptions in the statements, you can elaborate on it and create your chain of reasoning, and then collect the right data to prove or disprove the statements. Let’s take the first statement apart:
You can look further at what is not said in the statement. For example:
Statement: When people with different knowledge and skills work together they can combine what they know and come up with new areas of synergies.
- People need to have different areas of expertise and skills.
- New areas of synergies (innovation) require that diverse knowledge is combined.
Statement: New areas of synergies lead to innovation.
- The combination of diverse areas of knowledge leads to innovation.
- Innovation are things (processes, products, services, artifacts) that are different from the things that already exists.
Missing from this example are the assumptions about the value of of innovation for organizational performance. This statement ( Organization need to innovate) again has several assumptions.
The first statement Innovation requires people to collaborate can now be extended to show the chain of reasoning. I’m adding “link” in brackets to make the chain clearer:
- People have different areas of expertise (link 1). When people come together (link 2) they tell each other what they know (link 3).
- When people tell each other what they know (link 3) it is clear how their diverse knowledge can be combined (link 4) to create innovation (link 5).
Of course, this is just one chain of reasoning and it leaves many things unanswered. For example, how to make people come together? Do people share everything that they know or are they selective?
Statement 2: Remote work is bad for companies
The second statement was Remote work is bad for companies. The statement contains the following line of reasoning:
- When team members are not located in the same place (link 1) they perform less well (link 2). High performance (link 2 — just phrased differently) requires that team members interact with each other face to face (link 3).
- Employees who are not physically where their managers is (link 1), perform at a lower level (link 2), because they are distracted (link 4), or do not know what they need to do (link 5).
The way I phrased this chain of reasoning shows that the reasoning does not need to be linear in the form if A then B, if B then C, if C then D and so on. If can have different branches, as in If A then B, if B then D, if B then C.
It takes practice
It seems relatively easy to sit down and write out a chain of reasoning. While I was writing the example it was tempting to extent the chain of reasoning and add other branches. The goal of this exercise is to write down your chain of reasoning. You don’t need to include everybody’s thinking.
By writing down your chain of reasoning, you will have a better idea what data to look for. If you think that innovation requires people to collaborate and your company hasn’t been creating new products, services, features, then you’ll be looking for data that tells you why people are not collaborating. If, after collecting data on collaboration, you find that employees are collaborating you’ll know that your assumptions were wrong.
NetNigma helps teams improve their collaboration using data. Book a meeting if you like to know more.
Originally published at https://netnigma.com on April 14, 2020.
