Cognition
Thinking about thinking
August 08, 2024 Filed in: Thinking
Thinking about thinking
When we think of tools that support knowledge workers, we might consider the World Wide Web, the Microsoft Office suite of programs and even artificial intelligence. Paul Rogers explores their impact on the decision-making capabilities needed to make sense of the explosion in data and information.
I am so old I can remember a time before Skynet, when we had to write to our suppliers to find out how many widgets we had bought. We had invested millions in IT systems but couldn’t say how many blue biros had been purchased. Fast forward to today, and (many) organisations are awash with data and visualisation tools to analyse their spend by supplier, by category and by location. And they don’t buy blue biros anymore.
It can be difficult to judge the significance of these changes because they are happening right now and we do not have the perspective of time to reflect upon them. One suite of thinking tools that I have found helpful was created by Terry Borton in 1970. Borton suggested reflecting on the following three questions;
• What?
• So what?
• Now what?
Assault on batteries
I have created a data set on spend on batteries to try and bring to life the three questions. The data is fictitious, but the principles apply to many indirect categories.
The total spend on batteries is $250,000 a year. Of that spend, 90% is accounted for by AA and AAA batteries. I have used Microsoft Excel to sort the batteries by type (AA or AAA) and then by unit price. I have applied conditional formatting to colour code the unit price from low to high and also used data bars to highlight where the spend is concentrated. Not exactly cutting edge stuff, but hey! I started out with SuperCalc.
The ‘what?’ question
Whether you use VisiCalc (look it up) or a visualisation tool like Power BI or Tableau the What? question asks us:
• What does the data reveal?
• What is happening?
• What are the patterns or trends?
In the data above, the answers are:
• 90% of the spend is concentrated on two battery types, AA and AAA;
• There are at least four brands of each type of battery purchased
• There are multiple variants of each battery type, even within an individual brand
• The unit price of AA batteries varies from $0.60 each to $4.98 each
• The unit price of AAA batteries varies from $0.50 each to $4.75 each
• The lowest unit price battery accounts for less than one percent of all purchases
• There appears to be a strong preference for one brand, Energizer
In the past I may have written that the capabilities needed to answer the ’what?’ question included data analysis. But that is only true to the extent that I know what attributes of the data are important. I know that the unit of sale matters because every supplier uses a different pack size. In order to analyse the data we need to use unit price in order to compare like with like. That’s not going to earn me a Nobel Prize, is it? Could it be that the ability to devise and manipulate a pivot table in Excel is no longer as important as it once was?
The ‘so what?’ question
My contention is that the second and third questions in Borton’s framework are now more important for knowledge workers than they were historically.
What?
So what?
90% of the spend is concentrated on two battery types, AA and AAA
In order to maximise the return on investment of our time it makes sense to focus on these two types.
There are at least four brands of each type of battery purchased
Purchasing of batteries is relatively uncontrolled with users exercising personal discretion over which brand is bought
There are multiple variants of each battery type, even within an individual brand
Users need to know what is the most appropriate quality level (Everyday, Max, Max Plus, Ultimate) for their application
The unit price of AA batteries varies from $0.60 each to $4.98 each
In order to optimise value in purchasing AA batteries we need to match the battery type to the application
The unit price of AAA batteries varies from $0.50 each to $4.75 each
In order to optimise value in purchasing AAA batteries we need to match the battery type to the application
The lowest unit price battery accounts for less than one percent of all purchases
Users prefer to purchase more expensive batteries (Max and Max Plus) rather than the battery with the lowest unit price
There appears to be a strong preference for one brand, Energizer
We need to understand what the source of the user preference is, and how easy (or difficult) it may be to change users’ preferences.
The capabilities that I have (tried to) demonstrate in the table above are higher level cognitive abilities than sorting, counting or totalling data. I have applied knowledge and experience (and perhaps even some wisdom) to the data to interpret what is significant.
The ‘now what?’ question
‘Some men see things as they are and say why? I dream things that never were and say, why not?’ – George Bernard Shaw
If we are experiencing transformational change because of rapid advances in technology such as artificial intelligence it is inevitable that some of the frameworks which have served us in the past will become less relevant. In the example of the batteries, there are a number of options of what could be done next;
• Hard substitute generic equivalents for branded batteries in the purchasing system;
• Reduce the number of brands of battery bought from four to one;
• Disempower individual users from purchasing batteries and enforce policy by centralising responsibility for purchasing batteries;
• Address the number of different battery variants in use (Everyday, Max, Max Plus, Ultimate) by selecting a mid-quality solution e.g. ‘Max’;
• Address the number of different battery variants in use (Everyday, Max, Max Plus, Ultimate) by issuing guidance on which devices require something other than an alkaline battery (e.g. which are the high drain devices that may actually need a premium battery);
• Collect ‘spent’ batteries in battery recycling drop off boxes to avoid them going to landfill (or starting fires);
• Adopt rechargeable batteries to reduce waste and be more sustainable.
The choice of which options are right for your organisation requires higher level cognitive skills of evaluation, judgement and selectivity. In particular, while options four and five in the list above have the most potential to release value, they may also be the hardest to manage from a practical point of view. How do you change the behaviour of hundreds of individual decision-makers? It’s going to need more than sending them an email or drafting a carefully worded policy statement!
Positive thinking
I find Borton’s three questions to be deceptively simple. Answering the second and third questions requires different and higher level cognitive capabilities than answering the first question. There is no function in Excel that reads =RELEASEVALUE(A1:K33,567,2,FALSE)
When we think of tools that support knowledge workers, we might consider the World Wide Web, the Microsoft Office suite of programs and even artificial intelligence. Paul Rogers explores their impact on the decision-making capabilities needed to make sense of the explosion in data and information.
I am so old I can remember a time before Skynet, when we had to write to our suppliers to find out how many widgets we had bought. We had invested millions in IT systems but couldn’t say how many blue biros had been purchased. Fast forward to today, and (many) organisations are awash with data and visualisation tools to analyse their spend by supplier, by category and by location. And they don’t buy blue biros anymore.
It can be difficult to judge the significance of these changes because they are happening right now and we do not have the perspective of time to reflect upon them. One suite of thinking tools that I have found helpful was created by Terry Borton in 1970. Borton suggested reflecting on the following three questions;
• What?
• So what?
• Now what?
Assault on batteries
I have created a data set on spend on batteries to try and bring to life the three questions. The data is fictitious, but the principles apply to many indirect categories.
The total spend on batteries is $250,000 a year. Of that spend, 90% is accounted for by AA and AAA batteries. I have used Microsoft Excel to sort the batteries by type (AA or AAA) and then by unit price. I have applied conditional formatting to colour code the unit price from low to high and also used data bars to highlight where the spend is concentrated. Not exactly cutting edge stuff, but hey! I started out with SuperCalc.
The ‘what?’ question
Whether you use VisiCalc (look it up) or a visualisation tool like Power BI or Tableau the What? question asks us:
• What does the data reveal?
• What is happening?
• What are the patterns or trends?
In the data above, the answers are:
• 90% of the spend is concentrated on two battery types, AA and AAA;
• There are at least four brands of each type of battery purchased
• There are multiple variants of each battery type, even within an individual brand
• The unit price of AA batteries varies from $0.60 each to $4.98 each
• The unit price of AAA batteries varies from $0.50 each to $4.75 each
• The lowest unit price battery accounts for less than one percent of all purchases
• There appears to be a strong preference for one brand, Energizer
In the past I may have written that the capabilities needed to answer the ’what?’ question included data analysis. But that is only true to the extent that I know what attributes of the data are important. I know that the unit of sale matters because every supplier uses a different pack size. In order to analyse the data we need to use unit price in order to compare like with like. That’s not going to earn me a Nobel Prize, is it? Could it be that the ability to devise and manipulate a pivot table in Excel is no longer as important as it once was?
The ‘so what?’ question
My contention is that the second and third questions in Borton’s framework are now more important for knowledge workers than they were historically.
What?
So what?
90% of the spend is concentrated on two battery types, AA and AAA
In order to maximise the return on investment of our time it makes sense to focus on these two types.
There are at least four brands of each type of battery purchased
Purchasing of batteries is relatively uncontrolled with users exercising personal discretion over which brand is bought
There are multiple variants of each battery type, even within an individual brand
Users need to know what is the most appropriate quality level (Everyday, Max, Max Plus, Ultimate) for their application
The unit price of AA batteries varies from $0.60 each to $4.98 each
In order to optimise value in purchasing AA batteries we need to match the battery type to the application
The unit price of AAA batteries varies from $0.50 each to $4.75 each
In order to optimise value in purchasing AAA batteries we need to match the battery type to the application
The lowest unit price battery accounts for less than one percent of all purchases
Users prefer to purchase more expensive batteries (Max and Max Plus) rather than the battery with the lowest unit price
There appears to be a strong preference for one brand, Energizer
We need to understand what the source of the user preference is, and how easy (or difficult) it may be to change users’ preferences.
The capabilities that I have (tried to) demonstrate in the table above are higher level cognitive abilities than sorting, counting or totalling data. I have applied knowledge and experience (and perhaps even some wisdom) to the data to interpret what is significant.
The ‘now what?’ question
‘Some men see things as they are and say why? I dream things that never were and say, why not?’ – George Bernard Shaw
If we are experiencing transformational change because of rapid advances in technology such as artificial intelligence it is inevitable that some of the frameworks which have served us in the past will become less relevant. In the example of the batteries, there are a number of options of what could be done next;
• Hard substitute generic equivalents for branded batteries in the purchasing system;
• Reduce the number of brands of battery bought from four to one;
• Disempower individual users from purchasing batteries and enforce policy by centralising responsibility for purchasing batteries;
• Address the number of different battery variants in use (Everyday, Max, Max Plus, Ultimate) by selecting a mid-quality solution e.g. ‘Max’;
• Address the number of different battery variants in use (Everyday, Max, Max Plus, Ultimate) by issuing guidance on which devices require something other than an alkaline battery (e.g. which are the high drain devices that may actually need a premium battery);
• Collect ‘spent’ batteries in battery recycling drop off boxes to avoid them going to landfill (or starting fires);
• Adopt rechargeable batteries to reduce waste and be more sustainable.
The choice of which options are right for your organisation requires higher level cognitive skills of evaluation, judgement and selectivity. In particular, while options four and five in the list above have the most potential to release value, they may also be the hardest to manage from a practical point of view. How do you change the behaviour of hundreds of individual decision-makers? It’s going to need more than sending them an email or drafting a carefully worded policy statement!
Positive thinking
I find Borton’s three questions to be deceptively simple. Answering the second and third questions requires different and higher level cognitive capabilities than answering the first question. There is no function in Excel that reads =RELEASEVALUE(A1:K33,567,2,FALSE)