Scientific Management for the Information Age
There have been huge advances in the areas of data Science, big data, artificial intelligence and machine learning. However, our utilisation of information and data is generally poor. Society hasn’t been able to fully implement the benefits of information technology.
It has been difficult to get superior analytical insight or make better decisions. This article will explain why.
It will explain why we need a different approach to the science of managing and analysing information.
For those who manage to develop their thinking in this area the opportunities will be great. It will give first mover advantage in many instances. It will also give you the ability to analyse, understand and orientate in an ever changing and dynamic world.
For the other organisations they shall surely get left behind, suffering the fate of companies like Kodak and Blockbuster — Doing things right, but doing the wrong thing.
Scientific Management — “more, faster, with less”
Frederick Winslow Taylor (1856–1915) can be considered one of the most influential thinkers of the modern age. He was a key driver behind industrial engineering. What we think of today as mass production. It facilitated for the great US economic boom during the 20th century.
Peter Drucker, the most infamous management philosopher / guru of the last century, said that Taylor was “the first man in history who did not take work for granted, but looked at it and studied it. His approach to work is still the basic foundation”. Drucker maintained that Darwin, Freud and Taylor between them were the makers of the modern world.
In the beginning…
In 1873 Taylor started out at Enterprise Hydraulic Works, a small company in downtown Philadelphia that made steam pumps and hydraulic machinery. He later wrote that he could tell that industry was reshaping the world.
From his first day, he was struck by the surprisingly unscientific way workers approached working. Specifically, how they fashioned tool bits. Each worker had intuitively developed his own method for working the metal. Each claimed his own method was better than the others. No one had contemplated which way was the best… at least until Taylor came along.
Taylor perceived an incredible contrast. A contrast between the scientific precision of the machines contrasted with the intuitive, casual nature of the working methods of the laborers.
The human element — the way laborers worked and the way they were managed had changed little throughout the industrial revolution. The unchanged approach to work stood in contrast to the huge leaps forward in the capabilities of technology.
Taylor subsequently dedicated his life to making the relationship between workers and technology more scientific, more effective, and more efficient.
By studying workers’ practices and then developing the one best way Taylor created incredible efficiencies. To give one example, at one iron plant he increased output from 12.5 to 47 tons of steel per day. And decreased the number of workers from 600 to 140.
His ideas subsequently spread to the UK, France and even to Leninist Russia. His ideas have touched almost every part of society from business to the military, the education system and even how government is managed and organised.
But what did Taylor really do?
Taylor comprehended the scope of the technical revolution around him and how it was reshaping the world.
- He understood there had been huge advances technologically.
- He saw that neither labourers approach or the management style had not really changed.
- This created huge inefficiencies. It was an extremely inefficient and ineffective way of combining the resources of Labor, Technology and Materials.
- So he went about creating a new more systematic way of doing things — “scientific management”
Scientific management changed the world. Bringing with it both positives and negatives.
2018
Taylor was operating in the industrial age, the machine age. We have now entered a new age. Some call in industry 4.0. Other call it the information age.
Information on the internet is now measured in Zettabytes. Apparently by 2025 we will be producing 163 of them a year according to the International Data Corporation. A Zettabyte is 1 000 000 000 000 000 000 000 Bytes. That is a thousand trillion Bytes
These numbers so big that they actually become meaningless and unfathomable.
What is clear is that there is an almost unlimited amount of unstructured data out there. Staggeringly large amounts of information — the majority of which is ambiguous and poor quality. To put it another way, there is just a huge amount of noise.
What we have little of is more knowledge and wisdom.
Data — information — knowledge — understanding — wisdom
Russell Ackoff, the systems theorist and professor of organisational change, said the content of the human mind can be classified into five categories. (The following part is closely taken from his speech here.)
- Data -> relates to symbols
- Information: data that is processed to be useful; provides answers to “who”, “what”, “where”, and “when” questions
- Knowledge: application of data and information; answers “how” questions.
- Understanding: appreciation of “why”.
- Wisdom: Evaluated understanding.
The first four relate to four categories relate to the past and can even be stretched to the present. They deal with what has been or what is known
Only the fifth category, wisdom, deals with the future. It incorporates vision and design.
Wisdom allows people to create the future not simply comprehend the present and past.
However, achieving wisdom isn’t easy. It is the top of the pyramid and to get to the top one needs to pass successively through each level. Kind of like Super Mario progressing upwards to Donkey Kong.
Data… data is raw. It simply exists and has no significance beyond its existence (in and of itself). Example — Raw numbers in an Excel spreadsheet.
Information… information is data that has been given meaning by way of relational connection. This “meaning” may be useful, but not necessarily.
Knowledge… knowledge is the appropriate collection of information. The point is to make information useful. Knowledge is a deterministic process.
Knowledge is useful but limited. Think of your first years at school. You may have memorised the times table. When you got home from school you could proudly tell your mum that 2 x 2 = 4.
But when your annoying sister came over and said “but what is 120 x 97”. You were promptly taken down a notch (or at least I was!). This is because knowledge is limited.
To calculate 120x97 you need something more. You need cognitive and analytical ability. You need… understanding.
Understanding… understanding is an interpolative and probabilistic process. It is cognitive. It is analytical.
In this process we take our currently held knowledge and synthesise it with new knowledge or information.
Understanding is learning, whereas knowledge is memorising. People, businesses, organisations or experts with knowledge are not particularly useful.
People, businesses, organisations or experts with understanding, however, are useful. They are useful because they can synthesise new knowledge or information with previously held information and understanding.
Why is understanding so useful? Well understanding gives us the basis for making decisions.
Wisdom… wisdom is extrapolative. It is non-deterministic, non-probabilistic. It is about the future.
Wisdom allows us to gain understanding where there was previously none. It is like a torch illuminating the darkness.
Wisdom asks questions where there are no easily known answers. And to which there may be no answer. Philosophical reasoning is one example.
Wisdom involves judgement. When deciding what is right or wrong.
Determining what will happen in the future requires wisdom.
Back to 2018
People, businesses, organisations and experts have access to rather large amounts of data and information or even knowledge. However this isn’t particularly useful until it is turned into understanding or even wisdom.
My supposition is the following. We face the same situation that Frederick Winslow Taylor faced.
- We have moved from the machine age to the information age.
- We have had a huge technological revolution.
- Experts analyse data and information using their intuition and expertise to gain insight. These methods are outdated. Just like the metal workers Taylor observed.
- Put simply this method of combining labour, technology and information is outdated and unscientific.
- We need a new approach!
We need scientific management for the information age.
We live in a world filled with more and more information. Much of it poor quality.
This poses quite a specific challenge to us humans. This is because our brains are wired for the physical world not a conceptual world. Our many cognitive biases cause problems when we try to turn information into knowledge and understanding.
If I think about something and then create a working hypothesis, or thought about the world it is really very easy for me to go out into the digital world and find confirmatory information.
Furthermore, if I am uncritical of the source of information I can pretty much find supporting evidence for anything I want on the Internet. Hence the existence of the flabbergasting and ridiculous flat earth society!
What’s the problem?
The vast majority of institutions are still working in this way. They are employing experts that have built up their knowledge over several years. We employ internal experts (from within the company) or externally (consultants) in order to support planning and decision-making.
These experts have to the ability to relay information and knowledge. Sometimes possibly even understanding. But very rarely will they be able to offer any wisdom. What is more, how relevant will their analysis be to support your specific decision-making needs?
History has a tendency to repeat its self. What Taylor noticed is repeating itself again today.
We have individuals processing information using their intuition and expertise built up over the years. This method worked well before the information age.
During this period information flowed in slowly over the years. The only way to build knowledge and understanding was to slowly become an expert over time.
However this is no longer the case. There is more than enough data and information easily available.
Our current model of of processing and analysing information is a complete and utter ineffective use of labour, technology and information. I have developed the same disdain to this approach that Taylor had for the artisan approach to machining.
We require a new scientific approach to the processing information.
We need scientific management for the information age. Not literally but conceptually. We need to go from experts individually processing information to having a structured and scientific approach.
How does it work?
Well I have talked a little about this in my article here and here.
What we fundamentally need is a more systematic approach to the collection analysis and dissemination of information.
Mercyhurst model of intelligence
This is the Mercyhurst model of intelligence. They have spent a lot of time thinking about the creation of intelligence. What they show here is that intelligence and operations are inextricably linked.
The left circle (intelligence) represents the collection and analysis of information related to what is going on out there. Focused on external things things not under our control.
The right circle (operations) represents that which is going on internally. Things that are under our control.
Decisions happen when we combine information from these two circles. They are focused on moving us forward towards a goal.
This mental model applies just as much to any business or organisation.
In order for the analysis (and insight and understanding) you are providing to offer any value, then it has to be supporting some kind of decision or operation.
Insight for the sake of insight is nice. But it’s not exactly a good use of resources if its never used for anything.
To get that left hand circle to work so that it is supporting decision-making requires a new approach. The only way I know of is to do it in a much more structured, systematic and scientific way. Ive talked about how here and here.
If you know of any other way please let me know.
Getting it to work
Getting the two circles shown above to spin and support each other as an individual or small team isn’t particularly difficult. However, doing it as an organisation is hard.
But getting these two circles to spin in synchrony in an organisation is exactly where I like to think I can help.
