Architecture, Data and Intelligence: Business Science and its Enemies

Sunday, November 08, 2020

Business Science and its Enemies

#FollowingTheScience As politicians around the world struggle to contain and master the Covid-19 pandemic, the complex role of science in guiding decision and policy has been brought into view. Not only the potential tension between science and policy, but also the tension between different branches of science. (For example, medical science versus behavioural science.)

In this post, I want to look at the role of science in guiding business decisions and policies. Professor Donoho traces the idea of data science back to a paper by John Tukey in the early 1960s, and the idea of management science, which Stafford Beer described as the business use of operations research is at least as old as that. More recently, people have started talking about business science. These sciences are all described as interdisciplinary.

Operations research itself is even older. It was originally established during the second world war as a multi-disciplinary exercise, perhaps similar to what is now being called business science, but it lost its way in the years after the war and was eventually reduced to a set of computer programming techniques with no real impact on organization and culture.

In a recent webinar on Business Science, Steve Fox asked what business science enabled leaders to do better, and identified three main areas.

Firstly system-related - to anticipate requirements and resources, identify issues, including risk and compliance issues, and fix problems.

And secondly people-related - to tell the story, influence stakeholders and negotiate improvements. Focusing on message and communications to the various audiences we need to influence is a key part of business science.

And thirdly, thinking-related. When business science is applied correctly, it changes the way we think.

I agree with these three, but I'd like to add a fourth: organizational learning and agility. This is an essential component of my approach to organizational intelligence, which is based on the premise that serious business challenges require a combination of human/social intelligence and machine intelligence.

Steve Fox also stated that the biggest obstacles to creating data-driven business aren't technical; they're cultural and behavioural. So in this post, I also want to look at some of the obstacles of following the science in the context of business and organizational management.

Poor Data - Inadequate Measurement and Testing - Ignoring Critical Signals
Too Much Data - Overreliance on Technology - Abdication
Silo Culture - Someone Else's Problem
Linear Thinking - Denial of Complexity
Premature Attempts to Eliminate Uncertainty
Quantity becomes Quality

After I had initially drawn up this list, I went back to Tukey's original paper and found many of them clearly stated in there.

Poor Data - Inadequate Measurement and Testing - Ignoring Critical Signals

Empirical science relies heavily on a combination of observation, experiment and measurement.

Too Much Data - Overreliance on Technology - Abdication

Tukey: Danger only comes from mathematical optimizing when the results are taken too seriously.

Adrian Chiles reminds us that all the data in the world is no use to you if don’t know what to do with it. He quotes Aron F. Sørensen (via Chris Baraniuk) Maybe today there’s a bit of a fixation on instruments.

And in many situations, people overrely on algorithms. For example, judges relying on algorithms to decide probation or sentencing, without applying any of their own judgement or common sense. If a judge doesn't bother doing any actual judging, and lets the algorithm do all the work, what exactly are we paying them for?

Linear Thinking - Denial of Complexity

Tukey: If it were generally understood that the great virtue of neatness was that it made it easier to make things complex again, there would be little to say against a desire for neatness.

One of the best-known examples of linear thinking was a false theory about the vulnerabilities of aircraft during the second world war, based on the location of holes in planes that returned to base. People assumed that the vulnerabilities were where the holes were, and this led to efforts to reinforce planes at those points.

Non-linear thinking turns this theory on its head. If a plane makes it back to base with a hole at a particular location, this should be taken as evidence that the plane was NOT vulnerable at that point. What you really want to know is the location of the holes in the planes that did NOT make it back to base.

In 1979, C West Churchman wrote a book called The Systems Approach and its Enemies, about how people and organizations resist the ways of thinking that Churchman and others were championing. Among other things, he noted the way people often preferred to latch onto simplistic one-dimensional/linear solutions rather than thinking holistically.

Chris Baraniuk, Why it’s not surprising that ship collisions still happen (BBC News 22nd August 2017)

Christa Case Bryant and Story Hinckley, In a polarized world, what does follow the science mean? (Christian Science Monitor, 12 August 2020)

Adrian Chiles, In a data-obsessed world, the power of observation must not be forgotten (The Guardian, 5 November 2020)

C West Churchman, The Systems Approach and its Enemies (1979)

David Donoho, 50 years of Data Science (18 September 2015)

John Dupré, Following the science in the COVID-19 pandemic (Nuffield Council of Bioethics, 29 April 2020)

Faye Flam, Follow the Science Isn’t a Covid-19 Strategy: Policy makers can follow the same facts to different conclusions (Bloomberg, 10 September 2020)

Steve Fox, A better framework is needed: From Data Science to Business Science (Consider.Biz, 17 September 2020) via YouTube

Matt Mathers, Ministers using following the science defence to justify decision-making during pandemic, says Prof Brian Cox (Independent, 19 May 2020)

Megan Rosen, Fighting the COVID-19 Pandemic Through Testing (Howard Hughes Medical Institute, 18 June 2020)

John Tukey, The future of data analysis (Annals of Mathematical Statistics, 33:1, 1962)

Wikipedia: Data Science, Management Science