The Demon in the Machine
Davies, a physicist and cosmologist, has written an astonishing book about information and the role it plays in every level of our existence from the molecular level to the universal one.
Two things fascinate me:
firstly, the enormity of Davies’ thought and,
secondly, that we struggle to manage the information just in our own organisations.
Invisible Women: Data Bias in a World Designed for Men
Perez assesses everyday situations through the lens of gender bias and reveals a set of challenges and indignities (the size of i-phones, the design of automobiles) that many women are aware of but that most men do not notice, because they do not have to be. She then uses this lens to demonstrate how assumptions about gender influence what data we collect, why we collect it, and how we interpret its meaning. In the face of claims about the value of artificial intelligence, this book gives one a reason to pause and reevaluate the risks associated with allowing machines to take biased human thinking to its logical conclusions.
How to Measure Anything
Hubbard takes a concept that many find challenging, how to quantify, and turns it on its side: any measurement is, at its simplest, a comparison.
So measuring anything amounts to figuring out the appropriate comparisons and using them to learn more about the thing you want to measure. His choice to simplify the concept creates a new perspective that simplifies measurement and also shows the risks of failing to understand the assumptions built into measurement.
Good to Great
In “Good to Great” Collins researches why a few organisations outperform the market by a significant margin. The fundamental reason is cultural – the right people on the bus with a very clear vision of the destination.
Data and Reality
In Data & Reality, William Kent explores how we, people, make data, through a set of choices about what to represent and how to represent it. Written in the late 1970s, the book’s observations are even more important in the 21st century, as we are flooded with new and sometimes highly questionable forms of data.