Turning Data into Wisdom: How We Can Collaborate with Data to Change…

I had high hopes for this book, won through ER at a time when I happened to be doing some data analytics for work and was eager to improve my knowledge in this field, as aside from a stats class in undergrad, I have little schooling in the area. However, given that I started reading the book nearly six months ago and have yet to finish it, I am certain it is not living up to expectations. I find it tries to cover too much ground and doesn't do that very well. For example, it spend a whole page or more explaining the difference between mean, median and mode. Those are elementary school concepts, but I don't think the book is aimed at 10 year olds. Who is the audience? If they need to learn those basic concepts, the rest of the book is beyond reach. And yet it also introduces more complex topics like various analytic biases and others but only at a brief level. So much "depends" on context, that I found I was getting a wide range of topics with surface level depth. This turns out not to be very helpful. It's trying to cover too much ground and would be better off to assume basic math knowledge and spend more time on actual data analytics. I wanted very much to like it and learn from it, but it just wasn't working for me. ( )

I received a review copy of this through LibraryThing back in April. The timing for me wasn’t the best as my wife and I had just put our house on the market and were going through multiple rounds of showings, contracts, buyers backing out... well, that and other factors made it hard to focus on this. And this book needs a little more focus. (I started over a couple of weeks ago, reviewing all of my notes, and then was able to carve out the time to focus myself.) The concepts are far from difficult - you've seen them all before, but the reading, and my responding, were more difficult than I thought it should be. Part of it is the presentation; terms defined in a glossary are bolded, and there are some sections that have bold bleeding all over the pages (one paragraph has eight bold phrases!) And, a lot of italics. I get the idea, but it’s distracting from that focus. The overall impression is clinical, academic, cluttered, visually distracting. There is a lot of reinforcement, which will be repetitive to someone grasping the ideas quickly.

When I read books like this, I try to figure out who the target audience is. That was a challenge here. The chapter for his Analyze phase had some good information... mired with way-too-in-the-weeds details on statistical analysis, like showing how to manually calculate standard deviations?? I found it particularly interesting that the two sentences at the top of the back cover were “When you think of data do you think of complex charts and dashboards, things that are best left up to the experts to decipher? It’s time to debunk that myth.” and yet the author spent more than 50 pages on the minutiae of statistics. And I thought the presentation of causal loop diagrams less intuitive than Ackoff’s actual concepts.

The decision-making process Phases Hanegan offers -Ask, Acquire, Analyze, Apply, Announce, Assess - are alliterative, but they don’t have to be. Yes, it sells better though to have a handle. Also, I'm not fond of using definite articles, example:The 12-Step Methodology, is more authoritative than “A 12-Step”. That’s normal for these types of books, I know, but I always key on those word choices, like “Figure 3 shows the top-ten skills required within an organization to follow the data-informed decision-making process.” the top ten? what’s the source for that pronouncement? No source, it’s opinion and we’ve all read other books with different lists also making similar claims.

Pages 60-62 in the paperback copy I received had some of the best takeaways... “Ensuring that data can be trusted: Having a Data Strategy” and descriptions of characteristics of quality data are a good cheat sheet to hang on to. Later, on measurement and choosing the right key performance indicators, the author rightly notes that choosing the wrong ones can not only be useless, it could be harmful.

“Most strategic decisions within an organization are ripe for groupthink,...” Really? I disagree. General decisions maybe, but truly strategic decisions are either one person after assessing the options, or a team by a thought out process. Not groupthink.

Note to the author and the publisher: careful with quotes. Two epigraphs that I checked were sourced to sites that aren't that reliable when it comes to quotes. One, from Goodreads, is certainly not in itself questionable, but the quotes there are user curated and are misattributed many, many times. I found that particular quote from B.J. Neblett on his own blogspot page. Another, the Edison one about finding 10,000 ways to fail (I will often try to track down colloquial quotes from well-known persons...I don't always find the original source, and then I might say "attributed to"), was sourced from BrainyQuote. BrainyQuote is even less reliable that Goodreads, which is more disturbing as it purports to be a quote resource. Edison never actually said that, though he did say something close as recounted in a 1910 biography, and again, something similar in a 1926 interview.

So... the target? Student? Maybe. Mid-level manager, someone new to management? Also maybe. Certainly not a high level manager or executive who doesn’t need to do the heavy lifting herself. And if you are one of those higher on the higher levels, it might help to understand what is being asked (and maybe reconsider the asking). Still, most of the concepts once distilled and filtered are good to add to the toolbox. ( )

Kevin Hanegan presents a six-phase, twelve-step process for gathering, interpreting, and applying data for decision making. Hanegan makes what might seem to be a dry and imposing subject interesting and easy to understand. One does not need to be a mathematician to follow the process Hanegan describes. He provides examples throughout the book and provides a chapter with case studies to illustrate how the processes and tools are applied. He takes a multidisciplinary approach to data analysis. An entire chapter is devoted to explaining relevant tools, frameworks, and models. This is a useful book for anyone wanting to make data-informed decisions in a professional or personal context. ( )

Attention-grabbing title but no new insight or thinking. Perhaps best seen as marketing material for the author's company Qlik or supplementary manual for the company's software than essay on data or wisdom. Also poorly edited -- abbreviations used without definitions, running head on each page is the book title and author's name so that it is hard to find a chapter, and non-standard English grammar and usage in multiple places. ( )