On Reading: Second Year DMBA

As I wrap my head around the topics we covered throughout the DMBA, I’ve found it helpful to remember and assess the materials we used, including books. It should be noted that many of the courses heavily used articles and HBR case studies, which I’m not going to endeavor to list. Perhaps there will be a “best of” article list someday. Here’s the comprehensive book list from year two:

Brand Strategy:

Managerial Finance:

  • Financial Management, 13th Edition, Eugene F. Brigham, Michael C. Ehrhardt
  • Various HBR case studies

Operations:

Experience Studio:

Venture Studio:

Strategic Management:

Strategic Foresight: (Full reading list is too long to post, so I’m just posting the ones I selected or have read.)

Some of the books were wonderful, some forgettable, and there are a few books that I would never recommend. Books I have gone back to for reference include the Marty Neumeier selections, Blue Ocean Strategy (more the toolset than the book), Strategy Safari, and Kellogg on Branding.

A Natural History of the Senses and most of the strategic foresight books were enjoyable to read and provided a good sense of a field or concept, but haven’t been useful as references.

The concepts in The Lean Startup and Tribes have been useful and commonly referred to by practitioners I’ve spoken with, but both books could probably express their ideas in a few pages.

In my opinion, don’t bother with Operations Strategy or Experience Design 1.1. These books were not helpful, interesting, or useful. There are other, better ways to explore the content.

Second semester, I started to use Audible.com for some books, and would highly recommend listening to books at high speed. This method takes advantage of time running errands, baking, or cleaning. Good candidates for listening are books where the concept is more important than the prose, where storytelling isn’t key, and you won’t want to take copious notes. There’s a lot of stopping and starting involved in consuming books this way, and the high speed tends to lose nuance in the reader’s inflection. Although note taking isn’t super intuitive, I take notes on books in Audible through bookmarks and their speech recognition software. Because you can’t read the text, it’s harder to note specific sentences.

Advertisements

Summary: The Signal and the Noise

By exploring prediction methodologies as they pertain to events ranging from earthquakes to chess, The Signal and the Noise by Nate Silver offers insights on the art of prediction. As the title suggests, a central theme is separating signals (underlying truths), from the noise (the plethora of available data that forms meaningless patterns that can be mistaken for signals). The book makes a case for using Bayesian logic, thinking probabilistically, for better predictions. The premise is that by using information gathered from past events, you can predict future events.

Rather than read the physical copy, I listened to it at 2.5x the recorded speed. It was an interesting method to try.

Rather than read the physical copy, I listened to it at 2.5x the recorded speed. It was an interesting method to try.

Bayes’ theorem is an equation that takes multiple factors, expressed as probabilities, into account. Both general probabilities, things that are generally true, and conditional probabilities, probabilities based on if-then situations are used. For example, I eat hummus for lunch 50% of the time. (Probability.) If it’s raining, I only eat hummus 10% of the time. (Conditional probability.) The result of the equation is a likelihood of the event occurring. If you used the equation to evaluate whether or not I am likely to have hummus for lunch next Tuesday (using more information than I provided here) you may determine that there’s a 20% chance that I’ll have hummus, which does not rule out the possibility of a hummus lunch, but indicates that it is less likely than my having something else. For more information on Bayes’ theorem, check here.

Silver also addresses the value of the human versus computer in prediction. Despite a computer’s ability to sort and process massive amounts of data, humans sometimes have an edge, at least for now. In baseball, for example, the book examines predictions of player performance down the line. Computer programs made a list of players they predicted would do well, and human scouts made predictions. When the predictions were examined years later, the human scouts were more accurate. They were able to take in less quantifiable data that the computer did not consider, like personality.

Qualitative data plays a role in certain  types of predictions, although it is difficult, perhaps impossible, to take personal bias out of it, which may lead to error. Personal bias may lead us to over-emphasize certain information, while disregarding other data. The predictions that most obviously benefit from qualitative data involve human behavior. For example, predictions around poker, chess, basketball, and politics fit this category.

I was attracted to Silver’s assertion that there are uncertainties in prediction, and there always will be. There is no way to have access to all pertinent data relevant to a prediction.  Nor is it possible to un-biasedly and correctly analyze all available pertinent data. In part, this is because it is difficult to correctly discern which data is relevant. Otherwise stated: it is difficult to tell what is noise and what is signal. Silver asserts that it is important to accurately represent uncertainties, even when it makes the prediction less useful. For example, rather than stating that population growth will be P% in 30 years, it would be better to state that “pending X and Y conditions, if Z holds steady, population growth is projected to be between P% and Q% in 30 years.”

Silver also cites a Donald Rumsfeld quote: “…there are known knowns; there are things we know that we know. There are known unknowns; that is to say, there are things that we now know we don’t know. But there are also unknown unknowns – there are things we do not know we don’t know.” “Unknown unknowns” present the greatest chance for introducing danger or inaccuracy into predictions. In representing uncertainty accurately, it is important to take this under consideration. To combat unknown unknowns, Silver suggests absorbing as much data as possible by reading avidly. The more prediction-makers know, and the more they know that they don’t know, the more accurate they will be. The book asserts that most predictions go wrong due to human error, and the more data prediction-makers collect, the more human error is reduced.

When considering more data, though, there is potential to get caught up in “noise.” Rather than take “more data” as a net positive at face value, I believe there are criteria that data should meet before having equal consideration. Silver does not address this extensively in his book. The basic premise of the book is that there is so much data out there that it is easy to get stuck in the weeds, and so if we are supposed to absorb everything we can, without extensively filtering it, we are likely to become overwhelmed and confused. While I believe Silver understands this, it is not addressed and the basic idea of “get as much data as possible” is expressed throughout the book.

The concepts above were the ones that most interested me, although other gems are strewn throughout the book. Silver touches on betting strategy (always go for it if the likelihood greater than projected), poker strategy (learn the behavior of your opponents), and the relationship between extreme and non-extreme instances of events. After listening to the book, I feel much better informed about prediction strategy and am planning to incorporate representation of uncertainty into my predictions from here on out.

Indispensable Interweb Tools: The Study Edition

More learning happens online than ever before, and the trend will continue. I use online tools a ton, as my program accommodates a commuter schedule and involves a ton of online meeting, research, posting, and content generation. These are the tools I’ve found the most helpful this year:

Evernote: This tool has been wonderful. All of your notes for all of your classes and books and anything else…in the same place. You can insert videos, images and audios into your notes, and the search function searches text inside images as well as in your actual notes. You can also access your documents online or offline at any time– on your iPad, phone, or computer. It’s free.

Pandora: Electronic for Studying Mix. Everything you wanted from your study playlist but didn’t have time to curate. Free with commercials.

Zoom: The group video service that doesn’t crash, and connects people quickly. You can invite people via Google Chat or email. Allows for screen sharing, and is far more reliable than anything else I’ve tried. And I’ve tried a lot of services. Unfortunately, the free version only allows for 45 minute meetings before you have to re-connect. The paid version isn’t too expensive though.

Google Docs: When working with groups, Google Docs is a fairly intuitive and easy way to work on documents at the same time. We can take notes, keep track of action items, brainstorm, and generate content during meetings with each team member seeing the same thing. It’s brilliant. I can’t imagine writing a team paper or sharing research without it. Free.

LinkedIn: This tool has been surprisingly useful for connecting with visiting speakers, professors, and classmates. People post useful news articles and groups may offer insight into various industries and trends. Free.

Twitter: Another surprise find– when researching trends or trying to find general consumer data points (to get started in the right direction, not to be taken as a representative sample) Many of my projects involve industries that I haven’t worked in, and it’s been extremely helpful to create lists of key industry players and follow their tweets. Also free.

YouTube: Cute Baby Animal Videos. There’s nothing that’s quite as relaxing, brainless, and uplifting as watching videos of cute baby animals.

Honorable Mentions: Asana and Mural.ly

What are your favorites?