Staying Relevant

Being on top of cultural trends has never been more important. We have nearly unlimited access to national and international events, thought leaders, and products. Conferences like TED and media sharing sites like Twitter provide more ideas than it is possible to absorb. And this matters in the workplace. Because your consumers, whoever they are, are part of these trends and have access to that same information. Businesses need to anticipate their needs and understand the ever-evolving ecosystem your business operates in to be successful. Beyond workplace usefulness, it’s fun to be able to connect others with ideas that are relevant or interesting to them, and to try new things as they evolve.

That said, it’s not easy to stay relevant. Some people are more adept than others at being in the know about new products, business models, style trends, and popular media. In any case, it takes time and awareness to clue in to new things.

Keep your eyes, ears, and brain open. By being observant in daily life, you can absorb a lot. I find it’s a lot more interesting and relevant to talk to someone about their experiences with The Internet of Things than to read about it.

  • What are people wearing today? (Have you ever noticed that people walking together are likely to wear the same type of shoe? It’s weird.)
  • See a new business? Why not stick your head in, say hello, and find out why they’re there?
  • Did you have a great experience at a restaurant or retail space? Why was it awesome?
  • Do you or others have relationships with brands? What is it like?
  • Are people talking about experiences that were good or bad? Anything particularly interesting?
  • Are there recurring pain points in your life or the lives of others that can be solved and is anyone working on it? New products, services, and habits also come with their own set of problems to be solved, which results in new products, services and pain points.
  • Try new things and develop your own point of view.
After I'd read about the rising popularity of "bulletproof" coffee, I had to try it myself.

After I’d read about the rising popularity of “bulletproof” coffee, I had to try it myself.

Follow social media. I don’t post much, but it’s fascinating to see what friends post, especially if they are very different from me. It’s also helpful to follow thought leaders and news sites to see what’s current. My weapons of choice are Twitter for broad world happenings and trends and Facebook for social and opinion trends. LinkedIn and Medium also have timely articles and social components.

Seek out industry and interest relevant sources. As awesome and intelligent as you probably are, it’s pretty impossible to be an expert on everything. Figure out what areas you want to focus on, and deep dive. That might mean listening to podcasts, following hashtags, subscribing to newsletters or checking relevant sites daily, a magazine subscription, attending conferences, signing up for newsletters from your favorite businesses/bands/artists, or reading relevant books as they come out. I’ve recently been into:

One of my favorite ways to stay current is to try new, trendy restaurants. Yum!

One of my favorite ways to do research is to frequent popular/interesting restaurants. Yum!

I’d love to know how you follow trends and culture!

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.

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.

Summer Reading/Personal Leadership Plan

One of the most helpful deliverables we created last semester in our Leadership by Design course was a “Personal Leadership Plan.” This plan analyzed the results of our various assessments (See post titled “You: By the Numbers” for specifics) and identified two areas for growth for us to focus on. For each of these growth areas, we identified specific actions to take, feedback loops, success criteria, and a timeline for the plan. If you’re interested in the exact format, please message me directly.

The two areas I identified for myself were growing my capabilities and confidence as an ideator and better identifying and aligning my actions with my purpose. In addition to identifying actions and activities to support this growth, I’ve curated my summer reading list around these goals. I’d love your comments/feedback on other books that might support this.

Purpose focus: Identify a purpose that may lead to sustained happiness, satisfaction, and success.

Ideation focus: To look at opportunities instead of problems, and to practice letting go of some control to become more agile.

Some of these books are ones I’ve already read for school, and want to re-read on my own time to more fully absorb their lessons. And the journey continues.

On Reading: First Year DMBA

As you can imagine/have experienced/are experiencing currently, grad school requires a lot of reading. Almost through my first year, I feel ready to recommend some of the best reading we’ve done in the purple track of the DMBA.

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Here goes:

The top three on the list were pre-reading before the first semester started, and served to get my head in the game before starting my MBA. The second three were the Bibles of my first semester, and Business Model Generation and Designing for Growth have influenced each project I’ve worked on since meeting them. The last two books are both from my second semester marketing insights course. The Making Meaning book provided a whole new perspective on what needs to design for, and Design Research is full of tools for uncovering those needs.

Finally, cliche I know, but keeping a design journal has been wonderful. If you don’t already think of yourself as a person who draws or maps ideas on paper, having to keep a design journal as an assignment will open doors. I’m not a drawer and I do not sketch for fun. But, let me tell you, putting an idea on paper, literally, is the fastest way to try out a new idea and communicate it to people. Because it was a weekly assignment, I had to get used to it. I loved it when I did.

Anyone else have great grad school reads?