Opportunities to make change

I was listening to Freakonomics Radio podcast interview with Rahm Emanuel, who pointed out his somewhat famous quote is often shortened. Here is the full quote:

“You never want a serious crisis to go to waste. And what I mean by that is an opportunity to do things that you think you could not do before.”

Rahm Emanuel

I have taken this to heart in making changes at work, which means taking some professional risks. These are not substantial personal risks—they are trivial in the grand scheme of things, but meaningful to a small group of people where we can make work better. I am grateful that others have supported these changes.

We should not need a crisis to initiate change, and many managers have more authority to make changes than they think. We should want to make things better—always.

Applied Statistics

In some circles, “Statistics” has a bad reputation—primarily because most of us had limited training though the techniques are applied in numerous fields. The pharmaceutical industry uses statistical tests to determine if new drugs are effective (or harmful), manufacturing industries implement statistical process control to maintain ever higher quality standards, politicians have increased the use of polling to drive policy decisions, and the list could go on.

But in the current discussions on STEM education, I have yet to see an argument where statistics is elevated in the curriculum the way “computer programming” has been promoted.

Is it the way we teach math in general—heavy on theory and light on applications? For many, the applications are what makes the work interesting.

The $100,000 master’s degree?

The discussion on college debt seems to focus on extreme cases, and Six Figures in Debt for a Master’s Degree from Inside HigherEd pointed out several of these. While they did break down the averages by degree. It’s helpful to look at the type of institutions students attend and the programs of study. I’m personally interested in this analysis and started to explore the dataset and thought I’d share some of the first impressions.

Looking at the data from 2016–2017, the mean debt is following a log-normal distribution, so some care will need to be taken in making comparisons between the groups. Including proprietary schools may not be useful given the low number of programs, but the spread is similar to private institutions.


For all master’s degrees with reported data, the mean debt values are $52k, $44k, and $39k for private, proprietary, and public institutions, respectively. Looking at various programs, the number with student debt over $100,000 is low.

I am particularly interested in STEM fields associated with the Professional Science Master’s movement; however, a quick survey shows that many schools’ data is suppressed to maintain privacy. As a first pass, CIP descriptions aligned with keywords from the PSM programs of study were used to narrow the scope of the analysis, including:

  • Agricultural Science, Food Science, Nutrition
  • Biotechnology
  • Computational Science, Analytics, Big Data, Statistics
  • Environmental Science, Ocean Science, Sustainability, GIS
  • Physical Sciences, Chemical Sciences

Business Administration, Management and Operations shows a wide range and is included for comparison. Dietetics and Clinical Nutrition Services also shows a wide range of mean debt, but the remaining programs have more modest ranges.


These are preliminary numbers but would indicate most students are managing their graduate education debt responsibly.

Exploring​ the “data” before EDA

Whiteboard space is at a premium in my office, but I’m always willing to erase a section to brainstorm a potential solution to new problems—afterall if we can visually create a “product” that provides insight, we can work backward and determine what data we need to produce that work.

I read Steven Covey’s book The 7-Habits of Highly Effective People well over a decade ago; however, “Begin with the End in Mind” has stuck with me as I’ve worked on various projects since that first reading. Currently, I’m working on several data-driven activities that remind me why this habit is so important.

Data Analysis (and Data Science) are hot topics, but too often folks want to jump right in and start crunching numbers—”Let’s run an X test on the Y samples and calculate the Z results.” It is easy to create large datasets that miss the mark and become wasted effort. While it may be hard, we need to get everyone to step back and ask the simple questions: “What problem are we trying to solve?” “What will this look like when we are ‘done’?”

Silicon Slopes Tech Summit 2019 (Day 2)

I am glad I can get out of the office and learn what companies are doing in Utah and Day 2 of the SSTS 2019 offered some more reminders of what is on the mind of tech leaders. In no particular order, here are several items I noted:

  • Companies are looking for individuals with domain knowledge who can reach across disciplines.
  • Culture eats strategy (i.e., it doesn’t matter how great your corporate strategy is if the culture of your company is terrible.)
  • Good companies promote a learning culture.
  • As a leader, get your hands dirty.
  • Make hiring decisions based on core competencies even if the person looks different than you.
  • How do customers increase revenue when they adopt your product or service?
  • Start by measuring something.

Silicon Slopes Tech Summit 2019

Day 1 of the 2019 SSTS is in the books and my favorite session from the day was a panel discussion on The Future of AI Talent in Utah. Not because I know anything about AI, but because one can apply the points made by the panel across many disciplines.

When answering a question about talent in Utah, the panel consensus was something along the lines of “Most applicants look the same—1% of the applicant pool are getting all the offers.”

I suspect this is somewhat exaggerated, but it begs the question: What makes that 1% special? How do people differentiate themselves?

What did the panelist say to the young crowd looking at AI as a career path?

  • Don’t rely on “cookie-cutter” (or in software terms “hello world”) projects to demonstrate competency.
  • Show a passion for solving problems.
  • Demonstrate your capabilities by publicly sharing your work—in the area of software development, maintaining a public Github repository for one or more projects. For those of us not in the software space, publishing in a reviewed, online journal would be an option.
  • And while we’ve all heard the college dropout success stories, having a strong educational foundation and simultaneously learning how to solve NEW problems—the ones not in the textbook today—is needed to stay competitive.

These recommendations sound like good advice for everyone looking to differentiate from the crowd.

How long do you need to learn something new?

Or build on something old?

A recent article from the Inside Higher Ed highlighted how experimentation in the delivery of online courses are driving the discussion on what the proper length of a class should be.

The familiar 12 to 15-week blocks align with my experience, and it was only after starting my position at the University of Utah where I realized this was no longer the norm. In my department, two, 3-credit, semester-long courses, were broken up (long ago) into six, 1-credit, 5-week courses. For upper division and graduate level classes, other departments offer “first-half” and “second-half” courses during the traditional semester allowing students the opportunity to broaden their experiences as they can select from a broader range of topics than what might otherwise be available. What has been the shortest course length? A single 3-credit course over five days (8:00 am to 5:00 pm), with a caveat that readings and assignments are due before the first day of class (i.e., there is pre-work involved) and they should expect additional homework each evening.

Most academics consider the last example extreme; however, this model is typical for professional development in many industries. I was fortunate to work in a company that valued professional development and participated in two courses—each taught as full days over a single week—that were similar to a university course. While not graded in the traditional sense, managers have to approve the cost of the course and weigh the loss of immediate work against the promise of improved productivity in the future. Good luck getting additional professional development approved if you cannot demonstrate benefits from your previous development courses. One of the biggest challenges in professional development is getting people to focus on the course and set aside the distractions of work—easier said than done.

So, back to my original question: How long does it take to learn something new or to build upon a previous skill? Can this be done in a single week? Or does it take three-plus months? For a traditional course, the mantra is two to three hours of study per credit hour—for three hours in the classroom each week, the expectation is a minimum of six hours of work outside — a total of 9 hours per week or 135 hours over the 15-weeks of a traditional semester. Assuming that the class-to-study ratio is closer to 1:1, the total time is 90 hours; depending on the topic, 90 hours would be manageable, and this could be more viable with structured pre-work. Of course, one is not an expert at this point but has obtained a level of competency with the subject. As a “self-directed” learner, 45 to 90 hours is a good approximation of the time needed for learning as I’ve built up various skill sets.

Could this type of intense schedule work? Would it be possible to take a three-course calculus series over 15 weeks if that was the focus? Probably, but we also need to consider the instructors. University faculty members need time outside the class for non-teaching activities: research, service, administration, course preparation, and advising are the most visible out-of-class activities expected at the modern university, and this out-of-class engagement is needed. But there might be some appeal to faculty as completing a teaching assignment in five weeks may open up opportunities for focused work during the rest of the semester.

If universities can exploit technology to maximize the high-value activities of their faculty, the traditional classroom will change, and it may reflect the time-intensive learning environments used by industry for professional development. It is worth exploring as the need for life-long learning will force us to become more efficient in education.

Lifelong Learning

The concept of continual improvement is an established business practice in today’s economy. The idea was founded in the statistical process control methods developed by Walter A. Shewhart of Bell Labs and later generalized by W. Edwards Deming into the PDSA cycle:

  • Plan: What are the desired outputs? What can be changed to achieve the desired goal?
  • Do: Implement the plan and gather data.
  • Study: Review the outcomes based on the collected data (more commonly called the “Check” phase).
  • Act: If the outcomes were meet, act to make the plan the new standard.

Of course, when completed, we return to the planning phase and look for further improvement to continue the cycle. Are we communicating this idea to students and employees? Can we apply this principle to the concept of “lifelong learning?”

Selfishness and unintended consequences

The 5 Whys is a simple technique for looking at problems—if you participate in community discussion groups, the issue of property taxes will undoubtedly come up. Here is my take on the problem applying a 5 Whys analysis:

Why are property taxes increasing?
Because home values are going up.

Why are home values going up?
Because more people are willing to pay more money to buy a home in our community.

Why are people willing to pay more money to by a home in our community?
Because the demand for homes in our community exceeds the supply.

Why is the demand for homes exceeding the supply?
Because we won’t allow new homes to be built.

Why won’t we allow new homes to be built?
Because people won’t support policies that allow for new development.

I’ve seen this play out in California, and the unintended consequences for the community will be devastating. Maybe not next week or next year, but eventually, the market price will force people rooted to the community out, and it’s not the “transplant” from the Bay Area or Los Angeles or Portland or Seattle or “name a major metropolitan area here” who will be at fault, it will be us. Our children will pay the price.

One of the main reasons I returned to Utah was simple economics. From a family point-of-view, I could not see my children being able to enjoy the same, high quality of life I had experienced if they chose to stay in California. The cost of living was too high, and this was driven primarily by the cost of housing. Why were housing prices so high? (See above)

The residents of Holliday, Utah unanimously rejected a new development on a vacant mall site that would have added a 775-unit high rise apartment tower and 210 single-family homes which would have included higher density townhomes. These would have been new places to live for young families and first homes for others. The development would have added office space for businesses as well as dining and entertainment options for the local community. But the residents didn’t want to change; they didn’t want something different in their city; they didn’t want to open up their community to others who can’t afford a $700,000 home.

I only hope that my city of Millcreek is less selfish and works to find solutions that allow our children (and newcomers) to live next to us along the Wasatch front.

Unwritten rules?

Working with graduate students in higher education is a satisfying experience, and it is rewarding to watch their personal growth as they progress through their program of study—once a student is in our program, I see it as my job to provide guidance and make sure they don’t get stuck. It is upsetting to the student, but also to me when they get stuck or leave the program because they fail to follow a documented process. Even though there may be a breakdown in the process, individuals are responsible for becoming knowledgeable about the requirements that impact them. But what about undocumented procedures or “rules?” I struggled this week to resolve a complicated issue and part of the problem—I figured out at the end of the process—was that I did not have all the information. There were unwritten rules.

At the height of my frustration, I thought this was a problem only found in academics; however, after giving myself time to reflect, I realize it is a problem in industry settings as well.

So my question is why? Why do we have unwritten rules?

Of course, it could be that there are no rules, just guidelines that are intended to be flexible based on the situation. This distinction is an important point. Guidelines provide flexibility for both parties in a negotiation which may not be the case with rules. Until it is written down, I would argue any process is just a guideline and negotiation is an option.

Where the rules are documented, there may be unwritten exceptions—situations that warrant deviation from the normal process. Unwritten exceptions recognize that managers (or administrators) encounter problems that deserve consideration based on the merits of the case. But why are they unwritten?

I think this is an issue of trust. Do you trust people not to abuse the exceptions? In organizations that work with high levels of trust, a defined process that evaluates exceptions remove the possibility of arbitrary application or favoritism.

Documenting exceptions provides a level of transparency and levels the playing field for those not inclined to challenge the status quo. Individuals who are reluctant to ask the question “why?” effectively lose out on options that are available to those willing to test the system. I doubt this the intent, but it can be the result.

As managers, we have a choice on how we set up the rules. I would argue that we should write them down and strive for a transparent and fair process.