Planning – Jonathan Szeto

1 November 202623 October 2026

Who’s the Boss?

I learned the difference between being responsible and being accountable long before I ever worked with data. I hadn’t yet turned 23 when I arrived at my first duty assignment as a newly-minted lieutenant. I was the battalion chemical officer, responsible for contingency plans if weapons of mass destruction were ever used. Part of my responsibility was inventorying and maintaining the detection and decontamination equipment for a unit of 400 men. This included some very advanced electronics with low-radioactive isotopes. In today’s dollars all of it could be worth well over a million dollars. If something happened to that equipment, my career was on the line.

I saw the same lesson play out again when I was a manufacturing engineer. In manufacturing, the plant manager owns the line. If scrap rates climb or a shipment misses its window, the conversation starts and ends with that person. The process engineers, the quality technicians, and the operators on the floor are the ones who actually change the set points, quarantine the bad parts, and rewrite the work instructions. They are responsible for the work. The manager is accountable for the result.

Data isn’t any different, though looking at most corporate data org charts, you’d think we were all stuck in a mid-80s Tuesday night primetime loop—constantly asking, “Who’s the boss?” line-item by line-item while Tony Micelli and Angela Bower debate who actually holds the title.

23 October 202623 October 2026

The Top Line

A few years ago I asked a vice president how the business intelligence team I was on actually proved its value. His answer was straightforward: we made it easier for directors and executives to see the current state of the business and judge what was possible next.

That discussion stuck with me because it was so different from how most foundational work gets discussed. Data architecture, data governance, and the unglamorous parts of IT hygiene rarely get described in terms of decision quality or growth optionality. They get described as cost centers. When something is only a cost, the rational move is to minimize it, defer it, and hope the problem stays quiet.

The past few days of widespread vulnerabilities—especially the critical PHP issues that affected a large share of the web—made the pattern obvious again. Plenty of people have pointed out that basic patching, asset management, and maintenance were underfunded for years. The harder observation is why. Work that leadership can’t clearly connect to top-line outcomes tends to lose the budget fight, even when everyone agrees it is important in the abstract.

18 October 202617 October 2026

Going to Gemba

Source: Motor Trend (http://www.motortrend.com/news/miles-in-three-2013-honda-accords/)

In my past life before data architecture, I used to be a manufacturing engineer in the automotive industry. At the time, the popular approach was Lean Manufacturing and the Toyota Production System, driven by Toyota’s massive success in the United States.

Lean was—and still is—about increasing value to the customer by reducing and eliminating work that doesn’t add meaningful value. This spawned many tools and practices to surface inefficient habits. One that stayed with me was the concept of “going to gemba.” In Japanese, gemba means “the real place” where work actually happens. In practice, this meant that managers and engineers left the office and went to the shop floor. There they simply observed: stood and watched the work happening, and asked questions of the line operators in a curious and respectful manner. No decisions were made on the floor. Leadership took what they had learned back to the office and used those lessons to shape operational plans.

This concept came to mind as I read this post on LinkedIn:

5 September 20265 September 2026

The Stall

A while ago I was “gifted” with a gas grill. (It’s a long story—basically, someone I knew left behind the grill at my house and never came by to pick it up.) While I enjoy cooking, I’m usually not much of a grilling guy. However, I do have a fondness for barbecue. I lived in Kentucky for over two decades and traveled all over the South during that period. As a result, I was initiated into the finer details of pulled pork and smoked brisket.

As it turns out, there is actually a way to smoke with a gas grill; all you need is a smoker box to hold the wood chips, which you can get at any hardware store. So this past summer, I figured I’d put the gas grill to use and try my hand at smoking. It’s been a learning experience, and there were mistakes along the way. But with time and practice, I’ll get better at it.

23 August 20202 August 2022

Precipitous Terrain

*If you aren’t prepared for what life can throw at you, you may be in for a great fall.*

As I’m sure everyone understands all too well by now, life has a way of throwing monkeywrenches into the system. If there’s anything that’s been consistent about 2020, it’s that every month seemingly has a new surprise to spring upon humanity.

Instead of traveling over smooth ground, we are on precipitous terrain. Any kind of long term forecast is currently impossible.

When chaos is the order of the day, you must be able to make and act on snap judgements without overcommitting on your previous decision. How do you do that?

13 March 20202 August 2022

Keep Calm and Corona On

*Sources: CNN (left) / Money Inc (right)*

As I was folding my laundry tonight, I was thinking about everything that’s happened over the past few days, at the national level, but more importantly what was going around me in my local city. And some things aren’t adding up.

(As I’m writing this, President Trump has just declared a national emergency over the spread of COVID-19, aka the Coronavirus. It’s been only a few days since major sporting events were suspended and cancelled, most notably the NBA and March Madness. So I don’t know right now how things are going to play out over the next few weeks or months.)

Over a decade and a half ago, I used to be a manufacturing engineer in the automotive industry. One of my regular duties was to conduct something called an FMEA, Failure Modes Effects and Analysis. It was a risk assessment over what could possibly go wrong in a car (or a component of it), which guided what type of preventive measures and quality checks had to happen.

The Big Three automakers had a pretty standard system: there were three measures, rated on a scale from 1 to 10. You multiply the three numbers to get anything between 1 and 1 thousand. The bigger the number, the bigger the risk. If it was something under a hundred, you almost didn’t think about. However, if it was close to a thousand, it was all hands-on-deck, the-company-could-go-out-of-business bad.

These were the three measures: severity (what’s the worst case), occurrence (how often does it happen), and detection (how quickly can it be spotted).

I’m going to add in a fourth factor to the calculation: contagion. The automotive FMEA doesn’t account for this, because when a tire blows out on one Ford Explorer, it doesn’t cause three or four other Explorers to crash. So now our scale goes from one to ten thousand.

In thinking about the virus, I decided to apply my FMEA math. So let’s go:

(Disclaimer, I’m going to throw out some numbers, but all of these are second-hand with no references. This is a thought experiment, not a policy paper.)