Pattern Recognition

I watched my teammate shoot a three-pointer from the wing during an intramural basketball game a few years ago.

Before the ball reached its peak, I knew it would miss. I went for the rebound, but I didn’t run towards the basket as most people would. Instead, I sprinted away from the basket towards the opposite baseline.

The ball landed right in my hands.

Most of my rebounds throughout the seasons looked like this.

I’m only five feet seven inches tall, which is shorter than most of the league. My vertical jump was slightly above average, and while I was strong for my size, there were plenty of guys stronger than me.

I was at a physical disadvantage, yet I was one of the best rebounders on my team and consistently outrebounded larger, better opponents.

I rebounded well thanks to my pattern recognition skill.

Success didn’t come from luck or physical grit. It came from analysis and calculation.

Anytime a shot went up, I analyzed the ball’s arc, speed, and distance to predict the most likely landing location of a missed shot. Then, I’d run to the spot to increase my odds of getting the ball.

What fascinates me is that I didn’t do the mental math on my own. My mind subconsciously performed the calculations within fractions of a second.

I played basketball my whole life, so I witnessed tens of thousands of jump shots. Over time, my mind noticed patterns of where missed shots landed based on the shooter’s location and the ball’s arc and speed.

It recognized patterns through experience and repetition, helping me make better decisions on the court.

The same pattern recognition skill helps me off the court as a data analyst.

I’m given, or find on my own, problems to solve every day. The problems vary. Sometimes I write SQL queries to analyze data. Or I build new schema objects for our databases. Or I craft reports using SSRS and Power BI.

But the daily exposure to my company’s data ecosystem allows me to identify connections between business processes and our data. As I explore new sections of our systems, I uncover new relationships.

I don’t intentionally remember these connections and relationships. Rather, my brain subconsciously stores them—as it does when I watch people shoot a basketball. My mind is like a vector database that, when given new problems to solve, searches for previously stored knowledge that may prove useful.

I don’t know how or why my mind works this way. It just does.

Here are two examples.

During my first few months on the job, I needed to create an SSRS report with tabs that dynamically generated based on the user’s input parameters. For example, if the user selected multiple customers, the report would create a separate tab for each.

I had never done anything like this before, so I assumed it would take a while to build.

But I remembered seeing a report weeks earlier that contained the same dynamic tab functionality. So, I didn’t need to waste time consulting my teammates on how to build the report. Instead, I reverse-engineered the solution and deployed my report.

Recognizing the dynamic tab pattern from other reports allowed me to solve the problem on my own quickly.

The second example concerns a recent task.

My boss asked me to write a SQL stored procedure that recosted all inventory items. It required me to analyze packaging work orders to find the allocated manufacturing lots. Then I had to investigate the manufacturing lots to find the allocated purchased lots.

It seemed like a complex and laborious mess.

However, I quickly recognized a pattern. I remembered a task I completed a year ago that queried the tables containing lot allocations for packaging and manufacturing batches. So, I had no issues engineering the necessary SQL logic.

What initially seemed like one of the most complex problems thrown my way became fairly straightforward.

In each example, I didn’t purposefully try to relate the problems to previous ones. My mind worked on its own to identify the relationships and provide me with a strong starting point for finding solutions.

Pattern recognition helps me solve problems faster and solve more difficult problems.

This matters because, over my 2.5 years in the data industry so far, I’ve noticed that technical skills aren't the deciding factor that separates good from great analysts. The more important factors are the scale of the problems one solves and the speed at which they ship solutions.

Pattern recognition isn’t the only skill required to solve critical problems quickly, but it is one that has helped me most.