The Data Scientist's Dilemma: When NULL Isn't Just Nothing

As a data scientist in the shipping industry, I spend most of my time wrangling data, optimizing queries, and trying to extract meaningful insights from vast volumes of data. Theoretically, this job is very simple: run analyses, uncover insights, and help the business make data-driven decisions. However, in practice, it’s rarely that simple. Often, my time is consumed by a seemingly innocent but dangerous trap: technical issues- an innocent distraction that can easily pull focus away from the deeper business insights waiting to be uncovered.

Unexpected results, NULL values, or anomalies in the data lead me down a path of self-doubt and over-analysis. The trap? Assuming that if the data doesn’t look right, it must be my fault. More often than not, the technical side becomes a rabbit hole, diverting my attention from the business logic where the real insights often lie.

The Self-Doubt Spiral: "Is It Me?"

The first reaction to seeing unexpected results is almost always the same: "What did I do wrong?" Maybe I messed up the query. Maybe there’s a typo in the code. Maybe I joined the tables incorrectly. This self-doubt kicks off a spiral where I spend hours, even days, chasing a solution to what I perceive as a technical problem.

I can’t count the number of times I’ve meticulously debugged SQL queries and painstakingly reviewed every JOIN and filter clause, only to find that my logic was fine. Yet, each time the data returns NULL or seems "off," I instinctively assume that the error lies in my code.

Don’t get me wrong—technical accuracy is critical in this job. Data scientists need to ensure that the foundation of their analysis is solid. But the real problem arises when this technical focus becomes tunnel vision. I’ve learned the hard way that spending too much time chasing bugs in code can blind me to the bigger picture: the business context behind the data.

The Technical Rabbit Hole

In my early days as a data scientist, the technical rabbit hole was my default route. I spent an unreasonable amount of time troubleshooting technical issues, only to find that they were red herrings.

Some common distractions include:

• Query Syntax Errors: A missing GROUP BY or an incorrectly structured JOIN clause can drive hours of frustration.
• Data Pipeline Bugs: I’ve seen data pipelines fail midway through ingestion, leaving partial or incomplete data that leads to puzzling NULL values.
• System Updates Gone Wrong: Sometimes, a backend system update can break an existing query or change the format of incoming data.
• Data Type Mismatches: I've encountered instances where seemingly simple calculations returned strange results because of hidden type conversions or incorrect assumptions about the data format.

I recall a specific investigation where missing data points had me convinced there was a pipeline issue. I spent half a day combing through logs and rerunning ingestion scripts—only to realize that the data wasn’t missing at all. The client had simply stopped using a particular shipping lane, and the query was accurate in returning no data.

These technical challenges are unavoidable, but they can become time-consuming distractions. Many times, they turn out to be unrelated to the issue at hand, which leads me to the more valuable lesson: when the problem isn’t technical.

The Aha Moment: When the Problem Isn’t Technical

One pivotal experience stands out where hours of technical troubleshooting led to a completely different conclusion. I was analyzing the performance of our top shipping lanes and noticed that for a certain route, CA <> OK, no transit time data was available. My immediate thought? Something must be wrong with my query.

After checking and re-checking my SQL, I dove into our data pipeline, searching for any ingestion errors. After half a day of poking around the system, everything looked perfectly fine—technically speaking.

It wasn’t until I paused and thought about the actual business context that I had my "aha" moment. The customer had simply stopped shipping to Oklahoma. There was no bug, no query mistake—it was a business decision that caused the absence of data. The insight wasn’t technical; it was about understanding the customer’s changing shipping patterns.

Shifting the Mindset: Balancing Technical and Business Perspectives

This experience was a wake-up call. While it’s easy to get lost in technical details, the real insights often come from understanding the business context. By shifting my mindset, I started balancing technical troubleshooting with business analysis.

Here are some strategies I now use to approach problems:

• Consider the Business Early: When unexpected results appear, I ask myself, "Could this reflect a business decision or market change?" before diving too deeply into the technical side.
• Collaborate Across Teams: I work closely with product managers and customer success teams to understand changes in business operations that might be reflected in the data.
• Use Domain Knowledge: Knowing the ins and outs of shipping logistics helps me quickly recognize when data anomalies might point to real-world events, like changes in shipping lanes, customer behavior, or market dynamics.

By using this framework, I can shift focus earlier from technical issues to potential business insights, saving time and uncovering valuable information.

Case Study: From Query Debugging to Business Insight

One particularly eye-opening case was when I was investigating a Peak Season Plateau in our shipping volume data. Initially, I thought the plateau was the result of a technical issue—a bug in how we were aggregating shipping data for large retailers during the busy season. I spent hours reviewing the codebase and rerunning the numbers, only to find the data seemed correct.

After involving the business team, we discovered that the plateau wasn’t a data error. In fact, it reflected a deliberate shift in carrier strategy by several large merchants to handle their peak-season logistics differently by using regional hubs to manage overflow. The real insight was understanding how changes in business strategy were impacting overall shipping patterns.

Tools and Techniques for Broadening Your Perspective

To prevent myself from going too far down the technical rabbit hole, I’ve developed a few go-to methods for broadening my perspective:

• Quick Technical Validations: Before diving deep into debugging, I run quick checks on query logic, run-time metrics, and data types to eliminate obvious errors.
• Business Hypothesis Exploration: I incorporate exploratory data analysis (EDA) techniques early in the process to uncover any patterns that might suggest a business cause for anomalies.
• Data Visualization: Visualizing trends and patterns often reveals underlying business factors—like seasonality, customer segmentation, or market shifts—that aren’t immediately obvious in raw data.

These techniques help balance the focus between technical troubleshooting and uncovering business insights.

Embracing the Dual Role of Technical Expert and Strategic Problem Solver

As data scientists, our default is often to think that any issue we encounter is due to something technical—a bug in the pipeline, a query error, or a system outage. But the truth is, many times, the data is telling us something about the business itself. By shifting our mindset to think more broadly, we not only save time but also add real value as business analysts who can uncover insights that lead to strategic decisions.

My advice to fellow data scientists: embrace the dual role. Yes, we are technical experts, but we also sit at a unique intersection of technology and business. And that’s where the most impactful insights lie.