Data engineering is like being the caretaker of a bustling city. Everyone benefits from clean water, smooth roads, and reliable electricity, but few think about the behind-the-scenes work that makes it all possible. Now, let’s imagine trying to convince the city council to invest in invisible /not-so-fancy upgrades—a tougher sell, right? It is not visible or shiny. That’s the life of a data engineer when it comes to championing data quality and long-term goals.
Data Quality: Not a Magical Unicorn
Data quality isn’t some mythical creature that graces our pipelines uninvited. It’s the product of collaboration, planning, and painstaking effort across teams. Backend engineers, machine learning engineers (MLEs), data analysts, business intelligence analysts, and even marketing teams all have a stake in this game. Everyone wants reliable, clean data, but making it happen requires coordination and commitment. It might seem like a boring and tedious conversation, but trust me, it has longer-term benefits and becomes less painful over time.
Let’s not forget documentation—the unsung hero of data quality. Without clear documentation, MLEs struggle to understand what the data means, and everyone risks misinterpreting or misusing it. Documentation isn’t glamorous, but it’s foundational.
I invested much of my time trying to ensure data quality and pushing for initiatives to improve data quality and practices. Even for the ML processes, I pushed for an initiative to build a monitoring dashboard to keep us up-to-date on the effectiveness of our models. However, it was tough for me to sell, and sometimes, these efforts did not get much recognition beyond more visible products or products with visible outcomes like ML models or BI dashboards, etc., as they are not tangible or immediately shining outputs.
The Implicit Value of High-Quality Data
One of the biggest challenges is articulating the implicit value of data quality. It’s like explaining why a parachute is worth packing—you only really notice its absence when things go terribly wrong. For examples:
- Machine learning models require accurate, well-documented data. Without it, it’s garbage in, garbage out.
- Business metrics lose credibility if built on shaky foundations.
- Operational inefficiencies pile up when teams waste time untangling messy pipelines.
- Business logic is not kept up-to-date as the data organization becomes distant from the product engineering or other business units, and thus, the data team is out of sync with all the planning and changes. We only find our errors when somebody finally realizes something is off. For instance, the metrics are drifting. We become reactive instead of proactive.
Investing in data quality doesn’t yield immediate, shiny results. Instead, it quietly enables the success of countless other business areas.
The Cost of Neglecting Data Quality
We’re in the age of AI/ML, where everyone wants to ride the AI boom to success. Yet, no matter how advanced the algorithm, bad data will always derail it. Think of data quality as the engine oil in your AI/ML car. Skimp on it, and you’re headed for a breakdown—no matter how flashy the car looks. Without a solid data foundation, it is like a baby trying to sprint before being able to stand by itself.
What’s more, maintaining high data quality isn’t a one-and-done effort. There’s a recurring cost to ensure data remains reliable and meaningful as systems evolve. Cutting corners might save money now, but it’ll likely cost much more down the road in technical debt and missed opportunities.
The Big Question: How Do We Make the Case?
So, how do we, as data engineers, articulate the importance of these efforts to stakeholders who might only see us as bottlenecks? How do we bridge the gap between the technical value we understand and the business value they demand?
I’d love to hear your experiences and strategies. Have you found ways to quantify the ROI of data quality? How do you handle resistance when it feels like you’re swimming against the current? Let’s swap stories and ideas—because if we’re going to be the invisible heroes, we might as well share a playbook for success.
Please share your thoughts/experiences in the comments. It will be fantastic to learn from other data engineers who have faced similar situations and help each other.
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