Data Engineers, Stop Ignoring Documentation! Why Poor Data Governance Will Haunt You

When I started my journey as a data engineering intern, my manager gave me a piece of advice that stuck with me:

"Always ask yourself—where is the data coming from? How does it impact the business? Does your process follow a structured approach? And who in the business can help you when you’re stuck?"

At the time, these questions felt overwhelming. I was expecting to write SQL queries, build pipelines, and optimize performance. Instead, I was being asked to think like a detective—tracing the origins of data, understanding its meaning, and predicting its impact.

Now, imagine having to ask yourself these same questions every single day because there’s no clear documentation, no governance, and no structured processes in place. Would you be productive? Would you be confident that you’re delivering the right numbers?

For many data engineers, this is an everyday reality—navigating a complex web of scattered information, inconsistent data definitions, and missing context. But it doesn’t have to be this way.

Why Business Knowledge Matters for Data Engineers

Let’s take a simple example: we need to deliver a report on active customers for the last three months. How do we identify them?

• Employee 1: "Active customers are those who checked their balance at least once."
• Employee 2: "Active customers are those who made at least one successful transaction."
• Employee 3: "Active customers are those who made at least one successful transaction and generated revenue for the business."

Which definition is correct? As data engineers, we don’t decide that. If we assume the wrong definition, we risk providing misleading data, leading to flawed business decisions.

This is why a formalized approach to defining business metrics is essential. Without alignment, two teams solving the same problem might end up with completely different numbers, creating confusion in the data.

1. Misunderstanding Key Business Metrics and KPIs

Business teams rely on well-defined KPIs, but without clarity, data engineers risk misinterpreting them. For example:

• What qualifies as a "new user"—account creation, first login, or first transaction?
• What defines "churned customers"—three months of inactivity or six?

?? Solution: Work closely with business stakeholders to document metric definitions and ensure consistency across teams.

2. Overlooking Business Logic in Data Transformations

A data pipeline isn’t just about moving data from point A to point B—it transforms, aggregates, and filters information. If the business logic isn’t fully understood, critical nuances can be lost. For instance:

• Customer activity: Are failed transactions counted?

?? Solution: Always validate transformations with business teams before deploying them.

3. Data Isn’t Just Numbers—It Drives Decisions, Strategy, and Operations

Data isn’t just about building pipelines—it’s about enabling smarter decision-making. If the business doesn’t trust the data, it won’t be used effectively. A single miscalculated metric can lead to:

? Poor strategic decisions.

? Misdirected investments.

? Incorrect performance evaluations.

?? Solution: Data engineers must think beyond the technical side—understanding how data impacts real business outcomes is just as crucial as building efficient systems.

The Hidden Costs of Poor Data Governance

Now that we know business knowledge is critical, let’s talk about another silent killer of productivity—poor data governance.

Imagine this scenario: You finally know how to define active customers. Great! But now… where do you find that data?

• Employee 1: "Use the table T_ACTIVE_CUSTOMERS."
• Employee 2: "No, use T_ACTIVE_CUSTOMERS_2_LAST."
• Employee 3: "Actually, you need to join T_CUSTOMERS with T_TRANSACTIONS and filter the customers you need."

At this point, you’re completely lost. Which table is correct?

• What naming convention is being used?
• Which dataset is the most up-to-date?
• Are these tables even following the same logic?

Without proper documentation and governance, data engineers waste valuable time navigating inconsistent structures, outdated datasets, and tribal knowledge instead of focusing on solving actual business problems.

1. Wasted Time Spent Searching for the Right Data

When engineers don’t know where to find the right data, they spend hours—or even days—digging through databases, asking around, and second-guessing their choices. This leads to:

? Reduced productivity.

?? Slow delivery of reports and insights.

?? Frustration across teams.

?? Solution: Implement a data catalog that documents table structures, definitions, and sources in a centralized and accessible way.

2. Increased Risk of Errors and Inconsistencies in Reports

If different teams use different data sources to generate reports, inconsistencies are inevitable. The same KPI might show different numbers in different reports simply because they were pulled from different datasets. This results in:

?? Loss of trust in data.

?? Business leaders making decisions based on inaccurate insights.

?? Endless debates over which report is correct.

?? Solution: Establish clear data governance rules—define a single source of truth for each key metric and enforce standardized queries and processes.

3. Difficulty Onboarding New Engineers Due to Lack of Knowledge Transfer

Without proper documentation, new engineers struggle to ramp up quickly. Instead of focusing on building and optimizing pipelines, they spend weeks just figuring out where data lives and how it’s structured. This leads to:

? Longer onboarding times.

?? Higher dependency on a few senior engineers.

?? Increased risk of knowledge loss when employees leave.

?? Solution: Maintain comprehensive and up-to-date documentation on database schemas, table usage, and key transformations. Encourage a culture where documentation is part of the development process, not an afterthought.

How Data Engineers Can Take Action

? Collaborate with Business Teams – Don’t just work in silos. Engage with domain experts to ensure data is aligned with business needs.

? Advocate for Data Governance – Push for clear naming conventions, standardized metrics, and well-documented sources.

? Make Documentation a Habit – A well-documented table or pipeline today saves hours of confusion in the future.

? Encourage a Culture of Data Ownership – Everyone in the organization should be responsible for keeping data accurate and accessible.

Closing Thought ????: At the end of the day, data isn’t just about tables and queries—it’s about empowering businesses to make informed decisions. By prioritizing business understanding, governance, and documentation, we don’t just make our own jobs easier—we build data systems that truly support the success of the entire organization.