Observability finds the source
When your dashboard shows a sudden drop in revenue, Observability tells you that the ETL job failed at 2:00 AM and the data hasn't refreshed. It saves time by pinpointing the infrastructure failure immediately.
Thought Leadership
Data Observability vs. Data Quality: Understanding the Difference
By Tomás Velarde
•
October 24, 2023
•
5 min read
Data you can trust. Decisions that hold. But in the crowded market of data tooling, these promises are often wrapped in confusing terminology. Are you buying observability or quality? The answer matters.
The Industry Landscape
The marketing confusion
If you've been shopping for data tools lately, you've likely seen "Data Quality" and "Data Observability" used as interchangeable buzzwords. Vendors often bundle them together to create a comprehensive solution. But technically, they solve two distinct problems.
Why the mix-up?
Historically, data quality was a manual, spreadsheet-based process. When automated tools emerged, they were marketed as "Data Quality" tools. As the industry matured and pipelines became more complex, engineers realized they needed visibility into *why* data was failing, not just *if* it was failing. That gave birth to "Observability."
Today, because both concepts are essential for a healthy data stack, most modern platforms blend them. But understanding the core definitions helps you evaluate whether a tool fits your specific team's pain points.
The core distinction
Think of it this way: Data Quality is about the destination (is the data correct?), while Data Observability is about the journey (is the pipeline running?).
You can have a perfectly correct dataset that is never delivered because the pipeline crashed. You can also have a pipeline that runs perfectly but delivers garbage. You need both.
Observability
What happened?
Data Observability answers the question: Why is my data not arriving on time? It relies on metadata, logs, and lineage to paint a picture of your data infrastructure.
- → Alerts on broken pipelines or failed jobs.
- → Lineage graphs showing data movement between systems.
- → Freshness checks (Did the last run finish at 8:01 AM or 8:15 AM?).
Quality
Is the data good?
Data Quality answers the question: Is the data I received what I expected? It validates the content of the data against a defined schema or rule set.
- → Completeness checks (Are there nulls in a required field?).
- → Uniqueness and distribution checks (Are values within expected ranges?).
- → Logic and referential integrity validation.
The Intersection
While distinct, the two concepts overlap significantly. A broken pipeline (Observability) almost always results in "missing" data (Quality). However, a healthy pipeline can still deliver incorrect figures due to bad source data.
Data
Health
Health
Why you need both
Quality validates the content
Once the data is there, Quality tells you that the revenue figures are incorrect because a source system introduced a rounding error. It ensures that the decisions you make are based on accurate information.
Reader Poll: Which do you prioritize?
Which area is your team struggling with the most right now?
One platform for both
At Valido, we believe you shouldn't have to patch together a stack of tools to get a complete view of your data. Our AI-powered engine provides deep lineage and observability, while enforcing strict quality rules at the source.