Data Quality vs. Data Observability: What’s the Difference?

The concept of data quality is nothing new; for decades, businesses have been working to ensure the accuracy and completeness of the data they collect and use. However, in the last few years, we’ve seen a new term enter the data space: data observability. Because it’s so new, there’s inherent confusion around what it means—to the point where it’s even being used interchangeably with “data quality.” In reality, they’re two completely different concepts, so let’s demystify what they mean, how they differ, and what they mean for your business.

How do we define data quality?
Data quality is pretty straightforward: How well does your data meet standards for accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose? In the past, data quality existed as part of a master data governance program, which included cleanup like fixing address errors or duplicate customer names. Now, as every company in every industry wants to become more data driven, we’re seeing a broad shift toward establishing a strong acumen and a proactive approach to handling data that goes beyond master data. This means that for any type of data platform that people are relying on for operational and strategic decisions, they need to ensure that the data that’s present meets data quality standards.

How do we define data observability?
Data observability can be thought of as an extension of data quality, focusing not just on data but on the platforms serving data to downstream consumers. Are the processes that are feeding the data into the platforms healthy? Are the systems reliable? Because data observability is still so new, there’s a lack of consensus on how exactly we measure this. However, we can broadly think of data observability in five dimensions:

• Data volume: Evaluating whether data volume is up or down per the norms that we have, and detecting whether there are any sudden anomalies
• Consistency: Identifying if there are any sudden changes, like tables being dropped or added or a change to the structure of a table
• Freshness: Determining how up-to-date the data is by observing freshness throughout the data platform, instead of simply observing the data itself
• Distribution: Pinpointing when fields have too many nodes or if a distribution of a certain metric suddenly shifts from one pattern to another  
• Lineage: Tracking and tracing an issue from where it was first revealed all the way to the root cause

How are they different?
Data quality refers to the data itself, and data quality programs are typically owned by the line of businesses who are on the receiving end of it. Data observability adds in the context of the platform serving the data and the processes feeding into it to provide a more holistic and well-rounded view. Typically, data observability is owned by data platform owners who actually maintain and manage each individual platform.

Which should you invest in?
Considering the current state of data management and data platforms and how important they are for businesses, it’s crucial to have what we refer to as “holistic data observability,” encompassing data observability, data integrity audits, and data quality processes. Together, they provide a lens into the health of the platform while simultaneously giving you a 360-degree view of data quality.

Importantly though, because data quality has historically been more of a focus, any data observability solutions are newer to the market. Consequently, their capabilities are less familiar to many. Data observability is typically done using machine learning to generate rules and create anomaly detection models on metadata and on data itself to track any shifts, so the customer doesn’t have to write rules for every column and every table in a database.

Solutions like RightData’s DataTrust provide a unique blend of data quality and data observability, which allows businesses to achieve both without purchasing separate tools. It provides a complete suite of data observability, data validation, and data reconciliation tools that operate quickly and at massive scale. Plus, you’ll find continuous, automated data quality checks and alert notifications when issues are detected, as well as robust machine learning capabilities to speed up rule generation. Code-free and easy to use, it’s built to improve data quality, reliability, and completeness—giving you everything you need to gain the ultimate trust in your data.