Differences Between Data Quality and Data Observability

Do you know the differences between data quality and data observability? These two concepts are similar in some ways and different in others—and can work together to improve the insights you glean from the data you collect. When you want to gain the most value from your organization’s data, you need to maximize both data quality and data observability. 

Quick Takeaways

  • Data quality measures the accuracy, completeness, and consistency of data
  • Data observability monitors the overall health of data systems
  • Data observability builds on data quality to identify, troubleshoot, and prevent data-related issues
  • Organizations need to incorporate both data quality and data observability to ensure useful and actionable data 

What Is Data Quality?

Data quality measures the condition of a set of data—how suited it is for an organization’s needs. As you might suspect, high-quality data is more reliable and more usable than low-quality data. Organizations are constantly striving to improve the quality of the data they collect.

The six dimensions of data quality.

Organizations measure data quality along six distinct dimensions:

  • Accuracy, or how many errors there are in the data. To measure accuracy, compare a dataset to a reference set of data. 
  • Completeness, or whether all critical fields are fully entered. To measure completeness, calculate the percentage of records that contain incomplete data.
  • Consistency, or whether similar data pulled from two or more datasets agree with each other. Inconsistent data indicate inaccuracies in one or more datasets. 
  • Timeliness, or how old the data is. More recent data tends to be more accurate and relevant than older data. 
  • Uniqueness, or whether there are duplicates contained in a data set. Merge or purge duplicate data, as appropriate.
  • Validity, or how well data conforms to standard formats. It’s difficult to use data if it is the wrong data type. 

Data that is inaccurate, incomplete, inconsistent, untimely, duplicative, or formatted incorrectly can cost an organization both time and money. DAMA International estimates that handling data quality issues costs organizations between 10% and 30% of their revenues. According to the Gartner Group, businesses lose $15 million a year on average to bad data.  

What Is Data Observability?

Data observability builds on the concept of data quality to encompass the overall health of an organization’s data systems. The goal of data observability is to identify, troubleshoot, and work to avert data-related issues that affect data quality and system reliability. 

Data observability goes beyond data quality by not just describing a data-related problem but attempting to resolve the problem—and prevent the problem from recurring in the future. With data observability, an organization can better identify its most critical sets of data, users of that data, and problems arising from that data. 

The five pillars of data observability.

The concept of data observability rests on five essential pillars:

  • Freshness describes how current the data is and how often the data is updated.
  • Distribution details if data values fall within an acceptable range. Data outside this range may not be trustworthy. 
  • Volume gauges if data is complete. Inconsistent data volume indicates issues with data sources.
  • Schema tracks changes in data organization—who made what changes to the data, and when.
  • Lineage records and documents the entire flow of data from initial sources to end consumption. 

Together, these five pillars provide real-time insight into data quality and reliability. By constantly monitoring the health of your data, you’ll realize less downtime and spend less time correcting data errors. 

How Are Data Quality and Data Observability Similar—and How Are They Different?

Both data quality and data observability are concerned with the usefulness of an organization’s data. To this end, they are both immensely important to an organization and complement each other. 

That said, data quality and data observability have slightly different goals. Data quality aims to ensure more accurate, more reliable data. Data observability seeks to ensure the quality and reliability of the entire data delivery system. Data quality is concerned with data itself, while data observability is concerned with the system that delivers that data. 

To that end, data observability goes beyond monitoring data and alerting users to data quality issues. Data observability attempts to identify data collection and management issues and fix those big-picture issues at the source. When data observability works, it results in better quality data. 

Consider these key differences between data quality and data observability:

  • Data quality examines data at rest (in datasets), while data observability addresses data in motion (through data pipelines
  • Data quality focuses on correcting individual data errors, while data observability focuses on fixing systemic problems
  • Data quality utilizes static rules and metrics, while data observability uses machine learning to generate adaptive rules and metrics
  • Data quality deals with the results of data issues, while data observability deals with the root causes of those issues

How Data Quality and Data Observability Can Work Together to Improve Data Usefulness

Because data quality and data observability work towards the same goal of ensuring more useful and reliable data, many organizations use them together to improve the data they collect. Data observability can improve data quality over the long run by identifying bit-picture problems with data pipelines. With more reliable data pipelines, cleaner data comes in, and fewer errors get introduced into the pipelines. The result is higher quality data and less downtime because of data issues.

There are many ways to make data quality and data observability work together. These include:

  • Connecting data to scan and inspect data from a wide range of sources and pipelines
  • Gaining awareness by identifying relationships between different data sources
  • Automating data quality controls by using machine learning to generate new quality monitoring rules based on evolving data patterns and sources
  • Adapting business workflows and processes based on identified data patterns
  • Generating alerts when data quality deteriorates to quickly resolve issues

The more your organization relies on data to make day-to-day and long-term operational and strategic decisions, the more important data quality and the reliability of the data management process becomes. Access to data is critical, so ensuring the accuracy and useability of that data becomes even more critical. 

Ensuring actionable data insights is not a function of data quality or data observability alone. Data quality ensures reliable and useable data, while data observability ensures the reliability and usefulness of the entire data collection and delivery system. Reducing data errors isn’t enough. You must also predict how and where those errors occur and engineer your systems to produce higher-quality data. 

It’s a matter of combining the reactive defenses of data quality monitoring with the proactive error reduction measures of data observability. One builds on and enhances the other.

Ensure High-Quality Data with DataBuck

Improving the quality of your organization’s data is paramount, so you should turn to the data-quality experts at FirstEigen. Our DataBuck software is an autonomous data quality management solution that automates more than 70% of the data monitoring process and uses machine learning to automatically generate new data quality rules. Incorporate DataBuck into your organization’s data quality and observability efforts.

Contact FirstEigen today to learn more about data quality and data observability.

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