Data quality measures the accuracy, completeness, and consistency of data, while data observability monitors the overall health of data systems. Data observability builds on data quality by identifying, troubleshooting, and preventing data issues. Together, data quality and observability work to ensure data is useful and reliable.

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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.

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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.

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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 concept of data observability rests on five essential pillars:

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 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.

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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

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 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