Data ingestion monitoring and data observability are two different yet complementary approaches to improving the quality of an organization’s data. When it comes to ingesting data from various sources, monitoring the quality of that data is essential.

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Differences Between
Ingestion Monitoring and
Data Observability
Data ingestion monitoring and data observability are two different yet
complementary approaches to improving the quality of an organization’s data.
When it comes to ingesting data from various sources, monitoring the quality of
that data is essential. It’s also important that your data management systems work
properly and don’t introduce new errors into ingested data. That’s how ingestion
monitoring and data observability work together—they’re both effective on their
own but produce even better results when used together.
Quick Takeaways
 Ingestion monitoring looks at new data entering a system to ensure
adequate data quality
 Data observability looks at the data health of the entire system to identify
and repair systemic issues that affect data quality
 Organizations can use ingestion monitoring and data observability together
to clean today’s data and ensure that tomorrow’s data is even higher
quality
What Is Ingestion Monitoring?

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Data ingestion describes how data gets into a system. Data for ingestion can come
from a variety of sources, both internal and external. The system can ingest it in
real-time or in batches. It comes from existing databases, data lakes, real-time
systems and platforms (such as CRM and ERP solutions), software and
applications, and IoT devices.
A proper data ingestion process doesn’t just import raw data. Instead, it transforms
data in various formats from various sources into a single standardized format.
Data ingestion can even take unformatted data and fit it into an existing data
format.
When ingesting data, it’s important to ensure it is of the highest possible quality.
This is where ingestion monitoring comes in. Data ingestion monitoring involves
identifying poor-quality or incorrectly formatted data, cleaning and formatting the
data, and making the data ready for others to use.
Ingestion monitoring evaluates incoming data using the following metrics:
 Accuracy—whether the data is correct
 Completeness—whether all fields are populated
 Consistency—whether similar data from multiple databases are the same
 Timeliness—whether the data is recent
 Uniqueness—if there’s any duplicated data
 Validity—whether all data is in the proper format

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Identifying and dealing with poor-quality data is critical before it enters your
system. This could involve correcting inaccuracies, completing incomplete records,
formatting unformatted data, removing duplicates, and even deleting data that
you can’t repair easily.
Ingestion monitoring is important because it’s easier to catch and fix poor-quality
data before it enters a system. Once data enters the system, it’s mixed with your
existing data, which makes it more difficult to find and even more difficult to clean.
Because you don’t want poor-quality data to dilute the quality of your existing
data, you need to employ ingestion monitoring.
What Is Data Observability?
Ingestion monitoring deals with ensuring data quality as it moves into a
system. Data observability is about ensuring the quality of the data system itself.
Data observability tracks system quality in five key ways:
 Freshness—how current the data is
 Distribution—whether data values fall within an acceptable range
 Volume—whether the data is complete
 Schema—how the data pipeline is organized
 Lineage—how data flows through the pipeline

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By working through each of these key metrics, a data observability solution can
evaluate a data management system’s health, identify systemically affecting data
quality, and suggest changes to the system that address these issues. Unlike
ingestion monitoring, which evaluates the data itself, data observability is about
evaluating and troubleshooting the entire data management system to resolve any
issues that impact data quality.
When you want to ensure that your data management systems are working
properly and not introducing new errors into your data, employ data observability.
It’s essential for the long-term data health of any organization.
How Are Ingestion Monitoring and Data
Observability Similar—and How Are They
Different?
Data observability provides actionable insight into the quality of data and the
quality of data systems. One of the key differences between ingestion monitoring
and data observability is that ingestion monitoring fixes data while data
observability fixes data system problems.
Ingestion monitoring involves finding and fixing individual pieces of bad data but is
not concerned with what made that data bad. It fixes the immediate issue of poor-
quality data but isn’t involved with the longer-term issue of improving data
systems.
Ingestion monitoring, then, can fix data entering your pipeline today but can’t
ensure better quality data entering your pipeline tomorrow. Data observability
isn’t concerned with fixing today’s data but focuses on fixing systemic issues that
can affect data quality in the future.
In these ways, ingestion monitoring and data observability are significantly
different in what they do. However, they share a similar goal of improving data
quality and usability. With both ingestion monitoring and data observability in
place, data quality should improve today and in the future.

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(The following video further compares ingestion monitoring and data
observability.)
How Ingestion Monitoring and Data
Observability Can Work Together to
Improve Data Usefulness
Organizations of all types and sizes need high-quality data to inform their daily
operations and long-term decision-making. Experts say that poor-quality data can
cost an organization between 10% and 30% of its revenue.
For these reasons alone, organizations can and should employ both ingestion
monitoring and data observability with the shared goal of ensuring high-quality
data.
Ingestion monitoring is essential to ensure that no inaccurate or incomplete data
enters the system. This addresses immediate issues with data quality.
Data observability is essential to ensuring the quality of data over the longer term.
By identifying and helping to resolve systemic issues in a data pipeline, data
observability should result in fewer data quality issues affecting the ingestion
process.
How can your organization make ingestion monitoring and data observability work
together? Here are a few proven successful approaches:
 Identifying key relationships between a variety of data sources
 Designing new data quality rules for the ingestion process
 Developing new data workflows based on evolving data patterns
 Raising red flags when there is a deterioration in data quality during the
ingestion process and beyond
When ingestion monitoring and data observability work together, your data
management processes will run smoother, your data pipeline will be more
efficient, and your data quality will improve. You need to both identify data errors

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and prevent future errors from occurring, which you can only do by employing
ingestion monitoring and data observability.
Let DataBuck Help Improve Your
Organization’s Data Quality
The more your organization depends on data, the more you should turn to the
data-quality experts at FirstEigen. Our DataBuck data quality management
solution automates more than 70% of the data monitoring process and uses
machine learning to automatically generate new data quality rules. DataBuck
works with both ingestion monitoring and data observability to endure you’re
ingesting and using the highest-quality data possible.
Contact FirstEigen today to learn more about ingestion monitoring and data
observability.