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?

How data ingestion works.

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

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?

The five pillars of 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 

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. 

(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 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.

Check out these articles on Data Trustability, Observability, and Data Quality. 

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