You’ve heard the phrase “data observability,” but do you know what it means—or why it’s important? Data observability is all about becoming more knowledgeable about the state and health of your organization’s data. Robust data observability enables you to identify issues with your data, including poor data quality, and helps you to resolve those issues. Without data observability, bad data can cripple your organization. With data observability, your organization will run smoother and with fewer problems.
Quick Takeaways
- Data observability helps you monitor, understand, and trust your business’ data
- Data observability goes beyond data monitoring to determine why data behaves as it does
- The five pillars of data observability are freshness, distribution, volume, schema, and lineage
- Data observability is essential in managing and understanding the increasing amount of data generated in businesses today
- Data observability improves helps improve data quality and consistency, reporting and analytics, update and operational performance, and operating efficiency
What is Data Observability?
Data observability is the ability to monitor your system’s data, identify data-related issues, and rectify those issues. It’s about observing and understanding the health of your data and ensuring that identified errors do not reoccur. The goal of data observability is to guarantee a reliable flow of high-quality data throughout your organization, thus reducing downtime and improving both short- and long-term decision-making.
How Does Data Observability Differ From Data Monitoring?
To the uninitiated, data observability and data monitoring might seem like two ways of describing the same activity. In reality, data observability and data monitoring are closely related but subtly different.
Data monitoring examines data in your pipeline with the goal of identifying poor quality data and taking steps to remediate it. Data monitoring tools look especially for data that is incomplete, inaccurate, or doesn’t adhere to defined standards. It essentially asks the question “what’s broken and how can we fix it?”
Data observability goes beyond basic data monitoring to understanding the data being monitored. It examines not just the quality of the data but also its lineage and schema to provide insight into why it is as it is. It adds the question “why?” to the questions asked in data monitoring.
In fact, data observability can enable more accurate and efficient data monitoring and data quality management. By providing a better understanding of why particular data behaves in a certain way, you can better design data monitoring tools to look for specific data quality issues. While data monitoring does a good job looking for pre-defined issues, data observability helps identify new and evolving issues.
(The following video discusses the differences between data monitoring and data observability.)
What Are the Five Pillars of Data Observability?
Data observability tools evaluate a number of specific issues related to data quality and reliability. Experts often refer to these issues as the five pillars of data observability.
Freshness
Freshness (sometimes referred to as timeliness) concerns how recent a piece of data is. The goal is to use the most up-to-date data, knowing that data quality erodes over time. For example, customer information becomes less relevant over the years as people move, change email addresses, age, and experience lifestyle changes. Using older data will produce poorer results than working with more timely data.
Distribution
In this instance, distribution refers to the expected range of a set of data. If data points do not fall within an acceptable range, that may indicate data quality issues.
Volume
Tracking the consistency of data flow can help identify data pipeline issues. Erratic data volume can indicate problems with data intake.
Schema
Schema refers to how data is organized, such as the fields and tables in a database. Observing changes in schema can indicate broken data, often caused by someone making unauthorized changes to the data structure.
Lineage
Data observability pays particular attention to data lineage—that is, the history of a data set. A detailed lineage records every step in the data’s path, include data sources, transformations, and downstream destinations. It provides a complete picture of your firm’s data landscape.
Tracking data lineage helps identify where issues occur and why. It’s important for data governance, regulatory, compliance, and ensuring that the data is trustworthy.
Other Important Issues
Beyond the five pillars, data observability is also concerned with understanding:
- Data quality
- Data completeness
- Data security and privacy
- Data compliance
The first two of these issues are also in the purview of data monitoring, which reflects how these two activities often serve the same goals.
Why is Data Observability Important?
As the volume of data continues to grow, companies and organizations are expected to ensure the quality and usability of that data. According to IDC, more than 64 zettabytes (ZB) of data were created in 2020. The company expects the amount of data created to grow at a compound annual growth rate (CAGR) of 23% through 2025.
Much of this newly created data is not unique and instead replicated for consumption and analysis throughout an organization. IDC estimates that the ration of unique to replicated data is 1:9, which means that a typical organization will generate nine times as much data as it initially ingests.
Ensuring the accuracy, consistency, and reliability of this data is a significant challenge which can only be met by the use of sophisticated data monitoring and data observability tools. This is why 90% of IT decision makers say that data observability is both important and strategic to their business.
What Are the Benefits of Data Observability?
Embracing data observability offers significant advantages to any organization. By providing a 360-degree view of your data, data observability provides the following benefits:
Improves Data Quality
Data observability, much like data monitoring, helps improve the quality and integrity of your business’ data. It helps identify and resolve data quality issues.
Ensures Data Consistency
By monitoring data flow across your organization, data observability helps to ensure data consistency throughout the entire data pipeline.
Provides More Accurate Reporting and Analysis
The more accurate data resulting from the use of data observability improves the quality and reliability of your firm’s reporting. This, in turn, provides more accurate analysis of your business operations and help you make better informed business decisions.
Enhances Operating Efficiency
Higher-quality and more reliable data also improves your organization’s operating efficiency. This results in more uptime and enhanced operational performance.
Builds Trust in the Data
Ultimately, embracing data observability helps you better understand and better trust the data that your business relies on. Instead of questioning the data, you can now trust it to be fresh and accurate.
Turn to DataBuck to Improve Your Data Quality
When you want to improve the quality of your firm’s data, turn to the experts at FirstEigen. We provide an autonomous data quality management platform that automates more than 70% of the data monitoring process. Our DataBuck software is an essential component of any data monitoring or data observability solution.
Contact FirstEigen today to learn about improving data quality with DataBuck.
Check out these articles on Data Trustability, Observability, and Data Quality.
- 6 Key Data Quality Metrics You Should Be Tracking (https://firsteigen.com/blog/6-key-data-quality-metrics-you-should-be-tracking/)
- How to Scale Your Data Quality Operations with AI and ML (https://firsteigen.com/blog/how-to-scale-your-data-quality-operations-with-ai-and-ml/)
- 12 Things You Can Do to Improve Data Quality (https://firsteigen.com/blog/12-things-you-can-do-to-improve-data-quality/)
- How to Ensure Data Integrity During Cloud Migrations (https://firsteigen.com/blog/how-to-ensure-data-integrity-during-cloud-migrations/)
