What exactly is ‘Data Observability’? And why is it so important?

Avi Greenwald
CTO & Co-Founder | Aggua
March 7, 2023

Data is a precious resource. The insights it can provide are invaluable in helping companies make decisions that significantly affect their business, so it's important to ensure that the data you rely on is always accurate, reliable, and of high quality.

Gartner predicts that By 2025, 60% of data quality processes will be autonomously embedded and integrated into critical business workflows as opposed to being individual and distinct tasks — a testament to its importance*. But what if you're not prepared for how quickly things can go wrong with your data systems and processes? You could end up with a lot of downtimes.

That's where data observability comes in. It’s the organization’s ability to manage, monitor, and detect problems in its data and data systems before that can happen. With Aggua, you can avoid data downtimes by integrating automated anomaly detection into your ETL workflow, or triggering it from anywhere in your system.

[Source: Gartner 2022, The State of Data Quality Solutions: Augment, Automate and Simplify]

The 5 Pillars of Data Observability

There are five main pillars that make up the framework of data observability, each of which is meant to reveal information about the reliability and quality of data health and pipelines.

1. Freshness

Data freshness helps you make sure your organization’s data is up-to-date and in sync with the latest changes. It's concerned with whether the data is current or fresh, if any upstream data has been omitted or included, how long ago it was extracted or generated, and if it came on time.

2. Volume

Are all the data tables here? Is the data complete? Is the data intake in line with the projected thresholds? Is there enough data storage to meet data requirements? It's important to know how much data you have to ensure you're not exceeding your limit.

3. Distribution

Where did the data go? How valuable is the data? Is it accurate? What changes were made to the data? Are the data values acceptable? Distribution deals with the quality of data produced and consumed in a data system. It monitors for irregularities and prevents erroneous information from being inserted.

4. Lineage

Who produces and uses the data? How is the data linked? What data sources are being used? Lineage is all about making tracking the flow of data easy to provide a unified view of your data system.

5. Schema

Is the data formatted properly? Is there a change in the data schema? Who updated it? The database's schema—its tables, fields, columns, and names—should be regularly audited for accuracy and freshness.

Why Use a Data Observability Tool?

Data quality and observability are two different concepts with some similarities. Both are concerned with resolving data issues. However, data quality resolves data issues as they arise. It does not capture data journey from end to end so some data may be missed. With data observability, the entire data value chain is examined for issues. It keeps you informed of any problems in advance by proactively monitoring the health of your data systems.

If you don’t know what’s in your data pipeline and how it’s performing, you might as well be flying blind. Visibility is the key to understanding your data pipelines and knowing where problems may be occurring between data inputs and outputs.

When complex data pipelines and systems break, it takes data engineers a long time to fix the issues because they spend their time trying to understand what caused it. Organizations lose a lot more than just revenue and productivity. They also lose customer confidence and trust.

DataOps and data engineers are more likely to be successful in resolving issues when they can trace back to their roots. In this way, data observability improves the detection of data quality issues that may lead to system downtime and helps in the subsequent speedy restoration of services.

If you're looking for anomaly detection without the hassle of coding, Aggua can automatically add anomaly detection to your ETL workflow, or trigger it from anywhere in your system. Additionally, if your team ever has questions or needs support, Aggua’s expert support team is always available to help.

[Source: Gartner 2022, The State of Data Quality Solutions: Augment, Automate and Simplify]

Benefits of using Aggua:

● Detect and fix anomalies in your data with ease

● Automatically add anomaly detection to your ETL workflow

● Trigger anomaly detection from anywhere in your system

Even a few minutes of downtime can cost you thousands of dollars, so it's crucial to maintain proactive measures against it. Data observability tools can help you find and fix the root causes of downtime before they happen, improving your uptime.

* 2022 Gartner, The State of Data Quality Solutions: Augment, Automate and Simplify
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

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