Today, data is among a business’s most valuable assets because its insights drive crucial business decisions. This data should be accurate, of high quality, and reliable. While gaining data insights is essential, understanding your data’s health is vital to ascertain that it’s not missing, misused, or wrongly added. Thanks to data observability, your company can manage, track, and identify issues in its data systems and data before causing downtimes.

A. What is data observability?

Data observability involves a company’s ability to understand its systems and data state fully. With excellent data observability, your organization gets complete visibility into its data pipelines. It also enables teams to create processes and tools to understand data flow in the company and spot data barriers to prevent data inconsistencies and downtimes.

B. Importance of data observability

1. End-to-end data pipeline view

Data observability offers an end-to-end data pipeline view while eliminating data downtime using the pipelines’ best DevOps and observability principles. This means applying to alert, triaging, and automated monitoring to detect and assess data quality and discoverability issues.

Handling a broken data pipeline or data problem can be challenging. When your team doesn’t know that something in the data pipeline broke or the data is altered, they’re likely to jeopardize the experiments, stakeholders’ trust, and analytics’ integrity.

With a data observability platform like Streamdal, your DevOps team can gain an end-to-end data pipeline view while giving them split-second reaction times when things go wrong. It also lets them see beyond performance metrics by giving them answers as to why things are breaking.

2. Insights into data system health and performance

Data observability helps your team to ensure your data’s health by maintaining a constant throb of its volume, distribution, metadata, freshness, and lineage. Companies with many disconnected tools can use observability monitoring to help the IT department gain insights into your system’s health and performance.

One of the core data observability advantages is streamlining root cause analysis. It lets you enable end-to-end data monitoring and visibility across a complex IT architecture for your teams to easily detect data issues and bottlenecks, regardless of their origin.

3. Eliminates inaccurate reporting and analytics risks

Data observability enables businesses to detect irregularities and automatically alert the right users to warnings that might lead to dangerous data events. This can make your company more proactive in handling possible data problems that disrupt your business and develop expensive downstream issues.

4. Increases trust in data for crucial business decisions

While machine learning algorithms and data insights can be beneficial, poor data management and errors can be disastrous for your business. However, data observability allows your company to monitor and track situations quickly, boosting confidence when making data-driven decisions.

5. Ensures data accuracy, quality, reliability, and value

Your data team can use data observability to ensure that data accuracy, quality, reliability, and value aren’t compromised. It can also help them ascertain that data pipelines are not broken. Without a comprehensive data observability strategy, analytics and data teams may struggle to guarantee pipeline reliability and data consistency with an agile and iterative methodology or conduct centered root cause analysis yielding quick and effective solutions.

C. Endnote

Data observability is essential for any data-driven organization. Consider investing in data observability to reap these benefits.