- What is data observability?
- Why is data observability necessary for your data platform?
- What are the must-have features of a strong data observability platform?
What is data observability?
Data is increasingly important to today’s businesses — and ensuring the quality and reliability of that data is critical. High-quality data is the fuel for everything from building new products to driving accurate decision making. Data observability was created to make ensuring the quality of data easier, faster, and more scalable over the long term.Data observability gives organizations a complete view of their data's health at every stage — from data pipelines to infrastructure — as well as delivering at-a-glance views of dependencies and relationships between datasets. By leveraging data observability, data teams can quickly identify and resolve data quality issues before they reach data consumers, effectively reducing costs, minimizing impact, and driving confidence in the data products it protects.
Why is data observability necessary?
Data downtime — time when data is incomplete, erroneous, missing, or otherwise inaccurate — can be disastrous for organizations. From misallocated budgets to broken AI models, data quality issues can wreak havoc on organizations of all kinds.While data quality testing and monitoring are relatively common practices, data observability goes beyond the traditional methods of testing and monitoring. Data observability manages and improves data quality at scale by leveraging automated monitoring, custom rules, root cause analysis tools, and impact analysis to not only catch and resolve known data quality incidents faster but to detect and resolve unknown data quality issues as well.
Five must-have elements of a strong data observability platform
Choosing the right data observability tool can help your company avoid a menagerie of serious and costly data quality incidents, so it’s important to know what features you should have on your shopping list. Below are five features you should look for when considering a data observability solution for your data stack.-
ML-powered deep and broad data monitoring — both out-of-the-box and custom monitors
A key aspect of an effective data observability platform is its use of machine learning (ML) for data monitoring. Platforms with ML enable teams to programmatically identify data quality and performance issues, such as data freshness, volume issues, and schema changes out-of-the-box. Data observability platforms also offer the ability to create custom monitors that are tailored to your specific business needs and applied to your most critical tables, providing deep monitoring where you need it and allowing you to tackle recurrent data issues that can crop up within specific data environments.
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End-to-end integrations across cloud and on-prem tooling
An effective data observability platform should work with tools both in the cloud and on-prem. This necessary integration allows for comprehensive oversight of your data platform, from ingestion and storage to transformation and consumption. This integration helps track data movement across a variety of settings, which improves the platform's ability to find and fix quality issues quickly and effectively.
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Incident triaging and resolution workflows
To reduce the impact of data problems, it's important to have effective workflows for triaging and resolving incidents. A good data observability platform simplifies the steps to detect, triage, resolve, and measure data quality issues. This usually involves automatic alerts, tools to prioritize issues by severity and impact, and robust integrations with messaging and project management tools that complement existing workflows. Efficient prioritization means data teams can concentrate on the most urgent problems, which helps decrease delays and keeps the data accurate and reliable.
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Root cause and impact analysis via field-level lineage
Identifying the underlying cause of a data quality issue is essential to preventing it in the future. An effective data observability platform will provide field-level lineage, which provides an at-a-glance view of where the data came from, how it was changed, and what dependencies or data products are impacted by it. This information allows data teams to quickly understand the root-cause of an issue upon detection, decide who’s responsible for resolving it, and determine who should be informed to minimize cost.
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Performance monitoring — query optimization and cloud cost management
A key part of a strong data observability platform is performance monitoring, which includes improving query efficiency and managing cloud costs. This function helps you find and fix inefficient data queries and processes that could raise operating costs or slow down performance. By making queries more efficient and optimizing cloud resources, organizations can make their data operations more cost-effective and deliver greater value from their data platform at a significantly lower cost.
Pioneering the future of reliable data with data observability
Implementing a data observability platform that includes these five elements will empower your organization to reduce data downtime, improve data reliability, deliver more value for stakeholders, and foster an environment of data trust across your organization.Download Data Observability For Dummies to discover how data observability can help you improve your data reliability, build organizational trust, and deliver even more value from your data products.