The Definitive Guide to Data Observability for Analytics and AI

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Exploding data supply and demand are pushing modern data pipelines to the breaking point. Enterprise data consumers want to use more data from a wider variety of sources, often on a real-time basis, to improve decision-making and optimize operations. But data teams struggle to architect, build, and operate the data systems that can meet these rapidly expanding business requirements.

New tools and platforms, combined with bigger investments in engineering and operations, only partly ease the pain. The reality is that most enterprise data teams still spend the bulk of their time fire-fighting daily operational issues. The problem is only getting worse as massive data volumes, data pipeline complexity, and new technologies conspire to overwhelm data team capabilities and undermine the business value of data systems.

The paradigm of data observability seeks to address this new world of unprecedented data complexity. Data observability offers a systematic approach by building on its predecessor technology, application performance monitoring (APM). It seeks to monitor and correlate data events across application, data, and infrastructure layers. By doing so, it enables business owners, DevOps engineers, data architects, data engineers and site reliability engineers to detect, predict, prevent, and resolve issues—sometimes in an automated fashion—that would otherwise break production analytics and AI.

To succeed with data observability, data analytics leaders must assemble and prioritize requirements, then select a comprehensive data observability product that minimizes custom integration work. They should tackle small, achievable observability projects first, enlisting a cross-functional team of contributors to focus on key pain points, such as performance and efficiency. Success on early projects can lead to more ambitious observability efforts—provided business and IT leaders continue to replace and retire duplicative older tools.

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