Session: Top ten problems to solve with data observability

Kevin Petrie, Vice President of Research @ Eckerson Group

The emerging discipline of data observability optimizes data quality and pipeline performance by using techniques adapted from governance and application performance tools. This session explores ten common use cases for data observability, including the business and technical problems they solve. Together they enable enterprises to prepare, operate, and adjust data delivery across complex modern environments.

Read the Report: Top 10 Use Cases for Data Observability

Click here to download the report

To make data an asset, enterprises need data observability.

This emerging discipline includes data quality observability, which studies the accuracy and timeliness of data in flight or at rest, and data pipeline observability, which studies the performance of data pipelines as well as the infrastructure that support them.

Data observability programs and solutions should address these ten use cases across four categories:

  • Prepare. Infrastructure design, capacity planning, and pipeline design.
  • Operate. Performance tuning, data quality, and data drift.
  • Adjust. Resource optimization, storage tiering, and migrations.
  • Fund. Financial operations (FinOps).

This report from the Eckerson Group defines data observability, including its challenges and benefits. Then we explore use cases for preparing, operating, and adjusting data environments, as well as managing the business aspects of analytics projects and applications.

Get the Guide: The Definitive Guide to Data Observability for Analytics & AI

Click here to download the eBook

Enterprises have been running mission-critical data systems with outdated tools. Those tools aren't designed to manage the current exploding supply of data and complex data environments.

The problem is only getting worse as massive data volumes, complex data pipelines, and new technologies make it challenging for data teams to manage and optimize their data systems.

Data observability is a new technology that can help enterprises significantly improve data system performance, reliability, and cost.

Download Eckerson Group's guide to learn about:

  • How data observability can help you gain full visibility into data processing, data, and data pipelines
  • Why enterprises need data observability to accelerate data-driven transformation
  • Benefits for data engineers, DevOps, SREs, platform engineers, analysts, and IT/business leaders
  • How GE Digital uses data observability tools to optimize enterprise data system operations and performance and reduce annual operating costs by $millions.
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What is Enterprise Data Summit?

Enterprise data teams are moving from dashboards and reports to powering AI agents, real time decisions, and mission critical products. At large scale, that work is shaped by platform choices, governance, cost pressure, and the realities of operating complex data systems inside large organizations.

Enterprise Data Summit is a focused day for leaders who are modernizing AI ready data platforms. It is where enterprise data engineers, platform and analytics leaders, AI and ML teams, and partners in finance, risk, and security compare notes on how to ship reliable data and AI systems at scale.

Who comes to EDS?

  • Data and analytics engineering

  • Data platform and architecture

  • AI, ML, and LLM platforms

  • FinOps and cloud cost management

  • Data governance, security, and risk or compliance

Join us for talks that include:

  • Designing AI and agent ready data platforms, including lakehouse, streaming, and vector or retrieval layers

  • Using generative AI to accelerate data work while keeping quality, lineage, and controls in place

  • Data and AI governance as the control plane for trust, access, and regulatory compliance

  • FinOps for data and AI, including unit economics, chargeback models, and practical cost controls

  • Operating models for enterprise data teams, from platform teams to data products and self service

  • Data and AI reliability and observability in production, including real incidents and lessons learned

  • Hybrid and multi cloud data strategies, sovereignty, and regionalization in regulated environments

  • Moving from AI pilots to an enterprise AI platform backed by durable data foundations

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