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Enterprises have been running mission-critical data systems with outdated tools.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.
Where & when?

Enterprise Data Summit 2023 took place virtually on June 7th, 2023.

What is the cost to attend the virtual sessions?

EDS 2023 is always free and open for all to attend.

What is Enterprise Data Summit?

Data initiatives and priorities are often affected by resources, organizational structure, and data maturity, all of which can be different inside an enterprise-sized company or for teams processing data at scale.

Enterprise Data Summit is an annual day where leaders from large companies looking to succeed with large-scale data systems can explore ways to modernize their data platforms, enable engineering best practices, allocate resources efficiently, and wrangle topics like data observability.

Who comes to EDS?

Data, analytics, AI, data science, and other team and department leadership, engineers and administrators, FinOps, and risk/compliance officers.

Join us for talks that include:
  • Guiding enterprise-level data teams
  • Modernizing data platforms
  • Cloud financial management
  • Succeeding with large-scale data systems
  • Data reliability
  • Resource allocation
  • Use cases for data observability
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