Deep Dive on Data Quality Automation- 3 Tools to Consider

Deep Dive on Data Quality Automation

Eckerson Group Report, May 2022

Summary

Traditional techniques for ensuring data quality break at scale. As organizations deal with the ever-growing volume and velocity of data, data engineering teams can't keep up, leading business users to view their reports and dashboard with skepticism. Without trust in data, organizations struggle to become data-driven. Thankfully, machine learning (ML) offers a way forward. By using ML algorithms to automate aspects of the data quality workload, organizations can reassert control over their data pipelines and ensure that the data that business users consume is reliable no matter the quantity of data under management.

 

There are many ways to tackle data quality automation, however. This report profiles three tools that each take a different approach to reduce the burden of ensuring data quality. It gives data leaders context to better understand the strategies for data quality automation and to choose the approach best suited to the requirements of their organizations.

-Joe Hilleary, Eckerson Group

Eckerson Group Report

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Read this report to learn:

  • The components of data quality
  • How companies can build trust in their data
  • Why automated data quality tools are the answer to ensuring reliability at scale

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Eckerson Group Report recommends  DataBuck is ideal for customers who:

  • Want to automate the discovery and writing of the majority of their data quality rules.
  • Need a data quality solution for a cloud data lake environment
  • Would like to apply data quality checks at multiple stages of data pipelines
  • Require a solution that performs data quality analysis where that data resides.