In a recent survey by Great Expectations, 91% of respondents revealed that data quality issues had some level of impact on their organization. It highlights the critical importance of data quality in data engineering pipelines. Organizations can avoid costly mistakes, make better decisions, and ultimately drive better business outcomes by ensuring that data is accurate, consistent, and reliable.

However, 41% of respondents in the survey also reported that lack of tooling was a major contributing factor to data quality issues. Employing data quality management tools in data pipelines can automate various processes required to ensure that the data remains fit for purpose across analytics, data science, and machine learning use cases. They also assess existing data pipelines, identify quality bottlenecks, and automate various remediation steps.

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