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Innovations in Analytics: Elevating Data Quality with GenAI

Towards AI

Data analytics has become a key driver of commercial success in recent years. The ability to turn large data sets into actionable insights can mean the difference between a successful campaign and missed opportunities. Flipping the paradigm: Using AI to enhance data quality What if we could change the way we think about data quality?

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How to Assess Data Quality Readiness for Modern Data Pipelines

Dataversity

The key to being truly data-driven is having access to accurate, complete, and reliable data. In fact, Gartner recently found that organizations believe […] The post How to Assess Data Quality Readiness for Modern Data Pipelines appeared first on DATAVERSITY.

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Why Is Data Quality Still So Hard to Achieve?

Dataversity

In fact, it’s been more than three decades of innovation in this market, resulting in the development of thousands of data tools and a global data preparation tools market size that’s set […] The post Why Is Data Quality Still So Hard to Achieve? appeared first on DATAVERSITY.

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Who Is Responsible for Data Quality in Data Pipeline Projects?

The Data Administration Newsletter

Where exactly within an organization does the primary responsibility lie for ensuring that a data pipeline project generates data of high quality, and who exactly holds that responsibility? Who is accountable for ensuring that the data is accurate? Is it the data engineers? The data scientists?

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Testing and Monitoring Data Pipelines: Part One

Dataversity

Suppose you’re in charge of maintaining a large set of data pipelines from cloud storage or streaming data into a data warehouse. How can you ensure that your data meets expectations after every transformation? That’s where data quality testing comes in.

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It’s Essential – Verifying the Results of Data Transformations (Part 1)

Dataversity

Today’s data pipelines use transformations to convert raw data into meaningful insights. Yet, ensuring the accuracy and reliability of these transformations is no small feat – tools and methods to test the variety of data and transformation can be daunting.

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Testing and Monitoring Data Pipelines: Part Two

Dataversity

In part one of this article, we discussed how data testing can specifically test a data object (e.g., table, column, metadata) at one particular point in the data pipeline.