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The Race For Data Quality in a Medallion Architecture

DataKitchen

The Race For Data Quality In A Medallion Architecture The Medallion architecture pattern is gaining traction among data teams. It is a layered approach to managing and transforming data. It sounds great, but how do you prove the data is correct at each layer? How do you ensure data quality in every layer ?

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.

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Data Quality Is Free

Anmut

They made us realise that building systems, processes and procedures to ensure quality is built in at the outset is far more cost effective than correcting mistakes once made. How about data quality? What do we know about the cost of bad quality data? Authors, Tadhg Nagle, Thomas C.

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DataKitchen’s Data Quality TestGen found 18 potential data quality issues in a few minutes (including install time)!

DataKitchen

DataKitchen’s Data Quality TestGen found 18 potential data quality issues in a few minutes (including install time) on data.boston.gov building permit data! Imagine a free tool that you can point at any dataset and find actionable data quality issues immediately! first appeared on DataKitchen.

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Unlocking the full potential of enterprise AI

CIO Business Intelligence

Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs. [1] AI in action The benefits of this approach are clear to see.

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5 tips for better business value from gen AI

CIO Business Intelligence

Still, CIOs have reason to drive AI capabilities and employee adoption, as only 16% of companies are reinvention ready with fully modernized data foundations and end-to-end platform integration to support automation across most business processes, according to Accenture.

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7 types of tech debt that could cripple your business

CIO Business Intelligence

CIOs perennially deal with technical debts risks, costs, and complexities. CIOs who change the culture to be more data-driven and implement citizen data science are most impacted by data debt, as the wrong interpretation or calculation of a date, amount, or threshold can lead to the wrong business decisions.

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