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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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Why Data Driven Decision Making is Your Path To Business Success

datapine

The term ‘big data’ alone has become something of a buzzword in recent times – and for good reason. By implementing the right reporting tools and understanding how to analyze as well as to measure your data accurately, you will be able to make the kind of data driven decisions that will drive your business forward.

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Drug Launch Case Study: Amazing Efficiency Using DataOps

DataKitchen

A Drug Launch Case Study in the Amazing Efficiency of a Data Team Using DataOps How a Small Team Powered the Multi-Billion Dollar Acquisition of a Pharma Startup When launching a groundbreaking pharmaceutical product, the stakes and the rewards couldnt be higher. data engineers delivered over 100 lines of code and 1.5

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Data Quality Power Moves: Scorecards & Data Checks for Organizational Impact

DataKitchen

A DataOps Approach to Data Quality The Growing Complexity of Data Quality Data quality issues are widespread, affecting organizations across industries, from manufacturing to healthcare and financial services. 73% of data practitioners do not trust their data (IDC).

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Data Quality Is Free

Anmut

If quality is free, why isn't data? Crosby introduced a revolutionary concept: quality is free. Originally applied to manufacturing, this principle holds profound relevance in today’s data-driven world. How about data quality? What do we know about the cost of bad quality data?

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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

Data exploded and became big. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. 1) Data Quality Management (DQM). We all gained access to the cloud.