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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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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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Managing machine learning in the enterprise: Lessons from banking and health care

O'Reilly on Data

After the 2008 financial crisis, the Federal Reserve issued a new set of guidelines governing models— SR 11-7 : Guidance on Model Risk Management. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk. Machine learning developers are beginning to look at an even broader set of risk factors.

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Accomplish Agile Business Intelligence & Analytics For Your Business

datapine

Working software over comprehensive documentation. The agile BI implementation methodology starts with light documentation: you don’t have to heavily map this out. But before production, you need to develop documentation, test driven design (TDD), and implement these important steps: Actively involve key stakeholders once again.

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Generative AI in the Enterprise

O'Reilly on Data

Unexpected outcomes, security, safety, fairness and bias, and privacy are the biggest risks for which adopters are testing. And there are tools for archiving and indexing prompts for reuse, vector databases for retrieving documents that an AI can use to answer a question, and much more. Only 4% pointed to lower head counts.

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Bringing an AI Product to Market

O'Reilly on Data

This isn’t always simple, since it doesn’t just take into account technical risk; it also has to account for social risk and reputational damage. A product needs to balance the investment of resources against the risks of moving forward without a full understanding of the data landscape. Data Quality and Standardization.

Marketing 362
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Getting started with AWS Glue Data Quality from the AWS Glue Data Catalog

AWS Big Data

Data consumers lose trust in data if it isn’t accurate and recent, making data quality essential for undertaking optimal and correct decisions. Evaluation of the accuracy and freshness of data is a common task for engineers. Currently, various tools are available to evaluate data quality.