Remove Data Lake Remove Measurement Remove Scorecard
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Data Quality Power Moves: Scorecards & Data Checks for Organizational Impact

DataKitchen

These leaders are expected to influence organizational behavior without direct authority, leading to what DataKitchen CEO Christopher Bergh described as “data nags”—individuals who know what’s wrong but struggle to get others to act. Who should make the change (data engineers, system owners, or data quality professionals).

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Build an Amazon Redshift data warehouse using an Amazon DynamoDB single-table design

AWS Big Data

Typical use cases for DynamoDB are an ecommerce application handling a high volume of transactions, or a gaming application that needs to maintain scorecards for players and games. In traditional databases, we would model such applications using a normalized data model (entity-relation diagram).

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Visualize data quality scores and metrics generated by AWS Glue Data Quality

AWS Big Data

AWS Glue Data Quality allows you to measure and monitor the quality of data in your data repositories. It’s important for business users to be able to see quality scores and metrics to make confident business decisions and debug data quality issues. Avik Bhattacharjee is a Senior Partner Solutions Architect at AWS.

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Fact-based Decision-making

Peter James Thomas

This piece was prompted by both Olaf’s question and a recent article by my friend Neil Raden on his Silicon Angle blog, Performance management: Can you really manage what you measure? Data may be perfectly valid, but still not represent reality. It is hard to account for such tweaking in measurement systems.

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Redefining enterprise transformation in the age of intelligent ecosystems

CIO Business Intelligence

Ultimately, the intent, however, is generally at odds with measurably useful outcomes. The scorecard speaks for itself. By the peak of the pandemic, aggregated systems of record data in SaaS-based data lake houses became the preferred destination for global enterprises.