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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

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. AWS Glue Data Quality generates a substantial amount of operational runtime information during the evaluation of rulesets. Avik Bhattacharjee is a Senior Partner Solutions Architect at AWS.

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Build a real-time analytics solution with Apache Pinot on AWS

AWS Big Data

This has led to the emergence of real-time OLAP solutions, which are particularly relevant in the following use cases: User-facing analytics – Incorporating analytics into products or applications that consumers use to gain insights, sometimes referred to as data products. Anomaly detection – Identifying outliers or unusual behavior patterns.

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Two Acquisitions in Two Weeks!

Rita Sallam

Workday plans to embed Platfora technology into its technology foundation to complement existing operational reporting, dashboarding and scorecarding capabilities with Platfora’s ad hoc exploration features targeted specifically for Workday’s HR and Finance application customers.

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Fact-based Decision-making

Peter James Thomas

In our modern architectures, replete with web-services, APIs, cloud-based components and the quasi-instantaneous transmission of new transactions, it is perhaps not surprising that occasionally some data gets lost in translation [5] along the way. Regular facts are subject to data quality issues, or manipulation by creative humans.

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Redefining enterprise transformation in the age of intelligent ecosystems

CIO Business Intelligence

The scorecard speaks for itself. The mega-vendor era By 2020, the basis of competition for what are now referred to as mega-vendors was interoperability, automation and intra-ecosystem participation and unlocking access to data to drive business capabilities, value and manage risk.