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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).

Scorecard 180
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Build a strong data foundation for AI-driven business growth

CIO Business Intelligence

If the data volume is insufficient, it’s impossible to build robust ML algorithms. If the data quality is poor, the generated outcomes will be useless. By partnering with industry leaders, businesses can acquire the resources needed for efficient data discovery, multi-environment management, and strong data protection.

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Cloud analytics migration: how to exceed expectations

CIO Business Intelligence

A modern data and artificial intelligence (AI) platform running on scalable processors can handle diverse analytics workloads and speed data retrieval, delivering deeper insights to empower strategic decision-making. Business objectives must be articulated and matched with appropriate tools, methodologies, and processes.

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How to Do Data Modeling the Right Way

erwin

Data modeling supports collaboration among business stakeholders – with different job roles and skills – to coordinate with business objectives. Data resides everywhere in a business , on-premise and in private or public clouds. The post How to Do Data Modeling the Right Way appeared first on erwin, Inc.

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The Third Pillar of Data Culture: Data Governance

Alation

The same could be said about data governance : ask ten experts to define the term, and you’ll get eleven definitions and perhaps twelve frameworks. However it’s defined, data governance is among the hottest topics in data management. Organizations are governing data already, simply informally.