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Why data quality drives AI success

CIO Business Intelligence

Organizations must prioritize strong data foundations to ensure that their AI systems are producing trustworthy, actionable insights. In Session 2 of our Analytics AI-ssentials webinar series , Zeba Hasan, Customer Engineer at Google Cloud, shared valuable insights on why data quality is key to unlocking the full potential of AI.

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Data Observability and Data Quality Testing Certification Series

DataKitchen

Data Observability and Data Quality Testing Certification Series We are excited to invite you to a free four-part webinar series that will elevate your understanding and skills in Data Observation and Data Quality Testing. Slides and recordings will be provided.

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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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Webinar Summary: Driving Data Analytic Team Excellence Through Agility, Efficiency, and Aphorisms

DataKitchen

He drew from his twenty-five years of experience in business analytics, pharmaceutical brand launch strategy, and project management. The conversation then moved to the importance of logistics and data quality in analytics, particularly in the pharmaceutical industry. Click below to watch!

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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. They are often unable to handle large, diverse data sets from multiple sources.

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Data Governance Maturity and Tracking Progress

erwin

Data governance is best defined as the strategic, ongoing and collaborative processes involved in managing data’s access, availability, usability, quality and security in line with established internal policies and relevant data regulations. Beginning strategy processes. Predictability. Synchronicity. Benchmarking.

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Thoughts on Data Literacy & Data Quality

TDAN

Last week, we presented a webinar in our Data Governance — Best Practices series on data quality. Obviously, we’re a […].