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AI market evolution: Data and infrastructure transformation through AI

CIO Business Intelligence

This allows organizations to maximize resources and accelerate time to market. Data security, data quality, and data governance still raise warning bells Data security remains a top concern. Respondents rank data security as the top concern for AI workloads, followed closely by data quality.

Marketing 128
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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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It’s 2025. Are your data strategies strong enough to de-risk AI adoption?

CIO Business Intelligence

Despite decades of investment in data management solutions, many continue to struggle with data quality issues, either through their failure to modernise legacy investments or through the outcomes of acquisitions and business decisions, which in either instance have led to data existing in multiple silos across their organisations.

Risk 111
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5 tips for better business value from gen AI

CIO Business Intelligence

Raduta recommends several metrics to consider: Cost savings and production increases when gen AI targets efficiencies and automation; Faster, more accurate decision-making when gen AI is used to analyze large datasets; Time-to-market and revenue when gen AI drives product innovation by generating new ideas and prototypes.

Sales 143
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Alation 2022.2: Open Data Quality Initiative and Enhanced Data Governance

Alation

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

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AI adoption in the enterprise 2020

O'Reilly on Data

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.

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Top 10 Data Governance Trends for 2020: Data’s Real Value Comes Into Focus

erwin

Understanding the data governance trends for the year ahead will give business leaders and data professionals a competitive edge … Happy New Year! Regulatory compliance and data breaches have driven the data governance narrative during the past few years.