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Beyond “Prompt and Pray”

O'Reilly on Data

This approach delivers substantial benefits: consistent execution, lower costs, better security, and systems that can be maintained like traditional software. This translates to higher costs and slower response times. These workflows are then implemented as traditional software, which can be tested, versioned, and maintained.

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The Race For Data Quality in a Medallion Architecture

DataKitchen

The Race For Data Quality In A Medallion Architecture The Medallion architecture pattern is gaining traction among data teams. It is a layered approach to managing and transforming data. By systematically moving data through these layers, the Medallion architecture enhances the data structure in a data lakehouse environment.

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CIOs face mounting pressure as AI costs and complexities threaten enterprise value

CIO Business Intelligence

CIOs are under increasing pressure to deliver meaningful returns from generative AI initiatives, yet spiraling costs and complex governance challenges are undermining their efforts, according to Gartner. hours per week by integrating generative AI into their workflows, these benefits are not felt equally across the workforce.

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Call Center Dashboard – Reporting & Analytics In Our Data-driven World

datapine

That said, to improve the overall efficiency, productivity, performance, and intelligence of your contact center you will need to leverage the wealth of digital data available at your fingertips. Your Chance: Want to test a call center dashboard software for free? Your Chance: Want to test a call center dashboard software for free?

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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. 10) Data Quality Solutions: Key Attributes.

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7 types of tech debt that could cripple your business

CIO Business Intelligence

CIOs perennially deal with technical debts risks, costs, and complexities. These areas are considerable issues, but what about data, security, culture, and addressing areas where past shortcuts are fast becoming todays liabilities? Using the companys data in LLMs, AI agents, or other generative AI models creates more risk.

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Deep tech disruption: How advanced technologies are transforming businesses

CIO Business Intelligence

From AI models that boost sales to robots that slash production costs, advanced technologies are transforming both top-line growth and bottom-line efficiency. In finance, AI algorithms analyze customer data to upsell and cross-sell products at the right time, boosting revenue per customer. Thats a remarkably short horizon for ROI.