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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. But beneath the surface, its a patchwork of brittle improvisation and runaway costs.

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Jumia builds a next-generation data platform with metadata-driven specification frameworks

AWS Big Data

In this post, we share part of the journey that Jumia took with AWS Professional Services to modernize its data platform that ran under a Hadoop distribution to AWS serverless based solutions. These phases are: data orchestration, data migration, data ingestion, data processing, and data maintenance.

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A Guide to the Six Types of Data Quality Dashboards

DataKitchen

A Guide to the Six Types of Data Quality Dashboards Poor-quality data can derail operations, misguide strategies, and erode the trust of both customers and stakeholders. Data quality dashboards have emerged as indispensable tools, offering a clear window into the health of their data and enabling targeted actionable improvements.

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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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The Ultimate Guide To Data-Driven Construction: Optimize Projects, Reduce Risks, & Boost Innovation

Speaker: Donna Laquidara-Carr, PhD, LEED AP, Industry Insights Research Director at Dodge Construction Network

In today’s construction market, owners, construction managers, and contractors must navigate increasing challenges, from cost management to project delays. However, the sheer volume of tools and the complexity of leveraging their data effectively can be daunting. That’s where data-driven construction comes in.

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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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The key to operational AI: Modern data architecture

CIO Business Intelligence

From customer service chatbots to marketing teams analyzing call center data, the majority of enterprises—about 90% according to recent data —have begun exploring AI. For companies investing in data science, realizing the return on these investments requires embedding AI deeply into business processes.