This blog defines the governance requirements that streaming data pipelines must meet to make artificial intelligence/machine learning (AI/ML) initiatives successful. Published at: https://www.eckerson.com/articles/streaming-data-governance-three-must-have-requirements-to-support-ai-ml-innovation

This blog, the second in a series, explores the mix of infrastructure types that support modern AI. Published at: https://www.eckerson.com/articles/cloud-on-prem-hybrid-oh-my-where-ai-adopters-host-their-projects-and-why

Despite $180 billion spent on big data tools and technologies, poor data quality remains a significant barrier for businesses, especially in achieving Generative AI goals. Published at: https://www.eckerson.com/articles/poor-data-quality-is-a-full-blown-crisis-a-2024-customer-insight-report

This article breaks down the evolving landscape of AI/ML platforms, from AutoML to full-stack AI workbenches, and provides a structured tool evaluation framework to cut through vendor ambiguity. Published at: https://www.eckerson.com/articles/the-ai-ml-tool-evaluation-template-a-guide-to-smarter-selection

This blog defines streaming data, explains why companies need it, and explores how streaming data pipelines feed multi-faceted GenAI applications. Published at: https://www.eckerson.com/articles/why-and-how-streaming-data-drives-the-success-of-generative-ai

Change is inevitable, but adoption is key. From AI/ML tools to leadership shifts, success depends on aligning people, not just technology. Published at: https://www.eckerson.com/articles/managing-change-in-the-age-of-ai-the-head-heart-and-herd-framework

This blog covers integrating SAP and third-party systems to build a unified data foundation for analytics and AI in conversational use cases. Published at: https://www.eckerson.com/articles/analytics-and-ai-for-sap-environments-build-a-unified-data-foundation-to-drive-advanced-use-cases

This blog recommends four questions to help data and AI leaders compare homegrown and commercial options for retrieval augmented generation. Published at: https://www.eckerson.com/articles/build-or-buy-rag-four-questions-to-guide-your-approach-to-retrieval-augmented-generation-for-genai

What will change next year? In the age of generative AI, the answer is simple: Everything! Published at: https://www.eckerson.com/articles/predictions-2025-everything-is-about-to-change

Data teams must filter, blend, and refine raw data inputs to create the high-octane fuel that drives innovation with artificial intelligence and machine learning (AI/ML). Published at: https://www.eckerson.com/articles/refining-the-right-fuel-how-data-integration-drives-the-ai-ml-model-lifecycle

This blog describes the need for data teams to establish a flexible yet well-governed data architecture to support dynamic AI/ML projects. Published at: https://www.eckerson.com/articles/multi-style-data-integration-for-ai-ml-three-use-cases

Data analytics is a balance of flexibility for innovation and governance to control risks. This blog discusses its implications for artificial intelligence (AI), including machine learning (ML) and generative AI (GenAI). Published at: https://www.eckerson.com/articles/ai-ml-innovation-requires-a-flexible-yet-governed-data-architecture

Explore the reasons for data engineers to collaborate with data scientists, machine learning (ML) engineers, and developers on DataOps initiatives that support GenAI. Published at: https://www.eckerson.com/articles/dataops-for-generative-ai-data-pipelines-part-iii-team-collaboration

Data engineering is now considered a crucial job in IT as Generative AI, the hottest technology of this decade, relies on data engineers to provide accurate inputs. Published at: https://www.eckerson.com/articles/data-engineering-for-genai-how-to-optimize-data-pipelines-and-governance

Data engineers and data scientists must manage pipelines for unstructured data to ensure healthy inputs for language models. Published at: https://www.eckerson.com/articles/why-and-how-data-engineers-will-enable-the-next-phase-of-generative-ai

Companies that adopt DataOps increase the odds of success by making GenAI data pipelines what they should be: modular, scalable, robust, flexible, and governed. Published: https://www.eckerson.com/articles/dataops-for-generative-ai-data-pipelines-part-ii-must-have-characteristics

The success of Generative AI depends on fundamental disciplines like DataOps. Published at: https://www.eckerson.com/articles/dataops-for-generative-ai-data-pipelines-part-i-what-and-why

With the increasing adoption of Generative AI, learn how data governance will add value to and benefit from Generative AI. Published at: https://www.eckerson.com/articles/data-governance-in-the-era-of-generative-ai

The European Union recently passed the first of its kind legal framework on the development, use, and governance of artificial intelligence. It lays out rules and standards with the aim of ensuring technologies are safe and transparent, and do not violate the fundamental rights of an individual. Published at: https://www.eckerson.com/articles/the-eu-ai-act-and-the-emergence-of-new-global-standards

Most organizations are committed to responsible and ethical use of AI. Yet anticipating unintended consequences before designing and implementing AI can be challenging. This framework and process helps evaluate short-term and long-term impacts across multiple dimensions so you can mitigate AI’s unintended consequences. Published at: https://www.eckerson.com/articles/mitigating-ai-s-unintended-consequences

This blog examines the upcoming trend of domain-specific LLMs and evaluates three different methods of implementation. Published at: https://www.eckerson.com/articles/the-next-wave-of-generative-ai-domain-specific-llms

Many machine learning (ML) use cases center on real-time calculations. This article defines streaming ML and its architectural components. Published at: https://www.eckerson.com/articles/machine-learning-and-streaming-data-pipelines-part-i-definitions-and-architecture

Data leaders must prepare their teams to deliver the timely, accurate, and trustworthy data that GenAI initiatives need to ensure they deliver results. They can do so by modernizing their environments, extending data governance programs, and fostering collaboration with data science teams. Published at: https://www.eckerson.com/articles/the-data-leader-s-guide-to-generative-ai-part-i-models-applications-and-pipelines

Our industry’s breathless hype about generative AI tends to overlook the stubborn challenge of data governance. Data catalogs address this challenge by evaluating and controlling the accuracy, explainability, privacy, IP friendliness, and fairness of GenAI inputs. Published at: https://www.eckerson.com/articles/generative-ai-needs-vigilant-data-cataloging-and-governance

The need for an independent semantic layer continues to rise as data science gains traction in the enterprise. Its five primary elements—metrics, caching, metadata management, APIs, and access controls—support AI/ML use cases as part of data science projects. Published at: https://www.eckerson.com/articles/why-and-how-to-enable-data-science-with-an-independent-semantic-layer