Secrets of Data Analytics Leaders

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

Non-profit organizations are more mission-driven, consensus-driven, and resource-constrained than commercial organizations. As a result, it’s imperative that non-profits develop a data strategy before plunging into building data solutions. It will save them time, money, and burnout in the long run. Published at: https://www.eckerson.com/articles/why-non-profits-need-a-data-strategy

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

This blog explores three criteria to evaluate tools that manage unstructured data pipelines for GenAI. Published at: https://www.eckerson.com/articles/data-engineering-for-genai-three-criteria-to-evaluate-pipeline-tools

If your data team wants to implement data products, it would be wise to avoid these 12 pitfalls that can torpedo an initiative. Published at: https://www.eckerson.com/articles/12-pitfalls-to-avoid-when-implementing-data-products

This article compares data catalogs and data marketplaces and argues that you need both and will soon have both as vendors add data marketplace extensions. Published at: https://www.eckerson.com/articles/why-do-i-need-a-data-marketplace-when-i-have-a-data-catalog

This blog defines conversational BI, why companies should consider it, and how their power and casual users can best get the desired results. Published at: https://www.eckerson.com/articles/driving-results-with-conversational-bi-best-practices-for-power-and-casual-users

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