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

With numerous data catalog options available, all claiming to be the best, how do you make an informed decision without exhaustive research? Published at: https://www.eckerson.com/articles/unveiling-the-future-of-data-catalogs

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

Many data leaders want to implement self-service, but don’t realize that they first have to implement the right architecture, governance, operating model, project delivery approach, data, and change management plan. Published at: https://www.eckerson.com/articles/self-service-is-the-outcome-not-the-driver-of-a-data-driven-organization

Explore the essential characteristics to choose the right conversational query tool for your needs and environment. Published at: https://www.eckerson.com/articles/modernizing-analytics-with-conversational-query-tools-five-must-have-characteristics

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