Secrets of Data Analytics Leaders Eckerson Group

There’s so much hype surrounding data products that you have to wonder if it’s just another buzzword. But there’s more to data products than buzz. In this article, you’ll learn how the concept is a meaningful step forward in the art and science of data management.
Published at: https://www.eckerson.com/articles/best-practices-for-developing-and-scaling-data-products

A zone-based data refinery creates an agile, adaptable data environment that supports new and unanticipated business requirements quickly. It turns a monolithic data warehouse into a flexible data environment that gracefully adapts to new and unanticipated business requirements while maximizing reuse and standards.
Published at: https://www.eckerson.com/articles/how-zone-based-data-processing-turns-your-monolithic-data-warehouse-into-a-flexible-modern-data-architecture

Many data engineers already use large language models to assist data ingestion, transformation, DataOps, and orchestration. This blog commences a series that explores the emergence of ChatGPT, Bard, and LLM tools from data pipeline vendors, and their implications for the discipline of data engineering.
Published at: https://www.eckerson.com/articles/should-ai-bots-build-your-data-pipelines-examining-the-role-of-chatgpt-and-large-language-models-in-data-engineering

At IAPP Summit, privacy and data governance leaders expressed the importance of a collaborative operating model.
Published at: https://www.eckerson.com/articles/the-convergence-of-data-governance-and-privacy-takeaways-from-the-global-privacy-summit

Embeddings are a learned way of representing data in space. Vector databases make it easier to work with embeddings generated from deep learning models. They will become an essential tool in the AI stack because they reduce the time to structure data and train models.
Published at: https://www.eckerson.com/articles/the-why-what-who-and-where-of-vector-databases

A robust data workflow testing strategy helps ensure the accuracy and reliability of data processed within a pipeline. Use this checklist to meet your organization’s data quality requirements according to the dimensions of accuracy, completeness, conformity, consistency, integrity, precision, timeliness, and uniqueness.
Published at: https://www.eckerson.com/articles/developing-a-robust-data-quality-strategy-for-your-data-pipeline-workflows