Secrets of Data Analytics Leaders Eckerson Group

Most organizations view data as an asset to be actively managed with standards, controls, and discipline. Yet, they are passive and casual about metadata. Data is managed. Metadata happens. As data management becomes more complex, metadata management is becoming an essential discipline. It is time to think about metadata management from an architectural perspective.
Published at: https://www.eckerson.com/articles/an-architectural-view-of-metadata-management

Kevin Petrie, the Vice President of Research at Eckerson Group, and Dan O’Brien, research analyst, discussed large language models (LLMs), which are neural networks that analyze text to predict the next word or phrase. These models use training data, often from the internet, to understand word relationships and provide accurate answers to natural language questions.

Generative AI initiatives require new data pipelines that prepare text files for querying by language models. Data engineers, scientists, and other stakeholders collaborate to design and implement these pipelines, which span text sources, tokens, vectors, vector databases, and LMs.
Published at:
https://www.eckerson.com/articles/the-new-data-pipeline-for-generative-ai-where-and-how-it-works

Dan and Wayne discussed the concept of data and analytics operating models, which refers to how organizations organize their data and analytics resources for alignment and efficiency.

This final blog in our series on the ROI of master data management recommends ways for data teams to iterate their MDM initiatives based on the successes and failures of their first project.
Published: https://www.eckerson.com/articles/driving-roi-with-master-data-management-part-iii-project-iteration

Responsible AI ethical principles provide a clear, unifying purpose for the technological, business, and social goals of AI initiatives.
Published at: https://www.eckerson.com/articles/the-opportunity-and-risk-of-generative-ai-part-iii-responsible-ai-ethics