Many organizations abandoned data modeling as they embraced big data and NoSQL. Now they find that data modeling continues to be important, perhaps more important today than ever before. With a fresh look you’ll see that today’s data modeling is different from past practices – much more than physical design for relational data.

Published at:

https://www.eckerson.com/articles/a-fresh-look-at-data-modeling-part-1-the-what-and-why-of-data-modeling

Data democratization is the buzzword to describe empowering enterprise stakeholders with data. While there have been advances in data management, governance, and analytics, something keeps getting in the way of achieving data democratization.

Published at:

https://www.eckerson.com/articles/data-democratization-and-the-duties-of-data-citizenship

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

Business leaders can address AI bias and use it to have rational discussions about management and human bias.

Published at: https://www.eckerson.com/articles/weighing-the-risk-and-reward-of-ai-a-non-technical-guide-for-business-leaders

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