Secrets of Data Analytics Leaders

The unbundling of the data ecosystem is causing organizations to “duct tape” products and frameworks together to build their solutions and data delivery processes. Organizations fail to build and deploy end-to-end, automated, repeatable data-driven systems, ignoring data engineering & dataops principles as well as best practices.

Published at: https://www.eckerson.com/articles/dataops-in-data-engineering

This blog recommends guiding principles for successful implementation of language models to assist data engineering.

Published at: https://www.eckerson.com/articles/should-ai-bots-build-your-data-pipelines-part-iv-guiding-principles-for-success-with-language-models-and-data-engineering

An emerging approach to generative AI will help data engineering teams achieve much-needed productivity gains while controlling risk.

Published at: https://www.eckerson.com/articles/should-ai-bots-build-your-data-pipelines-part-iii-the-emergence-of-small-language-models-for-data-engineering

Dan O'Brien and Kevin Petrie discuss FinOps, which is a cost governance discipline for cloud-based analytics and operational projects.

An annual assessment of the positioning strategies of the leading 21 BI vendors finds a lack of differentiation that makes it difficult for buyers to compare products. In the BI market’s sea of sameness, Qlik is the only vendor that stands out with this clever, memorable position.

Published at: https://www.eckerson.com/articles/independent-study-bi-vendor-messaging-shows-lack-of-differentiation

MDM creates business value in three ways: it streamlines infrastructure, streamlines processes, and reduces risk.

Published at: https://www.eckerson.com/articles/driving-roi-with-master-data-management-part-1-build-your-business-case

“Universal” semantic layer tools introduced in recent years promise to standardize business metrics across the data stack, and eliminate silos of metrics trapped in semantic layers that are limited to specific data sources or BI platforms. This post offers considerations for adopting a universal semantic layer.

Published at: https://www.eckerson.com/articles/the-universal-semantic-layer-more-than-enough

An Analytics Center of Excellence empowers business teams to meet their own data needs by changing the role of IT from developer to facilitator. The reality, however, is that IT needs be both a facilitator and a developer.

Published at: https://www.eckerson.com/articles/analytics-center-of-excellence-part-i-how-to-shape-the-organization

Traditional companies must balance new and old technologies as part of an ever-modernizing data stack. This blog explores how companies strike the right balance to navigate economic uncertainty, AI disruption, and the need for tool consolidation.

Published at: https://www.eckerson.com/articles/the-modernizing-data-stack-three-ways-to-balance-new-and-old

LLMs are hugely popular with data engineers because they boost productivity. But companies must adapt their data governance programs to control risks related to data quality, privacy, intellectual property, fai-Datarness, and explainability.

Published at: https://www.eckerson.com/articles/should-ai-bots-build-your-data-pipelines-part-ii-risks-and-governance-approaches-for-data-engineers-to-use-large-language-models