The data mesh paradigm is in a nascent stage with data personas and organizations craving clarity and quick answers. Best practices are yet to be crystallized. Mesh done incorrectly runs the risk of degenerating into silos.

Published at:

https://www.eckerson.com/articles/caution-data-leaders-plan-carefully-before-rushing-to-data-mesh

I got energized walking the show floor at the Gartner Data & Analytics event last month and learned a few things about the future of our industry.

Published at:

https://www.eckerson.com/articles/quick-recap-of-gartner-conference-2023

The modern data stack is a loose collection of technologies, often cloud-based, that collaboratively process and store data to support modern analytics. It must be automated, low code/no code, AI-assisted, graph-enabled, multimodal, streaming, distributed, meshy, converged, polyglot, open, and governed.

Published at:

https://www.eckerson.com/articles/twelve-must-have-characteristics-of-a-modern-data-stack

AutoML and the emerging approach of declarative ML help simplify the process of creating and refining ML models.

Published at:

https://www.eckerson.com/articles/automl-and-declarative-machine-learning-comparing-use-cases

One version of the truth is the holy grail of data and analytics. However, the promise of one version of the truth still evades us because even with consistent data, the truth is, as the film My Cousin Vinny demonstrates, a matter of perspective and context.

Published at:

https://www.eckerson.com/articles/one-version-of-the-truth-according-to-my-cousin-vinny

Data leaders know the importance of change management, but few understand the dynamics involved in driving adoption. A new book by Damon Centola shows how social networks spread and inhibit change.

Published at:

https://www.eckerson.com/articles/how-change-happens-driving-technology-adoption

Over the past 20 years or more, data architecture practices have focused almost exclusively on managing data for analytics. Operational data is much more than source data for analytics. We must give attention to operational data architecture or pay the price in data disparity, data friction, and technical debt.

Published at:

https://www.eckerson.com/articles/operational-data-architecture

Designed and implemented well, automated workflows can make the modern business just a little less chaotic and complex. This blog explores the opportunity for automated workflows to help cross-functional teams collaborate and standardize organizational master data.

Published at:

https://www.eckerson.com/articles/master-data-management-and-operational-workflows-two-modern-use-cases

Data fabric is one of those buzzwords that’s used so much and in so many ways that it often elicits an eyeroll—undeservedly so. The phrase is shorthand for a complex and important set of issues that we’re all working to manage. In this article we’ll review what data fabric is and why it’s important.

Published at:

https://www.eckerson.com/articles/data-fabric-s-use-of-abstraction-and-metadata

The data pipeline market comprises four segments: data ingestion, data transformation, DataOps, and orchestration. This blog defines three principles for successful pipelines: (1) watch the innovative startups; (2) use suites where you can; and (3) use point tools where you must.

Published at:

https://www.eckerson.com/articles/modern-data-pipelines-three-principles-for-success