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

An operational data hub (ODH) is a pattern in data architecture that provides a central location and a standard protocol for operational systems to communicate about and share data among themselves. Operational systems post messages about data events (add, change, delete) and subscribe to messages of interest posted by other applications. The hub works to share data among applications without the clutter and chaos of point-to-point data feeds. Published at: https://www.eckerson.com/articles/operational-data-hub-responding-to-data-friction-and-technical-debt

Data mesh is a hot topic in the data world, generating conversations about the benefits and drawbacks of its decentralized approach. Concerns about an explosion of data silos and inconsistent data quality are justified. But to those who feel a bit like Chicken Little, maybe the sky is not falling. Published at: https://www.eckerson.com/articles/data-mesh-the-sky-is-not-falling

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