Secrets of Data Analytics Leaders

The data mesh framework doesn’t specify a key component that completes the last mile of the architecture: a data provisioning environment. New technology that underpins modern data marketplaces complement data mesh, providing a frictionless way for data providers and data consumers to exchange data.

Published at:

https://www.eckerson.com/articles/data-mesh-s-missing-ingredient-a-data-marketplace

Traditional techniques for managing data quality break at scale. Machine learning algorithms can automate aspects of the data quality workload, ensuring that the data the business users consume is reliable. This article profiles three tools and approaches that use ML to automate data quality.

Published at:

https://www.eckerson.com/articles/three-data-quality-automation-tools-you-should-consider

We must treat metadata like a fully-vested member of the enterprise data landscape. A unifying taxonomy is a good place to start making metadata a focus of data management rather than just a tool. This article explores how to start wrangling diverse and distributed metadata.

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https://www.eckerson.com/articles/wrangling-metadata-making-it-the-object-of-data-management

We enter 2023 in a haze of uncertainty. Enterprises must rationalize analytics projects, shift to lower-risk use cases, and control cloud costs. They also must measure the ROI of analytics projects and use data governance to reduce business risk.

Published at:

https://www.eckerson.com/articles/analyzing-a-downturn-five-principles-for-data-analytics-in-2023

This blog defines governed data integration and describes how it enabled two manufacturers to synchronize data flows from the factory floor to the customer.

Published at:

https://www.eckerson.com/articles/governed-data-integration-for-manufacturers

A rising number of financial services firms are adopting the discipline of governed data integration to build 360-degree customer views.

Published at:

https://www.eckerson.com/articles/governed-data-integration-for-financial-services

Synthetic data and artificial intelligence (AI) complement each other but are both subject to the risk of AI bias. Consequently, companies need to implement architectural and governance controls to reduce the bias that synthetic data can inject into AI models.

Published at:

https://www.eckerson.com/articles/mitigating-the-risk-of-bias-in-synthetic-data-for-ai

As enterprises grow more dependent on the cloud and as the economy convulses, FinOps will soon become mandatory.

Published at:

https://www.eckerson.com/articles/the-rise-of-finops-cost-governance-for-cloud-based-analytics

Business domains have a range of data & analytics capabilities that enterprise data teams must support. The key is to ensure domain activity aligns with enterprise standards and best practices to ensure data consistency and avoid silos.

Published at:

https://www.eckerson.com/articles/an-operating-model-for-data-analytics-part-iv-red-team-composition

Active metadata is not a type of metadata, it’s a way of using metadata to power systems. Active metadata is a critical feature of modern data architectures such as data fabric and data mesh. It makes things work such as data access management, data classification, and data quality management.

Published at:

https://www.eckerson.com/articles/active-metadata-the-critical-factor-for-mastering-modern-data-management