Secrets of Data Analytics Leaders Eckerson Group

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.
Published at:
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