Secrets of Data Analytics Leaders

This blog explores four use cases for data orchestration and examples of the supporting architectural elements.

Published at:

https://www.eckerson.com/articles/architecting-data-orchestration-four-use-cases

There are many models for bridging business and technical teams. These models can be more centralized or decentralized in nature, depending on the culture of the organization and nature of the business domain. Each requires a strong enterprise data teams comprised of multiple departments and roles.

Published at:

https://www.eckerson.com/articles/an-operating-model-for-data-analytics-part-iii-team-composition-and-dynamics

Data observability provides intelligence about data quality and data pipeline performance, contributing to the disciplines of DataOps and FinOps. Vendors such as DataKitchen, DataOps.live, Informatica, and Unravel offer solutions to help enterprises address these overlapping disciplines.

Published at:

https://www.eckerson.com/articles/the-blending-disciplines-of-data-observability-dataops-and-finops

Hybrid development teams are critical to the success of a data & analytics program. Data leaders must invest time, energy, and thought to the creation of these teams and how best to support them. It’s critical that they allocate staff time to nurture knowledge flows between component groups.

Published at:

https://www.eckerson.com/articles/an-operating-model-for-data-analytics-part-ii-knowledge-flows

“Metadata is data about data” is a bad definition. It’s vague and recursive. It’s like saying climate is the weather of weather. But if metadata is not data about data, then what is it? Follow my thought process working toward a better understanding of metadata and its role in today’s data landscape.

Published at:

https://www.eckerson.com/articles/fixing-metadata-s-bad-definition

Data Mesh gets a lot of discussion. Some see it as revolutionary—the first new data architecture thinking in years. Others view it as a dangerous backward slide to the chaos of data silos. The reality lies somewhere between—a big shift in architecture thinking with some inherent risk. A few good architecture practices help to realize the benefits while managing the risks.

Published at:

https://www.eckerson.com/articles/a-thoughtful-approach-to-data-mesh

Many machine learning projects fail because data scientists don’t have the right data. Techniques such as synthetic data is a novel algorithmic approach to address algorithmic risks.

Published at:

https://www.eckerson.com/articles/synthetic-data-for-ai-definition-risks-and-strategies

Many machine learning projects fail because data scientists don’t have the right data. Techniques such as synthetic data is a novel algorithmic approach to address algorithmic risks.

Published at:

https://www.eckerson.com/articles/synthetic-data-for-ai-definition-risks-and-strategies

Data orchestration uses caching, APIs, and centralized metadata to help compute engines access data in hybrid or multi-cloud environments. Data platform engineers can use data orchestration to gain simple, flexible, and high-speed access to distributed data for modern analytics and AI projects.

Published at:

https://www.eckerson.com/articles/data-orchestration-simplifying-data-access-for-analytics

Data access management (DAM) is the process of defining and enforcing policies that control access to application data throughout the enterprise. New approaches to DAM provide greater access and better protection through centrally managed policies that are universally enforced.

Published at:

https://www.eckerson.com/articles/data-access-management-zero-sum-game-over