Listen to data and analytics leaders share the secrets of their success. Wayne Eckerson, long-time g
This blog explores four use cases for data orchestration and examples of the supporting architectura
There are many models for bridging business and technical teams. These models can be more centralize
Data observability provides intelligence about data quality and data pipeline performance, contribut
Hybrid development teams are critical to the success of a data & analytics program. Data leaders mus
“Metadata is data about data” is a bad definition. It’s vague and recursive. It’s like saying climat
Data Mesh gets a lot of discussion. Some see it as revolutionary—the first new data architecture thi
Many machine learning projects fail because data scientists don’t have the right data. Techniques su
Data orchestration uses caching, APIs, and centralized metadata to help compute engines access data
Data access management (DAM) is the process of defining and enforcing policies that control access t
This article, the third in a series, dives into the technologies that underpin modern approaches to
The data mesh makes business domain experts the owners of their data, which they deliver as a “data
An operating model for data & analytics is critical for aligning resources across the enterprise and
In this article, Lawson Abinati lays out core principles for market positioning that apply across al
This article summarizes the major characteristics of a modern data architecture and serves as a guid
An analytics center of excellence is the cornerstone of every data strategy, yet few data leaders kn
Design patterns have proven valuable in many endeavors. Can data pipeline design patterns help to br
The need for adaptable data management architecture has never been more pressing. Yet getting there
Data mesh is an evolutionary concept that’s gained a lot of traction in the software engineering wor
This article, the second in a series, shares cutting-edge examples of location intelligence applicat
