Data leaders know the importance of change management, but few understand the dynamics involved in driving adoption. A new book by Damon Centola shows how social networks spread and inhibit change. Published at: https://www.eckerson.com/articles/how-change-happens-driving-technology-adoption

Over the past 20 years or more, data architecture practices have focused almost exclusively on managing data for analytics. Operational data is much more than source data for analytics. We must give attention to operational data architecture or pay the price in data disparity, data friction, and technical debt. Published at: https://www.eckerson.com/articles/operational-data-architecture

Designed and implemented well, automated workflows can make the modern business just a little less chaotic and complex. This blog explores the opportunity for automated workflows to help cross-functional teams collaborate and standardize organizational master data. Published at: https://www.eckerson.com/articles/master-data-management-and-operational-workflows-two-modern-use-cases

Data fabric is one of those buzzwords that’s used so much and in so many ways that it often elicits an eyeroll—undeservedly so. The phrase is shorthand for a complex and important set of issues that we’re all working to manage. In this article we’ll review what data fabric is and why it’s important. Published at: https://www.eckerson.com/articles/data-fabric-s-use-of-abstraction-and-metadata

The data pipeline market comprises four segments: data ingestion, data transformation, DataOps, and orchestration. This blog defines three principles for successful pipelines: (1) watch the innovative startups; (2) use suites where you can; and (3) use point tools where you must. Published at: https://www.eckerson.com/articles/modern-data-pipelines-three-principles-for-success

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. 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