The data mesh paradigm is in a nascent stage with data personas and organizations craving clarity and quick answers. Best practices are yet to be crystallized. Mesh done incorrectly runs the risk of degenerating into silos. Published at: https://www.eckerson.com/articles/caution-data-leaders-plan-carefully-before-rushing-to-data-mesh

I got energized walking the show floor at the Gartner Data & Analytics event last month and learned a few things about the future of our industry. Published at: https://www.eckerson.com/articles/quick-recap-of-gartner-conference-2023

The modern data stack is a loose collection of technologies, often cloud-based, that collaboratively process and store data to support modern analytics. It must be automated, low code/no code, AI-assisted, graph-enabled, multimodal, streaming, distributed, meshy, converged, polyglot, open, and governed. Published at: https://www.eckerson.com/articles/twelve-must-have-characteristics-of-a-modern-data-stack

AutoML and the emerging approach of declarative ML help simplify the process of creating and refining ML models. Published at: https://www.eckerson.com/articles/automl-and-declarative-machine-learning-comparing-use-cases

One version of the truth is the holy grail of data and analytics. However, the promise of one version of the truth still evades us because even with consistent data, the truth is, as the film My Cousin Vinny demonstrates, a matter of perspective and context. Published at: https://www.eckerson.com/articles/one-version-of-the-truth-according-to-my-cousin-vinny

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

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

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