Discover best practices to scale Power BI effectively—improving performance, governance, and self-service across enterprise environments. Published at: https://datalere.com/articles/power-bi-best-practices-from-the-field-lessons-from-enterprise-deployments
Chief Data Officers are expanding data sharing to drive productivity, innovation, and better decisions. Data marketplaces simplify access by connecting users to ready-to-use, high-value data through a centralized, self-service platform. Published at: https://www.eckerson.com/articles/how-data-product-marketplaces-enable-seamless-data-consumption-and-generate-value
All data management activities—whether internally or externally focused—should either reduce costs or grow earnings. Published at: https://www.eckerson.com/articles/book-review-how-to-make-money-with-data
This blog offers five guiding principles to help CIOs, CDOs, and team leaders optimize hybrid data environments. Published at: https://www.eckerson.com/articles/balancing-act-five-principles-to-optimize-hybrid-cloud-environments
This blog defines the governance requirements that streaming data pipelines must meet to make artificial intelligence/machine learning (AI/ML) initiatives successful. Published at: https://www.eckerson.com/articles/streaming-data-governance-three-must-have-requirements-to-support-ai-ml-innovation
Anthony Cosgrove, Co-founder at Harbr, tells us how successful data products focus on clear value propositions, tailored placement, intuitive packaging, and appropriate permissions. Published at: https://www.eckerson.com/articles/what-is-a-data-product
This blog, the second in a series, explores the mix of infrastructure types that support modern AI. Published at: https://www.eckerson.com/articles/cloud-on-prem-hybrid-oh-my-where-ai-adopters-host-their-projects-and-why
Despite $180 billion spent on big data tools and technologies, poor data quality remains a significant barrier for businesses, especially in achieving Generative AI goals. Published at: https://www.eckerson.com/articles/poor-data-quality-is-a-full-blown-crisis-a-2024-customer-insight-report
This blog discusses how GenAI and natural language processing are transforming SQL query generation, allowing non-technical users to access and query data easily. Published at: https://www.eckerson.com/articles/from-4gl-to-genai-how-sql-automation-has-evolved
This article by Piotr Czarnas, founder of DQOps, outlines a proven, team-based approach to tackling persistent issues like invalid data, delayed reporting, and inconsistent formats. Published at: https://www.eckerson.com/articles/overcoming-the-challenge-of-low-data-quality
This article explores how Product Information Management (PIM) helps manufacturers and retailers manage rich product data through phased attribution and collaborative workflows. Published at: https://www.eckerson.com/articles/keys-to-succeeding-in-your-product-information-management-pim-journey
As cloud adoption accelerates, not all analytics workloads are heading in the same direction. This blog explores three strategic options for data and IT leaders. Published at: https://www.eckerson.com/articles/are-you-cloud-bound-the-case-for-migration-repatriation-or-keeping-your-analytics-projects-on-premises
This article breaks down the evolving landscape of AI/ML platforms, from AutoML to full-stack AI workbenches, and provides a structured tool evaluation framework to cut through vendor ambiguity. Published at: https://www.eckerson.com/articles/the-ai-ml-tool-evaluation-template-a-guide-to-smarter-selection
This guide provides a step-by-step framework to assess vendors, align priorities, and make informed decisions about enterprise data and analytics tools. Published at: https://www.eckerson.com/articles/the-buyer-s-guide-to-selecting-the-right-enterprise-data-analytics-tool
This blog, the second in a series, recommends six criteria to help data leaders evaluate tools for managing streaming data pipelines. Published at: https://www.eckerson.com/articles/ultimate-guide-to-streaming-data-pipelines-six-criteria-to-evaluate-and-select-the-right-tool
This blog defines streaming data, explains why companies need it, and explores how streaming data pipelines feed multi-faceted GenAI applications. Published at: https://www.eckerson.com/articles/why-and-how-streaming-data-drives-the-success-of-generative-ai
Change is inevitable, but adoption is key. From AI/ML tools to leadership shifts, success depends on aligning people, not just technology. Published at: https://www.eckerson.com/articles/managing-change-in-the-age-of-ai-the-head-heart-and-herd-framework
This blog covers integrating SAP and third-party systems to build a unified data foundation for analytics and AI in conversational use cases. Published at: https://www.eckerson.com/articles/analytics-and-ai-for-sap-environments-build-a-unified-data-foundation-to-drive-advanced-use-cases
This blog recommends four questions to help data and AI leaders compare homegrown and commercial options for retrieval augmented generation. Published at: https://www.eckerson.com/articles/build-or-buy-rag-four-questions-to-guide-your-approach-to-retrieval-augmented-generation-for-genai
What will change next year? In the age of generative AI, the answer is simple: Everything! Published at: https://www.eckerson.com/articles/predictions-2025-everything-is-about-to-change
Exploring data observability’s limits in data migration, integrity audits, and the need for specialized tools for reliability. Published at: https://www.eckerson.com/articles/the-shiny-allure-of-data-observability-its-limits-in-data-migration-integrity-audits-and-certification
A strategic approach to data monetization covering key processes, risks, and organizational shifts needed for success. Published at: https://www.eckerson.com/articles/unlocking-data-monetization-success
A fresh perspective on data architecture, advocating for an 'anti-monolith' approach inspired by engineering best practices. Published at: https://www.eckerson.com/articles/good-data-architecture-anti-monolith
Many practitioners view data mesh and data fabric as mutually exclusive approaches to data strategy. However, these paradigms complement each other. Data mesh focuses on decentralization and autonomy; Data fabric ensures centralized integration and governance. Let’s dive into how blending elements of both can offer flexibility and control to create the right fit for your organization’s data strategy. Published at: https://www.eckerson.com/articles/blending-data-mesh-and-data-fabric-crafting-a-ba...
As organizations grapple with data spread across various storage locations, solutions like Coginiti Hybrid Query offer a much-needed alternative to fragmented tools. Published at: https://www.eckerson.com/articles/a-novel-approach-for-reducing-cloud-data-warehouse-expenses-from-coginiti
This blog post explores the evolving landscape of data catalogs, highlighting ten key market trends driving the adoption of next-generation solutions. Published: https://www.eckerson.com/articles/ten-key-market-trends-in-next-generation-data-catalogs
Data teams must filter, blend, and refine raw data inputs to create the high-octane fuel that drives innovation with artificial intelligence and machine learning (AI/ML). Published at: https://www.eckerson.com/articles/refining-the-right-fuel-how-data-integration-drives-the-ai-ml-model-lifecycle
With numerous data catalog options available, all claiming to be the best, how do you make an informed decision without exhaustive research? Published at: https://www.eckerson.com/articles/unveiling-the-future-of-data-catalogs
This blog describes the need for data teams to establish a flexible yet well-governed data architecture to support dynamic AI/ML projects. Published at: https://www.eckerson.com/articles/multi-style-data-integration-for-ai-ml-three-use-cases
Many data leaders want to implement self-service, but don’t realize that they first have to implement the right architecture, governance, operating model, project delivery approach, data, and change management plan. Published at: https://www.eckerson.com/articles/self-service-is-the-outcome-not-the-driver-of-a-data-driven-organization
