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-deployments저자 Eckerson Group

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-…

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-data저자 Eckerson Group

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-environments저자 Eckerson Group

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-innovation저자 Eckerson Group

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-product저자 Eckerson Group

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-why저자 Eckerson Group

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-report저자 Eckerson Group

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-evolved저자 Eckerson Group

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-quality저자 Eckerson Group

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-journey저자 Eckerson Group

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-tool저자 Eckerson Group

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-tool저자 Eckerson Group

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-ai저자 Eckerson Group

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-framework저자 Eckerson Group

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-cases저자 Eckerson Group

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-genai저자 Eckerson Group

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-change저자 Eckerson Group

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-certification저자 Eckerson Group

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-success저자 Eckerson Group

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-monolith저자 Eckerson Group

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…

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-coginiti저자 Eckerson Group

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-catalogs저자 Eckerson Group

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-lifecycle저자 Eckerson Group

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-catalogs저자 Eckerson Group

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-cases저자 Eckerson Group

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…

Explore the essential characteristics to choose the right conversational query tool for your needs and environment.Published at:https://www.eckerson.com/articles/modernizing-analytics-with-conversational-query-tools-five-must-have-characteristics저자 Eckerson Group

Data analytics is a balance of flexibility for innovation and governance to control risks. This blog discusses its implications for artificial intelligence (AI), including machine learning (ML) and generative AI (GenAI).Published at:https://www.eckerson.com/articles/ai-ml-innovation-requires-a-flexible-yet-governed-data-architecture…

Non-profit organizations are more mission-driven, consensus-driven, and resource-constrained than commercial organizations. As a result, it’s imperative that non-profits develop a data strategy before plunging into building data solutions. It will save them time, money, and burnout in the long run.Published at: https://www.eckerson.com/articles/why…

Explore the reasons for data engineers to collaborate with data scientists, machine learning (ML) engineers, and developers on DataOps initiatives that support GenAI.Published at:https://www.eckerson.com/articles/dataops-for-generative-ai-data-pipelines-part-iii-team-collaboration저자 Eckerson Group

This blog explores three criteria to evaluate tools that manage unstructured data pipelines for GenAI.Published at:https://www.eckerson.com/articles/data-engineering-for-genai-three-criteria-to-evaluate-pipeline-tools저자 Eckerson Group

If your data team wants to implement data products, it would be wise to avoid these 12 pitfalls that can torpedo an initiative.Published at:https://www.eckerson.com/articles/12-pitfalls-to-avoid-when-implementing-data-products저자 Eckerson Group

This article compares data catalogs and data marketplaces and argues that you need both and will soon have both as vendors add data marketplace extensions.Published at:https://www.eckerson.com/articles/why-do-i-need-a-data-marketplace-when-i-have-a-data-catalog저자 Eckerson Group

This blog defines conversational BI, why companies should consider it, and how their power and casual users can best get the desired results.Published at:https://www.eckerson.com/articles/driving-results-with-conversational-bi-best-practices-for-power-and-casual-users저자 Eckerson Group

Data engineering is now considered a crucial job in IT as Generative AI, the hottest technology of this decade, relies on data engineers to provide accurate inputs.Published at:https://www.eckerson.com/articles/data-engineering-for-genai-how-to-optimize-data-pipelines-and-governance저자 Eckerson Group

Data engineers and data scientists must manage pipelines for unstructured data to ensure healthy inputs for language models.Published at:https://www.eckerson.com/articles/why-and-how-data-engineers-will-enable-the-next-phase-of-generative-ai저자 Eckerson Group

Companies that adopt DataOps increase the odds of success by making GenAI data pipelines what they should be: modular, scalable, robust, flexible, and governed.Published: https://www.eckerson.com/articles/dataops-for-generative-ai-data-pipelines-part-ii-must-have-characteristics저자 Eckerson Group

Most data leaders want to deliver data products, but few are doing it. Let's face it: most data teams today function as internal service bureaus that fulfill customer requests that arrive via ticketing systems, email, handwritten notes, or calls from colleagues looking for a favor. Most work double time to keep their request backlogs from balloonin…

GenAI can help data engineers become more productive, and data engineering can help GenAI drive new levels of innovation.Published at:https://www.eckerson.com/articles/achieving-fusion-how-genai-and-data-engineering-help-one-another저자 Eckerson Group

Discover how master data management (MDM) provides language models with high-quality enterprise data to improve their response accuracy.Published at:https://www.eckerson.com/articles/improving-genai-accuracy-with-master-data-management저자 Eckerson Group

Explore our four primary criteria for evaluating conversational BI products.Published at:https://www.eckerson.com/articles/genai-driven-analytics-product-evaluation-criteria-for-conversational-bi저자 Eckerson Group

The success of Generative AI depends on fundamental disciplines like DataOps.Published at:https://www.eckerson.com/articles/dataops-for-generative-ai-data-pipelines-part-i-what-and-why저자 Eckerson Group

With the increasing adoption of Generative AI, learn how data governance will add value to and benefit from Generative AI.Published at:https://www.eckerson.com/articles/data-governance-in-the-era-of-generative-ai저자 Eckerson Group

"Meet the business where it is." If you're on the data team, that's what you're expected to do to empower stakeholders with data. But how far should you go to meet the business? And shouldn’t the business be expected to move a little toward meeting the data where it is?Published at:https://www.eckerson.com/articles/meeting-the-data-where-it-is-time…

The European Union recently passed the first of its kind legal framework on the development, use, and governance of artificial intelligence. It lays out rules and standards with the aim of ensuring technologies are safe and transparent, and do not violate the fundamental rights of an individual.Published at:https://www.eckerson.com/articles/the-eu-…

Most organizations are committed to responsible and ethical use of AI. Yet anticipating unintended consequences before designing and implementing AI can be challenging. This framework and process helps evaluate short-term and long-term impacts across multiple dimensions so you can mitigate AI’s unintended consequences.Published at:https://www.ecker…