Secrets Of Data Analytics Leaders

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-balanced-data-strategy-2118cd34-e463-4468-b150-bdaf9e1c541d

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

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-characteristics

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-non-profits-need-a-data-strategy

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-collaboration

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-tools

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-products

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-catalog

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-users

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-governance

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-ai