Listen to data and analytics leaders share the secrets of their success. Wayne Eckerson, long-time global thought leader interviews guests who run data and analytics programs at Fortune 2000 organizations around the world. Tune in to stay abreast of the latest technologies, techniques, and trends in our fast-paced industry.
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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-managementEckerson Group
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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-biEckerson Group
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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-whyEckerson Group
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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-aiEckerson Group
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"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…
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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-…
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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…
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It's not easy being the head of data & analytics at a large organization. You must align a large team across multiple disciplines; you must deal with oodles of legacy systems and tools that hamper innovation; and you must deliver business value fast to keep executives at bay and your job intact. You also need to recruit dynamic managers who can pus…
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Adopting community of practice principles, along with coaching and mentoring, is a practical approach to fostering and cultivating data literacy.Published at:https://www.eckerson.com/articles/a-people-first-approach-to-developing-data-literacyEckerson Group
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This blog examines the upcoming trend of domain-specific LLMs and evaluates three different methods of implementation.Published at:https://www.eckerson.com/articles/the-next-wave-of-generative-ai-domain-specific-llmsEckerson Group
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Machine Learning and Streaming Data Pipelines, Part I: Definitions and Architecture - Audio Blog
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Many machine learning (ML) use cases center on real-time calculations. This article defines streaming ML and its architectural components.Published at:https://www.eckerson.com/articles/machine-learning-and-streaming-data-pipelines-part-i-definitions-and-architectureEckerson Group
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Organizing for Success Part III: How to Organize and Staff Data Analytics Teams - Audio Blog
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Companies need to invest heavily in teams and people, both at corporate and in the field, if they want to become a data-driven organization.Published at:https://www.eckerson.com/articles/organizing-for-success-part-iii-how-to-organize-and-staff-data-analytics-teamsEckerson Group
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Data management practices have changed substantially since the early 1990s and the dawn of data warehousing.Published at:https://www.eckerson.com/articles/the-continuing-evolution-of-data-managementEckerson Group
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Conventional data governance conflicts with today’s world of self-service analytics and agile projects. Published at:https://www.eckerson.com/articles/modern-data-governance-problemsEckerson Group
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Let's reflect on the events of the past year and prognosticate on what may transpire in the months ahead.Published at:https://www.eckerson.com/articles/trends-for-2024-our-team-gazes-into-the-crystal-ballEckerson Group
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The Data Leader’s Guide to Generative AI, Part I: Models, Applications, and Pipelines - Audio Blog
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Data leaders must prepare their teams to deliver the timely, accurate, and trustworthy data that GenAI initiatives need to ensure they deliver results. They can do so by modernizing their environments, extending data governance programs, and fostering collaboration with data science teams.Published at:https://www.eckerson.com/articles/the-data-lead…
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A Fresh Look at Data Modeling Part 2: Rediscovering the Lost Art of Data Modeling - Audio Blog
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Data modeling is a core skill of data engineering, but it is missing or inadequate in many data engineering teams. These teams focus on moving data with little attention to shaping the data. They engineer processes, not products. Full data engineering is both process and product engineering, and that calls for data modeling.Published at:https://www…
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The hardest part about implementing data products is fostering a product mindset among the people responsible for defining, governing, building, and shipping data products. It’s also important that an organization establish processes to facilitate the work of the product team and review boards.Published at:https://www.eckerson.com/articles/data-pro…
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Many organizations abandoned data modeling as they embraced big data and NoSQL. Now they find that data modeling continues to be important, perhaps more important today than ever before. With a fresh look you’ll see that today’s data modeling is different from past practices – much more than physical design for relational data.Published at:https://…
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Data democratization is the buzzword to describe empowering enterprise stakeholders with data. While there have been advances in data management, governance, and analytics, something keeps getting in the way of achieving data democratization.Published at:https://www.eckerson.com/articles/data-democratization-and-the-duties-of-data-citizenship…
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Our industry’s breathless hype about generative AI tends to overlook the stubborn challenge of data governance. Data catalogs address this challenge by evaluating and controlling the accuracy, explainability, privacy, IP friendliness, and fairness of GenAI inputs.Published at:https://www.eckerson.com/articles/generative-ai-needs-vigilant-data-catal…
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The need for an independent semantic layer continues to rise as data science gains traction in the enterprise. Its five primary elements—metrics, caching, metadata management, APIs, and access controls—support AI/ML use cases as part of data science projects.Published at: https://www.eckerson.com/articles/why-and-how-to-enable-data-science-with-an-…
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Business leaders can address AI bias and use it to have rational discussions about management and human bias.Published at: https://www.eckerson.com/articles/weighing-the-risk-and-reward-of-ai-a-non-technical-guide-for-business-leadersEckerson Group
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Most organizations view data as an asset to be actively managed with standards, controls, and discipline. Yet, they are passive and casual about metadata. Data is managed. Metadata happens. As data management becomes more complex, metadata management is becoming an essential discipline. It is time to think about metadata management from an architec…
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Kevin Petrie, the Vice President of Research at Eckerson Group, and Dan O’Brien, research analyst, discussed large language models (LLMs), which are neural networks that analyze text to predict the next word or phrase. These models use training data, often from the internet, to understand word relationships and provide accurate answers to natural l…
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Generative AI initiatives require new data pipelines that prepare text files for querying by language models. Data engineers, scientists, and other stakeholders collaborate to design and implement these pipelines, which span text sources, tokens, vectors, vector databases, and LMs.Published at:https://www.eckerson.com/articles/the-new-data-pipeline…
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Analyst Series - Operating Models for Data & Analytics: How to Align Resources Across the Enterprise
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Dan and Wayne discussed the concept of data and analytics operating models, which refers to how organizations organize their data and analytics resources for alignment and efficiency.Eckerson Group
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This final blog in our series on the ROI of master data management recommends ways for data teams to iterate their MDM initiatives based on the successes and failures of their first project.Published: https://www.eckerson.com/articles/driving-roi-with-master-data-management-part-iii-project-iteration…
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Responsible AI ethical principles provide a clear, unifying purpose for the technological, business, and social goals of AI initiatives.Published at: https://www.eckerson.com/articles/the-opportunity-and-risk-of-generative-ai-part-iii-responsible-ai-ethicsEckerson Group
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Most definitions of a data product conflate it with a data asset. The only way to turn a data asset into a data product is to publish it in a data store along with metadata about subscription and delivery options, and terms of service that specify a bidirectional contract between data consumer and producer.Published at: https://www.eckerson.com/art…
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Responsible AI can help data leaders comply with the fast-evolving regulatory environment of data and artificial intelligence.Published at: https://www.eckerson.com/articles/the-opportunity-and-risk-of-generative-ai-part-ii-how-responsible-ai-assists-complianceEckerson Group
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US frontier history had races, risks, and rewards. Generative AI's future will follow a similar path.Published at: https://www.eckerson.com/articles/enterprise-data-and-the-taming-of-the-generative-ai-frontierEckerson Group
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Dan and Jay discussed the concept of Data Fabric, an automated and AI-driven approach to managing modern data environments.Eckerson Group
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Simba Khadder and Kevin Petrie discuss strategies to overcome technical debt in implementation, the pivotal role of data in the success of ML projects, navigating regulatory compliance in machine learning, and the future of AI governance.Eckerson Group
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Learn how to attain an optimal return on investment (ROI) with MDM by choosing the appropriate architectural strategy and evaluating progress during the initial project implementation.Published at: https://www.eckerson.com/articles/driving-roi-with-master-data-management-part-ii-your-first-projectEckerson Group
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As organizations strive to meet the ever-growing demand for data, they are adopting data products to streamline delivery and ensure solutions provide value to business stakeholders. Learn about four traps that can disrupt data product development and how to avoid falling into them.Published at: https://www.eckerson.com/articles/four-traps-to-avoid-…
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Generative AI brings a promise to improve lives in a blistering innovation race, but also a threat to people, corporations, and even nations. Data analytics leaders must understand the risks of generative AI, both societal and business-related, to use it positively and avoid the destructive consequences seen with nuclear energy development.Publishe…
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The unbundling of the data ecosystem is causing organizations to “duct tape” products and frameworks together to build their solutions and data delivery processes. Organizations fail to build and deploy end-to-end, automated, repeatable data-driven systems, ignoring data engineering & dataops principles as well as best practices.Published at: https…
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This blog recommends guiding principles for successful implementation of language models to assist data engineering.Published at: https://www.eckerson.com/articles/should-ai-bots-build-your-data-pipelines-part-iv-guiding-principles-for-success-with-language-models-and-data-engineeringEckerson Group
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An emerging approach to generative AI will help data engineering teams achieve much-needed productivity gains while controlling risk.Published at: https://www.eckerson.com/articles/should-ai-bots-build-your-data-pipelines-part-iii-the-emergence-of-small-language-models-for-data-engineeringEckerson Group
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Dan O'Brien and Kevin Petrie discuss FinOps, which is a cost governance discipline for cloud-based analytics and operational projects.Eckerson Group
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An annual assessment of the positioning strategies of the leading 21 BI vendors finds a lack of differentiation that makes it difficult for buyers to compare products. In the BI market’s sea of sameness, Qlik is the only vendor that stands out with this clever, memorable position.Published at: https://www.eckerson.com/articles/independent-study-bi-…
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MDM creates business value in three ways: it streamlines infrastructure, streamlines processes, and reduces risk.Published at: https://www.eckerson.com/articles/driving-roi-with-master-data-management-part-1-build-your-business-caseEckerson Group
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“Universal” semantic layer tools introduced in recent years promise to standardize business metrics across the data stack, and eliminate silos of metrics trapped in semantic layers that are limited to specific data sources or BI platforms. This post offers considerations for adopting a universal semantic layer.Published at: https://www.eckerson.com…
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An Analytics Center of Excellence empowers business teams to meet their own data needs by changing the role of IT from developer to facilitator. The reality, however, is that IT needs be both a facilitator and a developer.Published at: https://www.eckerson.com/articles/analytics-center-of-excellence-part-i-how-to-shape-the-organization…
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Traditional companies must balance new and old technologies as part of an ever-modernizing data stack. This blog explores how companies strike the right balance to navigate economic uncertainty, AI disruption, and the need for tool consolidation.Published at: https://www.eckerson.com/articles/the-modernizing-data-stack-three-ways-to-balance-new-and…
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LLMs are hugely popular with data engineers because they boost productivity. But companies must adapt their data governance programs to control risks related to data quality, privacy, intellectual property, fai-Datarness, and explainability.Published at: https://www.eckerson.com/articles/should-ai-bots-build-your-data-pipelines-part-ii-risks-and-go…
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Despite innovations in data architecture, infrastructure, and analytics, most organizations today still struggle to realize the promised value of data. Learn how the data mesh principle of data as a product can help, as part of a data mesh initiative or as a stand-alone strategy.Published at: https://www.eckerson.com/articles/data-products-part-of-…
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Data Mesh: Evaluating Your Organization's Readiness for a Decentralized Data Future - Audio Blog
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Data mesh is a new paradigm for fulfilling the promised value of data. It decentralizes both data ownership and the data itself, shifting them toward the functional domains that create and use data to operate. But data mesh is not for everyone. Learn how to assess if you’re ready for data mesh.Published at: https://www.eckerson.com/articles/data-me…
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There’s so much hype surrounding data products that you have to wonder if it’s just another buzzword. But there’s more to data products than buzz. In this article, you’ll learn how the concept is a meaningful step forward in the art and science of data management.Published at: https://www.eckerson.com/articles/best-practices-for-developing-and-scal…
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