Secrets of Data Analytics Leaders
A daily Business podcast
Secrets of Data Analytics Leaders
Episodes
Episodes of Secrets of Data Analytics Leaders
Mark All
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-ca
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-h
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.ec
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-characteris
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://w
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 w
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-dat
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-
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-
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-
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
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
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-pipelin
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 ca
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-another
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-management
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-bi
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-why
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-ai
"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 mee
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
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