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Secrets of Data & Analytics Leaders
Secrets of Data Analytics LeadersEpisodes
Secrets of Data & Analytics Leaders
Secrets of Data Analytics Leaders
A weekly Business podcast
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Episodes of Secrets of Data Analytics Leaders
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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
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
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
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-literac
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-llms
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-defi
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-sta
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-management
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-problems
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-ball
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 fo
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 engi
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 facilit
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 i
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.P
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 fairn
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 sc
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-leaders
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