Angel Valencia

I am looking forward to speaking at Big Data LDN on the 18th of September with Austin Price. "Force Multiply Your Delivery: How Data Platform Foundations Drive Efficiency" Watch this space as we have much more planned over the 18th/19th of September during Big Data London Looking forward to seeing many Friends, Customers, and Colleagues while I am over in London. LEIT DATA have a stand X753 which is just outside the Analytics and story telling theatre. Be sure to say Hi. LEIT DATA #meandatastreets #data #dataproducts

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35% of AI projects will either fail or experience delays because of poor data quality. There’s a huge gap between the data quality most companies have by default and the data quality needed for successful AI. #snowflake #aidataquality #ai

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Very nice overview of how to do data governance in a way that brings value by Nicola Askham, interviewed by Tobias Macey on the Data Engineering Podcast. https://lnkd.in/dnf7KtfQ #dataDriven #dataGovernance #dataEngineering

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It’s exciting, but certainly not easy… With centralised data teams navigating the delicate balance between innovation, federation and maintaining governance. A C-Suite hungry for rapid return on investment from years of building out their data and cloud capabilities. An ever evolving technology ecosystem developing capability possibly quicker than many enterprise architects can stay abreast of. Predicting where the tech talent gaps will exist in 24 months rather than now is as much art as a science. To unlock advanced analytics and AI, data quality and robust data management is a non-negotiable… Can we deliver more with less? Read our full report on the challenges of reshaping tech strategy for free now: https://lnkd.in/d3SMq3T4

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I wrote an article last week about three big cracks emerging in the Modern Data Stack in a non-clickbaity or sensationalist but honest and balanced way https://lnkd.in/egBpVibM #dataengineering #moderndatastack #cloud

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Gartner’s #AI #Leaders team: “Navigating #GenAI #Production and #Evaluation Challenges” Pleased to join our recent roundtable, with select peers in the AI Leaders group, on the above very engaging topic, expressing my personal views. We explored: Understanding GenAI production strategies * How do you define and prioritize best practices when implementing GenAI production strategies? * What considerations are essential when selecting workflows for managing GenAI models? * How do you balance the need for innovation and experimentation with the necessity of maintaining stability and reliability in GenAI production? GenAI model evaluation * Beyond traditional metrics, what additional factors do you consider when evaluating the performance of GenAI models? * How do you monitor and evaluate the performance of GenAI models in production for drift or degradation? * How do you approach the validation and verification of data sources used to train GenAI models? Integration Hurdles * What are the primary challenges you've encountered when integrating GenAI production into your existing development pipelines? How are you overcoming them?
 Can you share any "aha" moments or unexpected discoveries that have occurred during integration efforts, and how have they influenced your approach going forward? * Are there any regulatory or compliance considerations that have posed challenges during integration, and how do you address them? * What role does change management play in successfully integrating GenAI production into existing workflows, and how do you ensure smooth adoption by end users? As always, such conversations with senior leaders practicing AI and Generative AI are thought provoking and, in my view, enable us to go beyond the typical expectation of what is possible. Thank you Gartner, for hosting this event, and for your kind invitation. #GenerativeAI #Strategy #LLMs #Innovation #transformation #artificialintelligence #AI #GenAI #ChatGPT #disruptivetechnologies #cxos #ceos #cfos #ctos #caios #cios #cdos #excellence #GenAINY

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One of the most useful applications of a Large Language Model for data engineering is to make data labelling classification decisions Read this blog from Isabelle N. on my team where she covers exactly how to use this powerful technique - in this case to take highly varied job titles and map them to standardised departments & levels of seniority and thus easy to visualise in a BI dashboard.

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