Capco’s Post

In an artificial intelligence enabled organization, traditional data governance practices face challenges due to the complexity of #AI algorithms, utilization of unstructured data, dynamic #data transformations, integration with external data sources, and the lack of interpretability in AI models. Discover how to overcome these challenges in our paper 🔗 https://okt.to/eVWSzG

In an artificial intelligence enabled organization, traditional data governance practices face challenges due to the complexity of #AI algorithms, utilization of unstructured data, dynamic #data transformations, integration with external data sources, and the lack of interpretability in AI models. Discover how to overcome these challenges in our paper 🔗 https://okt.to/DnU5Eh

In an artificial intelligence enabled organization, traditional data governance practices face challenges due to the complexity of #AI algorithms, utilization of unstructured data, dynamic #data transformations, integration with external data sources, and the lack of interpretability in AI models. Discover how to overcome these challenges in our paper 🔗 https://okt.to/yOc0oJ

In an artificial intelligence enabled organization, traditional data governance practices face challenges due to the complexity of #AI algorithms, utilization of unstructured data, dynamic #data transformations, integration with external data sources, and the lack of interpretability in AI models. Discover how to overcome these challenges in our paper 🔗 https://okt.to/L8sZNM

In an artificial intelligence enabled organization, traditional data governance practices face challenges due to the complexity of #AI algorithms, utilization of unstructured data, dynamic #data transformations, integration with external data sources, and the lack of interpretability in AI models.. Discover how to overcome these challenges in our paper 🔗 https://okt.to/rm9PNk

The data center industry is seeing a transformative era marked by the rise of Generative Artificial Intelligence (#GenAI). This technology, which includes advanced models like GPT-4, is reshaping the landscape of data processing and management. For CEOs and CIOs, understanding the implications of this shift and preparing existing data center resources for the GenAI wave is crucial. Read our latest blog to learn more and how we can provide you with an actionable strategic plan for modernizing your data center for Generative AI. https://okt.to/vLRZO2 #LetsMeetTheFuture #newblog #GPT4 #AI #finops #ITChannel

Dataconomy is spot on. The accuracy of AI depends on the data it's trained on. 🎯 However, unlike traditional applications, incorporating enterprise data in AI/ML models is often difficult and time-consuming. That's why AI data development is so critical. Read the full story here: https://lnkd.in/gcEKtss4

🤖 🚀 Stay ahead of the curve and navigate the Gen. AI Revolution with proper Data Governance strategies in place. Read all about it: https://lnkd.in/gbQFDhxd 👨🚀 Navigate this breakthrough in the reshaping data landscape with us with a personalized demo today: https://lnkd.in/d3Aya2KC #WhyAlex #DataLineage #DataGovernance #CatalogofCatalogs #BusinessGlossary #BusinessIntelligence #DataQuality #DataPrivacyandSecurity #DataRiskandCompliance #MetadataManagement #AI #LLM

🚀 Performance vs quality dilemma in AI !! n 2021, AI pioneer Andrew Ng emphasized the need for high-quality data in AI systems, launching a data-centric AI initiative. Fast forward to 2024, and the focus is shifting back to data quality over model size. As GenAI and LLMs like OpenAI’s GPT and Anthropic’s Claude evolve, enterprises are discovering the need to customize these models with specific, high-quality data for real business applications. Microsoft’s Phi-3 models are a testament to this, excelling with a focus on curated datasets over sheer size. The conversation is shifting from which model to use, to how to best use, serve, and fine-tune these models on specific enterprise data. It’s clear: the future of AI will not just be about large models but also about how intelligently we can use data. Jim Rowan reiterates your point about how data quality is imperative when we spoke about agent based workflows #AI #DataQuality #MachineLearning #TechTrends