Intertech, Inc.’s Post

In this thought-provoking article, the importance of rethinking data management before scaling up generative AI projects is emphasized. After years of experimentation, IT leaders are eager to advance their AI initiatives but must first address critical challenges in data management. Data is the fuel that powers AI, and without proper data quality, governance, and privacy practices, the effectiveness of AI models can be compromised. At Sterling5, we follow best practices in Data Analytics, ensuring strong data management frameworks that support accurate and reliable AI outcomes. Our detailed understanding of both AI and data processes enables us to deliver optimal solutions for businesses looking to leverage AI. Partner with Sterling5 to harness our expertise in Data Analytics and Artificial Intelligence and take your AI projects to the next level.   #AI #DataManagement #GenerativeAI #TechnologyLeadership #DataAnalytics #ArtificialIntelligence Source: https://lnkd.in/eaFYq_JP  

Data is important for all companies. How they use it and have access to it is crucial. AI being used like KM and RAG will help. It just has to be done right.

Getting data management right is crucial for making generative AI work! As CIOs look to scale their AI initiatives, the importance of effective data management is front and centre. A recent article shares some great insights on what leaders need to focus on to get the most out of AI technologies. Here’s a big takeaway: not all data is created equal! Sure, having your structured data organised is important, but with the right tools, you can treat different types of data differently. This means you can get started without needing everything to be perfect. By leveraging vector-based strategies, organisations can tap into unstructured data, helping you figure out which bits are most impactful for boosting your AI outcomes. Plus, this approach allows you to kick off your initiatives and improve your data along the way. By treating data flexibly and being adaptable, you can unlock valuable insights that drive better decision-making and really enhance the AI experience. How is your organisation managing its data to make the most of generative AI? 👉 🤝 https://buff.ly/3Y5ym9c #AI #DataManagement #DigitalTransformation #CIO #BusinessStrategy

Implementing generative AI successfully? Most enterprises are focusing on two main use cases for gen AI:   1️, Knowledge Management (KM)   2️, Retrieval Augmented Generation (RAG) models  Both rely heavily on an organization's own data and IT leaders need to focus on 3 key aspects for gen AI projects. Key Aspect #1: Collect, filter, and categorize data using open-source tools, removing sensitive information, blending sources, and implementing quality controls. Key Aspect #2: Strengthen data governance by automating quality checks, adhering to AI regulations, and implementing robust security and classification systems. Key Aspect #3: Safeguard privacy and IP by reviewing access controls, understanding data sharing practices, and protecting valuable information when using public models. https://lnkd.in/eaFYq_JP  #GenAI #DataManagement #DataPrep #DataGovernance #DataPrivacy

As companies look to AI innovation, beware of taking short cuts due to pressure for the elusive quick returns. Having a solid data management strategy for structured and unstructured data sets will heavily increase the likelihood of future AI project success.

Need to make #data-driven decisions and operate with unprecedented efficiency? We got you. Add Red Hat #OpenShift AI, and get a robust platform that streamlines the entire AI/ML lifecycle, from data management to model deployment, across any #infrastructure. What are you waiting for? #strategicimperative #AI #businessprocessimprovement

“Everybody’s ready for AI artificial intelligence except your data” “You’re looking to bring together data that was otherwise siloed in different business units to actually deploy for a specific use case” 1,300 tech and data executives, just 18% of companies say they’re fully ready for AI artificial intelligence deployment, meaning their data is fully accessible and unified (another 40% consider themselves mostly ready, but not quite there) With AI artificial intelligence data governance isn’t so cut and dry : Hardest part of deploying gen AI artificial intelligence for most companies is having data that’s ready--- generative artificial intelligence ---

After almost two years of exploring generative AI, numerous IT leaders are now gearing up to expand. However, before scaling up, a critical aspect that requires attention is the reevaluation of data management. Check out this insightful article highlighting the crucial 3 elements essential for successful data management in gen AI projects: https://lnkd.in/gY6buGT9 #genai #datagovernance #dataprivacy #compliance

“The era of AI-powered business will lead to unintended consequences without advance planning...to safely harness this disruption, CIOs must work with executive leaders to define their ambitions for using everyday AI and game-changing AI, and to establish AI-ready principles, data and security.” Mary Mesaglio VP Analyst at Gartner With Red Hat #OpenShift #AI, we can give you a platform that streamlines the entire AI/ML lifecycle, from data management to model deployment, across any infrastructure. https://lnkd.in/erbVxzHs