dbt Labs’ Post

Put DATA strategy first and business impact through AI will follow...

🔍 Generative AI is enabling organizations to optimize their insights into diverse data sources, including customer data and risk assessments.   💡 These insights further help improve decision-making regarding pricing and fraud detection. For businesses to ensure AI applications are more precise, intelligent, and impactful, a trusted data infrastructure with consistent, real-time, and consented data is critical.   🏗️ Building a strong foundation for AI-ready can help guarantee that the AI models are fed the most accurate and timely data available to deliver appropriate results.   🔗 By unifying the most valuable data sources and focusing on the critical challenges, effective data integration will help fast-track the processes and drive a long-term competitive advantage.   For more insights: https://lnkd.in/exDaRia5 #insights #ai #data #genai #riskassessment #dataintegration #infrastructure #blog #linkedinblogs

There is lot of information to digest in this 27-minute presentation, but (shameless plug alert) it is clear that AI is going to increase the demand and need for tech docs and those who can craft them. (See the screen capture in the comments.) Will AI one day be able to meet that demand? Probably. But until then, there will be a lot of work for humans as we approach ever closer to the so-called singularity.

𝐄𝐯𝐞𝐫 𝐰𝐨𝐧𝐝𝐞𝐫𝐞𝐝 𝐡𝐨𝐰 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐞𝐧𝐬𝐮𝐫𝐞 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲, 𝐜𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞, 𝐚𝐧𝐝 𝐬𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲? Let’s explore the foundational layers of data modeling that make it all possible: - 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐋𝐚𝐲𝐞𝐫: This layer clarifies data meaning and context, ensuring alignment with legal and regulatory standards. Trust in AI starts here! - 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐮𝐚𝐥 𝐋𝐚𝐲𝐞𝐫: Define high-level business entities for good data quality. Consistency and accuracy lead to precise and reliable AI models. - 𝐋𝐨𝐠𝐢𝐜𝐚𝐥 𝐋𝐚𝐲𝐞𝐫: Detail data structure for efficient management. Seamless integration and flow are essential for handling vast datasets. - 𝐏𝐡𝐲𝐬𝐢𝐜𝐚𝐥 𝐋𝐚𝐲𝐞𝐫: Managing data storage and retrieval, this layer tackles Scalability and Performance Optimization. It ensures your system can grow and adapt without sacrificing speed or efficiency. Together, these layers form a robust data model, the backbone of successful AI deployment. They address critical factors like compliance, accuracy, and scalability, ensuring your AI initiatives succeed. 𝐂𝐮𝐫𝐢𝐨𝐮𝐬 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧 𝐦𝐨𝐫𝐞?  Check out our full article on how data modeling builds the foundation for AI! https://lnkd.in/eyqU2Gnc #AI #DataModeling #DataManagement #Scalability #TechTrends

I have noticed a very direct correlation between companies not getting value out of AI and companies not actually having any data. If your data is stupid, your AI will be stupid. You can't fix stupid without data. There is no technology you can invest in or alternative process you can build that will fix this problem for your organization. If you are in this current environment and unwilling to recognize that basic and fundamental step in the equation, good luck to ya laddie!

Want to Integrate AI into Your Business? 🚪🤖 I've got some bad news… Without clean data, AI won't do much for you. Why Clean Data Matters: - AI's Fuel is Data: Messy data leads to poor AI performance. - Accurate Insights: Clean data equals smart, actionable insights. - Efficiency Boost: Standardized data streamlines operations and reduces errors. - Easy Reporting: Clean data makes reporting simple and reliable. We Can Help: - Data Integration: Unify your data from various sources into one clean dataset. - AI Optimization: Prepare your data for top-notch AI performance. - Simplified Reporting: Get accurate reports effortlessly. At Buinsoft, we ensure your data is clean and AI-ready. Don’t let dirty data hold you back. Contact us today and unlock the full potential of your data! #DataIntegrity #AI #CleanData #Buinsoft #BusinessIntelligence #DataCleaning #DataIntegration #OpenSource

Building out your responsible AI takes planning and effort to get sustainable and reliable results.

Only 3% of companies’ newly-created data meets basic quality standards. Just think about that for a second. That’s a huge gap between the data quality most companies have by default and the data quality needed for successful AI. And that gap is directly affecting the performance and reliability of AI systems everywhere. Data teams know this all too well. For years, they’ve been on the front lines, battling to improve data quality because they’ve seen the data firsthand and understand the implications. Good data powers smart, reliable decisions; bad data undermines everything, affecting work across the board. Garbage in, garbage out. This has been true forever, but the stakes are even higher now. Without trustworthy data, AI can't make accurate predictions or decisions (and that applies to everything from customer support to healthcare diagnostics to financial forecasting). If our data quality is poor, we can’t trust it. And if we can’t trust the data, it’s useless — and our AI models are too. #dataengineering #genai #dataquality #ai