Garbage in, garbage out: Why data quality matters for AI

It’s all about data. Right now, everyone’s talking about AI, GenAI, and Agents. But here’s the reality: it all starts (and ends) with data. Garbage in → Garbage out. Always. Machine Learning + bad data = bad outcomes AI + bad data = flawed intelligence GenAI + bad data = misleading insights Agents + bad data = automation gone wrong At QuantiByteX, we believe: Before building “smart” systems, fix your data. Only then can AI truly create impact. #DataDriven #AI #GenAI #Agents #DataQuality #MachineLearning #QuantiByteX #Innovation #DataScience

Most teams use AI like a toy. Winners use it as infrastructure. AI isn’t just for content generation. #AI powers: ✅ Predictive lead scoring ✅ Deal health analysis ✅ Churn forecasting ✅ Fraud detection Don’t bolt on AI. Build AI-first stacks. #AI #RevOps #Stackvate

LLMs are smart, but they don’t know your business. Most companies excited about Generative AI run into the same two headaches 🙂↕️ : The model’s knowledge is frozen 🥶 in time. It doesn’t know your internal data. That’s where Retrieval-Augmented Generation (RAG) comes in. Instead of depending only on what the model was trained on, RAG lets it pull from your own documents, databases, or APIs in real time. The output isn’t just fluent—it’s grounded in facts that matter to your business. For companies 🏙️, that means more accurate insights, faster decisions, and better customer experiences—without giving up control of sensitive knowledge. Feels like RAG is less of a “nice-to-have” and more of the foundation for scaling AI responsibly. #GenerativeAI #RAG #AI #EnterpriseAI #DigitalTransformation

In the world of today with AI, what is different? What matters more now than before? You can make a query from an AI that can analyze your data in all sorts of new ways. We will trust the ability of AI to do more and more complex activities over time. But what will it work on? Your data has now become even more important. If you ask a question about something but the information about it is wrong or missing, then you will get either no answer or a wrong answer but you will think it is right because you are now trusting the AI. So I suggest that now is the time that we build tools to curate all of our data. We have always had this idea of curation, but I suggest that now is the time for this topic to shine. In this light, we should work and care about own own data, not just business data. #curation #contactmanager #people

GenAI lets you start even with messy data—that’s part of the magic. But turning pilots into production-grade results takes more than curiosity. Without clean, reliable data, AI can’t deliver consistent, trusted outcomes at scale. Foundations aren’t step five, they’re step one if you want AI to stick. 🔗 #AI #GenAI #DataStrategy #FirstTimeRight

Be Intelligent doesn’t mean slapping the AI label everywhere. How many times have we seen companies buy “intelligent” tools just because they were trendy… only to realize they didn’t actually improve anything? Real intelligence is something else: understanding which processes truly create value, using Data Science and Machine Learning to dig deep into the data, introducing AI only where it helps make better decisions and turn them into concrete actions. Being intelligent today isn’t about piling up technology. It’s about saying no to the unnecessary and yes to what delivers measurable results. #BeIntelligent #IBP #AI #DataScience #MachineLearning #Business

𝗧𝗵𝗲 𝗕𝘂𝗳𝗳𝗲𝘁 𝘃𝘀. 𝘁𝗵𝗲 𝗚𝗼𝘂𝗿𝗺𝗲𝘁 𝗣𝗹𝗮𝘁𝗲: 𝗥𝗲𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗧𝗣 In TP benchmarking, some believe 𝗺𝗼𝗿𝗲 𝗶𝘀 𝗯𝗲𝘁𝘁𝗲𝗿: with AI, large datasets seem to promise better AI training and accuracy in the selection of comparables, so the new trend and instinct is to collect and process as much as possible. Others insist 𝗹𝗲𝘀𝘀 𝗶𝘀 𝗺𝗼𝗿𝗲: cleaner, high-quality data delivers clearer insights, contextualized selection process and reduction in bias. The real question isn’t “𝘋𝘰 𝘸𝘦 𝘩𝘢𝘷𝘦 𝘦𝘯𝘰𝘶𝘨𝘩 𝘤𝘰𝘮𝘱𝘢𝘳𝘢𝘣𝘭𝘦𝘴?” but “𝘋𝘰 𝘸𝘦 𝘩𝘢𝘷𝘦 𝘵𝘩𝘦 𝘳𝘪𝘨𝘩𝘵 𝘰𝘯𝘦𝘴?” When benchmarking, what’s smarter: piling your plate at a good buffet or choosing the gourmet dish? 👉 What do you think? As TP professionals, do you lean towards the 𝗯𝘂𝗳𝗳𝗲𝘁 or the 𝗴𝗼𝘂𝗿𝗺𝗲𝘁 𝗽𝗹𝗮𝘁𝗲? Share your view in the comments and let’s compare notes! #TransferPricing #Benchmarking #InternationalTax #BigData #DataAnalytics #MachineLearning #AI #TPAnalytics

Don’t use a Ferrari to deliver pizza—unless speed truly matters! That line made me laugh and compelled me to write about today’s AI hype. Titles changing from “AI” to “Agentic AI,” but fundamentals still win. Before chasing the newest acronym, answer: What problem are we solving? What are the inputs and the required output? How often will it run? What response time is acceptable? What accuracy threshold is “good enough,” and what’s the cost of errors? Use the simplest approach that meets your targets—and upgrade only when data shows the added complexity pays back. So, Don’t follow the trend—follow the need😌 Reality is while I am writing this , I am learning about Agentic-AI , because who wants to be left behind 😂 Let me know what's your state? #aajkagyan #AI #AgenticAI #ProductThinking #PragmaticAI #MLOps #EnterpriseAI #BuildWhatMatters