How Data Science Drives AI Development

I’ve worked in data science for a decade, and I’ve seen the field evolve a lot. But nothing compares to what’s happened in the last three. Generative AI has completely reshaped our workflows. What used to take weeks of manual data prep and iteration now happens in days or even hours. The role of a data scientist is shifting fast: less about repetitive coding, more about designing intelligent workflows that solve real business problems. I recently came across Google's new Practical Guide to Data Science, and here are a few insights that stood out for me: ➝ The agentic shift Most of a data scientist’s day used to be cleaning data, tuning models, and writing the same pipelines again and again. Now AI agents automate those parts. The value we bring is moving to analysis, interpretation, and driving business outcomes. ➝ Multimodal data For years, our work was limited to structured tables. But most enterprise data is unstructured like images, PDFs, audio, and free text. With BigQuery, you can now analyze this directly with SQL. That means questions that used to be impossible, like combining sales data with call transcripts, are finally within reach. ➝ Blending external intelligence with enterprise data Foundation models bring real-world knowledge into the enterprise stack. Instead of writing rules for every scenario, you can ask nuanced questions like: Which of our products show high satisfaction based on quality? This type of reasoning used to take months of manual analysis. ➝ AI as a feature engineering engine Instead of just running basic sentiment analysis, you can extract structured insights at scale. For example, pulling out sentiment specifically around “battery life” or “user interface” and joining it with sales data. Raw text turns into powerful features that drive models. ➝ In-place model development Moving data around used to be the bottleneck. With BigQuery ML, you can now train and deploy models right where the data lives. Teams have seen deployment times cut by 10x, shifting the focus from infrastructure to speed of insight. ➝ Vector embeddings and semantic search Vector search used to mean adding another system. Now it’s built into BigQuery. That means semantic product discovery, document retrieval, and multimodal analysis all within your data warehouse. Data scientists role is changing, and now it's less about syntax, more about strategy. Less about writing every line of code, more about designing AI-powered workflows. If you want to dive deeper, I recommend checking out the full guide. It’s packed with practical examples that show just how much the landscape has shifted

𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘁𝗵𝗮𝘁 𝗔𝗜 𝗶𝘀 𝗮 𝘀𝘁𝗿𝗮𝗶𝗴𝗵𝘁 𝗽𝗮𝘁𝗵 𝗳𝗿𝗼𝗺 𝗱𝗮𝘁𝗮 𝘁𝗼 𝘃𝗮𝗹𝘂𝗲. The assumption: 𝗗𝗮𝘁𝗮 → 𝗔I → 𝗩𝗮𝗹𝘂𝗲 But in real-world enterprise settings, the process is significantly more complex, requiring multiple layers of engineering, science, and governance. Here’s what it actually takes: 𝗗𝗮𝘁𝗮 • Begins with selection, sourcing, and synthesis. The quality, consistency, and context of the data directly impact the model’s performance. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 • 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Exploration, cleaning, normalization, and feature engineering are critical before modeling begins. These steps form the foundation of every AI workflow. • 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: This includes model selection, training, evaluation, and tuning. Without rigorous evaluation, even the best algorithms will fail to generalize. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 • Getting models into production requires deployment, monitoring, and retraining. This is where many teams struggle—moving from prototype to production-grade systems that scale. 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀 • Legal regulations, ethical transparency, historical bias, and security concerns aren’t optional. They shape architecture, workflows, and responsibilities from the ground up. 𝗔𝗜 𝗶𝘀 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰. 𝗜𝘁’𝘀 𝗮𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲 𝘄𝗶𝘁𝗵 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗿𝗶𝗴𝗼𝗿 𝗮𝗻𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗮𝘁𝘂𝗿𝗶𝘁𝘆. Understanding this distinction is the first step toward building AI systems that are responsible, sustainable, and capable of delivering long-term value.

Most companies think they’re doing data science. The truth? Without AI-first workflows, they’re just doing data cleanup. If you use data at work, this new AI-first workflow will transform how you lead, decide, and deliver insights. I’ve spent the last several years building world-class conversational analytics platforms for some of the world’s top businesses, platforms that support billions of dollars in decisions every year. And I can tell you this: The Google Cloud Data Science Guide is groundbreaking. Why? Because it finally shows how to run data science in the age of AI, not in theory, but in practice. Here are 5 game-changing insights from the guide: ⨠ AI removes the grunt work. Agents now automate cleaning, feature engineering, and pipeline building so you stay in flow. ⨠ Notebooks go AI-first. Colab Enterprise and Vertex AI Workbench let you jump from SQL to Python to Spark seamlessly, with AI drafting code and fixing errors. ⨠ Unstructured data is unlocked. Images, audio, contracts, all queryable in BigQuery like regular tables. Multimodal analysis is now standard. ⨠ Modeling lives in the warehouse. BigQuery ML lets you train, evaluate, and deploy ML models with SQL, no messy data movement. ⨠ MLOps is finally integrated. From feature store to model registry to deployment, Vertex AI and BigQuery are stitched into a single workflow. This isn’t the future. This is available today. The future of analytics belongs to those who can blend business vision with cloud-native, AI-powered workflows. 👉 Download the guide. Study it. Build with it. It will redefine how you think about data science. ___________________________________________ 👋 I’m Amit Rawal, Director of AI-led Business Transformation at Google Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered. 🧠 Join my free masterclass: Design Your Life with AI Learn how to work smarter, live longer, and grow richer, with AI as your co-pilot. ♻️ Repost if you believe AI isn’t about replacing us… It’s about retraining us to think better.

Everyone’s talking about how AI is replacing data scientists. But few are talking about what’s actually changing and what’s not. → AI isn’t eliminating data roles. It’s compressing them. What used to take five tools and ten scripts can now be done in one prompt. That doesn’t mean less work, it means the work is shifting toward context, problem-solving, and interpretation. → Analysts who once focused on reporting are now becoming decision enablers. They need to connect outputs with business strategy and not just visualize data. → Engineers who only built pipelines are now curating data for models. The skill isn’t just knowing SQL anymore. It’s understanding what makes data trainable. → And data scientists? Now our job is about evaluating reasoning, ensuring explainability, and aligning outputs with business reality. The truth is: AI doesn’t remove the need for human reasoning, it magnifies it. Those who thrive in this new landscape are the ones who combine technical depth, clarity of thought, and the ability to communicate complexity. If you’re studying data science or starting your career, this is your competitive edge. AI can automate many things but it still can’t replace judgment. #DataScience #AI #MachineLearning #CareerDevelopment #FutureOfWork #Analytics

When Data Science Isn’t About Algorithms Everyone thinks data science is about picking the right model or tuning hyperparameters. I wish it were that simple. Here’s what my last week on a project looked like: ➡️ Three years of historical data, great for ML training but half the features I actually needed were never logged. I had to engineer proxies, validate assumptions with different teams, and test if the signal was even usable. ➡️ All the right features, clean and well-defined but only 6 months of data, barely enough for stable patterns. I had to constantly check if models would generalize. ➡️ A column looked consistent until I realized the business made some unit conversions. I had to decide whether to change all other factors, exclude, or split the dataset. ➡️ Sometimes, the tables make no sense at all, like trying to make a tire with 100 million interacting variables. You can’t just apply code; you need context, domain expertise, and yes, sometimes a bit of physics knowledge to understand what the numbers really mean! This is where data science stops being “apply an algorithm” and starts being judgment, intuition, and curiosity. ✅ What I actually did: 👉 Spent hours talking to the business to understand which numbers actually matter and learn every small thing associated with it. 👉 Created a reference dataset multiple teams could rely on. 👉Tested models on multiple versions of the data to see which assumptions held. 👉Documented every decision because in messy real-world data, the process matters more than the model. AI, automation, or fancy libraries can help with speed, but they cannot replace reasoning, context, and judgment. That’s the part that separates “looks like data science” from real impact. 💡 Real data work is messy, iterative, and deeply human. If you aren’t comfortable making decisions with imperfect data, you’re missing the point of the craft. Anyone else spend days untangling “perfect-looking” datasets that refuse to cooperate? #DataScience #MachineLearning #AI #Analytics #RealWorldData #MLProjects #DataEngineering #DataProblems #AIEngineering #DataScienceLife #DataStorytelling #BusinessImpact

Have you ever tried to bake a cake without any ingredients? 🍰 That's what AI is like without data. You can have the best recipe (algorithm) in the world, but if the pantry is empty, you're not getting a cake. You're just getting a very complicated stare from an oven. 😂 Why Data is the Unsung Hero of the AI Revolution 🦸♂️ : The Stats Don't Lie 📊 A recent study by Gartner found that 85% of AI projects fail due to a lack of quality data. Think about that for a second. We're investing billions into AI, but it's like buying a Formula 1 car and then trying to fill the tank with water. 💧 It just won't work! This isn't a new-age problem; it's a fundamental truth: garbage in, garbage out. The Need for a Dynamic Duo 🤝 👉 AI and data aren't just partners; they're the ultimate power couple. Data is the raw material, the fuel, and the history book for AI. AI, in turn, is the brilliant chef, the expert driver, and the insightful analyst. 👉 Data provides the what, and AI figures out the how and the why. Without data, AI is just a sophisticated calculator with no numbers to crunch. Without AI, data is just a vast, dusty library of books that no one has time to read. 📚 Three Ways Data and AI Collaborate 💡 💪 Feeding the Brain (Training): This is the most common form of collaboration. AI models, like large language models or image recognition systems, are trained on massive datasets. The quality, volume, and diversity of this data directly influence the model's performance. 🤫 Continuous Improvement (Feedback Loops): This is where the magic really happens! Once an AI model is in production, the new data it encounters helps it learn and adapt. 🤵♂️The "Data-First" Approach (Automated Pipelines): Think of this as the ultimate collaboration where data pipelines are built specifically to serve AI models. These pipelines clean, transform, and deliver high-quality, real-time data directly to the AI. Key Takeaways for Your Organization 🔑 For organizations and users alike, the lesson is clear: don't just focus on the shiny new AI toy. Focus on the ingredients—your data. 👉 Invest in Your Data Foundation: Prioritize data quality, governance, and accessibility. A messy data lake is a swamp, not a source of power. 🏞️ 👉 Foster a Collaborative Culture: Break down silos between data teams and AI/ML teams. They need to be in constant communication. 👉 Start Small, Think Big: Begin with a pilot project where you can demonstrate the value of this collaboration. Prove the concept, then scale. The figure is the source, the AI is the tool. The quality of the final product—the figurine and the generated image—is directly tied to the quality of the initial data. Remember to give your AI a solid foundation, and it will build something amazing for you! 🏗️ So, go on, show your data some love. Your AI will thank you for it! ❤️ Follow Omkar Sawant for more. #Data #Analytics #AI #DigitalTransformation #Innovation #GoogleCloud #Google #LifeAtGoogle

I chatted with Khalifeh, 𝘋𝘪𝘳𝘦𝘤𝘵𝘰𝘳 𝘰𝘧 𝘋𝘢𝘵𝘢 𝘚𝘤𝘪𝘦𝘯𝘤𝘦, at Google. Here's how AI is transforming the Data Science industry: 𝘛𝘩𝘦𝘴𝘦 𝘢𝘳𝘦 𝘒𝘩𝘢𝘭𝘪𝘧𝘦𝘩'𝘴 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 𝘢𝘯𝘥 𝘰𝘱𝘪𝘯𝘪𝘰𝘯𝘴, 𝘢𝘯𝘥 𝘥𝘰𝘯'𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵 𝘎𝘰𝘰𝘨𝘭𝘦'𝘴 𝘰𝘧𝘧𝘪𝘤𝘪𝘢𝘭 𝘷𝘪𝘦𝘸𝘴. #1 𝗧𝗵𝗲 𝗔𝗜-𝗳𝗶𝗿𝘀𝘁 𝗺𝗶𝗻𝗱𝘀𝗲𝘁 𝗳𝗼𝗿 𝗱𝗮𝘁𝗮 𝘁𝗲𝗮𝗺𝘀 There's a difference between being AI-assisted and being AI-first. 1. AI-assisted means you're using AI tools in your existing workflows. 2. AI-first means you're designing, implementing, and evaluating AI workflows from scratch. And Data Science teams naturally progress from AI assistance to implementation. #2 𝗧𝗵𝗲 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗲𝘃𝗼𝗹𝘃𝗶𝗻𝗴.. 𝘁𝗼𝘄𝗮𝗿𝗱𝘀 𝘀𝗼𝗳𝘁 𝘀𝗸𝗶𝗹𝗹𝘀 Coding is no longer your competitive edge. AI can do that now. Data scientists and engineers are shifting from code writers to strategic thinkers. Your competitive edge is being able to use the output of that code to drive business strategy. Data Scientists are now architects, not a coder. And there's a clear movement towards softer skills: • storytelling • creative thinking • strategic thinking #3 𝗗𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 𝗮𝗿𝗲 𝘄𝗲𝗹𝗹-𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻𝗲𝗱 𝘁𝗼 𝗹𝗲𝗮𝗱 𝗔𝗜 Data science is gaining more influence, not less. Why? Because there's an AI knowledge gap between technical teams and business stakeholders. And Data Scientists can bridge that gap, because we understand both the technical side of AI and the business side. This combination is rare. Data scientists can see where AI fits into a business process, understand the data it needs, evaluate whether it's actually working, and communicate the results. #4 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝗮𝗿𝗲 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗳𝗼𝗿 𝘂𝗽𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝘁𝗲𝗮𝗺𝘀 𝗶𝗻𝘁𝗼 𝗯𝗲𝗶𝗻𝗴 𝗔𝗜-𝗳𝗶𝗿𝘀𝘁 Khalifeh's team has been able to upskill quickly & effectively into become AI-first. Here is his advice on doing the same ↴ 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴. Managers go first and lead by example. They demonstrate AI usage in their own work: prep docs, 1:1 notes, agents for management tasks. 𝗣𝗿𝗼𝘁𝗲𝗰𝘁𝗲𝗱 𝗰𝗮𝗹𝗲𝗻𝗱𝗮𝗿 𝘁𝗶𝗺𝗲. Weekly and monthly blocked time on the entire team's calendar, managers and ICs, for learning and experimenting with AI tools. 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆. Team members are expected to their managers how they used their protected time. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝘀𝗵𝗮𝗿𝗶𝗻𝗴. Monthly sessions where team members show creative ways they've used AI in their day-to-day work. 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗮𝘁𝘁𝗲𝗻𝗱𝗮𝗻𝗰𝗲. Every team member gets budget to attend at least one AI conference per year, during business hours. Then they share what they learned with the team. TLDR: ↳ Your role is changing. But you're in a good spot. ↳ Focus on developing your soft skills ↳ Lead your team on AI design + implementation ♻️ Repost if you found this useful!

Artificial Intelligence is not a one-way activity like many think. It's a combination of many processes that involve much expertise. The process might be even more complicated in the companies as the requirements are bigger. But, the requirements have opened many new opportunities for people to gain employment. Why does it bring new opportunities? To bring value from AI, there are many aspects to cover: 🔍 Data: We need the AI- from selection sourcing to synthesis. Quality and relevant data are the roots of effective AI. Maintaining the data quality need someone who understands the business as well. 🔧 Data Engineering: Data must be available and ready from the source, which the Data Engineer helps. We cleaned, normalized, scaled, and explored the data for a better model. Feature engineering also transforms data into a format that algorithms can work with. 🤖 Modeling: Where data science shines and where the AI is created. Model selection, training, evaluation, and tuning are iterative processes that require deep expertise. ⚙️ Operationalizing: Bringing AI into the production. This involves registration, deployment, monitoring, and periodic retraining to keep the model up-to-date. Machine learning engineers and MLOps experts are required to ensure a smooth process. Don't forget about the constraints: ⚖️ Legal: Compliance with laws and regulations is non-negotiable, which is helped by the legal team. 📜 Ethical/Transparency: AI should be fair and its decisions, explainable. Function control such as data privacy is here to make sure about that. 🎓 Historical (Bias): We must constantly be wary of and correct biases in data. The business team must work together to ensure the bias isn't there. 🔒 Security: Protecting data and AI processes from threats. Data security is there to help. As you can see, AI is teamwork that is hard for one person to succeed in the company. Data Science is only one part of the team, and collaboration is the only thing that provides value. ——————— You don't want to miss #python data tips + #datascience and #machinelearning knowledge + #python. Follow Cornellius Yudha W. and press the bell 🔔 to learn together. ———————