How to Build a Data-Centric Organization for AI

Your AI isn’t hallucinating. It’s just accurately reflecting your messy data. "There is no AI - without IA." Seth Earley Your Information Architecture (IA) becomes your asset. Like Harari said: "𝙄𝙣𝙛𝙤𝙧𝙢𝙖𝙩𝙞𝙤𝙣 𝙞𝙨 𝙩𝙝𝙚 𝙖𝙩𝙩𝙚𝙢𝙥𝙩 𝙩𝙤 𝙧𝙚𝙛𝙡𝙚𝙘𝙩 𝙧𝙚𝙖𝙡𝙞𝙩𝙮, 𝙩𝙝𝙪𝙨 𝙩𝙝𝙚 𝙩𝙧𝙪𝙩𝙝." If you want your AI solution or Tool to add value to your business (which I think you do) - you need to make sure your model understands your business reality. Your data is that reality. Your IA is the foundation. Here are my 5 Pillars of Data Governance for making data your strategic asset: → 𝟭/ 𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻, 𝗔𝗰𝗾𝘂𝗶𝘀𝗶𝘁𝗶𝗼𝗻 & 𝗥𝗲𝘁𝗶𝗿𝗲𝗺𝗲𝗻𝘁 𝘏𝘰𝘸 𝘴𝘩𝘰𝘶𝘭𝘥 𝘥𝘢𝘵𝘢 𝘦𝘯𝘵𝘦𝘳 𝘢𝘯𝘥 𝘦𝘹𝘪𝘵 𝘺𝘰𝘶𝘳 𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯? - Define legal, ethical, and transparent acquisition channels. - Capture consent and regulatory compliance at source. - Set clear rules for retention and clean, timely deletion. → 𝟮/ 𝗗𝗮𝘁𝗮 𝗦𝘁𝗼𝗿𝗮𝗴𝗲, 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘏𝘰𝘸 𝘥𝘰 𝘸𝘦 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦, 𝘴𝘵𝘢𝘯𝘥𝘢𝘳𝘥𝘪𝘻𝘦, 𝘢𝘯𝘥 𝘶𝘴𝘦 𝘥𝘢𝘵𝘢 𝘦𝘧𝘧𝘦𝘤𝘵𝘪𝘷𝘦𝘭𝘺? - Data strategy that handles volume, velocity, and variety. - Ensure data marts are business-ready, FAIR, and MECE. - Centralize business rules, logic and KPIs as SSoT. → 𝟯/ 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆, 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 & 𝗦𝘁𝗲𝘄𝗮𝗿𝗱𝘀𝗵𝗶𝗽 𝘏𝘰𝘸 𝘥𝘰 𝘸𝘦 𝘦𝘯𝘴𝘶𝘳𝘦 𝘵𝘳𝘶𝘴𝘵 𝘢𝘯𝘥 𝘢𝘤𝘤𝘰𝘶𝘯𝘵𝘢𝘣𝘪𝘭𝘪𝘵𝘺? - Monitor data accuracy, completeness, and consistency. - Assign clear ownership and stewardship roles. - Establish accountability through data KPIs. → 𝟰/ 𝗗𝗮𝘁𝗮 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗔𝗰𝗰𝗲𝘀𝘀 & 𝗣𝗿𝗶𝘃𝗮𝗰𝘆 𝘏𝘰𝘸 𝘥𝘰 𝘸𝘦 𝘱𝘳𝘰𝘵𝘦𝘤𝘵 𝘰𝘶𝘳 𝘥𝘢𝘵𝘢 𝘢𝘯𝘥 𝘴𝘩𝘢𝘳𝘦 𝘪𝘵 𝘳𝘦𝘴𝘱𝘰𝘯𝘴𝘪𝘣𝘭𝘺? - Live data access via “right people, right data, right time”. - Apply anonymization and role-based access control. - Stay compliant (GDPR, HIPAA) and conduct audits. → 𝟱/ 𝗗𝗮𝘁𝗮 𝗨𝘀𝗮𝗴𝗲, 𝗘𝘁𝗵𝗶𝗰𝘀 & 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝘏𝘰𝘸 𝘥𝘰 𝘸𝘦 𝘢𝘱𝘱𝘭𝘺 𝘥𝘢𝘵𝘢 𝘪𝘯 𝘱𝘳𝘢𝘤𝘵𝘪𝘤𝘦? - Set clear AI ethics rules, and monitor bias and fairness. - Align with internal policies, laws, and social expectations. - Track data lineage and usage logs for transparency. On a scale of 1 to 10, what priority does Data Governance currently have in your company? 1-3: Data What? 4-7: We're trying, but it's messy. 8-10: It's a strategic pillar. Hi I'm Michael 👨💻 AI Strategist | Keynote Speaker | Executive Coach 👉 Follow to Gain Competitive Advantage through AI

Everyone celebrates the AI skyline. Almost no one wants to invest in the foundation. That foundation is data governance. Not as a policy exercise, but as an operating discipline. When governance is weak, AI looks impressive at first: fast demos clever outputs early wins Then reality shows up: inconsistent answers hidden bias teams arguing over whose data is “right” leaders quietly losing trust in the system That’s not an AI failure. It’s a foundation failure. Here’s the practical playbook I’ve helped organizations use to fix it: 1) Assign real ownership, not committees Every critical data domain needs a clear owner with actual decision rights. If no one owns the data, the model ends up guessing. → Leader question: Who is accountable when this data misleads a decision? 2) Define “good data” in business terms Quality only matters in context. Accuracy, timeliness, and completeness must be tied to how the data is used, not how it’s stored. → Leader question: What decision breaks if this data is wrong or late? 3) Design guardrails before scale Not every dataset should feed every model. Governance is about boundaries: what AI can see, what it can influence, what it can automate. → Leader question: Where must humans stay in the loop, no matter how good the model gets? 4) Treat data pipelines like production systems Monitoring, lineage, versioning, and rollback aren’t optional. If you can’t trace an output back to its source, you can’t trust it. → Leader question: Could we explain this answer six months from now? 5) Build governance where work actually happens Policies on slides don’t scale. Embedded checks in workflows do. → Leader question: Is governance preventing rework later, or just slowing teams down today? AI doesn’t fail because it’s too advanced. It fails because the groundwork was never finished. If you want a skyline that lasts, build where no one is looking. 📌 Save this if AI reliability is now a leadership issue 🔁 Repost to shift the conversation from demos to durability 👤 Follow Gabriel Millien for grounded insight on Enterprise AI and transformation

1/ You can't bolt AI onto chaos. In biotech, if your data is a mess, your AI won't save you. Build the data strategy first. Here's how. 2/ Real-world data isn't AI-ready. Without structure, governance, and clarity, it’s noise. AI needs fuel. And that fuel is clean data. 3/ At a biotech startup, we learned this the hard way. Here’s what I took from a panel and years of practice. The essentials: Governance Management Metadata Team dynamics Tool choices 4/ Start with data governance. Access control. Versioning. Basic security. Do it early. Fixing leaks later costs 10x more. 5/ Cloud is great—but only if you use it right. Define who sees what. Set folder rules. Use Google or AWS security playbooks. They’re free and solid. 6/ Next up: Data management. Chaos begins with "just toss it in the drive." Don’t. Structure folders. Standardize metadata. Make it findable again. 7/ Spreadsheets are fine—until they aren’t. Start smart: “Female” not “F” No weird characters Train your wet lab team. Seriously. 8/ You’ll accrue technical debt. That’s fine. If someone curses your naming scheme 5 years from now, congrats. You survived. 9/ But please—do the basics right. Read this paper. Print it. Frame it. “Data Organization in Spreadsheets” https://lnkd.in/ekEr6G28 10/ Public data is cheap. In-house data is gold. Use a LIMS to track it. Know where each sample came from. Know what each file means. 11/ Aim for FAIR: Findable Accessible Interoperable Reusable Even doing 70% right will put you ahead. 12/ Keep it simple: Internal/ ├── RNAseq/ ├── WGS/ Public/ ├── TCGA/ ├── ENCODE/ Each with a README. Just say what the data is and where it came from. 13/ README template: When was this data generated? What experiment? Where’s the preprocessing code? Who should I ask? 14/ You’ll get pressure to move fast. Investors want plots, not pipelines. But for big projects—do it right. Rushed analysis rots from the inside. 15/ Custom tools give you power. Commercial tools give you speed. Pick based on your team’s skill—not vendor marketing decks. 16/ And finally—people. Your computational and wet lab teams must sit together. Talk daily. Argue weekly. Trust always. 17/ Example: Bioinformaticians prep the Seurat object. Wet lab explores it in Shiny. This builds insight AND independence. 18/ Good data strategy isn't sexy. But it's the foundation. It makes your R&D faster, your AI smarter, and your team happier. 19/ Startups die by disorganized data. Don’t be one of them. Fix your foundation now—before the chaos scales. 20/ Have you seen data disasters in biotech? How did you fix it—or not? Reply and let’s trade war stories. I hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter chatomics to learn bioinformatics https://lnkd.in/erw83Svn

Too many AI strategies are being built around the technology instead of the business challenges they should solve. The real value of AI comes when it is directly tied to your goals. I have arrived at seven lessons on how to align your AI strategy directly with your business goals: 1. Start with the "why," not the "what." Before discussing models or tools, ask what business problem you need to solve. It could be speeding up product development, or cutting operational costs. Let that answer be your guide. 2. Think in terms of business outcomes. Measure AI success by its impact on metrics like revenue growth or employee productivity not by technical accuracy. 3. Build a cross-functional team. AI can't live solely in the IT department. Include leaders from all relevant departments from day one to ensure the strategy serves the entire business. 4. Prioritize quick wins to build momentum. Identify a few small, high-impact projects that can deliver results quickly. This builds organizational confidence and makes people ready to take on larger initiatives. 5. Invest in data foundations. The best AI strategy will fail without clean and well-governed data. A disciplined approach to data quality is non-negotiable. 6. Focus on change management. Technology is the easy part. Prepare your people for new workflows and equip them with the skills to work alongside AI effectively. 7. Create a feedback loop. An AI strategy is not a one-time plan. Continuously gather feedback from users and analyze performance data to adapt and refine your approach. The goal is to make AI a part of how you achieve your objectives, not a separate project. #AIStrategy #BusinessGoals #DigitalTransformation #Leadership #ArtificialIntelligence

Struggling to build a data foundation that helps you deploy AI models at scale? Regulation can help. Too often in my professional life I have heard the old adage that regulation is a blocker to innovation. In my experience, what actually impedes on innovation is uncertainty; specifically when relevant rules are missing, unclear, or poorly aligned. No doubt this was true for both the GDPR and AI Act, at least in the beginning. What is often overlooked, however, is that these laws also provide notable benefits: among others, guiding organizations how to approach data-driven innovation in a structured and sensible way. ➡️ How GDPR supports data readiness Art. 5 GDPR requires, e.g., purpose limitation, data minimization, accuracy, integrity, confidentiality, and accountability. Organizations must decide which personal data they need, why, and who is responsible. This amounts not only to a responsible but also strategic approach to handling data - and not just personal data. ➡️ How the AI Act builds on this Art. 6 AI Act links an AI system’s obligations to its intended use and impact on people’s health, safety, and fundamental rights. Art. 10 then mandates data governance requirements for high-risk AI systems, e.g., that training, validation, and test datasets are relevant, representative, complete, and documented. Providers must implement measures covering provenance, cleaning, annotation, assumptions, gap analysis, bias detection, and ongoing monitoring. These rules offer a practical blueprint for AI-ready data. ➡️ Why this matters for AI strategy A strong data foundation improves model performance, but also reveals when AI is not the right tool. A rules-based system might achieve the same outcome with less risk and less complexity. The decision when not to use AI should be part of any good AI strategy too. ➡️ What organizations should do ✅ Define the purpose of processing: What are you trying to achieve? How does this improve the status quo? What tradeoffs do you need to consider? ✅ Use Art. 5 GDPR to decide what personal data you need to achieve your processing purpose in the least intrusive way. ✅ Evaluate whether you need AI - or if a rules-based system suffices. ✅ If you do need AI, leverage the AI Act’s Art. 6 intended use test and Art. 10 data governance rules as a readiness checklist. In particular, if it looks like you would be developing or deploying a high-risk AI system, make sure you have the necessary resources to do so. ✅ Create clear roles and responsibilities along the lifecycle of data processing to continuously ensure the quality, consistency, and reliability of data. ✅ Delete data when you no longer need it. This not only saves resources, but minimizes your compliance exposure. Too often, regulation is framed as a constraint. In reality, it can help organizations plan and implement data projects in a strategic and purposeful way. #DataReadiness #AIGovernance #GDPR #AIAct #ResponsibleAI

Ambition sparks AI transformation, but readiness is what sustains it. The real differentiator is how ready your organization is in data, process, and leadership to absorb and scale what works.   The Frontier Playbook focuses on three essentials for building that foundation:   💡 Make your data and workflows AI-ready. AI transformation starts with clarity: knowing the value you’re driving and ensuring the data behind it is governed, connected, and accessible. Many organizations take a two-speed approach, modernizing legacy systems while capturing quick wins where data is already strong. Both paths matter.   💡 Invest in process excellence and change management. Transformation isn’t plug and play. It requires rigor, clear documentation, measurable workflows, and the discipline to embed AI into how work actually happens. Strong process leadership helps teams adopt new ways of working and sustain results.   💡 Build leadership and team readiness. Technology alone doesn’t make an enterprise AI-ready. Managers and teams need the capability to adapt how they work, integrate AI tools responsibly, and scale proven approaches. This operational readiness turns transformation from a one-time effort into a continuous advantage.   When the foundation is strong, innovation doesn’t just happen. It accelerates.   👉 How is your organization preparing its foundation for AI at scale?

42% of AI projects fail due to “poor data readiness.” And, it has nothing to do with data cleanliness. Snowflake's $250M acquisition of Crunchy Data, Databricks' $1B purchase of Neon, and Salesforce's $8B deal for Informatica - all within the last month - tell an important story. Tech giants aren't buying storage. They're buying understanding. I've seen this pattern repeatedly: organizations think clean data + RAG + LLM = intelligent chatbot. But their systems can't distinguish between revenue and sales, don't understand how to roll up employee expenses under company divisions and departments, and can't aggregate product purchases across client subsidiaries. The data is spotless. The context is invisible. So what's the fix? Before building any AI use case, understanding the meaning of your structured data is just as important as making sure it's clean: ☑️ Conduct comprehensive data analysis for each dataset: Map what's clean, what's missing, and identify gaps in your data. Consider which types of data, relationships, and descriptions are most important for your specific needs. You don’t need it all. ☑️ Establish data definitions and labels for each dataset: Define not just what each data element means, but how it's actually used in your business processes. Add explanations of table and column names, expected values, and business context to enhance understanding. ☑️ Build your ontology for the specific use case or dataset: Create the knowledge scaffolding by defining entities (e.g. customers, products, etc.), establishing hierarchies (e.g. corporate structures, escalation paths), and mapping relationships and hierarchies across entities. Example: "Employees belong to departments, departments roll up to divisions." ☑️ Create a knowledge graph: Populate your ontology with actual data instances so AI can work with real examples. Example: "John Smith belongs to Marketing Department, which rolls up to Sales Division." The lesson from these major acquisitions? AI without business context across your data simply doesn't work for AI to succeed. More about the acquisitions: https://lnkd.in/gTVJGqym #AIDataReadiness #DataContext #EnterpriseAI

😬 Many companies rush to adopt AI-driven solutions but fail to address the fundamental issue of data management first. Few organizations conduct proper data audits, leaving them in the dark about: 🤔 Where their data is stored (on-prem, cloud, hybrid environments, etc.). 🤔 Who owns the data (departments, vendors, or even external partners). 🤔 Which data needs to be archived or destroyed (outdated or redundant data that unnecessarily increases storage costs). 🤔 What new data should be collected to better inform decisions and create valuable AI-driven products. Ignoring these steps leads to inefficiencies, higher costs, and poor outcomes when implementing AI. Data storage isn't free, and bad or incomplete data makes AI models useless. Companies must treat data as a business-critical asset, knowing it’s the foundation for meaningful analysis and innovation. To address these gaps, companies can take the following steps: ✅ Conduct Data Audits Across Departments 💡 Create data and system audit checklists for every centralized and decentralized business unit. (Identify what data each department collects, where it’s stored, and who has access to it.) ✅ Evaluate the lifecycle of your data; what should be archived, what should be deleted, and what is still valuable? ✅ Align Data Collection with Business Goals Analyze business metrics and prioritize the questions you want answered. For example: 💡 Increase employee retention? Collect and store working condition surveys, exit interview data, and performance metrics to establish a baseline and identify trends. ✅ Build a Centralized Data Inventory and Ownership Map 💡 Use tools like data catalogs or metadata management systems to centralize your data inventory. 💡 Assign clear ownership to datasets so it’s easier to track responsibilities and prevent siloed information. ✅ Audit Tools, Systems, and Processes 💡 Review the tools and platforms your organization uses. Are they integrated? Are they redundant? 💡 Audit automation systems, CRMs, and databases to ensure they’re being used efficiently and securely. ✅ Establish Data Governance Policies 💡 Create guidelines for data collection, access, storage, and destruction. 💡 Ensure compliance with data privacy laws such as GDPR, CCPA, etc. 💡 Regularly review and update these policies as business needs and regulations evolve. ✅ Invest in Data Quality Before AI 💡 Use data cleaning tools to remove duplicates, handle missing values, and standardize formats. 💡 Test for biases in your datasets to ensure fairness when creating AI models. Businesses that understand their data can create smarter AI products, streamline operations, and ultimately drive better outcomes. Repost ♻️ #learningwithjelly #datagovernance #dataaudits #data #ai

🔄 Building a Practical Data & AI Strategy: A 6-Stage Roadmap After helping organizations implement AI, I've noticed a pattern: those who succeed focus on building strong foundations before rushing to deploy AI models. Here's a practical roadmap I've found effective: 1. Start with the Basics First, take a hard look at your data infrastructure. Are your data silos causing headaches? Is your security robust? Tools like Azure Purview comes in handy for understanding the data landscape. 2. Get Leadership On Board This is crucial - I've seen brilliant technical implementations fail without executive buy-in. Focus on concrete ROI metrics and compliance frameworks. Remember, leaders need to understand the value, not just the technology. never 3. Build Your Data Foundation Think of this as building a house - you need solid ground. I recommend starting with a hybrid approach: keep sensitive data on-prem with tools like MinIO, while leveraging cloud solutions like Azure Data Lake for scalability. 4. Set Up Your AI Platform Here's where it gets exciting. Tools like Red Hat OpenShift AI and Azure ML have made it much easier to build and deploy models across hybrid environments. The key is ensuring your models are containerized for flexibility. 5. Monitor & Scale Once you're live, keep a close eye on performance. I've found tools like Microsoft's Responsible AI Dashboards invaluable for tracking model drift and ensuring fairness. 6. Never Stop Evolving The AI landscape changes fast. Stay ahead by experimenting with edge AI and exploring synthetic data generation. Your strategy should grow with your business. Remember, this isn't a race - it's a journey. Take time to build strong foundations, and the results will follow. For details refer my blog link in comments. What stage is your organization at? #DataStrategy #ArtificialIntelligence

As a Global Capability Center(GCC) Leader, the Onus Is on You—Will You Drive AI Transformation or Get Left Behind? Most GCCs were not designed with AI at their core. Yet, AI is reshaping industries at an unprecedented pace. If your GCC remains focused on traditional service delivery, it risks becoming obsolete. The responsibility to drive this transformation does not sit with IT teams or innovation labs alone—it starts with you. As a GCC leader, you must push beyond cost efficiencies and position your center as a strategic AI hub that delivers business impact. How to Transform an Existing GCC into an AI-Native GCC This shift requires clear, measurable objectives. Here are five critical OKRs (Objectives & Key Results) to guide your AI transformation. 1. Embed AI in Core Business Processes Objective: Move beyond AI pilots and integrate AI into everyday decision-making. Key Results: • Automate 20 percent or more of manual workflows within 12 months. • Deploy AI-powered analytics in at least three business-critical functions. • Reduce operational decision-making time by 30 percent using AI insights. 2. Reskill and Upskill Talent for AI Readiness Objective: Develop an AI-fluent workforce that can build, deploy, and manage AI solutions. Key Results: • Train 100 percent of employees on AI fundamentals. • Upskill at least 30 percent of engineers in MLOps and GenAI development. • Establish an internal AI guild to drive AI innovation and best practices. 3. Build AI Infrastructure and MLOps Capabilities Objective: Create a scalable AI backbone for your organization. Key Results: • Implement MLOps pipelines to reduce AI model deployment time by 50 percent. • Establish a centralized AI data lake for enterprise-wide AI applications. • Deploy at least five AI use cases in production over the next year. 4. Shift from AI as an Experiment to AI as a Business Strategy Objective: Ensure AI initiatives drive measurable business value. Key Results: • Ensure 50 percent of AI projects are directly linked to revenue growth or cost savings. • Develop an AI governance framework to ensure responsible AI use. • Integrate AI-driven customer experience enhancements in at least three markets. 5. Change the Operating Model: From Service Delivery to Co-Ownership Objective: Position the GCC as a leader in AI-driven transformation, not just an execution arm. Key Results: • Rebrand the GCC internally as a center of AI-driven innovation. • Secure C-level sponsorship for AI-driven initiatives. • Establish at least three AI innovation partnerships with startups or universities. The question is not whether AI will reshape your GCC. It will. The time to act is now. Are you ready to drive the AI transformation? Let’s discuss how to accelerate your GCC’s AI journey. Zinnov Mohammed Faraz Khan Namita Dipanwita ieswariya Mohammad Mujahid Karthik Komal Hani Amita Rohit Amaresh