Data-Driven Decision Making

𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲. Because most people explain it from the inside out: policies, councils, standards, stewardship. But the business does not buy any of that. The business buys outcomes: → trustworthy KPIs → vendor and partner data you can actually use → faster financial close → fewer reporting escalations → smoother M&A integration → AI you can deploy without creating risk debt Most AI programs fail for boring reasons: nobody owns the data, quality is unknown, access is messy, accountability is missing. 𝗦𝗼 𝗹𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁. 𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗳𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗴𝘀: → ownership → quality → access → accountability 𝗔𝗻𝗱 𝗶𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘃𝗲𝗿𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗶𝗻 𝟰 𝗹𝗮𝘆𝗲𝗿𝘀: 1. Data Products (what the business consumes) → a named dataset with an owner and SLA → clear definitions + metric logic → documented inputs/outputs and intended use → discoverable in a catalog → versioned so changes don’t break reporting 2. Data Management (how products stay reliable) → quality rules + monitoring (freshness, completeness, accuracy) → lineage (where it came from, where it’s used) → master/reference data alignment → metadata management (business + technical) → access controls and retention rules 3. Data Governance (who decides, who is accountable) → data ownership model (domain owners, stewards) → decision rights: who can change KPI definitions, thresholds, and sources → issue management: triage, escalation paths, resolution SLAs → policy enforcement: what’s mandatory vs optional → risk and compliance alignment (auditability, approvals) 4. Data Operating Model (how you scale across the enterprise) → domain-based setup (data mesh or not, but clear domains) → operating cadence: weekly issue review, monthly KPI governance, quarterly standards → stewardship at scale (roles, capacity, incentives) → cross-domain decision-making for shared metrics → enablement: templates, playbooks, tooling support If you want to start fast: Pick the 10 metrics that run the business. Assign an owner. Define decision rights + escalation. Then build the data products around them. ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗮𝘀 𝗔𝗜 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

From Blueprint to Battlefield: Reinventing Enterprise Architecture for Smart Manufacturing Agility
   Core Principle: Transition from a static, process-centric EA to a cognitive, data-driven, and ecosystem-integrated architecture that enables autonomous decision-making, hyper-agility, and self-optimizing production systems.   To support a future-ready manufacturing model, the EA must evolve across 10 foundational shifts — from static control to dynamic orchestration.   Step 1: Embed “AI-First” Design in Architecture Action: - Replace siloed automation with AI agents that orchestrate workflows across IT, OT, and supply chains. - Example: A semiconductor fab replaced PLC-based logic with AI agents that dynamically adjust wafer production parameters (temperature, pressure) in real time, reducing defects by 22%.   Shift: From rule-based automation → self-learning systems.   Step 2: Build a Federated Data Mesh Action: - Dismantle centralized data lakes: Deploy domain-specific data products (e.g., machine health, energy consumption) owned by cross-functional teams. - Example: An aerospace manufacturer created a “Quality Data Product” combining IoT sensor data (CNC machines) and supplier QC reports, cutting rework by 35%.   Shift: From centralized data ownership → decentralized, domain-driven data ecosystems.   Step 3: Adopt Composable Architecture Action: - Modularize legacy MES/ERP: Break monolithic systems into microservices (e.g., “inventory optimization” as a standalone service). - Example: A tire manufacturer decoupled its scheduling system into API-driven modules, enabling real-time rescheduling during rubber supply shortages.   Shift: From rigid, monolithic systems → plug-and-play “Lego blocks”.   Step 4: Enable Edge-to-Cloud Continuum Action: - Process latency-critical tasks (e.g., robotic vision) at the edge to optimize response times and reduce data gravity. - Example: A heavy machinery company used edge AI to inspect welds in 50ms (vs. 2s with cloud), avoiding $8M/year in recall costs.   Shift: From cloud-centric → edge intelligence with hybrid governance.   Step 5: Create a “Living” Digital Twin Ecosystem Action: - Integrate physics-based models with live IoT/ERP data to simulate, predict, and prescribe actions. - Example: A chemical plant’s digital twin autonomously adjusted reactor conditions using weather + demand forecasts, boosting yield by 18%.   Shift: From descriptive dashboards → prescriptive, closed-loop twins.   Step 6: Implement Autonomous Governance Action: - Embed compliance into architecture using blockchain and smart contracts for trustless, audit-ready execution. - Example: A EV battery supplier enforced ethical mining by embedding IoT/blockchain traceability into its EA, resolving 95% of audit queries instantly.   Shift: From manual audits → machine-executable policies.   Continue in 1st and 2nd comments.   Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: Gartner

Robotics has a data scarcity problem - you simply can't scrape robot control data from webpages. Introducing GR00T-Mimic and GR00T-Gen: using both Graphics 1.0 & Graphics 2.0 to multiply your robot datasets by 1,000,000x. We trade compute for synthetic data, so we are not capped by the fundamental physical limit of 24 hrs/robot/day. Robotics is right in the thick of Moravec's paradox: things that are easy for humans turn out to be incredibly hard for machines. We are crushing the Moravec's paradox, one token at a time. > Graphics 1.0: Isaac simulators with manually written, GPU-accelerated physics and rendering equations.  > Graphics 2.0: big neural nets (Cosmos) that repaint the pixels from sim textures to real, given an open-ended prompt. Robot data multiplier workflow: 1. GR00T-Teleop: use XR device like Apple Vision Pro to map human finger poses to humanoid hands. 2. GR00T-Mimic: given a human-collected task demonstration, we augment the actions in Isaac and filter out ones that fail the task. 3. GR00T-Gen: apply Graphics 1.0 and then Graphics 2.0 to produce tons of visual variations. The above is an exponential pipeline, adding orders of magnitude at each step.

Over the past 10+ years, I’ve had the opportunity to author or contribute to over 100 #datagovernance strategies and frameworks across all kinds of industries and organizations. Every one of them had its own challenges, but I started to notice something: there’s actually a consistent way to approach #data governance that seems to work as a starting point, no matter the region or the sector. I’ve put that into a single framework I now reuse and adapt again and again. Why does it matter? Getting this framework in place early is one of the most important things you can do. It helps people understand what data governance is (and what it isn’t), sets clear expectations, and makes it way easier to drive adoption across teams. A well-structured framework provides a simple, repeatable visual that you can use over and over again to explain data governance and how you plan to implement it across the organization. You’ll find the visual attached. I broke it down into five core components: 🔹 #Strategy – This is the foundation. It defines why data governance matters in your org and what you’re trying to achieve. Without it, governance will be or become reactive and fragmented. 🔹 #Capability areas – These are the core disciplines like policies & standards, data quality, metadata, architecture, and more. They serve as the building blocks of governance, making sure that all the essential topics are covered in a clear and structured way. 🔹 #Implementation – This one is a bit unique because most high-level frameworks leave it out. It’s where things actually come to life. It’s about defining who’s doing what (roles) and where they’re doing it (domains), so governance is actually embedded in the business, not just talked about. This is where your key levers of adoption sit. 🔹 #Technology enablement – The tools and platforms that bring governance to life. From catalogs to stewardship platforms, these help you scale governance across teams, systems, and geographies. 🔹 #Governance of governance – Sounds meta, but it’s essential. This is how you make sure the rest of the framework is actually covered and tracked — with the right coordination, forums, metrics, and accountability to keep things moving and keep each other honest. In next weeks, I’ll go a bit deeper into one or two of these. For the full article ➡️ https://lnkd.in/ek5Yue_H

If your SQL tables are messy, your analytics will always lie to you. Data cleaning is not optional, it is the foundation of trustworthy insights. Here’s a simple breakdown of 13 essential SQL techniques every data engineer and analyst should know: 1. Replace NULL with a Default Value Use COALESCE to safely fill missing values during queries. 2. Delete Rows with NULL Values Remove incomplete records when they can’t be repaired. 3. Convert Text to Lowercase Standardize fields like names and emails for clean comparisons. 4. Find Duplicate Rows Identify values that appear more than once using GROUP BY. 5. Delete Duplicate Rows (Keep One) Remove duplicates while preserving a single valid entry. 6. Remove Leading & Trailing Spaces Trim whitespace so joins and comparisons don’t break. 7. Split Full Name into First & Last Extract components using SUBSTRING functions (simple cases only). 8. Standardize Date Formats Convert inconsistent date strings into a unified format. 9. Eliminate Special Characters Strip symbols while keeping alphanumeric data clean. 10. Identify Outliers Spot values outside expected upper/lower thresholds. 11. Remove Outliers Delete invalid or extreme values when necessary. 12. Fix Typo or Incorrect Values Correct inconsistent categories to avoid fragmentation. 13. Standardize Phone Number Format Keep only digits for clean, uniform phone fields. Messy data leads to messy decisions. Small SQL cleanup steps like these dramatically improve model accuracy, dashboards, and business reporting.

One of the most common mistakes I see in data technology strategies is failing to distinguish between operational and analytical data needs. These two areas have different requirements, use cases, and often, different user bases. 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐃𝐚𝐭𝐚 – Data produced by day-to-day operations (e.g., transactions) 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 – Data that is aggregated and curated to be analysed for business intelligence or fed into ML/ AI models Operational tools power daily business operations—think ERP and CRM systems—keeping the lights on with real-time, transactional or supply chain data. These tools are more rigid, rule-driven, and often introduce data quality issues if not set up properly. On the flip side, analytical tools like data warehouses and BI platforms derive insights from this operational data. These tools enable better decision-making and drive business value, but they depend heavily on the quality of the data fed from operational systems. Both need to work together seamlessly for a successful data strategy. But unfortunately, you often see teams spending more time on the analytical data and tooling. This is a mistake. Given their symbiotic nature, you need to think of them holistically. Ignore one, and your data efforts are at risk.

Hey Salespeople: Here is a collection of current use cases for AI in sales & CS: ** GenAI in Sales ** --> Draft messaging for personalized email outreach --> Generate post-call summaries with action items; draft call follow ups --> Provide real-time, in-call guidance (case studies; objection handling; technical answers; competitive response) --> Auto-populate and clean up CRM --> Generate & update competitive battlecards --> Draft RFP responses --> Draft proposals & contracts --> Accelerate legal review & red-lining (incl. risk identification) --> Research accounts --> Research market trends --> Generate engagement triggers (press releases; job postings; industry news; social listening; etc.) --> Conduct role-play --> Enable continuous, customized learning --> Generate customized sales collateral --> Conduct win-loss analysis --> Automate outbound prospecting -->Automate inbound response --> Run product demos --> Coordinate & schedule meetings --> Handle initial customer inquiries (chatbot; voice-bot / avatar) --> Generate questions for deal reviews --> Draft account plans ** Predictive AI in Sales ** --> Score leads & contacts --> Score /segment accounts (new logo) --> Automate cross-sell & upsell recommendations --> Optimize pricing & discounting --> Surface deal gaps / identify at-risk prospects --> Optimize sales engagement cadences (touch type; frequency) --> Optimize territory building (account assignment) --> Streamline forecasting (incl. opportunity probabilities; stage; close date) --> Analyze AE performance --> Optimize sales process --> Optimize resource allocation (incl. capacity planning) --> Automate lead assignment --> A/B test sales messaging --> Priortize sales activities ** GenAI in CS ** --> Analyze customer sentiment --> Provide customer support (chatbot; voice-bot / avatar; email-bot) --> Draft proactive success messaging --> Update & expand knowledge base (incl. tutorials, guides, FAQs, etc.) --> Provide multilingual support --> Analyze customer feedback to inform product development, support, and success strategies --> Summarize customer meetings; draft follow-ups --> Develop customer training content and orchestrate customized training --> Provide real-time, in-call guidance to CSMs and support agents --> Create, distribute, and analyze customer surveys --> Update CRM with customer insights --> Generate personalized onboarding --> Automate customer success touch-points --> Generate customer QBR presentations --> Summarize lengthy or complex support tickets --> Create customer success plans --> Generate interactive troubleshooting guides --> Automate renewal reminders --> Analyze and action CSAT & NPS ** Predictive AI in CS ** --> Predict churn; score customer health; detect usage anomalies, decision maker turnover, etc. --> Analyze CSM and support agent performance --> Optimize CS and support resource allocation --> Prioritize support tickets --> Automate & optimize support ticket routing --> Monitor SLA compliance

🍦 𝗔𝗜 𝗖𝗮𝘀𝗲: 𝗨𝗻𝗶𝗹𝗲𝘃𝗲𝗿 𝗜𝗰𝗲 𝗖𝗿𝗲𝗮𝗺 — 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗧𝗵𝗮𝘁 𝗥𝗲𝗮𝗰𝘁𝘀 𝘁𝗼 𝗪𝗲𝗮𝘁𝗵𝗲𝗿 & 𝗦𝘁𝗼𝗿𝗲 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 🤔 AI in supply chains isn’t just a promise — it’s already delivering measurable results. 🌡️ 𝗨𝗻𝗶𝗹𝗲𝘃𝗲𝗿’𝘀 𝗘𝘂𝗿𝗼𝗽𝗲𝗮𝗻 𝗶𝗰𝗲 𝗰𝗿𝗲𝗮𝗺 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 faces rapid, weather-driven demand swings. Seasonal volatility often outpaces traditional forecasts, leading to lost sales and waste. 📣 𝗛𝗼𝘄 𝗔𝗜 𝗵𝗲𝗹𝗽𝗲𝗱 𝗨𝗻𝗶𝗹𝗲𝘃𝗲𝗿’𝘀 𝗗𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 & 𝗱𝗲𝗺𝗮𝗻𝗱 𝘀𝗲𝗻𝘀𝗶𝗻𝗴 ▪️ Uses daily weather updates from hyperlocal data (temperature, rainfall by city). ▪️ Pulls live data from AI-enabled freezers with IoT sensors tracking SKU presence and quantities. ▪️ Combines POS and distributor sales to reconcile forecasts in near-real-time. ▪️ Adds event and promotion data to refine demand signals. 𝗧𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝘂𝘀𝗲𝘀 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝘀𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝗱𝗲𝗺𝗮𝗻𝗱 𝘀𝗲𝗻𝘀𝗶𝗻𝗴 𝘁𝗼 𝗱𝗲𝗹𝗶𝘃𝗲𝗿: 🔹 Weekly rolling forecasts that adjust monthly plans. 🔹 Daily alerts so teams can replenish high-demand SKUs fast (e.g., +5°C triggers orders within 48 hrs). 🔹 Inventory reallocation from low- to high-demand areas before expiry. 📈 𝗞𝗲𝘆 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: ✔️ 10% higher forecast accuracy, reducing waste and missed sales. ✔️ 30% higher retail orders due to proactive replenishment and SKU mix optimisation. ✔️ Lower waste through stock reallocation in cooler periods. ✔️ Faster decisions — from a week to hours. 📍 𝗧𝗵𝗶𝘀 𝘀𝗵𝗼𝘄𝘀 𝗵𝗼𝘄 𝗔𝗜 𝗰𝗮𝗻 𝘁𝘂𝗿𝗻 𝘄𝗲𝗮𝘁𝗵𝗲𝗿 𝗮𝗻𝗱 𝘀𝗮𝗹𝗲𝘀 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝘀 𝘁𝗵𝗮𝘁 𝗰𝘂𝘁 𝘄𝗮𝘀𝘁𝗲, 𝗯𝗼𝗼𝘀𝘁 𝘀𝗮𝗹𝗲𝘀, 𝗮𝗻𝗱 𝘀𝗽𝗲𝗲𝗱 𝘂𝗽 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲. 👇 𝘞𝘩𝘢𝘵 is 𝘩𝘰𝘭𝘥𝘪𝘯𝘨 𝘭𝘰𝘤𝘢𝘭 𝘤𝘰𝘮𝘱𝘢𝘯𝘪𝘦𝘴 𝘧𝘳𝘰𝘮 𝘭𝘦𝘷𝘦𝘳𝘢𝘨𝘪𝘯𝘨 𝘈𝘐 𝘪𝘯 𝘴𝘶𝘱𝘱𝘭𝘺 𝘤𝘩𝘢𝘪𝘯𝘴?

Underwriting is about to experience the same disruption payments saw with UPI silent, intelligent, and hyper-personalized. Traditional actuarial models, largely built on age, gender, and medical history, are no longer enough to accurately price risk. The future of underwriting is about 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞, 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐫𝐢𝐬𝐤 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧. A McKinsey study estimates that 𝐀𝐈-𝐞𝐧𝐚𝐛𝐥𝐞𝐝 𝐮𝐧𝐝𝐞𝐫𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐜𝐚𝐧 𝐫𝐞𝐝𝐮𝐜𝐞 𝐥𝐨𝐬𝐬 𝐫𝐚𝐭𝐢𝐨𝐬 𝐛𝐲 𝐮𝐩 𝐭𝐨 𝟐𝟎% through more accurate segmentation and predictive modeling. Insurers are already leveraging geolocation, wearable data, and transaction behavior to assess actual lifestyle risk, not just what’s declared on a form. Instead of pricing a policy once at issuance, underwriting will become continuous. Transactional data from IoT, telematics, and payments will enable dynamic risk tiers such as auto premiums recalibrating monthly based on real driving behavior. With explainability frameworks (like XAI), underwriters can ensure AI doesn’t become a black box. This is critical as 𝟖𝟐% 𝐨𝐟 𝐠𝐥𝐨𝐛𝐚𝐥 𝐫𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐬 𝐞𝐱𝐩𝐞𝐜𝐭 𝐬𝐭𝐫𝐨𝐧𝐠𝐞𝐫 𝐀𝐈 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐢𝐧 𝐢𝐧𝐬𝐮𝐫𝐚𝐧𝐜𝐞 over the next 3 years The top insurers are building ecosystems. Partnerships with mobility, fintech, and health platforms will give them richer, more reliable signals, transforming underwriting from risk prediction to risk prevention. The underwriting engine will sense, learn, and adapt in real time, turning insurance from reactive protection to proactive resilience. #DigitalIndia #Fintech #AI #technology #Fintech #technology

🚨 My dashboard is useless when the dataset is incorrect !!!!! I once made it to the final round of an interview for a Data Analyst role. The task? Build a dashboard in Excel or Power BI based on the company’s requirements. At that time, I was super confident in my Power BI skills. I built a beautiful dashboard with almost every feature from the meme — colorful visuals, interactive filters, drill-down magic, even a clean schema from Power Query. But… I forgot one small thing: removing duplicates. And here’s the truth: no matter how fancy your dashboard looks, stakeholders won’t care if the data feeding it is wrong. If your dataset isn’t reliable, your insights are useless. That experience taught me an important lesson: before you think about making a “wow” dashboard, make sure the dataset is correct. Here are a few expanded steps I now follow to keep my data clean: 1. Scan and understand your dataset - Start with a data audit — what kind of dataset is it? Transactional, customer, operational, or something else? - Understand the logic of rows and columns: are they events, unique IDs, or aggregated summaries? - Profile the data by running quick checks: number of rows, missing values, duplicate counts, and overall structure. - Treat duplicates carefully. Sometimes they’re errors, but sometimes they’re valid (e.g., multiple transactions from the same customer on the same day). 2. Check column types and validate formats - Classify every column: categorical (e.g., product category), numeric (e.g., sales amount), or time/date (e.g., transaction date). - Verify consistency: Categorical fields → spelling consistency (“USA” vs. “U.S.” vs. “United States”). Numeric fields → make sure they’re truly numeric and not stored as text. Dates → standardize to one format (e.g., YYYY-MM-DD) across the dataset. - Review NULL or missing values. Decide whether to impute, drop, or escalate — but never ignore them. 3. Spot anomalies and outliers - Check for extreme values that don’t make sense (e.g., negative sales, a customer age of 400). - Use descriptive statistics (mean, median, standard deviation) to highlight outliers. - Always validate with the business context before removing or adjusting. Sometimes outliers are the most important story! 4. Document every step of cleaning - Keep a “data diary” — document what transformations you applied, what errors you found, and how you handled them. - Track unresolved issues. For example: “Column X had 125 NULL values — awaiting stakeholder input.” “Customer IDs had 15 duplicates — validated as system error, removed.” - This makes your process transparent, reproducible, and easy to explain in future audits. ✅ In short: data cleaning isn’t “extra work,” it’s the foundation of reliable dashboards. A fancy front end might impress once, but clean, trustworthy data keeps stakeholders coming back. ✨ let’s connect and share ideas! #DataAnalytics #PowerBI #DataCleaning #DataStorytelling