Operations Analytics and Data Utilization

I started my first job purely in operations. No dashboards. No SQL. No Python. My work was not simple: → Manage warehouse & dark store operations → Launch new locations (including one in Peshawar) → Hit targets set for operational KPIs At that time, I didn’t know much about data — just worked based on gut, hustle, and on-ground realities. And it worked. But today, with the skillset I’ve built in data analytics, I look back and think: If I had these skills back then — I would’ve taken operations to another level. Here are a few initiatives I could’ve done from Day 1 👇 → Built a Dark Store P&L model To understand what city, shift, or zone was profitable vs. bleeding cash → Setup real-time fulfillment dashboards To track order delays, cancellations, and SLA breaches by zone → Ran stockout vs lost sales analysis To show how missing SKUs were directly hurting revenue → Automated daily operational KPI tracking Using Google Sheets + Power Query to show delay %, OTIF, and picking efficiency → Created a capacity vs. demand forecast So we could schedule riders, packers, and vehicles more smartly during peak hours → Identified city-level delivery cost trends So expansion decisions were backed by margin data, not just pressure to scale → Built a shift-level performance report To see how much was getting picked/packed/processed per FTE per hour These are small wins — but powerful when done consistently. And they’re not complex to build. You don’t need a data science team. You just need to know what problem to solve — and start from the data you already have. If you're in operations today: Don’t wait for a data team. Be the bridge between ops & data. Even a simple Excel dashboard can change how decisions are made on the floor. 💡 I’ve built these systems from scratch since then — and I can confidently say: The best ops teams aren’t just operationally strong — they’re data-aware. #Operations #Analytics #StartupExecution #WarehouseOps #DarkStore #Fulfillment #CapacityPlanning #InventoryControl #PakistanStartups #ZainUlHassan #CareerReflection #KPIFramework

Growth in today’s business environment is no longer driven by instinct or historical success alone. The integration of 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 into business development has redefined how companies strategize, operate, and scale. Let me share some case studies: 🎯 Asian Paints combined weather data with regional buying patterns to predict peak sales and optimize inventory. 🎯 Tata Consultancy Services (TCS) using advanced analytics for predictive maintenance. 🎯 Zomato and Swiggy leveraging real-time data for customer engagement and delivery optimization. We have to agree on this, data is the new oil powering business engines. In an era where organizations generate enormous volumes of data across touchpoints—from customer interactions and logistics to financial flows and market signals—the ability to harness and analyze this information has become a core differentiator between stagnation and sustainable success. Data analytics transforms raw, often unstructured data into actionable insights. Whether it is a mid-sized manufacturing firm optimizing production schedules or an IT services company evaluating expansion into new geographies, data analytics is foundational to clarity and confidence in every major decision. Across sectors, the impact is tangible. A 2023 NASSCOM report indicated that over 74% of Indian enterprises that adopted advanced analytics solutions reported measurable improvements in operational efficiency, while 63% experienced revenue growth through better customer targeting and service personalization. The analytics maturity of a business increasingly correlates with its ability to innovate, adapt, and lead. 𝐑𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 𝐚𝐧𝐝 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐦𝐨𝐝𝐞𝐥𝐬 now allow businesses to pre-empt disruptions, allocate resources with precision, and manage vendor performance based on historical data rather than assumptions. Indian manufacturing clusters, particularly in auto components and textiles, are using analytics to reduce rework rates, lower inventory carrying costs, and improve delivery timelines. Sales and marketing teams no longer rely solely on quarterly performance reviews. Data-driven customer segmentation, sentiment analysis, and behavioral tracking provide granular insights into consumer preferences and product lifecycle trends. An EY India study highlighted that predictive analytics tools are helping organizations reduce voluntary attrition by as much as 20% by identifying high-risk profiles and implementing timely interventions. One of the most powerful applications of data analytics is in product and service innovation. By analyzing structured feedback, usage patterns, and online reviews, businesses are able to accelerate time-to-market and design offerings that are more aligned with actual user expectations. In the financial sector, for instance, lending institutions now use analytics models to determine creditworthiness and reduce delinquency.

📊 How Modern Data Teams Really Work (End-to-End View) Many people see dashboards, charts, and AI predictions — but very few see the system behind them. This visual explains how modern data teams work together, from raw data to daily business decisions. It’s not theory. This is how real organizations turn data into impact. Let’s break it down 👇 🏢 Business Sources Everything starts with operational systems: Databases, APIs, applications, spreadsheets, ERPs — messy, inconsistent, and constantly changing. 🔄 Automated Data System (Pipeline) Raw data flows through a structured pipeline: • Bronze → ingestion (raw data) • Silver → cleaning & standardization • Gold → business-ready models This automation is the backbone. Without it, nothing scales. 👷 Data Architect & Data Engineer • Architects design the data foundation • Engineers build and maintain pipelines This layer decides whether analytics will be reliable or painful. 📊 BI Developer Transforms curated data into dashboards that refresh daily — trusted by leadership and operations teams. 📈 Data Analyst Uses SQL and ad-hoc analysis to answer: • What happened? • How much? • Why did it happen? This is where insights meet real business questions. 🧠 Data Scientist Moves beyond reporting: • Trains models • Experiments • Predicts what will happen next ⚙️ ML Engineer Takes models to production: • Builds deployment pipelines • Ensures models run reliably • Enables predictions at scale 🎯 Managers & Decision Makers Finally, all of this feeds decisions — every single day. From dashboards to predictions, the goal is simple: Better, faster, data-driven decisions. ⸻ 💡 From my own journey as a Supply Chain Analyst & AI Automation Specialist, this exact flow helped me: • Optimize inventory and demand • Reduce manual reporting • Build scalable Power BI systems • Connect analytics with real operations Modern data teams don’t work in silos. They work as systems. If you’re entering data analytics, supply chain analytics, or AI — understand the whole picture, not just one role. 👇 Which role are you currently working in — or aiming for? #DataAnalytics #ModernDataStack #SupplyChainAnalytics #BusinessIntelligence #PowerBI #SQL #DataEngineering #DataScience #MLengineering #AnalyticsArchitecture #DataTeams #LearningInPublic #CareerGrowth #TechCareers #DataDriven #Automation #AIinBusiness #ProfessionalJourney If anyone needs help with dashboards / reports / analytics, feel free to DM.

What does it mean when CPG giants start pulling operations leaders out of their operational roles and rewriting their jobs around data? Mars just did it twice in one month. Kristen Daihes was VP of supply chain. Now she's SVP of analytics, digital and data - overseeing data across supply chain, commercial, and R&D. Abhijit Dasgupta was running Mars Snacking operations. Now he's SVP global demand digital, data and analytics. Kenvue hired Mike Wondrasch as CTO and data officer. Then added Teresa Reilly as VP of data and AI governance. Then brought in Vikram Somaya (26 years at PepsiCo) as Chief Data & Analytics Officer. Hershey created its first VP of consumer and commercial intelligence role with Amy Roe. https://lnkd.in/gY86u3RG These aren't CIO hires. These are operators who built careers optimizing factories, managing supply chains, driving commercial performance. Now their titles say "data." Michael McGowan, who recruits for CPG leadership, sees the shift: "A supply chain officer used to be rewarded for efficiency. Today, leadership wants someone who can use data to drive demand planning, pricing, retailer negotiations and e-commerce performance." Think about that title change. It's not symbolic. When you elevate operations leaders into data roles, you're saying the competitive advantage isn't in running the process anymore. It's in what you learn from running the process. Manufacturing is heading the same direction. Your VP of Operations needs to own the manufacturing data strategy - not just consume dashboards IT builds. Because if they don't, operational improvement becomes an IT project managed by people who've never walked your floor. Data fluency isn't optional for operations leaders. It's becoming the job itself. Congratulations to all of them. These folks are on the frontier of something significant. #Manufacturing #DataStrategy #Operations #Leadership

This Confluent x Databricks partnership is a big step toward solving one of the most frustrating issues in the data space: the gap that exists between operational and analytical data. Right now, if you want to make sense of your operational data for analytics, you have to slog through building and maintaining ETL pipelines...especially the Transform part, which always ends up being way more work than you signed up for. What’s exciting here is the potential to streamline that entire process. Confluent’s real-time streaming paired with Databricks’ lakehouse could mean way less time spent moving and reshaping data, and way more time actually using it. In other words, it’s a shot at reducing toil and worse, endless delays we deal with every time we need to run an analytics deep dive on the business side. Wayne and I have been pounding the street for a year now (see our post from last August: https://lnkd.in/g9PVGYjp) that we need to shorten the distance between operational and analytical data, and this is a significant step to achieving that vision. If we can get to a world where real-time operational data flows into analytics without all the duct tape, it’s going to be a game-changer for cutting down on wasted time, endless rework, and those late-night "why isn’t this pipeline running” sessions. This shift raises an important question: if we can bridge the gap between operational and analytical data, how does that change the role of data teams? Will we finally move from being pipeline plumbers to strategic decision-makers? Thank you for the thought-provoking discussion on this over breakfast Naveen! #RealTimeAnalytics #OperationalToAnalytical #ETL #DataEngineering

The debate about data's role, optimal practices, and the utility of tools often revolves around a fundamental insight: analytics is now a dual mode discipline, a fusion of science and engineering. At its core, analytics is about uncovering and optimizing the input-output causal mechanisms with a business. The primary aim is to operationalize the scientific method within the organizational processes - formulating hypotheses, implementing interventions, and scrutinizing data to either confirm or challenge those hypotheses. This iterative loop of interventions, analysis and refinement drives continuous improvement in tactics and strategy. From this vantage point, doing data is doing science. But, in practice, this process is deeply intertwined with solving engineering problems. Efficiently building and maintaining data models and ensuring smooth data team collaboration require strong engineering solutions. Even at an atomic level, the efficiency of writing a SQL query matters, just as developers focus on optimizing their coding productivity. In fact, constructing even stronger abstractions on top of data models like metric trees can revolutionize organizational efficiency by allowing the entire organization to participate in this process without needing extensive knowledge of the data models. This represents the holy grail of “self serve analytics” that many in the industry aspire to achieve. However, we need to keep front and center the notion that solving these engineering problems is ultimately in service of the scientific method and the ultimate goal of data: to uncover and act upon the causal drivers of the business processes.

𝐇𝐨𝐰 𝐇𝐨𝐬𝐩𝐢𝐭𝐚𝐥𝐬 𝐂𝐚𝐧 𝐔𝐬𝐞 𝐋𝐎𝐒 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐭𝐨 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 Hospital resources are finite beds, staff, and equipment must be strategically allocated to keep operations smooth and patient care efficient. Length of Stay (LOS) analytics is the key to predicting demand, reducing inefficiencies, and ensuring the right resources are available at the right time. 𝐇𝐨𝐰 𝐋𝐎𝐒 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐬 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐀𝐥𝐥𝐨𝐜𝐚𝐭𝐢𝐨𝐧 𝟏. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐟𝐨𝐫 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 Forecast bed demand based on historical patterns. Identify high-risk patients likely to need extended stays and intervene early. 𝟐. 𝐒𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐧𝐞𝐝 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐅𝐚𝐬𝐭𝐞𝐫 𝐃𝐢𝐬𝐜𝐡𝐚𝐫𝐠𝐞𝐬 Real-time LOS data helps teams prepare for discharges in advance. Reduces bottlenecks, ensuring quicker bed turnover and smoother patient flow. 𝟑. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐒𝐭𝐚𝐟𝐟 & 𝐄𝐪𝐮𝐢𝐩𝐦𝐞𝐧𝐭 𝐔𝐭𝐢𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Align staffing levels with patient demand, minimizing overstaffing or shortages. Ensure critical resources (e.g., ICUs, ventilators) are available when needed. 𝟒. 𝐂𝐨𝐬𝐭 𝐒𝐚𝐯𝐢𝐧𝐠𝐬 & 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 Fewer unnecessary inpatient days mean reduced operational costs. Improves capacity management, allowing more patient admissions without expanding facilities. 𝟓. 𝐃𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭 Ongoing performance analysis highlights inefficiencies and improvement opportunities. Feedback loops help hospitals refine their approach to resource management. 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 Hospitals that integrate LOS analytics don’t just save money, they improve patient care, reduce wait times, and enhance overall efficiency. 𝐈𝐬 𝐲𝐨𝐮𝐫 𝐡𝐨𝐬𝐩𝐢𝐭𝐚𝐥 𝐥𝐞𝐯𝐞𝐫𝐚𝐠𝐢𝐧𝐠 𝐋𝐎𝐒 𝐝𝐚𝐭𝐚 𝐟𝐨𝐫 𝐬𝐦𝐚𝐫𝐭𝐞𝐫 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐚𝐥𝐥𝐨𝐜𝐚𝐭𝐢𝐨𝐧? Let’s discuss how real-time analytics can optimize operations. 𝐏.𝐒. 𝐖𝐡𝐚𝐭’𝐬 𝐲𝐨𝐮𝐫 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐢𝐧 𝐡𝐨𝐬𝐩𝐢𝐭𝐚𝐥 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭? Drop a comment or message me; I’d love to hear your insights.

Let's talk about how organizations use their data. When we look at the big picture, we can split data analytics systems into two categories. The first category is Business Analytics (BA) systems. These systems analyze large volumes of historical data to uncover strategic insights for decision-makers, supporting long-term planning and strategic decisions. E.g Business intelligence (BI) reporting The second category is Operational Analytics (OA) systems. They use real-time or near-real-time data from operational systems (e.g., transactional databases, ERP, CRM, IoT systems) to drive immediate decision-making and optimize business processes. Unlike traditional business intelligence (BI), which focuses on historical reporting, OA is focused on real-time insights that directly impact day-to-day operations. ✳️ Real-time and Near Real-time Data Analytics OA systems can be further divided based on the amount of data they use for decision-making and their time sensitivity. Real-time systems process data as it arrives and make immediate decisions based on the freshest data available. These systems typically handle individual events or very small windows of data, making split-second automated decisions. They typically operate in an automated, event-driven manner, making decisions without human intervention. For example, a credit card fraud detection system automatically blocks suspicious transactions based on predefined rules and patterns, or an automated trading system executes trades based on market conditions. Near real-time systems, while still focused on current operations, incorporate slightly larger datasets and may include some historical context in their analysis. These systems typically operate with data that's minutes or hours old and can handle more complex analyses. They can function either as decision-support tools for human operators or operate autonomously. For human decision support, these systems provide actionable insights. For example, a customer service dashboard alerts representatives to potential customer churn based on recent behavior patterns, enabling proactive outreach. In autonomous operation, these systems make decisions without human input—like an inventory management system that automatically generates purchase orders based on predefined rules and historical demand when it detects low stock levels. In the next post, we'll explore the implementation architectures for both OA and BA systems. #dataanalytics #operationalanalytics #sketchnotes

𝗪𝗵𝗼 𝗦𝗵𝗼𝘂𝗹𝗱 𝗢𝘄𝗻 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗶𝗻 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴? 🤔 Think advanced analytics is just an IT project? Or should operations take full control? The best manufacturers take a different route. They embed analytics where it drives real impact. 𝗧𝗵𝗲 𝗦𝗲𝗰𝗿𝗲𝘁 𝘁𝗼 𝗪𝗶𝗻𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 IT Teams (Manufacturing IT, Quality IT) – The backbone of data infrastructure, but their real power comes when they partner with business teams instead of playing gatekeeper. Value-Chain Teams (Quality, Maintenance, Safety, etc.) – Already influencing key plant decisions. When armed with analytics, they predict failures, optimize quality, and enhance safety—turning hindsight into foresight. Digital Councils (Cross-Functional Teams) – The real game-changer. The best manufacturers don’t silo analytics, they integrate IT, support teams, and operations into a single, data-driven powerhouse. 𝗪𝗵𝘆 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗦𝗵𝗼𝘂𝗹𝗱 𝗚𝘂𝗶𝗱𝗲, 𝗡𝗼𝘁 𝗢𝘄𝗻 Define strategy and KPIs—but let experts handle execution. Tap IT & data science teams for model building, and governance. Embed analytics into quality, maintenance & safety for real-time impact.  𝗧𝗵𝗲 𝗪𝗶𝗻𝗻𝗶𝗻𝗴 𝗙𝗼𝗿𝗺𝘂𝗹𝗮 • IT builds the foundation. • Value-chain teams turn insights into action. • Digital councils break silos and drive execution. • Operations use data-driven insights to stay ahead—without losing focus on production. The bottom line? Data alone doesn’t transform plants—embedding it where it matters does. Ref: LNS Research

Me again. Yikes! Yes I'm talking about the Operating Model again. Many people often tout data as the new something, or the key to better decision making. Well it may well be, but is simply having more data enough? For many companies, the harsh truth is that data doesn’t drive value and many forget that "data" is just an enabler. What drives the value is how you intentionally organise and this is called your "Operating Model" If you know me, you know that my mantra is that your "Operating Model is what makes your data strategy real" I’ve seen many companies that proudly call themselves “data-driven,” yet they find themselves at a standstill when it comes to achieving measurable outcomes. They just focus on collecting data, implementing dashboards, or investing in the latest analytics tools, but lack in the actual alignment with their #business operations. Take Company X, for example. They invested heavily in building a data lake, assuming that more data would mean more insights. Yet after months of effort, the insights they gathered had little impact on actual decision-making. Why? They hadn’t developed an #operatingmodel to embed the #data into their workflows, to ensure that insights were used where and when they were needed most. Data should never exist in isolation. It needs a purpose, a context within your business. Some would say it’s the lifeblood, I say it’s just a really smart teenager, full of potential but needs constant guidance to actually do something useful. I hope my teenage daughter doesn't read this! The companies that truly succeed with data understand that it’s not just about the data itself but about how that data flows through people, processes, and culture. This is where a robust operating model comes into play. An effective data operating model connects the dots between your data and your business goals. It ensures that: 👉Use cases are clearly defined and aligned with business outcomes. 👉People know how to use data in their day-to-day roles. 👉Processes are in place to turn insights into actions. 👉Culture embraces data-driven decision-making across all levels. Without these components in place, even the most sophisticated #datastrategy is just a collection of numbers, isolated from the realities of your business operations. So, the question isn’t “Do you have enough data?” but rather, “Is your operating model designed to turn your data into action?” Ask yourself these 3 questions: 👉Are your teams equipped to use the data in meaningful ways? 👉Are your processes aligned to support decisions? 👉Are you investing in the right use cases, not just technology? Data is powerful, but only when it’s embedded within an operational framework that drives real business outcomes. It’s time to move beyond the idea that more data equals better decisions—and start focusing on how you can transform that data into something actionable. Are you ready to build an operating model that makes data work for your business?