Operational Analytics

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

I’ve been in enough operational war rooms to know what data gets forgotten, even though it’s vital. Many will focus on arrival times, on-time departures, cargo volumes and more, but there are critical blind spots that I want to point out and discuss with you today. One not often seen metric is equipment idle time, i.e: idle time ‘over a ship’, during an operation, and the overall time laid idle not earning revenue. We track moves per hour, berth moves, ship productivity, and some ports are good enough to share that with everyone on LinkedIn - but how long do we ever get to see cranes or trucks that spend their time waiting to be seen, to have that box removed, or loaded? This is the often hidden, efficiency erosion. Second, shift change handover takes place - but are all the gaps taken on-board? Are any parts of the productivity chain reset or do they continue? Do we start to miss real context at that point? A third missing data point is proactive maintenance triggers. Waiting for a machine to fail means downtime; tracking trends could avoid it, or at least close the gap. Fourth, visibility over inbound supplier delays is crucial. When parts are not there when you need them, or arrive late, the whole schedule shifts. Lastly, error rates in reporting, documentation (missing paperwork, miswritten codes) slows customs and causes cascading delays. These are not glamorous. They don’t feature in dashboard-of-the-month slides. Yet they are where cost, trust, and performance quietly leak out. Do you have shared terminal KPI’s in your business - where many ‘Performance Indicators’ are owned by various stakeholders & departments - but all are aligned and link up to the overall ‘governing KPI’? This approach eliminates hiding, so everyone can see and resolve the right problems at the right time, informed by their collaborative ‘single source of truth’. At Trent Port Services and TrentGO, we build diagnostics to surface these hidden data points. Real operational clarity starts when you know where your system quietly falters, and then address it directly. https://lnkd.in/dzgM-P6A Find out more in the link above or get in touch with me today.

I'm excited to share my latest data analytics project: a comprehensive Retail Performance Analysis Dashboard. Problem: The retail company struggled with a lack of clear insights, making it difficult to track overall performance, understand customer behavior, and manage inventory efficiently. Solution: I developed and deployed an interactive, end-to-end Power BI dashboard. By connecting directly to SQL databases, the solution provides a real-time, holistic view of the business, analyzing key KPIs like sales, profit margins, customer segmentation, supplier performance, and stock health. 📊 Tools Used: Power BI | SQL | Excel | DAX | Data Modeling 💡 Key Insights & Highlights: • Total Sales: ₹5.34M • Profit Margin: 28.77% • YoY Sales Growth: 23.48% • Top Performers: The North Region (₹1.52M) and the supplier "Boat" (₹1.1M) were the primary drivers of sales. • Operational Health: Maintained a 65% delivery rate against a 9.17% return rate. • Actionable Inventory: Identified 3 critical products as "Low Stock" (Stock = Reorder Level), flagging them for immediate re-purchasing. Dashboard Link: https://lnkd.in/gHTPaTce #PowerBI #SQL #DataAnalytics #BusinessIntelligence #Dashboard #DataVisualization #RetailAnalytics #DataInsights

"We're burning $180K monthly processing items that will never turn a profit." That's what the data revealed when a legacy auction house analyzed its weekly item flow. Every item below their breakeven threshold wasn't just a loss - it was labor invested in failure. The Hidden P&L Killer: Over a third of items processed were destined to lose money. That's thousands of items weekly consuming photography, cataloging, and warehouse resources - all for a negative margin. The COO knew they needed a solution fast. The breakthrough wasn't optimizing pricing - it was building a pre-processing gate. The system we built now decides what NOT to process before any labor is invested. The Financial Impact (Q1 Results): → Labor costs: $540K/quarter reduced (equivalent to 15 FTEs redeployed) → Processing efficiency: 3x throughput on profitable items → Margin improvement: 23% increase on processed inventory → Payback period: 6.5 weeks (including implementation cost) → Risk mitigation: 76% accurate loss prediction prevents downstream waste The model paid for itself before the second invoice hit. The Lesson for Ops Leaders: When you process thousands of items daily, a single algorithmic decision - "skip this item" - compounds into a massive P&L impact. #OperationalExcellence #MLforOperations #PredictiveAnalytics #COO #DigitalTransformation

Operational efficiency isn’t always about doing more with less. Often, it’s about removing the invisible friction that slows teams down. We recently worked with a logistics provider whose data was scattered across systems - shipment statuses in one, cost data in another, delivery timelines somewhere else. The result? - Hours lost manually consolidating reports - Slow responses to client queries - Teams reacting instead of planning Here’s how we approached it at Fluidata: ● Unified dashboards – one reliable source of truth for shipments, costs, and timelines ● Automated pipelines – no more manual report prep ● Managed services – keeping systems healthy, without disruption The impact was clear: faster decisions, time saved on reporting, and teams shifting from firefighting to forward-planning. To me, this is the real promise of analytics, not prettier charts, but efficiency that compounds across an organization.

RISK isn’t a villain in the market. It is a blind spot in your operating system. 🧠 Buffett’s line is blunt because it is true: “Risk comes from not knowing what you’re doing.” In companies, not knowing shows up as fuzzy units, lagging indicators, and decisions made on vibes. Fix the knowledge gap, shrink the risk. Here is how operators de-risk in practice: ↳ Know the unit. Is your core unit a seat, job, shipment, subscriber, or cohort. price per unit, gross margin per unit, time per unit. ↳ Make time visible. Map the process, measure cycle time and variance at each step, not just the average. Queues create hidden risk. ↳ Promote leading indicators. Pipeline quality, win rates by segment, first-time-right, on-time-in-full, cash conversion days. If it moves the cash or the customer, track it. ↳ Write triggers, not slogans. “If churn for Cohort A hits 3.5 percent in week 8, then launch save flow B within 24 hours.” Decisions should be codified, not debated weekly. ↳ Shorten feedback loops. Smaller batch sizes, frequent releases, fast postmortems, quick refunds. Speed reduces uncertainty, which reduces risk. ↳ Price learning. Treat experiments as line items. 1️⃣ hypothesis, 2️⃣ time box, 3️⃣ decision rule. Learning is an asset when it compounds. Here’s a simple operating playbook: 1️⃣ Clarify the work ↳ One page that defines the unit, constraints, owner, and success metric. ↳ List the unknowns you must burn down this month. 2️⃣ Instrument the flow ↳ One page of leading indicators with thresholds and triggers. ↳ Daily glance, weekly review, monthly reset. 3️⃣ Decide in small bets ↳ Run tight experiments. Ship the smallest change that proves or disproves. ↳ Keep a running “What we learned” ledger. 💡 When you know your unit, time, and triggers, you stop gambling. You are operating. Do these now: ✅ Write your one-page “unit of value,” including price, margin, and cycle time. ✅ Pick three leading indicators and set explicit thresholds with If-Then triggers. ✅ Schedule a 30-minute weekly review to log decisions and lessons learned. ♻️Repost & follow John Brewton for content that helps. ✅ Do. Fail. Learn. Grow. Win. ✅ Repeat. Forever. ⸻ 📬Subscribe to Operating by John Brewton for deep dives on the history and future of operating companies (🔗in profile).

Your data strategy is built for a world that no longer exists. Last week at EPFL, we shared an uncomfortable truth. While you're generating Monday's reports, your competitors are already responding to what happened 5 milliseconds ago. The evidence is overwhelming: - DoorDash didn't grow from 17% to 50% market share with better dashboards. - They did it with operational systems that optimize 16.9M daily deliveries in real-time. - Netflix processes 1B+ events daily at sub-second latency. Not to analyze. To act. - Uber coordinates 500B daily events. Not in batches. Continuously. The market has already decided: - Traditional BI tools: Growing 6-8% annually (dying) - Operational data platforms: 26% CAGR (thriving) The new table stakes: ✅ Process 10M events per minute ✅ Respond in 5 milliseconds ✅ Sync bidirectionally without data loss This isn't about having faster analytics. It's about the difference between watching the game and playing it. By 2026, there will be two types of data products: Those that operate in real-time, and those that no longer exist.

🚀 Real-Time Operations & Sales Tracker – Part 2 (Live Page) In today’s environment, managers don’t just need static sales reports — they need real-time insights that combine sales performance, operational efficiency, forecasting, and risk monitoring in one place. That’s exactly what this Live Page delivers: a unified view that helps executives make faster, data-driven decisions. 🔹 Business Value: • Real-time monitoring of sales, margins, and customer orders • Early risk detection through forecasting and order status tracking • Target achievement visibility at regional, category, and daily levels • Better decision-making with interactive visuals and smart tooltips 🔹 Technical Highlights: • Dynamic KPI Cards – YTD & MTD Sales, Margin %, AOV with vs PM comparisons • Interactive Calendar Matrix – daily sales target indicators + advanced tooltips (sales, orders, items, gauge, category breakdown) • Sales by Region & Margin – dynamic field parameter switching • Prophet Forecast Integration (Python) – last 7d actuals vs next 7d forecast with dynamic % change subtitle • Sales vs Target (last 7d) – combined column & line chart • Smart Order Tracking Table – On Time, Late, Delayed, Pending statuses with delivery/transit days + customer/order details in tooltips 👉 By integrating operations, sales, forecasting, and order tracking into one interactive dashboard, this page bridges the gap between analytics and action, turning raw data into insights that directly support both strategic and operational goals. My Portfolio Website: https://lnkd.in/dEeazi3K My Youtube Channel: https://lnkd.in/dSp_j8k7 When I finish the project, I will release a presentation of it on my YouTube channel. #PowerBI #BusinessIntelligence #DataAnalytics #DataVisualization #Analytics #MAAB #iamKadirov

Operational excellence in modern applications depends on more than simply collecting data; it’s about activating the right insights, in the right place, at the right moment. The launch of Lakebase by Databricks is a significant step toward this vision. Lakebase integrates high-performance Postgres directly with your lakehouse, bringing trusted, curated data and AI insights out of the analytics layer and into the applications where decisions are made and action happens. Reverse ETL with Lakebase means that ML-powered recommendations, enriched customer profiles, or real-time fraud risk scores can flow seamlessly into your portals, dashboards, and operational tools, without fragile custom pipelines or fragmented infrastructure. What does this look like in practice? Teams can automatically sync fresh analytics from Delta tables into Postgres for live support portals, personalization engines, or interactive ML dashboards while preserving governance and ensuring rapid, reliable access. This managed, unified workflow seriously lowers complexity for developers and improves agility for the business. As the boundaries between analytics and operations continue to blur, Lakebase is making it easier to close the data-to-action gap and enable fully intelligent user experiences, all within the Databricks Data Intelligence Platform. Check out the blog for practical use cases and details: https://lnkd.in/emj_sNJ7 #Lakebase #ReverseETL #OperationalAnalytics #Databricks #DataIntelligence