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

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.

→ Most people use SQL. Very few understand the concepts that actually power modern data systems. And in 2026, SQL is no longer just a database skill. It is operational infrastructure for analytics, AI, and real time applications. → 𝐒𝐐𝐋 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐓𝐡𝐚𝐭 𝐐𝐮𝐢𝐞𝐭𝐥𝐲 𝐑𝐮𝐧 𝐌𝐨𝐝𝐞𝐫𝐧 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 • SELECT Core query operation powering dashboards, reporting, and analytics • JOIN Connects data across systems like users, orders, and transactions • WHERE Filters large scale datasets for APIs and operational workflows • GROUP BY Transforms raw data into business intelligence and performance insights • ORDER BY Ranks products, users, or events based on business metrics • INDEX Critical for query performance and production scale optimization • INSERT Drives continuous ingestion from apps, pipelines, and event systems • JSON Querying Handles semi structured data from APIs, logs, and streaming platforms • WINDOW FUNCTIONS Enables ranking, cohort analysis, and advanced analytical operations • CTEs Simplify complex transformations and improve query readability • DELETE / SOFT DELETE Manages lifecycle control and historical data retention strategies • UPDATE Keeps transactional systems and workflow states synchronized in real time → 𝐓𝐡𝐞 𝐁𝐢𝐠𝐠𝐞𝐫 𝐒𝐡𝐢𝐟𝐭 SQL is evolving beyond reporting. It now powers AI pipelines, event architectures, governance layers, and operational analytics. The engineers who understand these concepts deeply are not just querying data anymore. They are shaping how modern systems think and operate. → 𝐂𝐥𝐨𝐬𝐢𝐧𝐠 𝐓𝐡𝐨𝐮𝐠𝐡𝐭 Tools will continue to evolve. But structured data logic remains one of the most durable skills in technology. SQL is no longer optional infrastructure knowledge. It is foundational decision making language for modern systems. P.S. Which SQL concept do you think becomes most critical when systems scale from thousands to billions of records. Follow Ashish Joshi for more insights

A data analyst built an Order Completion Flowchart from real business data. What is revealed? Only 25% of orders were actually completed. 75% were failing somewhere in the process. Nobody in that business had named it. Nobody had seen it.  Because nobody had built the right analysis. This is the gap I see constantly in mid-sized enterprises. Not a shortage of data.  A shortage of people who can start with 𝐦𝐞𝐬𝐬𝐲, 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐝𝐚𝐭𝐚 the kind every business actually has and turn it into something that changes a decision. Most analysts can work with clean, structured datasets. Far fewer can take inconsistent records, broken pipelines, and incomplete logs and still produce an insight worth acting on. That's the difference between a reporting function and an analytics capability. 87% of hiring managers say they're more likely to interview candidates with proven, real-world project portfolios. The same logic applies to businesses. The organisations winning in 2026 aren't the ones with the most data tools. They're the ones with analysts who can look at a broken process map every step, find every drop-off, write the SQL to prove it and hand leadership a number they can't ignore. At 𝐕𝐢𝐞𝐫𝐚 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐢𝐧𝐠, we don't just connect systems. We design infrastructure that a business can actually operate, scale, and trust. Centralised. Documented. Because the data existed.  The capability to read it didn't. Does your team have the analytics depth to find what your data is actually hiding? #DataAnalytics #BusinessIntelligence #DataDrivenDecisionMaking #SQL #BusinessTransformation #OperationalExcellence

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.

Data teams often OVERLOOK OPERATIONAL USE CASES. The initial focus of BI teams is dashboards. Dashboards work great for high-level monitoring and basic deep dives, which cover the needs of C-level, VPs, Directors, and Managers. While budgets are decided at a higher level, the execution happens at the granular level of individual contributors. For marketing, Performance Marketers and UA managers are the ones optimizing their campaigns every day, ultimately driving ROAS. But dashboards are not enough for operational use cases. Operational use cases have 3 special requirements: 1) GRANULAR data (e.g., at the campaign level), often including multiple metrics 2) SPEED to make better decisions daily (it can't wait until tomorrow or next week) 3) FLEXIBILITY to enable granular deep dives, data exploration and hypothesis testing (Basically for quick feedback loops) Rigid solutions like dashboards fail to meet these requirements. Individual contributors like performance marketers / UA managers are often overlooked. They tend to find workarounds (spreadsheets) to run the analyses they need. And stop asking the data team for more developments. Data teams often believe the problem is solved. Because it lacks symptoms... But they are actually missing out on a big opportunity to improve data ROI. All these workarounds and fallback solutions have negative consequences: - Multiple sources of truths, silos, and mess - Time-consuming and error-prone manual workflows This leads to missed opportunities (i.e., wasted marketing budget) How are you covering operational analytics use cases? Let me know in the comments #data #bi #analytics #operationalanalytics #marketing

Most companies operate on gut feel and spreadsheets. The best companies use Operations Research (OR); the math behind smarter decisions. Here’s a quick way to think about OR: Imagine you run a delivery fleet. You have 50 trucks, hundreds of orders, and traffic delays. What’s the best way to deliver everything on time and at the lowest cost? 👉 A basic approach: Assign routes manually → Expensive and slow 👉 A data-driven approach: Use past data to plan better → Better, but not optimal 👉 An OR approach: Build a routing optimization model → Finds the best possible plan in seconds Real-world OR impact: ✅ Airlines schedule flights to minimize delays and costs ✅ Retailers optimize inventory to reduce stockouts and waste ✅ Automakers balance supply and demand to maximize profits If you’re working with complex decisions, constraints, and trade-offs, OR is the tool you need. Curious about how OR can help your business? Let’s discuss in the comments!

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.

Every organization has the same hidden cost: analytics built once, maintained forever, understood by no one. Someone builds a dashboard. It answers one question for one team. Six months later, twelve people depend on it, but the original analyst has moved on. No one knows what breaks it or how to fix it. This is what happens when you treat analytics as projects instead of products. A data product has an owner, a consumer contract, and a lifecycle. It's versioned, documented, and designed for reuse. When a new team needs customer segmentation logic, they don't rebuild it. They consume the existing product. The difference isn't technical. It's operational. Products get maintained. Projects get abandoned. The shift requires discipline: clear ownership, usage tracking, and deprecation policies. But the payoff is real. Less duplication, more reliability, and analysts freed from maintenance to do actual analysis.