Developing KPIs For Projects

Leading vs lagging indicators...what on earth are we talking about?? If you are not sure, read this. Most workplaces track what’s easiest to measure: 🔸 revenue 🔸 on-time delivery 🔸 customer complaints etc. Those are 'lagging indicators'. They’re the result of choices already made. 'Leading indicators' are the things that indicate what the results will be like: 🔸 preventative maintenance done on time 🔸 rework / repeat defects 🔸 near misses 🔸 spikes in absence etc. If you mainly manage lagging indicators, you’re managing history. You will notice a lot of time spent in meetings explaining what happened... Leading indicators are where learning starts. When we have them and use them, they trigger really useful conversation like: 💡 What is slipping?” 💡 What is going well and how will we recognize that? 💡 What patterns are we seeing? 💡 What do we need to adjust to get back on track? Essentially, you’re exploring cause and effect while the “cause” is still visible. Take a look at the chart below- it's a construction industry example. Notice how every KPI has a set of leading indicators beside it- I assure you it is entirely possible to find the early-warning, controllable stuff that drives the final result. (BTW, I have indicator charts for other industries I have worked in so feel free to send me a message if you want one for your specific industry) Note that companies don't use ALL these indicators- they choose the ones that are most important for them to work on right now. Are you familiar with leading indicators? Which ones do you use in your company? Share your experience below 🙏

Defining business-relevant KPIs for your dashboard can be a tricky task. Here is an example I encountered in my early career: 🎯 We were tasked with building a status dashboard for the warehouse management of a large e-commerce company. Together with the stakeholders, we identified the backlog in days as an important KPI that helps them decide on their capacity planning. The backlog should show a relationship between the unprocessed order pool and the next day's average daily processing capacity. We were happy to find an outbound backlog metric ready to be used in our BI system. After a quick review over several days, it looked like we had just found what we needed, so we included the metric in our dashboard. 🚨 Shortly after, our stakeholders complained that the numbers were extremely off compared to the business reality. We soon figured out that while the open order pool items were correct, the assumed average capacity was not. The BI system only contained the actual processed volumes instead of the planned future capacities. Due to the volatile nature of e-commerce, this definition difference of past vs. future values could lead to a completely opposite representation of the current backlog. With one definition, we showed a dramatic backlog of over 5 days, while the correct one would have been a healthy 0.5 days. 🔧 We were able to fix the metric by implementing a process to upload the planned capacities. 𝗟𝗲𝘀𝘀𝗼𝗻𝘀 𝗹𝗲𝗮𝗿𝗻𝗲𝗱: 1. Always check with the stakeholders to understand how they interpret the KPI.     2. Never assume that the number looks good. Check the definition, and if you are unsure, build your own metrics.     3. If you must choose between different definitions, choose the ones that best align with the stakeholder's decision. What challenges did you encounter when defining KPIs? Share your experience in the comments! ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find this post useful ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your data analyst career #dataanalytics #datascience #kpis #stakeholdermanagement #dashboards

Crowning a New Term: “Iceberg Metrics” 🧊 ✨ I’m calling it: Iceberg Metrics represent KPIs that only reveal the tip of what’s really happening below the surface. Metrics like abandoned carts seem simple but often mask much more—checkout friction, hidden costs, trust issues, and more. To truly understand and optimize, we need to dig deeper. Here’s how to dive into the “iceberg” of abandoned cart rates: 1. Establish Baseline Metrics: Start by gathering data on current abandoned cart rates, session times, and bounce rates using heat maps and session recordings to see where users drop off. 2. Segment the Audience: Analyze users by behavior (first-time vs. repeat visitors, mobile vs. desktop) and traffic source (organic, paid, email). 3. Experiment Hypotheses: Develop hypotheses for abandonment reasons—shipping costs, checkout friction, distractions, or lack of trust signals—and test them. 4. Run A/B Tests: Test variations like simplifying the checkout process, showing shipping costs earlier, adding trust badges, or retargeting abandoned cart emails. 5. Use Heat Maps & Session Recordings: Examine user behavior in real time. Look for confusion or hesitation, where users hover, and whether they engage with key information. 6. Contextualize Results: Analyze how changes impact overall user flow. Did simplifying checkout help, or did other metrics like bounce rate increase? 7. Ecosystem Approach: Examine how tweaks affect the full journey—from product discovery to checkout—balancing short-term improvements with long-term goals like lifetime value. 8. Iterate: Refine solutions based on experiment findings and continuously optimize the customer journey. This one’s mine, folks! #IcebergMetrics #OwnIt #DataDriven #EcommerceOptimization #NewMetricAlert Cheers, Your cross-legged CAC and CLV buddy 🤗

🚀How to choose the right KPIs for your tech scale-up I've noticed a consistent challenge Many businesses collect extensive data but struggle to identify which metrics actually matter. Here's my top tips on choosing KPIs that will genuinely drive your business forward. 1️⃣ Start with strategy, not metrics: Your KPIs should reflect your strategy through numbers. Before opening any spreadsheets, ask yourself: 🎯where are you aiming to get to? 🥅what specific goals have you set for your team? 🥸how do you differentiate from competitors? Your answers should guide your choice of metrics, not the other way around. 2️⃣Balance leading and lagging indicators: Here's a practical example. If your goal is to increase premium tier adoption from 15% to 25%, that percentage is your lagging indicator. But you need leading indicators to drive progress. For your sales team, this might mean tracking: ✅number of upgrade conversations with existing customers ✅weekly demos of premium features ✅customer feature usage patterns These leading indicators help predict whether you'll hit your target and allow for adjustments while there's still time to impact the outcome. 3️⃣The essential metrics: Some metrics need consistent monitoring regardless of your strategy. In my experience, these include: ☑️MRR ☑️EBITDA ☑️Cash runway ☑️Customer LTV ☑️Customer churn Consider these your fundamental business health indicators. 4️⃣Make data collection seamless: Even the best-designed KPI framework fails if data collection is manual and inconsistent. Two key principles: 🖥️automate wherever possible 🐣capture data at its earliest possible point For example, don't wait for finance to categorize sales by department at month-end. Build it into your invoicing process. 5️⃣Consider the human element: Numbers need context to drive action. For KPIs to create change: 🗣️share them with the people who can impact them 🤔explain the reasoning behind each metric 🔎make them visible and accessible 🫧create clear accountability I've consistently seen that teams who understand why they're tracking certain metrics perform better than those who are simply told what to track. What separates effective KPI frameworks from ineffective ones? Keep your regular reporting focused on metrics that are: 🔗directly linked to strategy 😕simple to understand ✔️actionable by your team ❤️🩹critical to business health But maintain other data points in your systems. They become valuable when investigating problems or identifying opportunities. If you're working on refining your KPI framework, what's the one metric that's transformed how you view your business performance? Want to dive deeper into building effective reporting structures for your scale-up? DM me for a copy of our KPI framework template. #techscaleup #startupmetrics #businessgrowth #datadrivendecisions

Using Data to Drive Strategy: To lead with confidence and achieve sustainable growth, businesses must lean into data-driven decision-making. When harnessed correctly, data illuminates what’s working, uncovers untapped opportunities, and de-risks strategic choices. But using data to drive strategy isn’t about collecting every data point — it’s about asking the right questions and translating insights into action. Here’s how to make informed decisions using data as your strategic compass. 1. Start with Strategic Questions, Not Just Data: Too many teams gather data without a clear purpose. Flip the script. Begin with your business goals: What are we trying to achieve? What’s blocking growth? What do we need to understand to move forward? Align your data efforts around key decisions, not the other way around. 2. Define the Right KPIs: Key Performance Indicators (KPIs) should reflect both your objectives and your customer's journey. Well-defined KPIs serve as the dashboard for strategic navigation, ensuring you're not just busy but moving in the right direction. 3. Bring Together the Right Data Sources Strategic insights often live at the intersection of multiple data sets: Website analytics reveal user behavior. CRM data shows pipeline health and customer trends. Social listening exposes brand sentiment. Financial data validates profitability and ROI. Connecting these sources creates a full-funnel view that supports smarter, cross-functional decision-making. 4. Use Data to Pressure-Test Assumptions Even seasoned leaders can fall into the trap of confirmation bias. Let data challenge your assumptions. Think a campaign is performing? Dive into attribution metrics. Believe one channel drives more qualified leads? A/B test it. Feel your product positioning is clear? Review bounce rates and session times. Letting data “speak truth to power” leads to more objective, resilient strategies. 5. Visualize and Socialize Insights Data only becomes powerful when it drives alignment. Use dashboards, heatmaps, and story-driven visuals to communicate insights clearly and inspire action. Make data accessible across departments so strategy becomes a shared mission, not a siloed exercise. 6. Balance Data with Human Judgment Data informs. Leaders decide. While metrics provide clarity, real-world experience, context, and intuition still matter. Use data to sharpen instincts, not replace them. The best strategic decisions blend insight with empathy, analytics with agility. 7. Build a Culture of Curiosity Making data-driven decisions isn’t a one-time event — it’s a mindset. Encourage teams to ask questions, test hypotheses, and treat failure as learning. When curiosity is rewarded and insight is valued, strategy becomes dynamic and future-forward. Informed decisions aren't just more accurate — they’re more powerful. By embedding data into the fabric of your strategy, you empower your organization to move faster, think smarter, and grow with greater confidence.

Day 6 of whatever it takes, Continuing our discussion on the optimization of Fraud Model & thereby reducing the False positives to improve the Customer Satisfaction & Reduce the revenue loss. Below were the KPIs we talked about: 1. Business Impact: False Positive Rate (FPR), Revenue Loss Due to False Positives, Customer Churn Rate Post-False Positive, Business Impact Score (BIS) & finally, Returns on Investment (ROI). 2. Customer Experience: Customer Complaint Volume (Related to Fraud Flagging), Average Resolution Time for False Positives, Customer Satisfaction Index (CSI), Revenue Reinstatement Rate Post-Resolution, Net Promoter Score (NPS) Post-Incident, Customer Account Blocking Rate, Repeat Incidence Rate of False Positives. 3. Model Drift: Population Stability Index (PSI), Feature Importance Drift, - Concept Drift Metrics (Sudden and Gradual Drift Detection): By monitoring for both sudden shifts and gradual pattern changes, these metrics help capture new fraud tactics quickly. - Model Performance Degradation Over Time: Observing declines in core metrics like precision and recall provides a direct indication of how well the model is coping with drift. - Retraining Frequency and Adaptation Impact: Tracking retraining frequency shows how adaptable the model is to changing patterns and can indicate areas where the data environment is volatile - Drift Detection Using Adversarial Validation: By creating a classifier to detect differences between past and current data, this KPI provides a nuanced look at subtle shifts that may otherwise go unnoticed - Error Rate Increase Across Data Segments: Monitoring error rates for specific customer groups helps to localize drift impacts, ensuring that segments like small businesses are not disproportionately affected. - Time to Detection of Drift-Triggered Performance Drop: This KPI ensures that any significant performance drop is quickly flagged and corrected, minimizing the risk of ongoing misclassifications. The revenue part of the business which also gets impacted due to High Number of False positives are also important to interpret: - Revenue Impact of False Positives: Total revenue of legitimate transactions blocked or delayed due to incorrect fraud flags. - Average Revenue per Blocked Transaction: Total Revenue Impact of False Positives/Total number of such transactions - Lifetime Value (LTV) Loss from False Positives:  Sum of LTV for customers who churned due to false positives. - Average Revenue Delay Due to False Positives: Average time delay multiplied by revenue for transactions wrongly flagged as fraud. - False Negative Revenue Impact (Missed Fraud): Estimated revenue lost to undetected fraud cases. These KPIs across Business Impact, Customer Experience, Model Drift and Revenue Loss will help us in understanding the technical details of the optimization. #FraudOptimization #ML #Data #Analytics #DataDrivenDecisionMaking #Intelligence #consistency

I've spent over 4,000 hours in stakeholder requirement-gathering meetings! Save hours of your life by asking these questions: 1. What do they plan to use the data for? 1. What initiative are they working on? 2. How will this initiative impact the business? 3. Is this for reporting or optimizing existing workflows? Understanding the purpose of the data helps you define its impact. 2. How do they plan to use the data? Will they access it via SQL, BI tools, APIs, or another method? 1. Do they have a workflow to pull data from your dataset? 2. Do they just do a `SELECT *` from your dataset? 3. Do they perform further computations on your dataset? This determines the schema, partitions, and data accessibility needs. 3. Is this data already present in another report/UI? 1. Is this data already available in another location? 2. Do they have parts of this data (e.g., a few required columns) elsewhere? Ensuring you're not recreating work saves time and avoids redundancy. 4. How frequently do they need this data? 1. How frequently does the data actually need to be refreshed? 2. Can it be monthly, weekly, daily, or hourly? 3. Is the upstream data changing fast enough to justify the required latency? Understanding frequency helps you determine the pipeline schedule. 5. What are the key metrics they monitor in this dataset? 1. Define variance checks for these metrics. 2. Do these metrics need to be 100% accurate (e.g., revenue) or directionally correct (e.g., impressions)? 3. How do these metrics tie into company-level KPIs? Memorize average values for these metrics; they’re invaluable during debugging and discussions. 6. What will each row in the dataset represent? 1. What should each row represent in the dataset? 2. Ensure one consistent grain per dataset, as applicable. 7. How much historical data will they need? 1. Does the stakeholder need data for the last few years? 2. Is the historical data available somewhere? Ask these questions upfront, and you'll save countless hours while delivering exactly what stakeholders need. - Like this post? Let me know your thoughts in the comments, and follow me for more actionable insights on data engineering and system design. #data #dataengineering #datastakeholder

HSE Leading & Lagging Indicators 🔹 Leading Indicators Proactive, preventive, and predictive measures that focus on activities, behaviors, or conditions before an incident occurs. They help organizations identify weaknesses and prevent accidents. 🔹Benefits of Leading Indicators: Encourage proactive safety culture. Provide early warnings to prevent incidents. Help management measure the effectiveness of safety programs. Improve worker engagement and awareness. 🔹Examples: Number of safety trainings conducted. Percentage of employees attending toolbox talks. Number of safety audits and inspections performed. Near-miss reporting frequency. Percentage of corrective actions closed on time. Behavior-based safety observations. Preventive maintenance completed as scheduled. 🔹 Lagging Indicators Reactive measures that reflect events that have already happened — often used to measure outcomes of safety programs in terms of failures, accidents, or losses. 🔹Benefits of Lagging Indicators: Provide measurable results and statistics for performance evaluation. Help identify trends of recurring incidents. Useful for regulatory reporting and benchmarking against industry standards. Show the consequences of gaps in safety management. 🔹Examples: Number of Lost Time Injuries (LTI). Total Recordable Incident Rate (TRIR). Number of fatalities. Days Away, Restricted, or Transferred (DART rate). Number of property damage incidents. Medical treatment cases. Workers’ compensation claims. 🔹 Comparison Leading indicators = proactive (inputs, prevention, actions). Lagging indicators = reactive (outputs, results, outcomes). The best HSE systems use both indicators: Leading indicators to predict and prevent. Lagging indicators to measure performance and outcomes. #KPI #HSE #HSEProfessional #HSEManagement #Leading_Indicators #Lagging_Imdicators

🚀 Understanding OEE (Overall Equipment Effectiveness) – The Core Indicator of Manufacturing Excellence In manufacturing, production quantity alone does not define operational performance. A line may achieve target output while still suffering from hidden losses affecting productivity, quality, machine utilization, and operating cost. This is where OEE (Overall Equipment Effectiveness) becomes critical. OEE is a comprehensive KPI used to measure how effectively a manufacturing process or equipment is utilized compared to its full potential under ideal operating conditions. 📊 OEE Formula: OEE = Availability × Performance × Quality Each parameter represents a major dimension of manufacturing effectiveness: ✅ Availability Measures actual operating time against planned production time. Availability losses mainly occur due to: • Equipment breakdowns • Tool failures • Setup & changeover losses • Planned maintenance overruns • Material unavailability • Utility interruptions Formula: Availability = Operating Time / Planned Production Time High availability indicates strong equipment reliability and maintenance effectiveness. ✅ Performance Measures whether the machine is running at its designed or standard production speed. Performance losses include: • Minor stoppages • Idling • Reduced cycle speed • Operator inefficiencies • Improper machine settings • Feed interruptions Formula: Performance = Actual Output / Target Output A machine running continuously at lower speed may appear productive, but performance analysis exposes the hidden capacity loss. ✅ Quality Measures the percentage of good parts produced against total production. Quality losses arise from: • Rejection & scrap • Rework • Dimensional variation • Process instability • Welding defects • Surface defects • Startup rejection Formula: Quality = Good Parts / Total Parts Produced Quality directly impacts customer satisfaction, COPQ (Cost of Poor Quality), and process capability. 📉 The Six Major Losses impacting OEE: 1️⃣ Equipment Failure Losses 2️⃣ Setup & Adjustment Losses 3️⃣ Minor Stoppages 4️⃣ Reduced Speed Losses 5️⃣ Process Defects/Rejections 6️⃣ Startup Yield Losses These losses are the foundation of TPM (Total Productive Maintenance) and Lean Manufacturing improvement activities. 🎯 World-Class OEE Benchmark • Availability → >90% • Performance → >95% • Quality → >99% • Overall OEE → ≥85% An OEE below target indicates hidden inefficiencies within the manufacturing system. 📌 Improving OEE results in: ✔ Reduced downtime ✔ Higher machine utilization ✔ Improved throughput ✔ Better First Pass Yield (FPY) ✔ Lower rejection & rework ✔ Improved process stability ✔ Increased operational profitability OEE is not just a production metric; it is a strategic tool for identifying losses, improving process capability, and driving continuous improvement across the shop floor. In Modern Way “Machines do not create losses silently — poor monitoring does.”

Why BRD Changed My Analytics Career: I spent 2 years building dashboards that nobody used. This wasn't due to poor SQL or unattractive Tableau visuals; it was because I never asked why anyone needed them. The turning point came when a stakeholder said five words: "This isn't what we wanted." After three weeks of work, zero bugs, and beautiful visuals, I realized I had delivered something completely useless. I initially blamed shifting requirements, vague briefs, and the stakeholder. Then, my manager asked, "Did you write a BRD before you started?" 🫥 A Business Requirements Document (BRD) isn't just a formality; it's a contract established before any code is written. It answers critical questions: - What problem are we actually solving? - What does success look like, and how is it measured? - What's in scope, and what's not? - What assumptions are we making, and what could go wrong? Without a BRD, you're guessing. With it, you're aligned. The modern BRD has evolved from the traditional document that nobody reads. It now integrates: - AI/ML requirements: model type, explainability, human-in-the-loop - Cloud & API specs: latency SLAs, fallback logic - Data privacy: GDPR, DPDP Act, access controls - KPI definitions: formula, source, refresh frequency - Model drift & feedback loops The technology has changed, and so should the BRD. Here is my refined framework, developed over 200+ projects, includes: 1. Problem Statement — what decision are we enabling? 2. Business Objective — revenue, risk, efficiency? 3. Scope Boundary — write OUT OF SCOPE first 4. KPI Definitions — formula, source, target 5. Functional Requirements — with priority ratings 6. AI/Cloud Requirements (often overlooked) 7. Non-Functional Requirements — SLA, concurrency, latency 8. Assumptions & Risks — the honest section 9. Stakeholder What changed after I started using BRDs: ✅ Rework dropped ~40% ✅ Stakeholder escalations — nearly zero ✅ Scope creep became controllable ✅ I stopped being a dashboard developer ✅ I became a problem solver The biggest shift wasn't in my technical skills. It was in the conversation I had before opening a laptop. SQL won't save you. Python won't save you. Clarity will. Built a modern BRD template — updated for AI, cloud & compliance sections built in. 🔗 Download link in comments ↓