Performance Measurement Systems

100 percent on KPIs .....0% bonus 😭😭😭 The misalignment noone talks about !!! Last week, I sat with a frustrated CEO who said something that every leader should pay attention to… “My managers are all scoring 100% on their KPIs… but the business is scoring 0% on its strategy targets.” He wasn’t exaggerating. When we opened their performance files, every manager had neat, well-completed KPIs all pulled straight from their job descriptions. ✔️ “Prepare monthly reports” ✔️ “Attend weekly meetings” ✔️ “Supervise the team” ✔️ “Submit budgets” ✔️ “Manage stakeholder relationships” All ticked. All done. All… irrelevant to the company’s actual strategy. Because here’s the twist: Their bonus wasn’t linked to job descriptions. It was linked to strategy. And the strategy said: ❌ Grow revenue by 50% ❌ Reduce cost-to-serve by 10% ❌ Improve turnaround time by 5% ❌ Expand two new markets ❌ Digitise three processes None of that was in anyone’s KPIs. Not one person was actively carrying the strategy on their shoulders. Everyone was performing… but no one was delivering. So the company had: A team that scored 100% A business that scored 0% And a bonus pool that had nothing to pay out And suddenly the tension made sense. When KPIs come from job descriptions, you get activity. When KPIs come from strategy, you get results. That day, the CEO looked at me and said: “We don’t have a performance problem. We have an alignment problem.” And that’s the truth in many organisations today. People are working hard , very hard , but not always on the things that shift the business forward. 👉 If you want accountability, align KPIs to strategy. 👉 If you want growth, cascade the strategy into every role. 👉 If you want bonuses to make sense, measure what matters. Because nothing is more painful than a team doing their best… on the wrong things. #leadership #strategy Winfield Strategy & Innovation - Winfield Business School

Has Amazon cracked the code on developer productivity with its cost to serve software (CTS-SW) metric? Amazon applied its well-known "working backwards" methodology to developer productivity. "Working backwards" in this case starting with the outcome: concrete returns for the business. This is measured by looking at the rate of customer-facing changes delivered by developers, i.e. "what the team deems valuable enough to review, merge, deploy, and support for customers", in the words of the blog post by Jim Haughwout https://lnkd.in/eqvW5wbi . This metric is different from other measures of developer productivity which look only at velocity or time saved. Instead, "CTS-SW directly links investments in the developer experience to those outcomes by assessing how frequently we deliver new or better experiences. Some organizations fall into the anti-pattern of calculating minutes saved to measure value, but that approach isn’t customer-centered and doesn’t prove value creation." This aligns with Gartner's own research on developer productivity. In our 2024 Software Engineering survey, we asked what productivity metric organizations are using to measure their developers. We also asked about a basket of ten success metrics, including software usability, retention of top performers, and meeting security standards. This allowed us to find out which productivity metric was associated most with success. What we found in our survey was that *rate of customer-facing changes* is the metric most associated with success. Some other productivity metrics were actually *negative associated* with success. But *rate of customer-facing changes* is what organizations should focus on. Sadly, our survey found that few organizations (just 22%) use this metric. I presented this data at our #GartnerApps summit [and the next summit is coming up in September: https://lnkd.in/ey2kpc2 ] Every metrics gets gamed. So I always recommend "gaming the gaming". A developer might game the CTS-SW metric by focusing more on customer-facing changes. But... this is actually a good thing. You're gaming the gaming. We will be watching closely how this metric gets adopted alongside DORA, SPACE, and other metrics in the industry.

In Q1 2025, LTI (Ongoing Equity) Programs Had 4x the “Pay for Performance” Differentiation for Promoted Employees Vs. Salary Raises Companies generally reward top performers through three types of compensation programs: [A] Salary Raises [B] Long Term Incentives (LTI)–often ongoing equity grants [C] Short Term Incentives (STI)–often called a bonus program Today, let’s compare how much differentiation there is across the market for top performers between [A] and [B]. ________________ 𝗠𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝘆: We recently took a look at Q1 2025 merit cycle data across 46k+ employees from Pave's dataset. 1st, our data science team grouped and analyzed employees across four groups:  • [1] Promoted  • [2] Above expectations (no promo)  • [3] Meets Expectations or equivalent (no promo)  • [4] Below Expectations (no promo) 2nd, our data science team looked at two dimensions across salary and ongoing equity grants  • [1] What % of employees received a compensation update?  • [2] For those who received, what was the size of the increase? Note that for equity, this was measured by the % increase in net equity value compensation vesting over the next 12 months 3rd, our data science team multiplied “participation” with “amount” to find the “𝗲𝘅𝗽𝗲𝗰𝘁𝗲𝗱 𝘃𝗮𝗹𝘂𝗲 𝗼𝗳 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲” as a method of measuring pay for performance. ________________ The Results: ✅ 𝗣𝗿𝗼𝗺𝗼𝘁𝗲𝗱  => Salary: +9.7% expected value increase => Ongoing Equity: +38.6% expected value increase ✅ 𝗔𝗯𝗼𝘃𝗲 𝗘𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀 (𝗡𝗼 𝗣𝗿𝗼𝗺𝗼)  => Salary: +4.5% => Ongoing Equity: +11.0% ✅ 𝗠𝗲𝗲𝘁𝘀 𝗘𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗿 𝗘𝗾𝘂𝗶𝘃𝗮𝗹𝗲𝗻𝘁 (𝗡𝗼 𝗣𝗿𝗼𝗺𝗼)  => Salary: +3.1% => Ongoing Equity: +3.8% ✅ 𝗕𝗲𝗹𝗼𝘄 𝗘𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀 (𝗡𝗼 𝗣𝗿𝗼𝗺𝗼)  => Salary: +0.3% => Ongoing Equity: +0.0% expected value increase ________________ 𝗠𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: 1️⃣ 𝗣𝗿𝗼𝗺𝗼𝘁𝗲𝗱 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀 𝗿𝗲𝗰𝗲𝗶𝘃𝗲 𝗮 𝗺𝗲𝗱𝗶𝗮𝗻 𝗲𝘅𝗽𝗲𝗰𝘁𝗲𝗱 𝘃𝗮𝗹𝘂𝗲 𝟯𝟴.𝟲% “𝗲𝗾𝘂𝗶𝘁𝘆 𝗿𝗮𝗶𝘀𝗲” 𝘃𝘀 𝗮 𝟵.𝟳% 𝘀𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗶𝘀𝗲. This means that for promoted employees, the equity comp is ~4x as outsized from a pay for performance standpoint. 2️⃣ 𝗠𝗲𝗮𝗻𝘄𝗵𝗶𝗹𝗲, 𝘁𝗵𝗲 “𝗲𝗾𝘂𝗶𝘁𝘆 𝗿𝗮𝗶𝘀𝗲𝘀” (𝟯.𝟴%) 𝗮𝗿𝗲 𝗺𝘂𝗰𝗵 𝗰𝗹𝗼𝘀𝗲𝗿 𝘁𝗼 𝘀𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗶𝘀𝗲𝘀 (𝟯.𝟭%) 𝗳𝗼𝗿 “𝗺𝗲𝗲𝘁 𝗲𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀” 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀. This suggests that the real LTI/ongoing equity comp differentiation is happening for top performers (both those in the “promoted” and “above expectations (no promo)” buckets. ________________ 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗦𝘂𝗴𝗴𝗲𝘀𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗖𝗼𝗺𝗽𝗲𝗻𝘀𝗮𝘁𝗶𝗼𝗻 & 𝗛𝗥 𝗟𝗲𝗮𝗱𝗲𝗿𝘀: Analyze your company’s “expected value” salary and equity raise amounts. How do your outcomes compare to the Q1 2025 benchmarks from this post? And where + how should you consider tweaking your "recommendation logic” to guide your company towards more or less merit cycle differentiation for different cohorts of employees?

Don’t Lose Your RAG: How to Use Red, Amber & Green Responsibly in Dashboards As data professionals, we’ve all heard it: “Don’t use red and green in your dashboards - people with colour vision deficiency won’t be able to tell the difference.” Wrong. The truth is, you can use red, amber, and green - as long as you do it properly. What do I mean, and why should you care? 🧐 Why RAG Still Matters Whether you like it or not, RAG status indicators are embedded in business culture - especially in executive reports and performance dashboards. 📌 Executives love a quick-glance signal. 📌 Red = urgent. Green = all good. Amber = needs attention. 📌 It’s familiar. And familiarity drives faster decisions. So rather than throwing RAG out entirely, we need to be smarter about how we use it. 🧐 The Accessibility Issue Roughly 1 in 12 men and 1 in 200 women have some form of colour vision deficiency (CVD) - commonly referred to as colour blindness. The most common type, deuteranopia, affects red-green perception. So yes - your standard green-to-red gradient is a mess for a sizeable chunk of your audience. Especially when: ❌ You're using continuous colour scales (e.g. red to green heatmaps) ❌ The colours are just slightly different hues with no other visual cue ❌ There's no text, label or tooltip to clarify meaning 🧐 What Works Better? ✅ Use discrete colours instead of gradients When you’re working with Red, Amber, and Green KPIs, use clearly separated blocks of colour, not a continuous scale. This removes the visual ambiguity. ✅ Choose a colour-safe palette For example, the IBCS colour palette provides Red and Green colours that remain distinguishable even with CVD filters applied. ✅ Add labels or tooltips Text clarifies what colour alone might not. A simple “Red: Critical”, “Amber: At Risk”, “Green: On Track” label or hover tooltip can make your dashboard instantly more accessible. ✅ Give users control Where appropriate, consider letting users switch views or toggle labels to meet their accessibility needs. 🧐 A Real Example I recently tested two RAG designs (see in the attached video): 1) A standard green-to-red diverging palette (looked fine at first - but ran into problems under a CVD filter) 2) A discrete IBCS-style palette with clearly separated colour blocks (remained readable even with filters applied) The difference was clear - literally. And yet, the RAG meaning remained intact for all users. 🧐 Final Thoughts Stop treating red and green as the villains of dashboard design. Used well, they’re clear, powerful, and intuitive. Just make sure you’re thinking about everyone who uses your dashboards - not just those with perfect vision. 🎯 Want to try this yourself? Test your colour palettes with ColorOracle.org and build with confidence. And as I always say…give your stakeholders what they WANT, and a little bit more of what they NEED, each and every time… #DashboardDesign #ColourAccessibility #TableauTips #KPIReporting

🚜🌾 Reimagining Agriculture with AI — My New Field Monitoring System is LIVE! Farming has always been about precision — and now, AI is bringing a whole new level of intelligence to the field. I just built an end-to-end, production-ready video pipeline that transforms raw footage into real-time insights for agriculture. Here’s what it delivers: 🔹 Smart Row Line Detection – YOLOv11 instance segmentation draws razor-sharp field lines with rotated boxes for precision planting & navigation. 🔹 Worker Behavior Analysis – Pose estimation tracks “working” vs. “resting” to monitor activity and support productivity & safety. 🔹 Consistent Tracking – Stable IDs and center numbering make rows and workers easy to reference across frames. 🔹 Smooth Real-Time AI – Runs at 720p with crisp overlays, frame stats, and polished UI (logo, aligned panels, clean footer). 🔹 Actionable Insights – Every video is saved for review, reporting, and farm decision-making. ⚙️ Under the Hood: Python | YOLOv11 (Ultralytics) | OpenCV | NumPy | Pillow Why does this matter? ✅ Precision farming = less waste, more yield ✅ Real-time monitoring = proactive decisions ✅ Automation = reduced labor costs & higher efficiency This is just the beginning 🚀 — AI has the power to reshape how we monitor, manage, and optimize agriculture fields. 👉 Want to try it yourself? I’m making the source code + trained models available here: https://lnkd.in/dydPQwJs #AI #ComputerVision #AgricultureTech #YOLOv11 #PoseEstimation #MLOps #Sustainability #FutureOfFarming

Step-by-Step Guide to Measuring & Enhancing GCC Productivity - Define it, measure it, improve it, and scale it. Most companies set up Global Capability Centers (GCCs) for efficiency, speed, and innovation—but few have a clear playbook to measure and improve productivity. Here’s a 7-step framework to get you started: 1. Define Productivity for Your GCC Productivity means different things across industries. Is it faster delivery, cost reduction, innovation, or business impact? Pro tip: Avoid vanity metrics. Focus on outcomes aligned with enterprise goals. Example: A retail GCC might define productivity as “software features that boost e-commerce conversion by 10%.” 2. Select the Right Metrics Use frameworks like DORA and SPACE. A mix of speed, quality, and satisfaction metrics works best. Core metrics to consider: • Deployment Frequency • Lead Time for Change • Change Failure Rate • Time to Restore Service • Developer Satisfaction • Business Impact Metrics Tip: Tools like GitHub, Jira, and OpsLevel can automate data collection. 3. Establish a Baseline Track metrics over 2–3 months. Don’t rush to judge performance—account for ramp-up time. Benchmark against industry standards (e.g., DORA elite performers deploy daily with <1% failure). 4. Identify & Fix Roadblocks Use data + developer feedback. Common issues include slow CI/CD, knowledge silos, and low morale. Fixes: • Automate pipelines • Create shared documentation • Protect developer “focus time” 5. Leverage Technology & AI Tools like GitHub Copilot, generative AI for testing, and cloud platforms can cut dev time and boost quality. Example: Using AI in code reviews can reduce cycles by 20%. 6. Foster a Culture of Continuous Improvement This isn’t a one-time initiative. Review metrics monthly. Celebrate wins. Encourage experimentation. Involve devs in decision-making. Align incentives with outcomes. 7. Scale Across All Locations Standardize what works. Share best practices. Adapt for local strengths. Example: Replicate a high-performing CI/CD pipeline across locations for consistent deployment frequency. Bottom line: Productivity is not just about output. It’s about value. Zinnov Dipanwita Ghosh Namita Adavi ieswariya k Karthik Padmanabhan Amita Goyal Amaresh N. Sagar Kulkarni Hani Mukhey Komal Shah Rohit Nair Mohammed Faraz Khan

The Hidden Risk of Misaligned MSOPs: Balancing Incentives with Long-Term Growth When not properly structured, Management Stock Option Plans (MSOPs) can shift from being a powerful incentive to a strategic liability—where short-term stock gains take precedence over long-term enterprise value While MSOPs are designed to align leadership ambition with shareholder interests, a misalignment often creates a performance paradox—driving executives to optimize for immediate stock price movements rather than fostering enterprise resilience and transformational growth. This short-sighted approach doesn’t just distort decision-making; it gradually erodes a company’s ability to maintain a sustainable competitive advantage Where Does the Disconnect Happen? ⚠️ Over-fixation on stock price incentives leads to risk aversion, discouraging bold, transformative decisions ⚠️ Rigid vesting timelines fail to accommodate the unpredictable pace of innovation and market evolution ⚠️ Misaligned MSOPs reward individual achievements over collective success, fragmenting leadership focus How Can We Realign MSOPs with Strategic Vision? 📌 Integrate broader KPIs: Move beyond stock price metrics—incorporate innovation milestones, strategic impact, and stakeholder value. 📌 Shift to milestone-based vesting: Replace time-based rewards with goal-oriented incentives that drive meaningful outcomes. 📌 Encourage cross-functional collaboration: Design MSOPs that align executive incentives with company-wide synergies, preventing siloed decision-making. 📌 Extend vesting horizons: Link MSOPs to long-term strategic initiatives, ensuring leadership remains focused on sustainable growth. When leadership incentives are tied solely to short-term performance, true innovation suffers. The most significant victories aren’t achieved overnight—they are the result of forward-thinking strategies that may seem small today but position companies far ahead of the competition tomorrow MSOPs should be more than just incentives; they should be the cornerstone of sustainable leadership and long-term value creation What are your thoughts on structuring MSOPs for lasting impact? Let’s discuss!

Industrial reliability and uptime in the era of Industry 4.0 The gap between world-class facilities and average performers often comes down to how well they manage three critical metrics: MTTR, MTBF, and OEE. These are interconnected indicators that reveal the health of your entire operation. MTTR (Mean Time to Repair): Measures how quickly your team responds to failures. The formula is straightforward: Total Downtime divided by Number of Repairs. But the real insight comes from understanding what drives these numbers → spare parts availability, technician skill levels, diagnostic capabilities, and repair procedures. MTBF (Mean Time Between Failures): Reflects asset reliability through the lens of Total Uptime divided by Number of Failures. Higher MTBF indicates more dependable equipment, but achieving it requires systematic approaches to root cause analysis, proactive component replacement, and condition monitoring. OEE (Overall Equipment Effectiveness): The comprehensive metric that multiplies Availability × Performance × Quality. OEE reveals how effectively your assets convert planned production time into quality output. World-class facilities typically achieve OEE scores above 85%. Here’s the Critical Interconnection: These metrics don't operate in isolation. Improving MTBF directly impacts MTTR by reducing the frequency of repairs. Both contribute to better Availability within the OEE calculation. Performance and Quality factors add additional layers that complete the operational picture. Modern facilities are enhancing these traditional metrics with real-time monitoring and predictive analytics. IoT sensors provide continuous condition data that helps predict failures before they occur, while automated algorithms identify patterns that improve both repair efficiency and failure prevention. The most sophisticated operations integrate these metrics into digital dashboards that provide instant visibility into equipment performance trends and maintenance effectiveness. *** P.S.: Looking for more in-depth industrial insights? Follow me for more on Industry 4.0, Predictive Maintenance, and the future of Corrosion Monitoring.

Has the recent us-east-1 outage got you thinking about your cloud region policy? Then it's a great time to consider sustainability in your decision making. Remember, the environmental impact of your cloud workloads isn't only affected by WHAT you're running - but WHERE you're running it. Due to the differing availability of renewables around the globe, local grids vary in their carbon intensity. This means the same power consumption will emit more or less carbon depending on where the computation is happening. Lots of wind and solar energy = less carbon emissions Lots of fossil fuels = more carbon emissions As an example... moving from us-east-1 to us-west-2 equals a 45% decrease in carbon emissions for the same workload. Interested? I've created a really easy-to-use scorecard for engineers and decision makers to refer to when checking Cloud Region Carbon Intensity for AWS Azure and GCP. Here's how the scoring works... A+ The lowest gco2/kwh regions available A: ~1.4× to 3× the carbon intensity of the A+ average B: ~3× to 6× the carbon intensity of the A+ average C: ~6× to 10× the carbon intensity of the A+ average D: ~10× to 15× the carbon intensity of the A+ average E: ~15× to 25× the carbon intensity of the A+ average F: Above ~25× the carbon intensity of the A+ average These scores are all based on region-specific, hourly carbon intensity data provided by Greenpixie's GPX-Grid product. I will be releasing the scorecard soon via the GreenOps Academy and for download. I'm curious: Region selection is a multi-level strategic decision... would your teams use this resource before selecting region?