Using Analytics to Measure Productivity

Every week for the past five years, I’ve calculated a single number that determines whether I’ve been productive. It isn’t a revenue or product-related stat. It’s the percentage of my time spent on tasks I actually PLANNED to do. Giving yourself a weekly success score doesn’t work for everyone, but it’s been an insane productivity hack for me because it gives visibility into my work AND gives me something to improve upon. This concept came from Intercom co-founder Des Traynor, who created the perfect Venn diagram of productivity: find the overlap between your email, your to-do list, and your calendar so you can stop letting everyone else control your time. The solution is to track how much of your time aligns with your intentions, AKA your alignment score. Here’s what to do, using this doc that lets you sync your email, calendar, and to-do list: https://lnkd.in/gHyBvgKv 1. Work through your emails and identify which ones have actions. 2. Turn the emails into entries on your to-do list. 3. Slot each entry into a specific time block on your calendar (the template will do it for you). 4. Now, your to-do list has two new columns: when you’re supposed to work on a task and where it came from. At the end of the week, you get a chart that shows what percentage of your time is spent on your planned to-dos vs. reactive work. The system triages emails into different buckets, ensures the important ones make it to your to-do list, merges them with what you already planned to accomplish, then helps you allocate time for each task. Try calculating your score for a month and see what changes! And don’t feel bad if you’re not at 100%—for me, any week that crosses 50% is a good week. 🙂 Are there any productivity hacks you swear by?

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

📈 Unlocking the True Impact of L&D: Beyond Engagement Metrics 🚀 I am honored to once again be asked by the LinkedIn Talent Blog to weigh in on this important question. To truly measure the impact of learning and development (L&D), we need to go beyond traditional engagement metrics and look at tangible business outcomes. 🌟 Internal Mobility: Track how many employees advance to new roles or get promoted after participating in L&D programs. This shows that our initiatives are effectively preparing talent for future leadership. 📚 Upskilling in Action: Evaluate performance reviews, project outcomes, and the speed at which employees integrate their new knowledge into their work. Practical application is a strong indicator of training’s effectiveness. 🔄 Retention Rates: Compare retention between employees who engage in L&D and those who don’t. A higher retention rate among L&D participants suggests our programs are enhancing job satisfaction and loyalty. 💼 Business Performance: Link L&D to specific business performance indicators like sales growth, customer satisfaction, and innovation rates. Demonstrating a connection between employee development and these outcomes shows the direct value L&D brings to the organization. By focusing on these metrics, we can provide a comprehensive view of how L&D drives business success beyond just engagement. 🌟 🔗 Link to the blog along with insights from other incredible L&D thought leaders (list of thought leaders below): https://lnkd.in/efne_USa What other innovative ways have you found effective in measuring the impact of L&D in your organization? Share your thoughts below! 👇 Laura Hilgers Naphtali Bryant, M.A. Lori Niles-Hofmann Terri Horton, EdD, MBA, MA, SHRM-CP, PHR Christopher Lind

*** SPOILER *** Some early data from our 2025 LEADx Leadership Development Benchmark Report that I’m too eager to hold back: MOST leadership development professionals DO NOT MEASURE LEVELS 3&4 of the Kirkpatrick model (behavior change & impact). 41% measure level 3 (behavior change) 24% measure level 4 (impact) Meanwhile, 92% measure learner reaction. I mean, I know learner reaction is easier to measure. But if I have to choose ONE level to devote my time, energy, and budget to… And ONE level to share with senior leaders… I’m at LEAST choosing behavior change! I can’t help but think: If you don’t measure it, good luck delivering on it. 🤷♂️ This is why I always advocate to FLIP the Kirkpatrick Model. Before you even begin training, think about the impact you want to have and the behaviors you’ll need to change to get there. FIRST, set up a plan to MEASURE baseline, progress, and change. THEN, start training. Begin with the end in mind! ___ P.S. If you can’t find the time or budget to measure at least level 3, you probably want to rethink your program. There might be a simple, creative solution. Or, you might need to change vendors. ___ P.P.S EXAMPLE SIMPLE WAY TO MEASURE LEVELS 3&4 Here’s a simple, data-informed example: You want to boost team engagement because it’s linked to your org’s goals to: - improve retention - improve productivity You follow a five-step process: 1. Measure team engagement and manager effectiveness (i.e., a CAT Scan 180 assessment). 2. Locate top areas for improvement (i.e., “effective one-on-one meetings” and “psychological safety”). 3. Train leaders on the top three behaviors holding back team engagement. 4. Pull learning through with exercises, job aids, monthly power hours to discuss with peers and an expert coach. 5. Re-measure team engagement and manager effectiveness. You should see measurable improvement, and your new focus areas for next year. We do the above with clients every year... ___ P.P.S. I find it funny that I took a lot of heat for suggesting we flip the Kirkpatrick model, only to find that most people don’t even measure levels 3&4…😂

🛠️ Measuring Developer Productivity: It’s Complex but Crucial! 🚀 Measuring software developer productivity is one of the toughest challenges. It's a task that requires more than just traditional metrics. I remember when my organization was buried in metrics like lines of code, velocity points, and code reviews. I quickly realized these didn’t provide the full picture. 📉 Lines of code, velocity points, and code reviews? They offer a snapshot but not the complete story. More code doesn’t mean better code, and velocity points can be misleading. Holistic focus is essential: As companies become more software-centric, it’s vital to measure productivity accurately to deploy talent effectively. 🔍 System Level: Deployment frequency and customer satisfaction show how well the system performs. A 25% increase in deployment frequency often correlates with faster feature delivery and higher customer satisfaction. 👥 Team Level: Collaboration metrics like code-review timing and team velocity matter. Reducing code review time by 20% led to faster releases and better teamwork. 🧑💻 Individual Level: Personal performance, well-being, and satisfaction are key. Happy developers are productive developers. Tracking well-being resulted in a 30% productivity boost. By adopting to this holistic approach transformed our organization. I didn’t just track output but also collaboration and individual well-being. The result? A 40% boost in team efficiency and a notable rise in product quality! 🌟 🚪 The takeaway? Measuring developer productivity is complex, but by focusing on system, team, and individual levels, we can create an environment where everyone thrives. Curious about how to implement these insights in your team? Drop a comment or connect with me! Let’s discuss how we can drive productivity together. 🤝 #SoftwareDevelopment #Productivity #TechLeadership #TeamEfficiency #DeveloperMetrics

I recently had the opportunity to work with a large financial services organization implementing OpenTelemetry across their distributed systems. The journey revealed some fascinating insights I wanted to share. When they first approached us, their observability strategy was fragmented – multiple monitoring tools, inconsistent instrumentation, and slow MTTR. Sound familiar? Their engineering teams were spending hours troubleshooting issues rather than building new features. They had plenty of data but struggled to extract meaningful insights. Here's what made their OpenTelemetry implementation particularly effective: 1️⃣ They started small but thought big. Rather than attempting a company-wide rollout, they began with one critical payment processing service, demonstrating value quickly before scaling. 2️⃣ They prioritized distributed tracing from day one. By focusing on end-to-end transaction flows, they gained visibility into previously hidden performance bottlenecks. One trace revealed a third-party API call causing sporadic 3-second delays. 3️⃣ They standardized on semantic conventions across teams. This seemingly small detail paid significant dividends. Consistent naming conventions for spans and attributes made correlating data substantially easier. 4️⃣ They integrated OpenTelemetry with Elasticsearch for powerful analytics. The ability to run complex queries across billions of spans helped identify patterns that would have otherwise gone unnoticed. The results? Mean time to detection dropped by 71%. Developer productivity increased as teams spent less time debugging and more time building. They could now confidently answer "what's happening in production right now?" Interestingly, their infrastructure costs decreased despite collecting more telemetry data. The unified approach eliminated redundant collection and storage systems. What impressed me most wasn't the technology itself, but how this organization approached the human elements of the implementation. They recognized that observability is as much about culture as it is about tools. Have you implemented OpenTelemetry in your organization? What unexpected challenges or benefits did you encounter? If you're still considering it, what's your biggest concern about making the transition? #OpenTelemetry #DistributedTracing #Observability #SiteReliabilityEngineering #DevOps

5 Ways to Use Data to Improve Your Company Culture Culture isn’t just feelings. It’s measurable. Here’s how data can transform your workplace. 1. Track employee engagement.  ➜ Use surveys to understand what your team needs.  ➜ Example: Ask questions like, “Do you feel valued at work?”  ➜ Data reveals trends. Trends guide action. 2. Measure workload balance.  ➜ Analyze hours worked versus output.  ➜ Example: Spot burnout early by tracking overtime trends.  ➜ Balanced workloads lead to happier, more productive teams. 3. Monitor feedback patterns.  ➜ Collect and analyze peer-to-peer and manager feedback.  ➜ Example: Look for themes in quarterly reviews.  ➜ Patterns show areas for growth or celebration. 4. Analyze retention rates.  ➜ High turnover is a sign something’s wrong.  ➜ Example: Use exit interview data to uncover root causes.  ➜ Retention data helps build a culture people want to stay in. 5. Use recognition metrics.  ➜ Track how often employees are recognized for their work.  ➜ Example: Monitor shoutouts in meetings or team platforms.  ➜ Frequent recognition creates a positive feedback loop. Great cultures don’t happen by chance. They’re built with intention—and data. ❓ Which of these steps will you take today? Let’s discuss in the comments. Data drives change. ♻️ Repost to your network. ➕ Follow Nathan Crockett, PhD for actionable insights.

MACHINE MONDAY | A 𝗦𝗹𝗼𝘄𝗲𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 Line Might 𝗣𝗿𝗼𝗱𝘂𝗰𝗲 𝗠𝗼𝗿𝗲 — Find Your 𝗚𝗛𝗢𝗦𝗧 𝗕𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀 Identifying bottlenecks in complex assembly lines can be tough due to the multiple processes happening at once and the lack of recorded 𝗶𝗻𝗱𝗶𝘃𝗶𝗱𝘂𝗮𝗹 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻 cycle times; at best, only the 𝘀𝘁𝗮𝘁𝗶𝗼𝗻 𝗰𝘆𝗰𝗹𝗲 𝘁𝗶𝗺𝗲 is recorded. Additionally, when industrial engineers observe the line, workers may change their pace, which doesn't help. The real issue, though, is the 𝘃𝗮𝗿𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝗳 𝗶𝗻𝗱𝗶𝘃𝗶𝗱𝘂𝗮𝗹 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝘁𝗶𝗺𝗲𝘀, affecting the entire production cycle time. With the Odin workstation, every individual operation's time on the line is recorded, allowing us to 𝘀𝗽𝗼𝘁 𝘁𝗵𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘁𝗶𝗺𝗲 𝘃𝗮𝗿𝗶𝗮𝘁𝗶𝗼𝗻. We use a 𝗹𝗶𝘃𝗲 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗱𝗲𝘃𝗶𝗮𝘁𝗶𝗼𝗻 calculation for this. The chart we've created from this data has now become a key resource for engineers to find and fix these 'ghost bottlenecks.' By focusing on these #ghostbottlenecks, we can make the production process more stable and improve the line's productivity and output. That's how sometimes, a slower but more stable line can end up producing more.

Every transaction tells a story. Don't just read the first and last chapters. 𝗙𝗶𝗻𝗱 𝘆𝗼𝘂𝗿 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀 𝘄𝗶𝘁𝗵 𝗘𝟮𝗘 𝗧𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀. A delayed approval. A mismatched invoice. A system glitch. These tiny hiccups in the middle can snowball into massive headaches—delays, upset customers, and endless firefighting to get things back on track. That’s why End-to-End Transaction Analysis matters. It forces you to stop and look at the entire process—not just the highlights—and figure out where things slow down or break. Here are some tips that have worked for me: 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝗦𝗺𝗮𝗹𝗹. Pick one process, maybe vendor payments or procurement, and map out every step. Look for the obvious bottlenecks. 𝟮. 𝗔𝘀𝗸 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀. Where do things slow down? Who’s always waiting on who? What’s the one step everyone complains about? 𝟯. 𝗨𝘀𝗲 𝗗𝗮𝘁𝗮 𝘁𝗼 𝗦𝗽𝗼𝘁 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀. Track what’s happening, not just what went wrong. Look for trends in delays or errors. 𝟰. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲. If the same problems keep happening, find a way to streamline the process. Tools like Germain UX give you visibility across the whole process to pinpoint and fix inefficiencies. Smooth workflows don’t just happen. They’re built by paying attention to the things most people ignore. What's your tip for keeping transactions running smoothly? #SessionReplay #CustomerExperience #ProcessMining #DigitalExperience #Observability #UX Follow me for weekly updates on the latest tools and trends in UX and productivity.