Using Analytics to Measure Productivity

#PeopleAnalytics: Turning #HRMetrics into #Strategic Insights In today’s data-driven organizations, HR is evolving from a support function to a strategic powerhouse. These HR Metrics are more than just numbers; they’re lenses through which we can understand workforce dynamics, organizational health, and business impact. Let’s break it down: 🔹 Absenteeism Rate: A high rate may signal burnout, disengagement, or systemic issues in workplace culture. Tracking it helps identify patterns and intervene early. 🔹 Employee Attrition & Retention: These twin metrics reveal the stability of your workforce. High attrition can be costly and disruptive, while strong retention often reflects good leadership and employee satisfaction. 🔹 Internal Promotion Rate: A key indicator of talent mobility and succession planning. Promoting from within boosts morale and reduces hiring costs. 🔹 Cost Per Hire & Time to Hire: Efficiency metrics that reflect the effectiveness of your recruitment strategy. Long hiring cycles or high costs may point to process inefficiencies or misaligned sourcing channels. 🔹 Offer Acceptance Rate: A direct measure of your employer brand and candidate experience. Low acceptance rates might mean your value proposition isn’t resonating. 🔹 Human Capital ROI: This is the ultimate business case for HR—how much return you’re getting from your investment in people. It’s a powerful metric for aligning HR with financial performance. 🔹 Employee Engagement: Often measured through surveys, this metric captures how emotionally and cognitively invested employees are in their work. High engagement is correlated with productivity, innovation, and employee retention. 💡 Why it matters: These formulas empower HR teams to move from reactive to proactive. They help diagnose problems, forecast trends, and make evidence-based decisions that drive business value. People analytics isn’t just about tracking—it’s about transforming. #PeopleAnalytics #HRStrategy #HumanCapital #WorkforceInsights #EmployeeExperience #DataDrivenHR #Leadership #FutureOfWork #LinkedInHR #HRLeadership

I wish someone taught me this in my first year as a PM. It would��ve saved years of chasing the wrong goals and wasting my team's time: "Choosing the right metric is more important than choosing the right feature." Here are 4 metrics mistakes even billion-dollar companies have made and what to do instead with Ron Kohavi: 1. Vanity Metrics They look good. Until they don’t. A social platform he worked with kept showing rising page views… While revenue quietly declined. The dashboard looked great. The business? Not so much. Always track active usage tied to user value, not surface-level vanity. 2. Insensitive Metrics They move too slowly to be useful. At Microsoft, Ronny Kohavi’s team tried using LTV in experiments. but saw zero significant movement for over 9 months. The problem is you can’t build momentum on data that’s stuck in the future. So, use proxy metrics that respond faster but still reflect long-term value. 3. Lagging Indicators They confirm success after it’s too late to act. At a subscription company, churn finally spiked… but by then, 30% of impacted users were already gone. Great for storytelling but let's be honest, it's useless for decision-making. You can solve it by pairing lagging indicators with predictive signals. (Things you can act on now.) 4. Misaligned Incentives They push teams in the wrong direction. One media outlet optimized for clicks and everything was looking good until it wasn't. They watched their trust drop as clickbait headlines took over. The metric had worked. They might had "more MRR". But the product suffered in the long run. It's cliche but use metrics that align user value with business success. Because Here's The Real Cost of Bad Metrics - 80% of team energy wasted optimizing what doesn’t matter - Companies with mature metrics see 3–4× stronger alignment between experiments and outcomes - High-performing teams run more tests but measure fewer, better things Before you trust any metric, ask: - Can it detect meaningful change in faster? - Does it map to real user or business value? - Is it sensitive enough for experimentation? - Can my team interpret and act on it? - Does it balance short-term momentum and long-term goals? If the answer is no, it’s not a metric worth using. — If you liked this, you’ll love the deep dive: https://lnkd.in/ea8sWSsS

She was one of our brightest talents Smart. Committed. A quiet force that lifted the whole team And then... she resigned No warning. No second thoughts. Just… gone. We were stunned. She had everything: a promising future, fair pay, great feedback. So we asked her why. Her words hit like a punch: "I didn’t feel seen. I didn’t feel like we mattered." That moment changed everything. Because the truth is, we missed the signs: - Her engagement score had dropped - Her internal applications went nowhere - She kept going the extra mile with no recognition We had the data. We just didn’t use it wisely. Today, we have no excuse. AI and predictive analytics give us a head start. They help us spot patterns before they become problems: - Who might be silently disengaging? - Where are we overlooking skills and potential? - Are we creating an inclusive space where everyone feels they belong? This isn’t about replacing human connection, it’s about deepening it. When we pair data with empathy, we lead smarter, faster, and more human. Because great HR doesn’t just prevent risks. It unlocks possibility. If we reinforce our data and tools, we can spend even more time on what matters most: making sure people remain at the heart of our organizations. #Talents #PredictiveHR #DataDrivenLeadership #EmployeeExperience #humanresources

Measuring productivity affects productivity! As a product manager, I'm often asked to measure the work of engineers. It's a difficult task, but it's important to do it right. The wrong metrics can lead to bad decisions, and the right metrics can help me make better decisions about what features to build and how to prioritize my team's work. There are a few challenges to measuring engineering work. First, it's often hard to quantify the work that engineers do. Software development is a creative process, and it's not always easy to measure the value of a new feature or bug fix. Second, even if you can quantify the work, it's hard to do it accurately. Engineers often work on multiple projects at the same time, and it can be difficult to track their time and effort. Third, the metrics you choose can have a big impact on how engineers behave. If you focus on the wrong metrics, you can end up encouraging bad behavior. So how do you measure engineering work in a way that is fair and accurate? 👉🏻 Start with the business goals. What are you trying to achieve with your engineering team? Once you know the business goals, you can start to identify the metrics that will help you measure progress toward those goals. 👉🏻 Focus on outcomes, not activities. Don't just measure how many hours engineers work. Measure the results of their work, such as the number of features released or the number of bugs fixed. 👉🏻 Use multiple metrics. No single metric is perfect. Use a combination of metrics to get a more complete picture of engineering work. 👉🏻 Be transparent about your metrics. Make sure engineers understand how their work is being measured. 👉🏻 Be flexible. The metrics you use will need to change over time as your company grows and changes. Measuring engineering work is challenging, but it's important to do it right. The wrong metrics can lead to bad decisions. Do you have any other tips for measuring engineering productivity?

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

How do you measure developer productivity? It's always been a tricky thing for me, but I think this 👇🏼 could be a good solution. According to Emilio Salvador, VP of Developer Relations & Community at GitLab, 3 metrics are key: 1. Task-based: it's not about measuring the NUMBER of tasks a developer completes, but about the TYPE of tasks completed. Some tasks require advanced critical thinking or outside-the-box thinking. They should be evaluated as such. 2. Time-based: measuring the time needed to complete tasks and release features. Using Google's DORA framework to measure both production and deployment times helps identify process weaknesses and bottlenecks. 3. Team-based: no developer works isolated from their colleagues. Measuring the team's delivery performance in terms of business outcomes gives an indication on the productivity of developers. -- Combining these 3 metrics would help engineering managers have a broader view on how the work environment is helping/hindering developer productivity. I would add one more "human" dimension: how the presence of a developer in a team affects the whole team. Factors such as helping teammates, coming up with new concepts, or being proactive on process enhancement count towards a developer's productivity. How do you go about measuring productivity in your company? Share your thoughts!

📈 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

📌 Power BI Breakdown # 3: HR Analytics HR teams have more data than ever before. But are they using it effectively? Employee turnover, absenteeism, and engagement levels all hold critical insights that can shape the success of an organization. Yet, many HR teams still rely on fragmented reports and manual analysis. This is where a well-built HR Analytics Dashboard comes into play. In this 3rd post of the Power BI Breakdown series, I’m sharing a demo I’ve recently built for HR teams. The dashboard can help companies tackle key workforce challenges: ⤷ Why are employees leaving? ⤷ Which departments have the highest turnover? ⤷ What factors contribute to employee satisfaction? But realistically, what data do you need? Building a similar dashboard in Power BI requires integrating multiple data sources: 🔹 HRIS (e.g., Workday or SAP) → Employee records, tenure, salary, job position 🔹 Payroll System (ADP, Paycom, QuickBooks Payroll) → Compensation and salary trends 🔹 Engagement & Performance (SurveyMonkey, Lattice, Culture Amp) → Satisfaction scores, turnover risks 🔹 Recruitment Data (LinkedIn, Indeed, etc.) → Hiring sources, candidate pipeline Once you bring all these data sources into a centralized data warehouse, you can merge them and unlock critical insights such as: ☑ Turnover Rate by Department → Identify which teams struggle with retention ☑ Departure Reasons → Analyze why employees leave (salary, engagement, career growth) ☑ High-Risk Employees → Spot individuals with low satisfaction & high turnover risk ☑ Recruitment Effectiveness → Find out which hiring sources bring long-term employees Power BI can help you solve all these problems and truly leverage your HR data, but only if you do it properly :) 🟢 Live Demo Here: https://lnkd.in/egHAqBdg #PowerBI #DataAnalytics #BusinessIntelligence

Want better sprints? Start with better metrics. Agile success isn’t about guessing it’s about tracking the right data. ✓ Sprint Velocity & Story Points Gauge your team’s delivery capacity and fine-tune sprint planning with historical data. ✓ Sprint Progress Visualization Visual cues like burndown charts help monitor scope creep and pacing in real time. ✓ Cycle Time vs. Lead Time Understand time efficiency Cycle Time reflects execution, Lead Time reveals delivery performance. ✓ Task Management Efficiency Too many WIP (Work in Progress) items? That’s a signal to reduce multitasking and improve focus. ✓ Team Happiness Index Morale impacts productivity. Regular pulse checks lead to better engagement and retention. ✓ Defect Density Track bugs early. Low defect density means higher product quality and team effectiveness. ✓ Sprint Goal Success Rate Did the team meet the sprint goal? This shows alignment between planning and execution. ✓ Release Frequency Frequent releases mean faster feedback loops and better adaptability to change. ✓ Technical Debt Tracking Identify patterns in rushed work or rework. Addressing this early saves future costs. ✓ Team Collaboration Health Better collaboration leads to shared ownership and faster problem-solving. Common Myths Agile doesn’t believe in metrics. → Agile isn't anti-data it’s anti-waste. Good metrics inform, not control. Velocity is the only metric that matters. → Velocity without quality or context can be misleading. Focus on outcomes, not just speed. Metrics are for managers, not teams. → The best teams track their own metrics to inspect, adapt, and grow. All metrics should be quantitative. Why does this matter? ✓ These KPIs help teams improve sprint over sprint. ✓ Scrum Masters use them to remove blockers and coach teams. ✓ Stakeholders gain visibility into team performance and product health. What’s the toughest KPI to measure in your team? #BusinessAnalyst #ProjectManager #AgileLeadership #ScrumMaster #AgileMetrics

Everyone says “change is happening” But how do you know it’s actually working? Change initiatives are easy to start. Harder to measure. Without clear indicators, leaders guess if progress is real And guesswork rarely works Top change leaders track these metrics to stay ahead: 1/ Achievement → How close did we get to our change goals → Focus on learning first, then performance Example: % of project milestones met vs. planned 2/ Completion → How well did we execute on schedule, scope, and budget → Example: Tasks finished on time and within budget 3/ Acceptability → Stakeholder satisfaction with the process and solution → Example: Survey scores, qualitative feedback 4/ Engagement → How involved are teams and stakeholders in the change → Example: Attendance in workshops, participation in feedback sessions 5/ Adoption → Are people actually using new systems, behaviors, or processes → Example: % of employees actively using a new tool or workflow 6/ Sustainability → Are changes sticking over time or fading → Example: Reassess behaviors 3–6 months post-change 7/ Impact → The measurable difference on business outcomes → Example: Efficiency gains, revenue growth, or error reduction Stop hoping for progress. Start proving it. P.S. Which of these metrics do you track most closely in your change initiatives? -- Follow me, Daniel Lock, for practical tips for leading change, consulting & thought leadership