Why your HR dashboard is killing your business and how GenAI will fix it Last week, I sat with a CHRO who was proud of their "data-driven" HR approach. Then I asked one simple question: "How does your compensation strategy impact customer satisfaction?" Silence. This is the common problem in HR today. We're drowning in data but starving for insights. The uncomfortable truth is that current HR dashboards are like looking at a complex symphony through a keyhole. You might see the violinist, but you're missing the entire orchestra. The silo trap Think about how your organization handles people data today. Your engagement scores live in one system or Excel, while compensation data sits in another. DEI metrics are tracked separately, and retention numbers exist in their own universe. It's a fragmented picture of what should be a seamless story. What happens in reality is far more intricate and interconnected: • That 5% boost in compensation → drives 12% higher engagement • Higher engagement → leads to 20% better customer service • Better service → results in 15% higher customer retention • Higher retention → generates 25% more revenue The real cost of disconnected data 💸 When HR operates in silos, the impact ripples far beyond HR metrics. We're making hiring decisions without understanding retention patterns, implementing training programs without connecting them to performance outcomes and adjusting compensation without seeing the full productivity picture. This disconnected approach is expensive. You will probably ask what to do with it? Make a system that you can ask how increasing your learning budget will impact customer satisfaction scores. Within seconds, you get an answer that weaves together historical training ROI, employee productivity trends, customer feedback patterns, and financial implications. Or consider investigating high turnover in sales. Instead of looking at isolated metrics, you'll see a rich tapestry of interconnected factors – from leadership effectiveness to market compensation benchmarks, all analyzed in real-time with contextual recommendations. Winning organizations won't be those with the most data, but those who best understand the interaction of people metrics and business outcomes. Every HR decision creates ripples across the organization, affecting customer satisfaction, market performance, innovation, and financial results. Start by examining how your HR functions truly connect to each other and to the business. Map out these relationships. Look for the hidden connections between employee satisfaction and customer loyalty, between training investments and innovation outcomes and you can all do it by connecting genAI dots and tools. Far away form CRMs high fees. Interested? Ping me, in AI Superpowers we do solutions that work, not just look good. Photo: worklife.news
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𝗛𝗲𝘆 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻𝘀. 👋 Embarking on a quest to revolutionize HR practices, I recently spearheaded a transformative project in the realm of HR Analytics. Leveraging the power of Excel, Through a combination of innovative KPIs and visually compelling analytics, this project aimed to empower organizations with actionable intelligence to drive strategic decision-making. 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲𝘀: 🎯 The project encompassed a comprehensive analysis of diverse HR datasets, spanning recruitment, employee performance, retention, and engagement metrics. By delving deep into the data, I aimed to unearth patterns, trends, and correlations that would unveil invaluable insights into workforce dynamics. 𝗗𝗮𝘁𝗮 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻: I explore the data and find some data errors, missing values and set their data types. 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: 🧹 I handled the errors, missing values, Eliminating duplicates and diving into advanced data transformations. 𝗧𝗼𝗼𝗹 𝗨𝘁𝗶𝗹𝗶𝘇𝗲𝗱: 🔰 Microsoft Excel: Pivot tables, charts, and data manipulation. 𝗞𝗲𝘆 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀: Throughout the project journey, several notable achievements were realized: 1. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗼𝗳 𝗖𝘂𝘀𝘁𝗼𝗺 𝗞𝗣𝗜𝘀: I engineered a suite of bespoke Key Performance Indicators (KPIs) to measure critical HR metrics, including employee turnover rates, recruitment efficiency, and performance benchmarks. 2. 𝗩𝗶𝘀𝘂𝗮𝗹 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: Through the creation of dynamic charts, graphs, and dashboards, I translated raw data into visually compelling representations, enabling stakeholders to intuitively grasp complex trends and patterns. 3. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: Leveraging predictive analytics techniques, I developed models to forecast future workforce trends, enabling proactive decision-making and strategic planning. 4. 𝗔𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀: Based on the insights gleaned, I formulated actionable recommendations to optimize HR processes, enhance employee engagement, and foster a culture of continuous improvement within the organization. 𝙏𝙤𝙥 𝙄𝙣𝙨𝙞𝙜𝙝𝙩𝙨; 📈 The project's impact was profound, fostering data-driven decision-making and tangible improvements in recruitment, talent management, and overall organizational performance. It instilled a culture of innovation, paving the way for ongoing optimization and excellence in HR practices, marking a transformative shift towards strategic HR management. 𝗙𝗶𝗻𝗮𝗹 𝗧𝗵𝗼𝘂𝗴𝗵𝘁𝘀: 😍 In conclusion, this project exemplifies the transformative potential of data analytics in HR management. By leveraging Excel's capabilities, we've unlocked insights driving strategic decisions, fostering a culture of innovation. As we propel forward, committed to data-driven excellence, we continue reshaping HR practices for organizational success. 𝗟𝗶𝗻𝗸𝘀. My Resume: My Portfolio: Github: https://github.com/tayymus
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A compelling example of HR analytics in action is from Google and its famous "Project Oxygen." Google applied a mix of descriptive and diagnostic analytics to analyze employee performance reviews, feedback, and other data to uncover what makes a great manager. Initially, they relied on descriptive analytics to look at past data and trends, then moved into diagnostic analytics to dig deeper into why certain managers were performing better than others. The insights showed that "soft skills" like coaching and listening were more important than technical skills in effective management, which led to changes in their manager training programs. They later introduced predictive analytics to identify employees who might need extra coaching or support and even to help predict which new managers might struggle. This resulted in improved management performance and higher employee satisfaction. By combining different types of analytics—descriptive, diagnostic, and predictive—Google was able to make data-driven decisions that directly impacted its culture and productivity, demonstrating how powerful HR analytics can be in shaping organizational success. This leads to an important question: Are we equipped to handle our own HR analytics, or are there gaps in our historical data? Many companies struggle with incomplete or inconsistent data, which can limit the effectiveness of their analytics efforts. However, it's never too late to start. By putting the right measures in place—such as tracking key performance indicators (KPIs) from today, improving data collection processes, and ensuring consistency—organizations can begin to build a solid foundation for future analytics. Even if you don't have years of data, starting now will help you establish the framework needed for meaningful insights in the future.
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(A UNIVERSAL HRBP PRACTITIONERS AMPLIFIED INTELLIGENCE SOFTWARE)- HUMAN RESOURCE BUSINESS PARTNER SPACE- A NUCLEUS TO REVOLUTIONIZE FULL SPECTRUM OF HR JOURNEY MAKING TRANSFORMATIVE EPOCH IN HRMS SOFTWARE (PRODUCTIVITY, TASK, ACTIVITIES, INITIATIVE, BEHAVIOUR, SKILLS, CAPABILITIES FOCUS) WITH KNOW/DO GUIDANCE SYSTEM -BUSINESS INSIGHTS -BUSI PERF. ANALYTICS & IMPROVISATION -BUSINESS IMPACT -BUSI FUNCTIONAL PROCESS IMPROVISATION -BUSI INTELLIGENCE 1-ASSESSMENT OF- INITIATIVES FOR⤵️ -WORKFORCE ANALYTICS -HR OPER. MODEL DESIGN -HR PROCESS EFFECT, LEADERSHIP EFFECT -HR FUNCTIONAL PROCESS IMPROVISATION -HR VALUE STREAM -HR CAPABILITIES BUILDING PRACTICES IMPLEMENTATION, PRODUCTIVITY -HR TALENT MGMT, ACQUI. PROCESS,TALENT VALUE CREATION, VALUE CONTRIBUTION --HR CULTURE COMPATABILITY, LEADERSHIP CULTURE -HR DIGITIZATION ECO-SYSTEM⬇️ 2-HR ANALYTICS ASS⤵️ FOCUS HOW HR ACCESS INFORMATION TO MAKE BETTER DECISION MAKING (OFFER GUIDANCE NOT JUST BENCH MARKING OR BEST PRACTICES) 3-HR DESIGN ASS⤵️ FOCUS (MATCHING HR DEPART TO BUSI DESIGN CONNECT SEPECIALIST (EXPERT) TO GENERALIST (EMBED IN BUSI) WITH AGILITY -HR RELATIONSHIP ASS FOCUS INTERNAL & EXTERNAL STAKEHOLDERS 4-HR COMPETENCY FRAMEWORK ASS 5- STRA. HR WORKFORCE PLAN -JOB A&E, TALENT ASS⤵️ OF -DYNAMIC SKILL INVENTORY -CORE COMPETENCIES INVENTORY GAP ANALYSIS -CULTURE COMPATABILITY -JOB ARCHITECTURE -JOB FAMILY, DESIGN, BANDS -JOB LEVELING -JOB SEGMENT -SALARY BANDS -BUDGETING & COST -INCENTIVE & BENEFITS STRUCTURING -WORK FLOW -WORK VALUE INVENTORY -PERSONAL CORE VALUE -WORK TASKS/ACTIVITIES -BEHAVIORAL INVENTORY -VALUE BASED INITIATIVES -ONBOARDING & ORIENTATION 6-HR INITIATIVES FOR TALENT ASS OF⤵️ -TALENT INFRASTRUCTURE -TALENT ACQUISITION/HIRE QUALITY -TALENT SEGMENT, INTELLIGENCE ANALYTICS -TALENT IMPACT, ENGAGE, DEI -TALENT CARRIER & PROMOTION (POTENTIAL) UPSKILLING, RESKILLING NEW SKILLING 7- A HR INITIATIVES FOR ORG. CAPABILITIES BUILDING PRACTICES ASS⤵️ OF -ORG.TRANSFORMATION -ORG. DESIGN REQUIRE -LEADERSHIP -INDIVIDUAL -TEAM ASS-CROSS FUNCTION -ORG.LEARNING -ORG.WORK FLOW DESIGN 8-HR INITIATIVES FOR PMS ASS OF⤵️ -BUSI PERF. MGMT (CORPORATE, BU, DEPART, TEAMS, INDIVIDUALS) BUSINESS ANALYTICS & PROCESS IMPROVISATION 9-HR WORK PLACE ASS OF⤵️ -WORK PLACE ENGAGE & ENVIRONMENT -WORK PLACE SATISFACTION -WORK CONTENT -WORK RELATIONSHIPS -HR TEAM SKILLS & LEADERSHIP 9-HR CAPABILITIES-⤵️ ASS OF -HR CUSTOMERS -HR PURPOSE -HR DESIGN -HR CAPABILITIES -HR TECH -HR PRACTICES -HR RELATIONSHIPS -HR VALUE CHAIN CREATION -HR STRATEGIC PLANNING -HR COMMUNITY REPUTATION -HR PERSONALIZING WORK -HR CULTURE -HR INNOVATION & SYSTEM DESIGN -HR PERF ANALYTICS -HR EFFECTIVENESS 10-SURVEYS & ANALYSIS-FOCUS ON⤵️ -ENGAGE -EXPERIENCE -SATISFACTION -WELL-BEING -CAPABILITIES -CULTURE -ATTITUDE -SCALE/ IMPACT -PERF 11-ASS OF⤵️ -R & R -REWARDS & PERF. PAY STRUCTURE - INCREMENT, BONUS, ESOP -ASSETS (LAPTOP, MOBILE) -COST & BUDGETING To IT/ SOFTWARE CO. LET'S DEVELOP A HRBP SOFTWARE HRMS HOUSE:
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Leveraging Data Analytics for Effective HR Decision Making In the age of information, data analytics has emerged as a beacon of insight in the vast sea of human resources management. Leveraging data analytics for HR decision-making isn't just about crunching numbers; it's about unlocking the stories those numbers tell about your workforce. Let's explore how to harness this power effectively, with a touch of friendliness and the right amount of professionalism. Understand Your Objectives Before diving into data, it's crucial to have a clear understanding of your objectives. Are you looking to improve employee retention, enhance recruitment strategies, or boost employee engagement? Defining your goals will help you focus on the relevant data and analytics tools. Collect the Right Data HR data can come from various sources – from employee surveys and performance reviews to social media and wearable technology. Ensure you're collecting data that is accurate, relevant, and legally compliant. Remember, garbage in, garbage out; the quality of your insights is directly related to the quality of your data. Use the Right Tools The market is flooded with HR analytics tools, each promising to be the solution to all your problems. Whether it's a sophisticated AI-driven platform or a simple spreadsheet model, the right tool is the one that matches your organization's size, complexity, and specific needs. Don't get dazzled by features you don't need; focus on tools that deliver actionable insights. Analyze for Insights This is where the magic happens. Data analysis can reveal patterns and trends that were not apparent before. For instance, analyzing turnover data can help identify the reasons behind employee departures and pinpoint departments or roles with higher attrition rates. These insights can inform targeted retention strategies, reducing turnover costs in the long run. Turn Insights into Action Data analytics is not just an academic exercise; it's a springboard for action. Use the insights gained from your analysis to inform HR strategies and decisions. This could mean tweaking your recruitment process, modifying your training programs, or introducing new employee benefits. The key is to make data-driven decisions that align with your HR objectives and business goals. Measure, Refine, Repeat Finally, the cycle of data analytics is ongoing. Measure the outcomes of the actions you've taken, refine your strategies based on what you learn, and repeat the process. This continuous improvement loop will help you stay responsive to the changing dynamics of your workforce and the external environment. In essence, leveraging data analytics in HR is about turning information into insight and insight into action. By following these steps, HR professionals can make more informed decisions that not only benefit the organization but also enhance the employee experience. #hr #dataanalytic #talent #recruitment #newwork #employeeretention #employerbranding
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⚠️ Why Multiple Comparisons Are Risky in HR Analytics: The Role of Bonferroni Correction in People Analytics In people analytics, we often compare multiple groups across metrics like: 1️⃣ Which department has the highest employee satisfaction? 2️⃣ Does turnover vary across job levels? 3️⃣ Are performance ratings different between demographics? Each comparison increases the risk of false positives (Type I errors), leading to flawed decisions. The more comparisons, the higher the risk. This is where the Bonferroni correction comes in. It helps ensure the differences we see are real, not just chance. 🤔 Why Multiple Comparisons Increase Error Rates: In statistical testing, we typically use a significance level of 0.05, meaning there’s a 5% chance of finding a significant result by chance. But with multiple comparisons, this error rate applies to each test. For example, with 10 tests, the likelihood of at least one false positive increases to about 40% (1 - 0.95^10 ≈ 0.40). The Bonferroni correction adjusts the significance level to account for the number of comparisons, minimizing false positives and ensuring more accurate, reliable insights. 🔍 How the Bonferroni Correction Works: Normally, we use a significance level of 0.05, but with multiple tests, this threshold needs to be adjusted. The Bonferroni correction divides the standard significance level by the number of comparisons. For example, if you’re making 10 comparisons, the new significance level would be 0.05 ÷ 10 = 0.005. 🔧 Practical Applications in People Analytics: 1️⃣ Engagement Survey Analysis: Ensures that differences across gender, tenure, or department are substantive, not due to random chance. 2️⃣ Pay Equity Studies: Reduces the risk of identifying false pay gaps when analyzing compensation across demographics. 3️⃣ Diversity & Inclusion Metrics: Maintains validity when making multiple comparisons across demographic groups on metrics like promotion rates or leadership representation. The Bonferroni correction is a crucial tool for improving the accuracy of your people analytics insights. By adjusting for multiple comparisons, you can avoid false positives and ensure that your HR strategies are based on robust, reliable analysis. To learn more about Bonferroni correction, check out our blog post linked below. 📘 Curious to learn more but need guidance? Let’s connect: If you're ready to dive deeper and apply such concepts on a real-world dataset, we're here to help! Work with us on guided HR data projects on real-world datasets. We provide personalized training to help you thrive in people analytics. 🔗 Learn more about our programs here: https://lnkd.in/ghwaFNWm 🔗 Book a free 60-minute discovery call to discover how we can help your career here: https://lnkd.in/gYsHGEF7 🔗 Read more about the Bonferroni Correction here: https://lnkd.in/gXwDjT4C #PeopleAnalytics #HRData #BonferroniCorrection #DataDrivenHR #HRAnalytics #DataSkillUp #Coaching
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I’ve seen analysts run over 100 tests of means and be excited when they see results. If a good way to describe a p value is “the likelihood this would occur due to chance, if their means were the same” and you give 100 chances - odds are something will pop. Controlling for this is critical, either with a regression or a penalty like bonferonni correction #peopleanalytics #analytics #r #python
⚠️ Why Multiple Comparisons Are Risky in HR Analytics: The Role of Bonferroni Correction in People Analytics In people analytics, we often compare multiple groups across metrics like: 1️⃣ Which department has the highest employee satisfaction? 2️⃣ Does turnover vary across job levels? 3️⃣ Are performance ratings different between demographics? Each comparison increases the risk of false positives (Type I errors), leading to flawed decisions. The more comparisons, the higher the risk. This is where the Bonferroni correction comes in. It helps ensure the differences we see are real, not just chance. 🤔 Why Multiple Comparisons Increase Error Rates: In statistical testing, we typically use a significance level of 0.05, meaning there’s a 5% chance of finding a significant result by chance. But with multiple comparisons, this error rate applies to each test. For example, with 10 tests, the likelihood of at least one false positive increases to about 40% (1 - 0.95^10 ≈ 0.40). The Bonferroni correction adjusts the significance level to account for the number of comparisons, minimizing false positives and ensuring more accurate, reliable insights. 🔍 How the Bonferroni Correction Works: Normally, we use a significance level of 0.05, but with multiple tests, this threshold needs to be adjusted. The Bonferroni correction divides the standard significance level by the number of comparisons. For example, if you’re making 10 comparisons, the new significance level would be 0.05 ÷ 10 = 0.005. 🔧 Practical Applications in People Analytics: 1️⃣ Engagement Survey Analysis: Ensures that differences across gender, tenure, or department are substantive, not due to random chance. 2️⃣ Pay Equity Studies: Reduces the risk of identifying false pay gaps when analyzing compensation across demographics. 3️⃣ Diversity & Inclusion Metrics: Maintains validity when making multiple comparisons across demographic groups on metrics like promotion rates or leadership representation. The Bonferroni correction is a crucial tool for improving the accuracy of your people analytics insights. By adjusting for multiple comparisons, you can avoid false positives and ensure that your HR strategies are based on robust, reliable analysis. To learn more about Bonferroni correction, check out our blog post linked below. 📘 Curious to learn more but need guidance? Let’s connect: If you're ready to dive deeper and apply such concepts on a real-world dataset, we're here to help! Work with us on guided HR data projects on real-world datasets. We provide personalized training to help you thrive in people analytics. 🔗 Learn more about our programs here: https://lnkd.in/ghwaFNWm 🔗 Book a free 60-minute discovery call to discover how we can help your career here: https://lnkd.in/gYsHGEF7 🔗 Read more about the Bonferroni Correction here: https://lnkd.in/gXwDjT4C #PeopleAnalytics #HRData #BonferroniCorrection #DataDrivenHR #HRAnalytics #DataSkillUp #Coaching
DataSkillUp Blog - The Role of Bonferroni Correction in People Analytics
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People Analytics takes center stage in HR, as decisions regarding personnel increasingly rely on analytical and data-driven approaches 📊 Here’s your essential Guide to People Analytics in 2024, featuring definitions, examples, tools, and more: https://aihr.ac/3OXaY7M ✅ #HRanalytics #PeopleAnalytics #HR #HumanResources
People Analytics
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This is an EXCELLENT deep dive into the significant role people analytics play in organizations. The importance of having a clear people analytics strategy is critical in our global workforce. Organizations not adopting this mindset will be left behind. #peopleanalytics #datadrivendecisions #peopleoperations
People Analytics takes center stage in HR, as decisions regarding personnel increasingly rely on analytical and data-driven approaches 📊 Here’s your essential Guide to People Analytics in 2024, featuring definitions, examples, tools, and more: https://aihr.ac/3OXaY7M ✅ #HRanalytics #PeopleAnalytics #HR #HumanResources
People Analytics
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✨ Have you ever wondered how companies use data to keep employees engaged and satisfied? 🌟 In today’s competitive business landscape, I believe retaining skilled talent is crucial to long-term success. One effective strategy for achieving this is HR analytics—a data-driven approach that helps organizations understand what truly drives their people. By delving into employee data, companies can identify factors like motivation, satisfaction, and the likelihood of staying with the organization. 🌟 From my perspective, HR analytics enables organizations to go beyond guesswork. For instance, by examining patterns in employee behavior, companies can pinpoint who may be at risk of leaving and why. This insight allows them to proactively address potential issues, such as workload imbalance or job dissatisfaction. With these insights, they can effectively reduce turnover rates, saving both time and resources. 🌟 I also see how HR analytics supports a more tailored approach to employee engagement. With this data, organizations can design targeted programs, such as flexible work schedules or career development pathways, that directly impact job satisfaction. In my opinion, predictive models within HR analytics are especially valuable, allowing HR teams to anticipate potential challenges and intervene before they escalate. This timely intervention creates a workplace that is both supportive and responsive to individual needs. 🌟I also find the role of technology fascinating in amplifying HR analytics. AI and machine learning have become key players, enabling HR teams to process vast amounts of data quickly, uncover patterns, and generate actionable insights. With these tools, HR can operate more strategically, aligning closely with organizational objectives and contributing directly to the company's overall success. 🌟 Looking to the future, I think HR analytics will continue to grow in importance. Companies may increasingly focus on identifying early signs of stress or burnout, allowing for targeted support that helps employees thrive. By using data to create a more meaningful employee experience, organizations foster a culture of engagement and loyalty. In my view, this approach isn’t just a passing trend—it’s becoming an essential strategy for forward-thinking companies committed to building a dedicated, high-performing workforce. 🌟 So, as HR analytics continues to evolve, one question remains: How can organizations strike the right balance between data-driven insights and the human touch that makes work truly fulfilling? Source: Ravesangar, K., & Narayanan, S. (2024). Adoption of HR analytics to enhance employee retention in the workplace: A review. Human Resources Management and Services, 6(3), 3481. hashtag #Talentmanagement #TalentAquisition #Psgim #EmployeeRentention #PriyadharshiniInsights5
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AI in HR is no longer a future concept—it's reshaping talent management today. Discover insights from our CMO, Marc Ramos, as he explores the AI-driven transformation in HR at the HR Technology Conference & Exposition 2024. Learn how generative AI can accelerate recruitment and elevate employee engagement strategies. Read more on ERP Today! - https://bit.ly/4eiY2Fh #AIinHR #reporting #analytics #hranalytics #hrreporting #CSRD #ESG #SplashBI
HR Tech Con and Expo Europe 24: AI, people analytics and an improved user experience
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