{"id":497,"date":"2025-04-27T08:11:41","date_gmt":"2025-04-27T08:11:41","guid":{"rendered":"https:\/\/andre.lagelab.kozow.com\/?p=497"},"modified":"2025-05-06T08:22:37","modified_gmt":"2025-05-06T08:22:37","slug":"peopleanalytics","status":"publish","type":"post","link":"https:\/\/andre.lagelab.kozow.com\/en\/peopleanalytics\/","title":{"rendered":"People Analytics with Turnover Analysis"},"content":{"rendered":"
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\ud83d\udccb Project Summary<\/h2>\n\n\n\n

Development of a complete People Analytics<\/strong> solution, focused on analyzing turnover, retention, absenteeism, hours worked and salary mass.<\/p>\n\n\n\n

\ud83c\udfaf Goals<\/h2>\n\n\n\n
    \n
  • Analyze the history of hirings, dismissals and internal movements.<\/li>\n\n\n\n
  • Identify turnover patterns by age group, gender, education level and position.<\/li>\n\n\n\n
  • Evaluate the financial impact of absenteeism and absences.<\/li>\n\n\n\n
  • Monitoring the distribution of the wage bill in different groups.<\/li>\n\n\n\n
  • Supporting talent retention strategies based on data.<\/li>\n<\/ul>\n\n\n\n

    \ud83d\udee0\ufe0f Methodology<\/h2>\n\n\n\n
      \n
    • Gathering and preparing HR data<\/strong>: hiring, firing, salaries, absenteeism and hours worked.<\/li>\n\n\n\n
    • Data processing<\/strong> (cleaning, normalization and integration between sources).<\/li>\n\n\n\n
    • Building dynamic dashboards<\/strong> in Power BI, with filters by position, age, education and race\/color.<\/li>\n\n\n\n
    • Exploratory analysis<\/strong> and visualization of trends<\/strong> in turnover, absenteeism and salary distribution.<\/li>\n\n\n\n
    • Calculation of strategic KPIs<\/strong>: turnover rate, % of bad hires, % of overtime, among others.<\/li>\n<\/ul>\n\n\n\n

      \ud83d\udcc8 Main Results<\/h2>\n\n\n\n
        \n
      • Clear visualization of turnover rates and their variations over the years.<\/li>\n\n\n\n
      • Identifying the positions and profiles most prone to turnover.<\/li>\n\n\n\n
      • Complete analysis of the wage bill by gender, age group, education level and race.<\/li>\n\n\n\n
      • Evaluation of the main reasons for dismissal and the impact on retention.<\/li>\n\n\n\n
      • Estimativas de custos financeiros associados ao absentismo.<\/li>\n<\/ul>\n\n\n\n

        \ud83d\udea7 Difficulties Overcome<\/h2>\n\n\n\n
          \n
        • Overcoming gaps in historical data and standardizing categorical variables.<\/li>\n\n\n\n
        • Integration of different databases for a 360\u00ba view of the workforce.<\/li>\n\n\n\n
        • Optimization of the dashboard model to facilitate analysis by non-technical users.<\/li>\n<\/ul>\n\n\n\n

          - Technologies and Tools<\/h2>\n\n\n\n
            \n
          • Power BI<\/strong> - Interactive dashboards and reports.<\/li>\n\n\n\n
          • Python<\/strong> \u2014 Data pre-processing (pandas, numpy).<\/li>\n\n\n\n
          • SQL<\/strong> - Extracting and manipulating data in relational databases.<\/li>\n<\/ul>\n\n\n\n

            \ud83d\udd0d Final Reflection<\/h2>\n\n\n\n

            This project shows how data analysis can strengthen people management strategy, promoting fact-based decisions and improving talent retention.
            Transforming data into useful information is the first step towards building more efficient, humane and sustainable organizations.<\/p>\n\n\n\n

            <\/p>","protected":false},"excerpt":{"rendered":"

            \ud83d\udccb Resumo do Projeto Desenvolvimento de uma solu\u00e7\u00e3o completa de People Analytics, focada na an\u00e1lise de turnover, reten\u00e7\u00e3o, absentismo, horas trabalhadas e massa salarial. \ud83c\udfaf Objetivos \ud83d\udee0\ufe0f Metodologia \ud83d\udcc8 Principais Resultados \ud83d\udea7 Dificuldades Superadas \ud83e\uddf0 Ferramentas e Tecnologias \ud83d\udd0d Reflex\u00e3o Final Este projeto evidencia como a an\u00e1lise de dados pode fortalecer a estrat\u00e9gia de gest\u00e3o […]<\/p>","protected":false},"author":1,"featured_media":780,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":"","footnotes":""},"categories":[25],"tags":[],"class_list":["post-497","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-power-bi-dashboards"],"_links":{"self":[{"href":"https:\/\/andre.lagelab.kozow.com\/en\/wp-json\/wp\/v2\/posts\/497","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/andre.lagelab.kozow.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/andre.lagelab.kozow.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/andre.lagelab.kozow.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/andre.lagelab.kozow.com\/en\/wp-json\/wp\/v2\/comments?post=497"}],"version-history":[{"count":17,"href":"https:\/\/andre.lagelab.kozow.com\/en\/wp-json\/wp\/v2\/posts\/497\/revisions"}],"predecessor-version":[{"id":901,"href":"https:\/\/andre.lagelab.kozow.com\/en\/wp-json\/wp\/v2\/posts\/497\/revisions\/901"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/andre.lagelab.kozow.com\/en\/wp-json\/wp\/v2\/media\/780"}],"wp:attachment":[{"href":"https:\/\/andre.lagelab.kozow.com\/en\/wp-json\/wp\/v2\/media?parent=497"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/andre.lagelab.kozow.com\/en\/wp-json\/wp\/v2\/categories?post=497"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/andre.lagelab.kozow.com\/en\/wp-json\/wp\/v2\/tags?post=497"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}