{"id":623,"date":"2019-08-05T16:49:50","date_gmt":"2019-08-05T16:49:50","guid":{"rendered":"https:\/\/courses.lumenlearning.com\/wm-humanresourcesmgmt\/?post_type=chapter&p=623"},"modified":"2024-04-24T23:22:24","modified_gmt":"2024-04-24T23:22:24","slug":"why-it-matters-people-analytics-and-human-capital-trends","status":"publish","type":"chapter","link":"https:\/\/courses.lumenlearning.com\/wm-humanresourcesmgmt\/chapter\/why-it-matters-people-analytics-and-human-capital-trends\/","title":{"raw":"Why It Matters: People Analytics and Human Capital Trends","rendered":"Why It Matters: People Analytics and Human Capital Trends"},"content":{"raw":"

Why learn about\u00a0people analytics and human capital trends?<\/h2>\r\n\"A\r\n\r\nThe quantity of \u201cbig data\u201d\u2014data sets that are too large or too complex for traditional data processing applications[footnote]Van Vulpen, Erik. \"15 HR Analytics Case Studies with Business Impact.<\/a>\" AIHR Analytics. July 30, 2019. Accessed August 06, 2019.[\/footnote]\u2014is growing exponentially. By 2020, it\u2019s estimated that 1.7 MB of data will be created per second for every person on earth.[footnote]\"Data Never Sleeps 6.0<\/a>.\" Domo Resource. Accessed August 06, 2019.[\/footnote] In 2019, the size of the \u201cglobal datasphere,\u201d or quantity of new data captured or created globally,[footnote]\"Global DataSphere<\/a>.\" IDC. Accessed August 06, 2019.[\/footnote] is projected to be 41 zettabytes. By 2025, that number is forecast to be 175zettabytes. To put this in perspective, see Cisco\u2019s Visualized: A Zettabyte<\/a> graphic.\r\n\r\nWell . . . so what? Consider this: What if, instead of simply reporting human resource metrics and attempting to trouble-shoot a \u201cblack box,\u201d you applied predictive analytics to employee data to identify probable causes and change outcomes? And that is precisely what Experian did when faced with a turnover challenge.\r\n
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Turnover and Data<\/h3>\r\nIn 2016, Experian\u2019s global human HR management found that the company\u2019s resignation rates were 4% over the industry benchmark.[footnote]\"Case Study: Experian Predictive Workforce Analytics<\/a>.\" Experian. 2019. Accessed August 06, 2019.[\/footnote] This was not only a staffing issue; the company determined that every 1% increase in turnover cost the business approximately $3 million and that the churn was constraining growth and innovation. Additionally, higher turnover diverted HR staff from core culture- and employee-building initiatives.\r\n\r\nIn order to identify employees who were a flight risk, Experian built a predictive model that factored in 200 attributes, including team size and structure, supervisor performance and commute distance.[footnote]Van Vulpen, Erik. \"15 HR Analytics Case Studies with Business Impact<\/a>.\" AIHR Analytics. July 30, 2019. Accessed August 06, 2019.[\/footnote] The model identified both risk factors and flight risk triggers, one of the latter being a move that increased the employee\u2019s commute. Analytical insights, combined with good management practices, allowed management to address issues on both an individual level and at scale. Business impact: attrition was reduced by 4% globally, saving the business $14 million over two years. Experian Group HR Director Mark Wells observed that applying analytics not only saved the company millions, it has become \u201cthe backbone of how we make the best decisions for our people. We\u2019re now better able to anticipate and predict what our employees value and that\u2019s helped us to retain talent that keeps Experian innovating.\u201d Experian has also turned their predictive model into a \u201cWorkforce Analytics for Retention\u201d service.\r\n\r\n<\/div>\r\nBeing able to see into the black box of employee motivations and behavior is powerful. However, the ability to change HR outcomes is transformational. In this module, we\u2019ll explore that potential, including people analytics and human capital trends and implications.","rendered":"

Why learn about\u00a0people analytics and human capital trends?<\/h2>\n

\"A<\/p>\n

The quantity of \u201cbig data\u201d\u2014data sets that are too large or too complex for traditional data processing applications[1]<\/sup><\/a>\u2014is growing exponentially. By 2020, it\u2019s estimated that 1.7 MB of data will be created per second for every person on earth.[2]<\/sup><\/a> In 2019, the size of the \u201cglobal datasphere,\u201d or quantity of new data captured or created globally,[3]<\/sup><\/a> is projected to be 41 zettabytes. By 2025, that number is forecast to be 175zettabytes. To put this in perspective, see Cisco\u2019s Visualized: A Zettabyte<\/a> graphic.<\/p>\n

Well . . . so what? Consider this: What if, instead of simply reporting human resource metrics and attempting to trouble-shoot a \u201cblack box,\u201d you applied predictive analytics to employee data to identify probable causes and change outcomes? And that is precisely what Experian did when faced with a turnover challenge.<\/p>\n

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Turnover and Data<\/h3>\n

In 2016, Experian\u2019s global human HR management found that the company\u2019s resignation rates were 4% over the industry benchmark.[4]<\/sup><\/a> This was not only a staffing issue; the company determined that every 1% increase in turnover cost the business approximately $3 million and that the churn was constraining growth and innovation. Additionally, higher turnover diverted HR staff from core culture- and employee-building initiatives.<\/p>\n

In order to identify employees who were a flight risk, Experian built a predictive model that factored in 200 attributes, including team size and structure, supervisor performance and commute distance.[5]<\/sup><\/a> The model identified both risk factors and flight risk triggers, one of the latter being a move that increased the employee\u2019s commute. Analytical insights, combined with good management practices, allowed management to address issues on both an individual level and at scale. Business impact: attrition was reduced by 4% globally, saving the business $14 million over two years. Experian Group HR Director Mark Wells observed that applying analytics not only saved the company millions, it has become \u201cthe backbone of how we make the best decisions for our people. We\u2019re now better able to anticipate and predict what our employees value and that\u2019s helped us to retain talent that keeps Experian innovating.\u201d Experian has also turned their predictive model into a \u201cWorkforce Analytics for Retention\u201d service.<\/p>\n<\/div>\n

Being able to see into the black box of employee motivations and behavior is powerful. However, the ability to change HR outcomes is transformational. In this module, we\u2019ll explore that potential, including people analytics and human capital trends and implications.<\/p>\n\n\t\t\t

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Candela Citations<\/h3>\n\t\t\t\t\t
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CC licensed content, Original<\/div>