{"id":1083,"date":"2020-08-04T08:18:11","date_gmt":"2020-08-04T12:18:11","guid":{"rendered":"http:\/\/opentextbooks.concordia.ca\/mana362sandbox\/?post_type=part&p=1083"},"modified":"2020-08-28T13:33:22","modified_gmt":"2020-08-28T17:33:22","slug":"chapter-14-talent-anaytics","status":"publish","type":"part","link":"https:\/\/opentextbooks.concordia.ca\/mana362sandbox\/part\/chapter-14-talent-anaytics\/","title":{"raw":"Chapter 11: HR Analytics","rendered":"Chapter 11: HR Analytics"},"content":{"raw":"To explain and demonstrate typical analytical process, CGI Advanced Analytics Teamperformed advanced analysis over anonymous corporate employees\u2019 data. The sample data set represents prepared and clean data integrated from several HR information systems.The dataset contains common and specific HR-oriented features for utmost 1500 individual employees regarding:\r\n\r\nDemographics\r\n\r\nJob and company environment satisfaction\r\n\r\nTravelling\r\n\r\nEducation and field\r\n\r\nJob type, level and status\r\n\r\nTime reports and absences\r\n\r\nRates, salary and project utilization For each individual record there is an information determining whether the corresponding employee left the company at the end of analyzed period. This information is used to identify key features connected to attrition issue.Uncovering hidden data patterns to predict present employees in the risk of attrition, outcomes of this study show the way how to save a value by identifying possible causes of talents.\r\n\r\n4\u201cUsual approach included in black-box analytical tools is rather problematic. It leads to inaccurate solutions and very incomprehensive conclusions. \u201dCase-Study Prediction and understanding the attrition of employees To explain and demonstrate typical analytical process, CGI Advanced AnalyticsTeamperformedadvanced analysis over anonymous corporate employees\u2019 data. The sample dataset represents prepared and clean data integrated from several HR information systems. The dataset contains common and specific HR-oriented features for utmost 1500 individual employees regarding:\uf0b7Demographics\uf0b7Job and company environment satisfaction\uf0b7Travelling\uf0b7Education and field\uf0b7Job type, level and status \uf0b7Time reports and absences\uf0b7Rates, salary and project utilization. For each individual record there is an information determining whether the corresponding employee left the company at the end of analyzed period.This information is used to identify key features connected to attrition issue.Uncovering hidden data patterns to predict present employees in the risk of attrition, outcomes of thisstudy show the way how to save a value by identifying possible causes of talents.\r\n\r\nANALYTICAL APPROACHES The selection and right execution of suitable analytical approach and modelling method is essential for the precision and usability of final outcomes and its utilization.Usual approach included in black-box analytical tools,such as automatic feature selection and choosing from predefined prediction algorithms,is rather problematic.This leads to inaccurate solutions and chiefly, very limited and incomprehensive conclusions.CGI Advanced Analytics methodology brings solid and proven approach defining how to correctly manipulate the data, test and select suitable modelling techniques and discover reliable and highly applicable insights.For the purpose of the case-study, whole dataset was inspected and using logistic regression method, two different questions were answered:\uf0b7Which specific factors increases or decreases the probability of attrition?\uf0b7Which individual employees across different jobs are in high risk of attrition?Regarding the first question, advanced modelling techniques like neural networks or logistic regression are able to identify \u201cdrivers\u201d that influence target variable \u2013risk of attrition in this case. In opposite to traditional and trivial methods such as simple correlation, advanced methods are able to get further information and uncover more complex patterns.\r\n\r\n\"\"\r\n\r\nThe figure bellow shows far most relevant factors influencing attrition:This kind of information provides seemingly straightforward insight. However,in order to deliver more thorough and usable conclusions it is necessary to go a little bit deeper.One possible way how to do that is for example to perform such an analysisseparately for different job types and roles.The next figure shows the importance of various factors for three different job categories: technician, scientist and salesman\r\n\r\n\"\"\r\n\r\n \r\n\r\n \r\n\r\nIn the general meaning,social network analysis term is not only associated to social media like Facebook or Twitter. It also describes a way how to explore various type of social interaction. In the context of HR analytics, this can be used as an instrument how to enhance standard models by adding information about interactions among employees.Real social networks and communication flows in companies often differ from structural organization charts. Social leaders might be identified even they are not on the top of structural hierarchy.\r\n\r\nInteractionscan be identified by:\r\n\r\nList of calls\r\n\r\nMail communication\r\n\r\nInstant messages\r\n\r\nMutual meetings\r\n\r\nWork on same project and shared folders access\r\n\r\n \r\n\r\nAnswering the second question -who are the employees in a danger of the attrition, prediction model was developed and applied. For evaluating purposes, one third of thedataset was separated to test the model accuracy. The rest was used to train the model and perform previous analysis.Developed model is able to predict 88.9%of employees withleft-the-company flags.\r\n\r\n\"\"","rendered":"

To explain and demonstrate typical analytical process, CGI Advanced Analytics Teamperformed advanced analysis over anonymous corporate employees\u2019 data. The sample data set represents prepared and clean data integrated from several HR information systems.The dataset contains common and specific HR-oriented features for utmost 1500 individual employees regarding:<\/p>\n

Demographics<\/p>\n

Job and company environment satisfaction<\/p>\n

Travelling<\/p>\n

Education and field<\/p>\n

Job type, level and status<\/p>\n

Time reports and absences<\/p>\n

Rates, salary and project utilization For each individual record there is an information determining whether the corresponding employee left the company at the end of analyzed period. This information is used to identify key features connected to attrition issue.Uncovering hidden data patterns to predict present employees in the risk of attrition, outcomes of this study show the way how to save a value by identifying possible causes of talents.<\/p>\n

4\u201cUsual approach included in black-box analytical tools is rather problematic. It leads to inaccurate solutions and very incomprehensive conclusions. \u201dCase-Study Prediction and understanding the attrition of employees To explain and demonstrate typical analytical process, CGI Advanced AnalyticsTeamperformedadvanced analysis over anonymous corporate employees\u2019 data. The sample dataset represents prepared and clean data integrated from several HR information systems. The dataset contains common and specific HR-oriented features for utmost 1500 individual employees regarding:\uf0b7Demographics\uf0b7Job and company environment satisfaction\uf0b7Travelling\uf0b7Education and field\uf0b7Job type, level and status \uf0b7Time reports and absences\uf0b7Rates, salary and project utilization. For each individual record there is an information determining whether the corresponding employee left the company at the end of analyzed period.This information is used to identify key features connected to attrition issue.Uncovering hidden data patterns to predict present employees in the risk of attrition, outcomes of thisstudy show the way how to save a value by identifying possible causes of talents.<\/p>\n

ANALYTICAL APPROACHES The selection and right execution of suitable analytical approach and modelling method is essential for the precision and usability of final outcomes and its utilization.Usual approach included in black-box analytical tools,such as automatic feature selection and choosing from predefined prediction algorithms,is rather problematic.This leads to inaccurate solutions and chiefly, very limited and incomprehensive conclusions.CGI Advanced Analytics methodology brings solid and proven approach defining how to correctly manipulate the data, test and select suitable modelling techniques and discover reliable and highly applicable insights.For the purpose of the case-study, whole dataset was inspected and using logistic regression method, two different questions were answered:\uf0b7Which specific factors increases or decreases the probability of attrition?\uf0b7Which individual employees across different jobs are in high risk of attrition?Regarding the first question, advanced modelling techniques like neural networks or logistic regression are able to identify \u201cdrivers\u201d that influence target variable \u2013risk of attrition in this case. In opposite to traditional and trivial methods such as simple correlation, advanced methods are able to get further information and uncover more complex patterns.<\/p>\n

\"\"<\/p>\n

The figure bellow shows far most relevant factors influencing attrition:This kind of information provides seemingly straightforward insight. However,in order to deliver more thorough and usable conclusions it is necessary to go a little bit deeper.One possible way how to do that is for example to perform such an analysisseparately for different job types and roles.The next figure shows the importance of various factors for three different job categories: technician, scientist and salesman<\/p>\n

\"\"<\/p>\n

 <\/p>\n

 <\/p>\n

In the general meaning,social network analysis term is not only associated to social media like Facebook or Twitter. It also describes a way how to explore various type of social interaction. In the context of HR analytics, this can be used as an instrument how to enhance standard models by adding information about interactions among employees.Real social networks and communication flows in companies often differ from structural organization charts. Social leaders might be identified even they are not on the top of structural hierarchy.<\/p>\n

Interactionscan be identified by:<\/p>\n

List of calls<\/p>\n

Mail communication<\/p>\n

Instant messages<\/p>\n

Mutual meetings<\/p>\n

Work on same project and shared folders access<\/p>\n

 <\/p>\n

Answering the second question -who are the employees in a danger of the attrition, prediction model was developed and applied. For evaluating purposes, one third of thedataset was separated to test the model accuracy. The rest was used to train the model and perform previous analysis.Developed model is able to predict 88.9%of employees withleft-the-company flags.<\/p>\n

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