A Decision Making Model for Human Resource Management in Organizations using Data Mining and Predictive Analytics

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1 A Decision Making Model for Human Resource Management in Organizations using Data Mining and Predictive Analytics Sujeet Narendra Mishra Faculty of Computer Science and Engineering Shri Ramswaroop Memorial University Lucknow-Deva Road, India Dev Raghvendra Lama Faculty of Computer Science and Engineering Shri Ramswaroop Memorial University Lucknow-Deva Road, India Abstract The development of a decision-making model for Human Resource Management (HRM) in organizations especially for multinational companies can be encouraged considering the fact that HRM plays a lead role in determining the effectiveness of organizations endurance. The HRM generally maintains evaluation practices and systems impelling employee behavior, commitment, and performance. It is the responsibility of the HRM to dig best talents around the world, look after training, evaluate employee performance, give away rewards and ultimately keep a right environment in the company. As every strategy of an organization is totally or somewhat related to the talents present, it becomes essential to provide a framework that accurately predicts talent and workforce. When human resource data is accessed for decision making, different methods can be used to extract the best knowledge out of available data. Data mining is widely considered for extracting insights from data to make decisions whereas predictive analytics is known for state-of-the-art accuracy in decisions. The proposed model can be helpful in improving effectiveness and efficiency of HR system that will optimize business outcome. This paper is an attempt to provide a decision-making framework for Human Resource related decisions comprising data mining and predictive analytics. Index Terms Data Mining, Decision Making, Human Resource Management, Predictive Analytics, Talent Management I. INTRODUCTION Advancement in technology has boost HR functioning in business organizations. They are now able to add more time to strategic business decisions as automation on data handling has provided better decision-making power to HR. Data mining has been a reliable technology which gives a deeper understanding of data for decision making. But a challenge associated with HRM especially in terms of accurate decision making is worth consideration. Data mining is often called Knowledge Discovery in Data (KDD) which is believed to be a combination of descriptive and predictive techniques. Even if predictive techniques are referred as part of data mining due to its similar nature, but the term prediction in data mining differs broadly from predictive analytics as the later one focus on the accuracy of prediction with better decision making. [1] Combining data mining with the advanced predictive techniques has enabled HR to understand and improve HRM functions; HR Predictive Analytics (HRPA) is one such advancement. A hierarchy of analytics applied on HR data such as descriptive, correlation, predictive and prescriptive analytics combined together with HR metrics and advanced machine learning techniques for decision making. [3] [4] specifically, predictive models are prepared from insights of mined data from human resource data collection. [5] In this paper Section-I is an introduction, Section-II gives definitions and concepts of HRM, data mining and predictive analytics, Section-III describes Methodology, Section-IV are Discussions on application of data mining and predictive analytics in HRM, Section-V explains the components of Proposed framework, Section-VI is the Concluding statements on future of decision making in HR. A. HRM II. DEFINITIONS AND CONCEPTS In any organization HRM plays an important role in strategy making and are responsible for organizational decision making. HRM manages workforce by keeping them satisfied and also lifts work environment. The work includes assessment, hiring, training and rewards for deserving employees at the right time. HRM is believed to hire or retain best talents but often miss out on them due to either loose recruitment policy or attrition. B. Data Mining Data Mining has gained a lot of significance in the society as it helped to make prediction methodologies easier in various fields. It is applied in almost all the fields like medical, telecommunication, manufacturing, health care and customer relationship apart from Human Resource fields. It may be viewed as the extraction of patterns and models from observed data or repositories. It is applied after data preprocessing i.e. cleaning of data. Information has to be mined for intelligent decision making from rich and colorful data warehouse. There are six tasks covered under data mining; anomaly detection, association, clustering, classification, regression, and summarization. It is an important step ahead in the process of predictive analytics

2 Data Collection Identify Suitable Case Apply Data Mining Techniques Record Data Insights Prepare Predictive Model Fig 1: Methodology Some well-known software for data mining is Weka, SPSS, SAS, Orange, RapidMiner, KNIME etc. [6] C. Predictive Analytics Predictive analytics can be referred as an analyst-guided subject that studies data patterns to make forward-looking predictions and can make complex statements by evaluating various data patterns. If data mining quests for hints, predictive analytics deliver answers that guide to what next action. Whereas data mining techniques separate the highly valuable data variables within a large dataset, the analyst uses these variables and their patterns to provide a predictive model that formulate the relationships and predict forthcoming behavior consistently. [7] HR professionals boost their strategic contribution with the help of advanced statistical techniques. The predictive metrics are a byproduct of predictive insights that enhances reports and dashboards. Predictive analytics provide predictive solutions which can efficiently solve talent management issues. [1] [4] [5] III. METHODOLOGY HR requires certain steps to identify data and apply decisive techniques into the functioning of HR. The methodology is as follows; Step 1: Data Collection A continuous collection of data in warehouse pertaining to the human resource is maintained such that it includes employee records, psychometric test, and performance and business outcome results. HR needs to make sure every important measure is included in data records. Step 2: Identify Suitable Cases Variables list selected from data collection are according to the need of data mining. [8] Correlation based association rule is generated from data warehouse by applying data mining techniques to reduces dependent variables. [9] Step 3: Apply Data Mining Techniques Knowledge Management is essential for on time predictions and has measurable benefits in management decisions. C4.5 classifier technique has a potential accuracy for talent management. [10] Better decisions can also opt with other data mining techniques such as J48, KNN and Apriori algorithms. [11] [12] Step 4: Record Data Insights A collective experience is produced from mined data in the form of records. These records are analyzed for leveraging analytics on data. This step invokes value from mined data by HRM. Step 5: Prepare Predictive Model A number of predictors combined to prepare a predictive model from data insights which can either be linear models or business intelligence rules. A careful formulation of predictors derives result in the form of a rank or a score, known as the predictive score. This score is used for decision making. [5] IV. DISCUSSION Data mining and predictive analytics techniques are automating the process of decision making for various human resource 218

3 functions. Some case analysis has been discussed here on the impact of these techniques and their application in HRM. These case studies are presented below; A. Data Mining in HR Employee Recruitment (Personnel Selection) Hiring of employees can be subdivided into two parts first being recruitment while next being selection. Decision trees are a useful tool to explore data for predictions in the form of rules. Classification and regression tree (CART) algorithm is the most famous method used in HRM for decision making on hiring. [13] Other data mining techniques such as clustering using fuzzy c-means and k-means algorithm are also used for this purpose. A regression method is too applied in data mining that removes dependent variables and provides accuracy on performance assessment. [14] [15] Talent Management Major data mining techniques for talent management are classification, clustering, association and prediction rules. Some classification algorithms i.e. decision tree (C4.5, random forest), neural network (multilayer perceptron, radial basis function) or nearest neighbor (instance based learning) are applied for prediction of potential talent for a specific task in organizations. [16] Mining of talent can also be integrated with information retrieval techniques for processing text in resumes and filled forms. [17] Employee Turnover Analysis Decision trees, Logistic regression, and neural network are the data mining techniques applied to understand the problem of turnover. SAS institute has developed SEMMA (Sample, Explore, Modify, Model, and Assess) to create a classification of data and allows application of statistical and visualization techniques, to select and signify predictor variables, to model predicting output and confirm the credibility of the model. [18] International Journal of Computer Science and Information Security (IJCSIS), leverage complex statistical distribution allowing more accurate predictions in marketing, management and operations. Predictive analytics is offering accurate decisions for human resource functions using quantitative measures such as location, attendance, age, and skill level for employee satisfaction and retention. [20] Oracle Fusion Workforce Predictions Predictive analytics provides progressive insights into workforce trends and help to predict performance and attrition. Also, a What-If predictive model on cloud improves performance and reduces attrition. The blending of internal information with external benchmarks and market indicators are done to provide the statistical models to make highly accurate predictions. [21] Attrition Risk Prediction from PricewaterhouseCoopers Human Resource System (PWC-HRS) A six-phase approach was applied to an organization having 16,000 employees to predict attrition risks. First was design phase where 75 hypotheses were considered after that second phase was data extraction in which predictors were chosen from data history. Then 3 models were developed which then deployed to produce a predictive score. 427 high-risk employees were identified in next phase. The final phase was redoing of this approach after every 6 months. [22] A white Paper from Tata Consultancy Services Predictive analytics is adapted only when the data source is aligned with strategic business outcomes. HR processes such as recruitment, workforce management, performance management and risk management are integrated to predictive analytics. Key aspects of HRM like employee recruitment, profiling and attrition are predicted with the help of predictive scores. [23] B. Predictive Analytics in HR Employee Fight Risk Prediction by HP HP helped Global Business Services (GBS) to lower down their attrition rate by application of predictive analytics. A predictive model considering all the attrition factors zipped into one to calculate flight risk scores. And the score on careful handling able to reduce attrition rate by 5%. [19] A report from SAS institute Predictive analytics is equipped with relevant data having volume, velocity, variety i.e. big data which in turn is optimizing business outcomes. Enhancement in computing power has enabled predictive models to V. PROPOSED FRAMEWORK Figure 2 presents the architecture of the proposed system aggregating four components in the model as follows; 1. Human Resource Data: Data acquisition and storage of human resource s information to a data warehouse. The data is a continuous collection of records by Human Resource Management and is extensively used for decision making

4 Human Resource Data Data Mining Data Insights Predictive Analytics Predictive Score Decisions Fig 2: The Architecture of Proposed HR Decision System Model 2. Data Mining: Mining information to generate insights from HR data and obtain knowledge after mapping of the pattern through generated rules. Appropriate classification and clustering techniques are applied, specifically decision tree and neural networks which return insights of data with high predictive accuracy. 3. Predictive model: It has two sub-components, first being predictive analytic technique guided by an analyst to formulate data insights in statistical form. Second being predictive scores used for a range of decisions. 4. Predictive decision: Depending on if else based necessity it is used for either immediate action or action in future. Decisions are delivered after comparing predictive scores. VI. CONCLUSION Data mining and predictive analytics are much-needed techniques for decision making for human resource management in organizations. The application of modern techniques provided better solutions to the existing limitations. The ordering of components in the designed framework was well chosen to complement existing system that relies on data warehouse and mining. The framework if fully implemented will enhance decision-making capability of HR. And also, it will optimize the decision-making speed, provide reliability and enhance organizational outcome. REFERENCES [1] C. Elkan, Predictive Analytics and Data Mining, May 28, 2013, pp 7-12 [2] J. C. Sesil, Applying Advanced Analytics to HR Management Decisions, Pearson Publication, New Jersey, USA, March 2014, pp 1-26 [3] J. Fitz-Enz and J. R. Mattox II, Predictive Analytics for Human Resource, Wiley Publication, SAS Institute Inc., USA, 2014, pp [4] S. N. Mishra, D. R. Lama and Y. Pal, Human Resource Predictive Analytics (HRPA) for HR Management in Organizations, International Journal of Scientific & Technology Research, Vol. 5, Issue 5, May [5] E. Siegel, Predictive Analytics with Data Mining: How It Works, DM Review's DM Direct, February 2005 (Available: [6] What is Data Mining, Predictive Analytics Today, 2013 (Available: [7] FICO, How does predictive analytics differ from data mining and business intelligence? Business Intelligence, Data Mining, Predictive Analytics, June 2006 (Available: [8] Z. Cheng and Y. Chen, Data Mining Applications in Human Resources Management System, Journal of Convergence Information Technology, Vol. 7, No. 8, May 2012 [9] Z. Danping and D. Jin, The Data Mining of the Human Resources Data Warehouse in University Based on Association Rule, Journal of Computers, Vol. 6, No. 1, January 2011 [10] L. Sadath, Data Mining: A Tool for Knowledge Management in Human Resource, International Journal of Innovative Technology and Exploring Engineering, Vol. 2, Issue 6, April 2013 [11] A. Fatima and S. Rahaman, Mining System in HR: A Proposed Model, International Journal of Computer and Information Technology, Vol. 03, Issue 05, September 2014, ISSN: [12] J. Ranjan, D. P. Goyal and S. I. Ahson Data Mining Techniques for Better Decisions in Human Resource Management Systems, International Journal of Business Information Systems, Vol. 3 (5), 2008 [13] A. Azar, M. V. Sebt, P. Ahmadi and A. Rajaeian, A Model for Personnel Selection with a Data Mining Approach: A Case Study in a Commercial Bank, Journal of Human Resource Management, 11(1), Art. 449, 15 Apr [14] N. Sivaram and K. Ramar Applicability of Clustering and Classification Algorithms for Recruitment Data Mining, International Journal of Computer Applications, Vol. 4, No. 5, July

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