Introduction to HR Analytics Presentation

HR Analytics

1. What is Business Intelligence?

2. HR Analytics as a function

3. HR Analytics in HR Delivery Model

4. HR Analytics

5. History of BI and Analytics

6. Phase 1: Traditional HRM Traditional HRM is considered as a reactive approach to HRM. Although it included major HRM functions such as recruitment and selection, training of employees, payroll administration, performance management system, salary and compensation benefits, maintaining other compliances, it lacked the strategic orientation. Traditional HR managers were followers of the top management and the strategic think tank. Inputs were received from the top management, and HR managers used to comply with the broad directions with little or no personal inputs. There are three phases in traditional HRM: (1) Personnel management phase, (2) Industrial relations phase, (3) HR maintenance phase.

7. 1. Personnel Management Phase (1900–35) The period from early 1900 to 1935 majorly focused on personnel management. Rapid growth in industrialization, mainly the manufacturing economy, shifted the focus of engineers to productivity and efficiency of work and not just completion of work. The primary objective of scientific management was to figure out the best and fastest way of doing work, which could ultimately lead to enhanced productivity of the employees. Ultimately, the total number of tasks completed in a day by a worker formed the basis of his or her compensation, eventually giving rise to piece-rate pay systems. The piece-rate payment system proved to be highly motivating for workers and ultimately increased the overall productivity of the organization. The emergence of the personnel system in India is traced to the early 1920s.

8. 2. Industrial Relations Phase (1935–45) Various labor laws were enacted to strengthen the welfare conditions of workers by providing the appointment and chartering duties of a welfare officer in all the factories. By the mid-1950s, unionism increased to 35% from a mere 10% in the 1930s. Hence , HR once again became a prominent function in organizations, but the nature of functions had changed. HR was now mainly involved in dealing with unions and industrial disputes. The industrial relations (IR) aspect of HR was more significant now, and personnel activities were only observed as administrative or maintenance-oriented.

9. 3. HR Maintenance Phase (1945 to 1970s) After World War II from 1945 to the 1970s, when the economy was in the booming phase, more and more employment and labor laws were passed so that a peaceful and sustainable business environment was ensured. As a result, HR witnessed a relatively stable mode for both personnel management and IR. Personnel management was now more involved in record keeping and maintenance of personnel-related data such as recruitment, training, and benefits. On the other hand, IR was more engaged in maintaining legal compliances and coordinating with government offices for the smooth functioning of the business . The personnel management and IR practices had started showing impact on the bottom line, and hence advancement in the HRM department was bound to happen. One interesting phenomenon that evolved during this period was the rationalization of the internal labor market (ILM), which meant building leaders from within and ensuring job security to employees. HR functions ensured a fixed time-based promotion and increase in compensation to employees joining at the entry level.

10. Phase II: Emergence of Strategic HRM Wright and McMahan defined SHRM as a discipline concerned with the planning and deployment of human resources (people) and HRM (functions) to increase organizational effectiveness. Thus , we can say that all activities and functions of HR that add business value and enhance its efficiency can be termed SHRM. Miles and Snow classified firms into three broad categories: ( 1) Defenders- These organizations are capital-intensive with a relatively narrow product market domain. The top management of these organizations mainly works to increase the efficiency of the existing system. ( 2) Prospectors- These are innovative organizations known for their diversification. These are change agents who spend extensively on research and development to have the first-mover advantage. These organizations are not highly efficient because of the high diversification value. ( 3) Analyzers- These organizations are balanced because they operate differently for stable market areas and innovative market areas. In the stable areas, they mimic the characteristics of a defender, while they act as prospectors in the innovative areas. Thus, we can say that they develop a stable product mix and work extensively to increase efficiency in the system .

11. Phase III: HR Analytics Phase (2010 Onward) Professor Jac Fitz- enz talked about the five necessary steps for doing HR analytics. (1) recording, (2) relating, ( 3) comparing , (4) understanding , (5) predicting He suggested recording the work as the beginning of the process followed by relating it with organizational goals, comparison of outcomes with other organizations, understanding the past business and behavioral results, and finally predicting the future likelihood of an HR-related event . This was the first comprehensive model on HR analytics for HR professionals . The greater realization of the impact generated by the use of HR analytics in organizations helps the field to grow substantially in the near future. Automation in HRM ( DeSanctis 1986, Taylor and Davis 1989) coupled with the growth of HR information systems (HRIS) (Haines and Petit 1997) or electronic HRM (Crestone 2005) gradually led to a decision-oriented HRM system (Boudreau and Ramstad 2005), which eventually is evolving as HR analytics ( Heuvel and Bondarouk 2017).

12. Summary of the Growth of Automation and Analytics in HR Year Analytical Advancement in HRM Rationale/Need for Advancement Author 1980–90 Automation in HRM processes (payroll and data a dministration ). Identify HR area for automation and reducing administration cost. De Sanctis (1986); Taylor and Davis (1989) 1990–99 Increased adoption of computers in HRM and popularity of HRIS. Reduced administration cost, avoiding dupli - cation of data, quick access to information. Haines and Petit (1997) Early 2000 Internet accessibility, increased ICT. Emergence of E-HRM. Instant access to information, better decision-making, linkage to business decision, better planning. Crestone (2005) 2005–10 Fast-paced Internet access, penetration of ERP etc. Need for decision-oriented HRM from traditional service-oriented HRM. Boudreau and Ramstad (2005) 2010 onward HR analytics stage. HR as business enabler. Strategic partner. Fecheyr-Lippens et al. (2015)

13. Evolution of HR Analytics

14. Steps in HR Analytics

15. HR Analytics Process Cycle

16. Stages of Analytics

17. Stages of Analytics

18. Descriptive analytics Descriptive analysis is mainly concerned with the description of the data. It helps us in segregating macro-level data from micro-level data. Through this analysis, we can understand the nature of data. Generally, management is a victim of oversimplification bias. It treats employees as a group of people who are working for the top management with a common objective. However, it tends to forget that these employees (larger group) are an aggregation of several subgroups with several distinct and similar features or characteristics such as age, domicile, gender, language, education, work experience, and tenure. The two steps in descriptive analysis, as proposed by Fitz- enz and Mattox (2014), are organize and display. Organize: This is the first step of analytics and the basis for further steps. In this level, the emphasis is on data collection. It is advised to ensure data accuracy. Process checks such as missing data and normality of data are adhered to. Display: Once the data collection and basic statistics such as mean, t-statistics, and correlation are done, it is advised that these relevant patterns or trends should be presented in the form of tabular reports or dashboards. Tables and dashboards help the reader to comprehend the pattern in a quick span of time. This stage becomes the basis for further analyses, such as predictive and prescriptive.

19. Diagnostic Analytics Diagnostic analytics is a type of data analytics that examines data to understand why specific events or outcomes happened.  It's used to identify patterns, trends, and connections to determine root causes. This helps businesses make better decisions and plan for the future. It often follows descriptive analytics, which focuses on what has happened in the past. Diagnostic analytics relies on information from descriptive analytics to proceed, as you need to know  what  happened before you can ask  why  it happened. This is followed in turn by prescriptive analytics, which focuses on what to do in the future. Following the order of “what?” then “why” then “what next?” is a sensible way to do data analytics, as you need to know what happened and why before you can decide what to do next .

20. Predictive analysis This step includes relatedness and modeling for predicting some relationship in the future. In the first step of predictive analysis, it is advised to relate your data with an internal or external benchmark. The relating step gives a clear notion of where we are and where we want to go. Modeling, the next step of predictive analysis, guides us in developing a model that has a possible arrangement of human or/and structural or/and relational capital to lead to the desired state (Fitz- enz and John Mattox 2014). Now the predictive model aimed for the desired state is ready for testing. Thus, predictive model formulation (expected relationships that may appear soon) and hypothesis testing are key features of this step.

21. HR Predictive Analytics examples:

22. Prescriptive analysis This step is also known as the evaluation step. In this step, statistical analysis is used to test the model developed in the predictive stage. The validity and reliability of the model are ensured. Eventually, the impact of the model on shareholder value creation is also determined.

23. Types of Analytical Models

24. What is generally measured or tracked?

25. What could be measured?

26. Eight Step Model of Purposeful Analytics

27. Conducting HR Analytics (Scott Mondore et al. 2011) A six-step model of HR analytics was also proposed in 2011 by Scott et al. ( Mondore et al. 2011). The basic steps of the model are as follows: determine critical outcomes, create cross-functional data team, assess outcome measures, analyze data, build program and execute, measure and adjust.

28. Conducting HR Analytics (Scott Mondore et al. 2011) Determine critical outcomes: The first step in conducting an HR analytics is determining the critical outcome of the analytics project. The desired outcomes can be decided in consultation with the top management, such as Chief Operational Officer (COO), Chief Financial Officer (CFO), and Chief Executive Officer. Some of the critical outcomes of a project can be a quality improvement, reducing accident rates, addressing turnover, improving employee satisfaction, etc . Create a cross-functional data team: In the second step, a cross-functional team of data analysts, line managers, process owners, and HR business partners needs to be constituted. Team members should have complete confidence in each other. Otherwise, there may be reluctance in data sharing, which will defeat the entire purpose of the project . Assess outcome measures: In the next step, it is essential to define the frequency of data capturing, unit of analysis, and process owner of each measurement. For example, it should be clarified in the beginning whether the outcomes will be measured monthly or quarterly and also whether the analysis will be at the individual level, department level, or functional level.

29. Conducting HR Analytics (Scott Mondore et al. 2011 )(Contd.) Analyze data: Once the data are collected, an appropriate measurement tool must be used to analyze the data. If necessary statistical skills are absent in internal employees, the organization can hire a professional analyst for data analysis. It is advisable to initially concentrate on only a few variables in each project. Next, these models can be tested based on the data using statistical analysis such as regression and structural equation modeling. For example, to understand the underlying mechanism of turnover among employees, HR analytics experts can identify variables based on the opinion of the critical stakeholders and relevant literature . Build program and execute: Based on the results, HR analytics experts should build an intervention program to reduce the problem at hand. To implement the program effectively, it is also suggested that an action plan at the individual, department, and organization levels should be rolled out. Return on investment (ROI) is one of the critical features of this stage. To have a full-fledged outcome, support of the top management is required at all juncture . Measure and adjust: The final step of the HR analytics project is the frequent measurement of the applicable model. This measurement or checking helps in tracking the progress and making necessary adjustments as per the desired objectives. It is advisable not to measure on a persistent basis.

30. Key Influencers in the HR Analytics Process The objective of the HR analytics process is ensuring that HR investments support the business goals. However, in the business, various stakeholders are interested in the investments and their return to the company. The key influencers are not only insiders who provide solutions to the problem, such as HR analytics experts, but also several other beneficiaries such as the operations department, marketing department, finance department, information and technology (IT) department, subject matter experts, and top management. .

31. HR Analytics Professionals Definitely, HR analytics professionals are the service providers and are the most critical influencers in the entire process. If the HR analytics experts are of top quality, users will automatically have trust in the processes and interventions of the HR analytics team. Thus, it is suggested that recruiting and selecting a highly competent HR analytics expert is one of the most critical aspects of building an analytics-driven culture in the organization. Once the HR analytics team is recruited, it should undertake small and quick-win projects as it further affirms the trust level in users. A few of the essential competencies for an HR analytics expert are (a) sound conceptual knowledge of HRM, (b) business acumen, (c) documentation, (d) statistical analysis using software, and (e) storytelling ability to influence the stakeholders.

32. Information and Technology (IT) Department The IT department is one of the key influencers in developing an HR analytics culture. The IT department has to provide all the necessary software and hardware to facilitate the data collection and analysis process. A highly upgraded IT system in the organization can be a boon for the HR analytics team. Appropriate training by the IT department to all the employees on updated software can help in facilitating the purpose of the HR analytics team.

33. User Department (Marketing/Sales/Production) User departments are prime influencers in the HR analytics journey because based on their feedback, the future of HR analytics will prevail in the organization. The user department can help in providing timely data and other feedbacks, which are ultimately the raw materials for the HR analytics team. If the user department is not supportive or their buy-in the process is absent, it will be challenging to conduct an HR analytics project. It is suggested that to have timely data, the HR analytics team should try to innovate on the data capturing process so that it is not taxing for the user department.

34. Top Management (COO, CFO, CEO) The support of the senior management in the entire process is critical. The HR analytics team should buy in the confidence of the top management team in communicating that they can make the difference. In other words, the HR team has to convince the senior management first that if people investments are made appropriately, it can drive business and positively impact the bottom line. If the top management is convinced about the positive ROI of the HR analytics projects, then the further steps become smoother.

35. Chief Human Resource Officer The Chief Human Resource Officer (CHRO) is the leader of the HR analytics team. The CHRO’s confidence in the idea that HR investments can earn a positive return to business is critical for the entire HR analytics culture in the organization. Research indicates that if the CHRO has a high technological and quantitative self-efficacy, his/her attitude toward the adoption of HR analytics will be high. In other words, if the CHRO has a low technical self-efficacy, the level of adoption will be really low.

36. Model for Adoption of HR Analytics Technical self efficacy of the top management is one of the key attributes which plays an enabling role in the adoption of HR analytics in an organization. Vargas et al., in 2018, proposed and tested a model explaining the level of adoption of HR analytics by an individual . According to the model, the technological and quantitative self-efficacy determines an individual’s attitude toward HR analytics. If the self-efficacy is high, the individual’s attitude will be positive toward analytics. Further, the positive attitude coupled with positive social influence and ease of trial ability can positively impact the level of HR analytics adoption. We can now summarize the key success factors for building an HR analytics culture in an organization. First of all, if an organization is trying to build an HR analytics culture, it should have a buy-in of the senior management. This step is most critical as it is the top management that will back it up financially, administratively, and morally. If the user and service department does not collaborate, the reliability and validity of the process will be at stake. Recommendations and interventions will be of no use because all the departments are working in silos. However, if there is collaboration between the user department (for example, marketing) and the service department (HR analytics), then access to clean and sufficient data is possible. This data can be statistically utilized and can help generate meaningful recommendations.

37. Tools, Software: Reporting

38. Tools, Software: Analysis & Monitoring

39. Tools, Software: Analysis & Monitoring

40. Tools, Software: Predictive Analytics

41. HR Analytics and Metrices

42. Data Sources for HR Analytics

43. 7 Pillars of HR Analytics

44. Metrics: Recruitment

45. Metrics: Retention

46. Metrics: Performance and Career Management

47. Metrics: Training and Development

48. Metrics: Compensation and Benefits

49. Metrics: Workforce

50. Metrics: Organizational Effectiveness

51. The Metrices Trap

52. Drivers and barriers

53. Benefits

54. Benefits

55. Key Take Away

56. Ethical Issues with HR Analytics

57. Potential risks

58. Balancing benefits of HR analytics with the potential risks to employee privacy Employee Trust: Employees are more likely to trust their employer if they believe their personal data is being handled responsibly and ethically. If employees feel that their privacy is being violated or their data is being used in ways they did not anticipate, they may be less willing to participate in HR analytics programs or to provide personal data . Legal Compliance: Businesses must comply with a range of laws and regulations governing the collection, use, and protection of personal data. Failure to comply with these laws can result in legal penalties and damage to the company's reputation. Balancing the benefits of HR analytics with the potential risks to employee privacy is essential for legal compliance . Ethical Responsibility: Businesses have an ethical responsibility to protect employee privacy and ensure that their use of HR analytics aligns with ethical principles and values. This involves being transparent with employees about how their data will be used, obtaining explicit consent before collecting personal data, and ensuring that the data is protected from unauthorized access or misuse . Business Benefits: Balancing the benefits of HR analytics with the potential risks to employee privacy can benefit businesses in the long run. By protecting employee privacy, businesses can build trust with their employees and avoid legal penalties or damage to their reputation. Additionally, by ensuring that HR analytics is fair and equitable, businesses can avoid perpetuating biases and discrimination, which can lead to a more diverse and inclusive workforce .

59. Best Practices for Ethical Use of HR Analytics Minimize Collection: Only collect personal information that is necessary for the intended purpose. Avoid collecting personal information that is not relevant to the purpose of data collection. Obtain Consent: Obtain consent from employees before collecting their personal information. Inform them of the purpose of data collection, how the information will be used, and who will have access to the information. Protect Privacy: Ensure that collected personal information is kept secure and confidential. Establish policies and procedures to safeguard personal information, such as access controls, encryption, and secure storage. Limit Use: Use the collected personal information only for the intended purpose. Do not use or share the information for any other purpose unless explicit consent is obtained from the employee. Be Transparent: Be transparent about the data collection process, including the purpose of data collection, who will have access to the information, and how the information will be used. Educate Employees: Educate employees about responsible data collection and the importance of protecting personal information. Provide training and resources to employees to help them understand their rights and responsibilities. Re gularly Review and Update Policies: Regularly review and update data collection policies and procedures to ensure they align with industry standards and legal requirements. Stay informed of changes to data privacy laws and regulations .

60. Protection of E mployee Privacy De-identification of Data: Organizations can de-identify data by removing or encrypting personal identifiers, such as names, addresses, and social security numbers. This ensures that the data cannot be linked back to individual employees and reduces the risk of unauthorized access or disclosure. Access Controls: Access to sensitive information should be restricted to authorized personnel only. Organizations can implement access controls, such as password protection, two-factor authentication, and role-based access, to limit access to sensitive data. Encryption: Sensitive data should be encrypted both at rest and in transit. This ensures that the data is protected even if it falls into the wrong hands. Data Retention Policies: Organizations should establish data retention policies that dictate how long sensitive data should be kept and when it should be destroyed. This helps to minimize the risk of unauthorized access or disclosure of sensitive data. Regular Audits: Regular audits should be conducted to ensure that privacy safeguards are being properly implemented and followed. This helps to identify any weaknesses in the system and allows for corrective action to be taken.