In Part 1 of this series, we learnt about the basics of HR Analytics for business decision.
Application of HR analytics for facilitating business decision-making process depends on two things – collection of quality data and then analyzing data for business objectives. This requires conscious planning and adoption of HR analytics at every stage of HR operations. Organizations today already mine and collect enough employee data which gives insights into employee demographics, academic and professional background, completed pieces of training and their impact as well as performance ratings. But to use this data to derive meaningful business solutions is where the key to HR analytics lie.
HR Analytics is, therefore, not just a statistical method for defining HR strategy, but is a full-fledged discipline in itself. To be able to assist in the decision-making process for the organization, HR Analytics is essentially integrated into the business process, through the following steps –
1. Determine business outcomes critical to growth: The more focused the information search is, the lesser time it requires and the more productive it can be. Hence, the first step in the adoption of HR analytics itself entails determining critical business outcomes that need information based solutions.
2. Identify cross-functional data contributors: The process then requires bringing data contributors on-board. Before data analysis, quality data needs to be extracted from across functions. That is why bringing department leaders on-board and convincing them about the analytics process is important. They are the largest sources of employee information and while contributing to the analytics process, they can also help refine the business outcomes towards which the analytical effort is aimed.
3. Define frequency and level of measurement: The data is then to be collected at daily, weekly, monthly, quarterly or annual intervals. The levels also have to be determined for data collection. While some employee data is more significant at the job level, other could be more consequential at work unit, store, department or function level. This step involves establishing the level at which the data will be collected from employees and the frequency with which the data will be collected for better analytical accuracy.
4. Conduct data analysis: HR analytics employ information management tools, Big data tools, statistical analysis tools, and predictive modeling methods to process the employee data collected. Not only does it enable HR leaders to identify human resource gaps causing business problems, but also help determine possible solutions. Oracle and Workday offer embedded analytics programs that are popular in the industry for ready implementation of HR analytics. Ultimately, effective data analysis depends on automation of metrics and refinement of employee data before processing. Data analysis is done using a top-down, objective-driven approach and focuses on finding concrete answers.
5. Design interventions and business strategies: Once the data is scrubbed and converted into relevant information, HR analytics enable development of a feasible business plan. Business leaders have been asking for tactical solutions from HR concerning recruitment, training, employee engagement, talent retention and succession planning; which could directly enhance Top-line and Bottom-line. With HR Analytics, interventions in on-going and ineffective HR practices can be designed and executed.
6. Evaluate and modify adopted strategies: Despite the radical advancements which the industry has made in adoption and application of data sciences, HR is still a field that is learning to apply data analytics. The strategies thus developed with the help of HR analytics are not perfect and require frequent modifications and up-gradations. With the slow evolution of HR analytics from operational reporting to advanced reporting, advanced analytics and predictive analytics; this stage entails constant monitoring and improvement of business strategies proposed by HR analytics.Go ahead and check out in Part 3 of this series 'How it helps in taking business decisions'.