In the current highly competitive globalized business environment with a fluctuating economy, it has become imperative for companies to go an extra mile and utilize the available resources, capture value, and manage risks more effectively. Intensified decision making through clear interpretation of information and efficient utilization of human capital have emerged as drivers of sustained economic value. Analytics has progressed out of these market needs to become a key tool for organizations to make well-informed and strategic decisions. With enhanced focus on human capital, the application of analytics in HR is the current wave of evolution for businesses.
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When we get started with Human Resource Analytics we create a new way of working and looking at data in an out-of-date- thinking human resource department. This deviation from the status quo can be compared to the process of new venture creation within an existing organization. One of the key approaches to this value creation is through a form of reasoning whereby one starts with the available means instead of a number of set goals, which are framed based on the available means.
In this article, we look into the three major building blocks that need to be put in place for the Human Resource Analytics journey to kick off. While the drive cannot proceed without any of the three, the actual level of involvement and investment of each of the building blocks depends on a number of considerations.
1. Business Problems
The golden rule in any business area is that if you solve a problem, you create value and the same applies to HR analytics. If you can leverage people data to solve a business issue, you are off to a good start as analytics only adds value if it solves real organization problems. One should begin by establishing the existing problem before you can proceed to gather people data to inform your hypothesis.
For instance, if you’re trying to measure employee satisfaction, analyze the turnover rate and absenteeism levels of your workforce. Measuring employee attrition using HR Analytics Turnover is a big problem in developing countries because global businesses that are looking for lower-skilled and easy-to-replace labor are actively competing with each other. More established companies on the other hand usually have older, more loyal and higher-educated workers yet they cope with higher levels of absenteeism. Leverage your data to inform the specific business problem you are experiencing – or if your specific pain point is a direct result of your workforce’s performance.
Employee satisfaction survey is a very effective tool that measures and maintains a positive culture within the organization. It gives the employees an opportunity for both, personal and professional development that contributes to the overall performance. Predictive Analytics Lab allows you to Design and deploy questionnaires for your staff here
2. People Data
Data being used for HR analytics can be classified, based upon its source and ownership. On one end it is purely internal data generated within the organization and owned by it e.g. employee-related data and performance management data. This data may often be found in discrete databases and systems .On the other end, we have external data which is generated outside the organization and owned by external agencies, e.g., economic or demographic data. To add on this, we have data which is generated outside the organization but owned by it, e.g., data on the social media page of the organization or information on job applicants residing in an applicant tracking system.
Integration of internal and external data creates new possibilities in effective decision-making, as it takes into account the real-life dependencies that businesses have on the external environment.
For example, someone’s career path can be used to measure intention to quit. If someone has been waiting for a promotion for a long time, or received a promotion without a salary increase, these may increase one’s intention to leave the company. When all these different possible variables are combined in a predictive model, it can help to predict turnover.
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Similarly, previous absence, age, work intensity, and other stress factors can be used to analyze employee absenteeism and risk factors for long-term absence. Not all data are available as sometimes you have to collect new data to properly analyze an issue. This is often a time-consuming process, and it is a best practice to do this only when necessary and for a small group of critical employees. Critical employees are, in this case, the people whose performance will make the biggest impact on the issue you’re researching. When you’re trying to improve customer satisfaction, you will want to start with front-office employees because they are assumed to make the largest impact on your business outcome.
You can still keep track of your employees in real time, even as they work remotely through the Predictive Analytics Lab Location Intelligence Software
While a robust integrated data warehouse gives better insights, it is not necessary to implement a comprehensive data warehouse before commencement of analytics usage. Simpler analytics can be used with limited or no data warehousing capability. To help reduce the initial investment cost and help in create returns earlier, data warehousing can also be implemented as an ongoing project, running simultaneously and in-line with analytics usage.
Creating an HR analytics unit requires different skills. It includes the ability to connect people, build alliances over functional divisions (with IT, legal and compliance, and marketing or finance), and making progress towards a goal that may challenge conventional HR practices. The people working in the HR analytics department need to be able to conceptualize problems, define data requirements and work with data
When the results of the analysis are in, your business partners need to implement it. Roughly around one-third of HR professionals actually see data as something that is important and that can help to make more impact. Attending Free Online HR Events helps HR professionals to get the most out of data and therefore build their HR analytics capabilities.
As the business environment continues to remain dynamic, alignment of human capital to business strategy is more critical than ever. It is therefore no longer a matter of why, but rather when, where, and how to utilize the power of analytics in human resource. Advancements in big data technologies combined with emergence of focused solutions and providers, are making it possible for companies to address the traditional challenges and start their Human Resource Analytics with confidence. Again, applying the best practices can enable you to become a competent human resource analytics professional in the job market .
When the three blocks discussed in this article are combined, then your organization will be assured of having the basics for value-adding analyses. These practices enable you to create early wins that will help in establishing HR analytics in your organization. Lastly, the principle of effectuation, will help you define a number of best practices and work on analyses that is within the team’s skill set.
Predictive Analytics Lab has been approved by NITA to offer training in Data Science and analytics. Enroll today for our HR Analytics masterclass and change your HR skills to what is currently on demand. Drop us an email at email@example.com or call us on +254725349693 for any inquiries on our Data science courses, predictive analytics software and consultancy services.