Efficient data management in HR

Data analytics for efficient HR management
Perhaps all companies that regularly face mass hiring understand that without certain software tools and robotization, this task can become impossible. Large companies are implementing a wide range of tools that can make the work of HR easier: from interviewer robots to candidate assessment assisted with Artificial Intelligence. Such methods work well in retail or for mass vacancies in banking, but how to build a process for a young IT company with high growth rates and a different set of tasks?

There is a lot of software that collects data about candidates from almost everywhere. It is used for searching not only on job sites where a candidate can post his CV, but also on social networks and other platforms. The algorithm collects information about the specialization of a candidate, professional interests, contact information and forms data in a single database for the recruiter. After that, the recruiter can select candidates for a specific task, considering the requirements of the project. A number of such software also provides the opportunity for the recruiter to interact with the candidate and track the process of hiring in the company.

After successful onboarding, the employee is integrated into the staff of the company, and vital information is recorded in a standard set of systems (for instance, payroll and other payments, time tracking, internal KPI systems, and so on).

All these systems produce a huge array of data that is used in descriptive analytics. Descriptive analytics helps determine business performance at a certain moment. Descriptive analytics can identify labor cost, optimal staff amount, analyze salary ranges, and calculate business process efficiency metrics.

However, organizational processes are not limited by the present time, each business has its own busy season. In this case, forecasts can be very useful. A forecast model based on determined dependencies and existing statistics, allows users to assess the required number of staff and define the workplan. So, when signing a new project for the development of, for example, an application, the company already understands that it will need 5 testers. Why are there exactly 5 employees? The company has already accumulated statistics on the scope of work and the number and types of staff engaged in it, as well as the proportions of the number of developers and testers.

However, hiring new employees always involves additional investments: from remuneration of the recruiter, allocation of the team’s time for interviews with candidates, to training and adaptation of the new employee. In this case, it may provide more benefits to work on retaining existing staff. This is the field where predictive analytics helps. By processing a large amount of data, establishing links between performance indicators and socio-demographic characteristics, predictive analytics may identify employees who are about to leave the company.

Of course, the last stage of analytics is the most difficult. It requires both the most complete descriptive database and the link between all enterprise systems, as well as assessment tools obtained using machine learning. Can predictive analytics become a panacea for outlooks of prospective events? Perhaps yes, but in the long run, when a large amount of data accumulates.