What does data-driven HR look like and how can technology help achieve it? In its simplest form, it involves data collected from current employees and then used to obtain key information about the organization. This knowledge can be used as a lever to make more effective human resources decisions, design efficient human resource processes and improve the general well-being of the company’s employees.
The main trend behind a data-driven HR organization is the concept of datafication – the transformation of HR data into new forms of value. This approach will allow HR to better understand their employees, candidates, HR processes and the sectors in which they compete.
With intelligent technologies such as predictive analytics, the Internet of Things, independent learning and artificial intelligence, organizations can leverage this human resource data to uncover meaningful patterns that not only respond to questions why things have happened, but especially about what will or should happen in the form of advanced and predictive analysis.
For example, by evaluating candidate data and comparing their attributes to those of the current workforce, HR may be able to predict the quality and future success of our new employees. Examples of predictive and advanced analysis like this can be used across the HR spectrum:
- Recruitment: Exercise discernment in acquiring talent and identify the best ways to attract suitable candidates who stay in the company longer.
- Employee engagement: Using sentiment analysis in emails and other communications to determine what employees actually think and feel.
- Talent retention: Obtain information on staff turnover, identify people likely to leave the company and take proactive measures to prevent it.
- Learn: Create evidence-based links between training and employee and company performance, thereby identifying the skills that employees should acquire.
Data: internal versus external, structured versus unstructured
Human resources data can be classified as internal or external. Internal data includes information that belongs to the organization and that is found on various platforms in the company. External data may be openly available on the Internet or held confidentially by another organization. This could include, for example, social media profiles, recruiting data from LinkedIn and job boards, economic data, and data relating to exit interviews.
Data can also be classified into two categories: structured or unstructured (or semi-structured). Structured data implies that data can be properly organized into rows and columns. Personal, organizational, job, time and absence, activity tracking and other similar employee data would be considered structured data. Unstructured data is information that cannot be organized in a spreadsheet. Unstructured data could include social media posts, emails, survey data, photos, videos, and audio recordings.
For many HR issues, access to structured data is often not enough. For example, with structured internal data, you might be able to determine that the staff turnover rate is 15%, but without the unstructured external data contained in employee termination interviews, you would not know. not why the turnover rate is so high.
Indeed, structured data represents only about 20% of all data in the world. The rest are considered unstructured. Over time, the ability to analyze unstructured data will become increasingly essential for the HR business. Fortunately, with advances in storage and computing power, the exploitation of unstructured data has become a reality.