HR analytics is the application of mathematical techniques and data mining statistics, regarding the data generated by Human Resources practices and organizations in general, for the exploration of concepts and ideas that allow solving organizational problems associated to human resources. HR analytics makes it possible to improve decision making, to the extent that it allows the organization to understand aspects associated with its people, work processes, policies and results.
Why is HR Analytics important?
It helps the company understand more about their organization and the people who make it up, how they perform and how they are creating value for the organization. It allows the management taking the best possible decisions while giving the possibility to demonstrate the impact that management, processes and policies have on the overall performance of the organization. Business leaders have a growing interest in how to use HR concepts more efficiently, so HR analytics has become a key tool for evaluating and improving organizational performance.
What is HR Data?
HR Data is information about any aspect of employees or HRIS. This data can be generated in many ways and expressed in different ways, and can be classified as quantitative or qualitative: Quantitative data can be measured and illustrated through numbers. Qualitative data cannot be measured and is usually associated with someone’s perception. For example, in HR we can consider that the average age of the population and its standard deviation is quantitative data, while a team’s commitment to a goal is qualitative data.
In generic terms, HR data is stored in different systems, but it should be ideally centralized under a single administrative tool, which would be responsible for keeping it updated and secure. Only those responsible for the data should have access to change some aspect of it, such as classification terminologies or derivative.
How does HR Analytics work?
HR analytics uses HR data to investigate concepts with the help of modeling systems. There are three central levels of analytics. Most organizations are able to manage level 1, a few are able to generate and manage the second level and very few achieve level 3.
Basic analytics: use of descriptive data to illustrate a particular aspect of HR, such as absenteeism records, turnover rates, ratios recruiting, etc. At this level, no analysis is applied beyond the description or comparative monitoring over time.
Multidimensional Data: Combination of different types of data for specific research of ideas or situations that allows understanding the correlation between different activities and processes. An example for this level is the relationship between leadership skills data and commitment scores, to determine leadership effectiveness.
Predictive analytics: Some of the HR data can be used to predict future behaviors or trends. The use of data in this regard can help HR departments in planning future events, ensuring adequate responsiveness as well as predict data and scenarios that require robust and high-quality data and specialized technology and capabilities.
What is HR analytics strategy?
HR managers should tie their results with the strategy of both HR department and the company. Connecting human resources data to strategic business objectives can help HR managers demonstrate the return on investment (ROI) of the human resources area itself. The type of data that can be used will depend on the strategy and the type of operations. For example, a centralized sales organization will focus more on data collection as sales per employee, to differentiate and remunerate them properly.
An HR analytics strategy must have 3 central objectives:
(1) Connecting HR data with the business strategy to demonstrate a particular aspect of the organization that leaders should know to make better decisions. (2) Allowing the right managerial techniques to be implemented and (3) Supporting managers to measure the effectiveness of HR results in relation to objectives.
What applications does HR analytics have?
- Increasing employee morale
Instead of absorbing the cost of losing key employees, organizations can mitigate attrition levels by measuring the well-being and satisfaction of their workers, adapting their efforts according to the efforts of the employees. Career development planning for employees, as well as learning for high-performance employees, are measures where HR can use data to increase workforce morale.
2. Targeting business results
HR analytics can help achieve the required performance by identifying employees with strong leadership, mixing them with those who do not feel integrated into the company’s culture. Similarly, it can help to increase the fit between applicants with the organization and with their successors in key positions.
3. Reducing attrition
Employers who suffer from talent losses can benefit from HR analytics by improving employee retention based on various parameters.
The deployment of cloud-based technologies and the increasing digitalization of business activities, as well as the level of usability adopted by the majority of users make these HR Analytics much more attainable and affordable for the vast majority of companies.