There has been a revolution in the way business is done in many companies in the past ten years. Predictive talent models are being deployed by Human Resources managers which are very effective in identifying, recruiting, developing and retention of the correct talent. There is one small issue though, the data is not always accurate in terms of direction that the HR officers expect. There are three examples:
- Figuring out where the majority of the talent comes from
All major organizations follow a simple process of hiring the top talent from the best universities. It is important to look at these organizations data analytics to gain insight into the performance indicators of its employees. It may not be necessary that high performing students end up being high performing colleagues too.
It is widely accepted that various institutions provide employees with varying degrees of efficiency who excel in particular duties. This means hiring staff from one single demographic location limits the level of diversity within an organization. Using analytics data about the high performing individuals within the organization recruitment officers can focus on the correct location to look for in the talent pool.
2. Paving way to hire without being biased
Whilst keeping the best interest at heart unconsciously a lot of managers do end up hiring an unequal ratio of staff that may seem biased. Companies can choose to adopt an algorithm which would help them to take into account their previous recruitment analytics. This people’s analytics is useful for Human Resources. Using this data recruitment experts are able to concentrate on the candidates that would fit right into the organization. This helps organizations save a lot of time and money while sorting through numerous candidate profiles.
3. Improved management to reduce attrition
The war to win over good talent is won by offering a better salary package to the employees. Gathering data to figure out the workers who were at a higher risk of leaving their job was done by measuring their education and professional capabilities, their age, how well they perform and their salaries. Data analytics applications helped to figure out these high risk people. The main efforts taken to retain these employees were providing them with monetary compensation.
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They were also given the opportunity for learning and development. This in turn helps increase the performance of the employees and better training for managers. Thus, retention and overall performance of the organization improved, showing that it is important to collect the data available. The application of this people analytics has a great impact on the health of the organization.
References:
- “People analytics reveals three things HR may be getting wrong” By Henri de Romrée, Bruce Fecheyr-Lippens, and Bill Schaninger for McKinsey July 2016
- “Workforce Analytics and HR Metrics” SHRM
