Big data is coincidentally major news when it comes to HR and in extension talent management. Over the years, the notions of how big data will significantly transform the universe and how with it the world will consequently shift from darkness towards light have continued to grow.
Nonetheless, data is not a new discovery in HR, so why the sudden fuss?
Conventionally, the main focus for HR analytics has been majorly been suppressed to the present especially items like cost per hire and turnover. However, with predictive analytics, the scope goes beyond the present and seeks to give solutions to queries such as:
How will the employee turnover be in 2 years?’ OR What steps do we need to take to manage the predicted turnover and guarantee future competitiveness relative to the talent warfare?’
True to the word, predictive analytics seems to be boldly approaching previously untouched HR realms. But why the sharp increase or expected increase in demand for HR predictive analytics? There are numerous reasons to which this can be accredited to; although there are profound changes which have sparked a sudden hunger for HR predictive analytics.
- Momentous increase with respect to computing power and its subsequent affordability.
- Significant increase as compared to digitalized form of HR data which is accessible for processing through the cloud.
- Worldwide talent war which is disorienting talent strategies for firms and threatening the enduring integrity relative to inner talent streams.
Predictive analytics happens to be a portion of a wave of big HR data which is sweeping the globe in numerous disciplines like sales and marketing-with HR now following suit. If truth be told, for HR to retain its commercial relevance, there is a necessity to tag along with the momentous big HR data debate; to have the ability to offer chief executives a predictive-centred validation relative to its principal talent-related resolutions.
Below are some of the ideas on how HR predictive analytics will be of use to HR as derived from its application in different fields.
# 1 Turnover modeling: It has the capacity to forecast future turnover with respect to your business though in particular business units, specific functions, geographies and even nations through checking on factors like performance-over-time, commute time and time since role change was last undertaken. As such, you are better placed to accordingly scale your business hiring efforts; minimizing panic hiring and empty desk period. This can result in lower costs and higher quality when hiring.
# 2 Measurement of Recruitment advertising: you can utilize the experience as amassed from former campaigns so as to avoid using channels or contacting candidates who don’t yield responses instead focusing on those that bring results.
# 3 Targeted retention: Identify workers or a collection of staff more prone to future churns then divert your focus to offering retention activities to those that need it the most while minimizing where the need is slightly insignificant.
# 4 Risk Management: Come up with an exclusive profile featuring individuals with a slightly higher possibility of performing below par or departing prematurely.
# 5 Talent Forecasting: This is practically having the capacity of forecasting new hires with a higher possibility of flying high basing it on their profiles then subsequently directing them to your business programs that are high potential.
In a nutshell: While this is not an attempt to scale down the mountain’ of HR predictive analytics in such few words, it is just an attempt to spark a discussion on the precise queries predictive analytics can subsequently help answer. It is no secret that predictive analytics applications are now literally around us and as such, there is a need to have an idea of exactly what it can do for you and your business.
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