When is it Time to Explore Predictive Analytics in HR CareerMetis.com

When is it Time to Explore Predictive Analytics in HR Original Image Source â€" Depositphotos.comMany companies worldwide have already passed the stage of HR-processes automation, and now they are facing the task of extracting additional value from the data accumulated in the automation systems.The first steps are usually rather simple reports on the basis of this data, which then becomes more complicated. The next logical step for such companies is predictive analytics, the economic effect of which can be many times stronger.Compare how a company can benefit from departmental turnover reports and forecasts of employee departures.evalThink about this:Back in 2016, only 32% of employerswere ready to build a predictive analytics management model but 2018 has already seen that figure rise to 69%with companies actively taking steps to improve the way they view people data.Despite a clear trend, there is, of course, a question of the size of the company and the amount of data it has and keeps accumulating.In addition, the effect of improveme nts in business processes will usually be a tenth of a percent, if it has been optimized enough, respectively, you need a sufficiently large scale to pay off such projects.In addition, it is too early and useless to start doing predictive HR-analysis until the company systematically collects data on employment and work of employees, has not put in order the data on vacancies, resumes, positions, KPI and employee evaluations in electronic form, does not count on them on a regular basis metrics with the use of conventional HR-analysis, has not started to make and apply specific decisions based on the results of conventional analytics.The main limiting factor of HR-analysis development, in general, is the immaturity of the market, which, fortunately, gradually ceases to be a problem, as our industry is developing. This is reflected in the fact that there are specialists and numerous training events on this topic.For example, the largest conferences for HR-specialists in the last few ye ars are focused on the “digitalization“.evalAccordingly, now the application of HR-analysis and the development of solutions on the basis of HR are already must-have for large companies.Similar to pricing automationin e-commerce, today it is impossible to imagine, for example, a large retailer that does not use this approach â€" such a company will be uncompetitive and its losses in recruiting alone will amount in significant profit shortfall.Who is it for?In my experience, predictive analytics is usually used by large companies, first of all, from retail and banking, because it helps them to achieve a competitive advantage in the objective conditions of high staff turnover and large budgets for selection.It should also be added that the traditional innovators in the HR-technology market are IT-companies, which also use different analytics, but here the effect of its use is not so tangible compared to retail and banks.evalIndividual companies in many industries are already start ing to apply HR forecasts. As soon as it starts to affect the profitability and margins of a given business due to the fact that some of them attract and retain the best specialists much better, while others have to work with others, a “boom” will begin.The HR-forecast boom will be connected with the mass spread of technologies based on artificial intelligence To familiarise yourself with the features and costs I recommend to stud off-the-shelf solutions first: HR Analytics SoftwareOr better schedule a demo or two ??On a company level:1) Get over old habitsevalIn order for the tools of predictive HR-analysis to work as efficiently as possible, first of all, it is necessary to change the habit of HR people Both data quantity and quality are important. The data should be “clean” and it often takes more time to clean, prepare and normalize the data than to develop predictive models themselves.In addition to the data, people and tools are needed that can benefit from them, for e xample, through machine learning. At the same time, the company can develop models on the machine learning independently (this is actively done, in particular, by banks), and work through outsource providers.Can your in-house guy fixing computers and resting passwords do that? Probably not.As an example, we can consider the task of ranking or scoring candidates for a particular vacancy. The company can make such a model on the basis of previous years’ data and cut off at an early stage those candidates who are not suitable and communicate more quickly with those candidates who are suitable.Optional: continuous fine-tuningIt is important to consider that the lifecycle of models is just beginning: the company needs to continually assess whether it is worth investing additional resources in improving its models to make them more accurate and efficient.Often such development can have an even greater effect than the initial implementation. I also refer here to the possibility of reusin g models to solve tasks that might not even be expected when creating them.Here are two examples from our practice.We mentioned above a clever search and recommendations for resumes: while developing our Virtual Recruiter service, which provides lead generation of candidates for mass positions, we realized that we could add our machine learning models, which were previously used for searching and recommending resumes. Almost free of charge. Thus, our product gained an additional competitive advantage.A similar story was with ClickMe’s advertising service, where we added ready-made models to search for and recommend vacancies.To sum it up on predictive analytics in HRGiven certain company size and ability to adapt predictive analytics used for more efficient human resources management can be huge.It also seems to be hindered by the fear of new things, something that requires us to recognize that everything is not perfect, to review the processes that have developed over the years w ith all their compromises and the status quo.And even to realize that it is necessary to take off habitual, simple, half-life executed and perfected to shine routine which the programmer can automate for half a day, and to do something more useful and meaningful.