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Data Science methods and techniques allow new approaching the solution of complex tasks in terms of mathematics, and statistics for the various aspects and areas of our life, work, and business. Therefore, this makes it possible to produce the most unobvious results.

Humans-based decision tasks are not an exception. This article focuses on using Data Science and Machine Learning techniques for HR tasks. It is one of the topical problems of a good HR manager to be well familiar with modern smart tools helping to manage large data flows and automate the recruitment process. Thus, many companies are leveraging information on performance, engagement, retention, recruitment process, and employees’ satisfaction to facilitate the decision-making process.

To familiarize you with the benefits that data science brings to the sphere of HR, we tried to compile a list of the most important data science use cases. Hopefully, these examples will disclose some secrets of modern recruiting and human resources management.

  • Talent analytics maturity model

Concerning all the fuss around data use and analytics in HR it would be good to know how it has started. Regarding thetalent analytics, data science has proved its efficiency in the course of the years. First of all, an employer should find his place at the talent analytics learning curve. Analytics have been implementedon various levels of business organization practically in all companies, no matter how big they are. Building the talent analytics maturity model is a complex, long-term and voluntary process. It is usually performed step by step.  

Here are the major levels of analytics implementation in HR:

Operational reporting

This level involves developing dashboards and reports presenting the measurement of efficiency and compliance. 

Advanced reporting 

Knowing where you can go deeper into details. Further filtering, analysis and processing the data allows building a multi-dimensional dashboard presenting data for each separate employee. 

Advanced analytics 

This level concerns segmentation, statistical analysis, and models' development. At this level, specific steps to solve the problem are defined. The massive amount of HR data such as demographics, performance and hiring data, financial and operational data, are combined for statistical analysis. Thanks to this analysis you can find answers to your most important questions. This answer may be transformed into actionable decisions for your company.

Predictive analytics 

Using statistical data gained at levels 1, 2, and 3 you can create and develop predictive models. Reaching this level you are firmly stating that HR analytics plays one of the key roles in strategic decision-making. 

The sphere of HR has been adopting the elements of analytics for the year. Nowadays, the organization is moving from the simple use of steps of descriptive and diagnostic analytics to somewhat mature steps of a predictive analytics application. This is along the path to take, and there is no chance to skip one of the steps. Yet their importance for the company development can hardly be underestimated

  • Recruitment 

Uncovering the insights is always beneficial. That is why predictive analytics is making its way into the recruitment process. This is a technology capable of learning on the historical data and making predictions about the top-performing hires, selection process, cognitive skills assessment. 

Data science in recruitment can help in improving talent acquisition process, employee assessment, recruitment etc.

Let us consider several vivid use cases as a proof of HR analytics vast potential and influence.

  • Use case #1 Predicting top-performing hires

The HR manager faces the problem of choosing the best employee among a considerable number of candidates. A key point to take into account is the ability of a candidate to perform a specific task. Here Google serves as a fascinating example. Google has probably the best human resources in Silicon Valley or even around the world. They are famous for the study on connection between performance and interview results. The candidates often seem to have perfect answers to your questions at the interviews and get a high score, though they fell flat on their faces performing the task and gaining the performance score. The correlation between interview score and performance score bears precious insights. In this case, predictive models come to the rescue. 

 

  • Use case # 2 Workforce forecasting 

Predictive analytics provides deep insights into the company recruitment needs well in advance. These forecasts are usually made on the basis of the historical data and present business model.  A key benefit of these forecasts is in the wide range of areas they cover. Due to the HR analytics mechanism, you can make a prediction of the demand for hourly-employees for the following month or for the next few years.    

  • Use case #3 Cognitive Based Talent Acquisition

Cognitive ability tests are gaining popularity. These tests belong to psychometric assessment measuring numerical, verbal, abstract, spatial, mechanical reasoning. Specially developed software and tools pose questions aimed to evaluate the aspects of human cognition and intelligence. After getting one of possible answers, the model estimates it. By score gained a general conclusion regarding knowledge may be made in comparison to the maximum score. In HR it allows to evaluate the existing skills and knowledge as well as to determine general predispositions of a candidate. Modern tools developed for the testing allow to list the hiring profiles containing desirable traitsto which the candidates’ results are compared. 

Retention

Retention of employees depends on many factors. Losing an employee even if he is not so good, results in costs for the company. Therefore, companies are interested to increase the  retention rates. Thanks to modern tools and platforms employers develop particular strategies to retain the employees and increase their job motivation and satisfaction.

  • Flight risk assessment

It seems so natural to use previously received data and their analysis to forecast some future trends, events or behavior. Monitoring KPIs (Key Performance Indicators) help to define whether the actions of staff members, teams, departments, and individuals were successful and enables to foresee possible risks in the future. 

Almost every aspect of HR may be automated, accelerated and streamlined starting with the job advertising to performance analysis.

The most vivid representation of the practical use of predictive analyticsis defining the so-called Flight Risk score. This score reflects the employee's likelihood to quit. This is how it works in the nutshell. Specially trained Machine Learning model uses employee data in the HR system to highlight employees under risk of leaving the company. That allows the managers to act on the retention risks before they are invited for the exit interview when it’s obviously too late. And what is more important: it helps in spotting up general attrition patterns and changing retention practices for the better. 

  • Performance Management 

One of the key responsibilities of an HR manager is to regulate performance management and a general environment within the company. The leading indicators of successful performance management are leadership, feedback, teamwork, and internal relations. A specially developed performance management software is called to facilitate this job. Due to the ability of big companies to get enormous data amounts every day, it may be turned into precious insights. These insights shed light on the performance and may help to improve the performance indicators. Data-driven performance management can really change the way people work. Let us find several vivid examples demonstrating these changes. 

  • Sales team productivity management

Having proper tools at your hand will help you to manage teamwork and increase productivity. It becomes possible due to modern technologies allowing not to lose customers out of the email conversations or for a salesperson not to invest time into not-responsive customers` accounts. Moreover, the employees themselves can track their productivity and define preferable working hours, shifts or spheres.

  • Succession planning

Succession planning is a process aimed at recognition and further training of the prospect leaders. It may also be regarded as replacement training. This type of planning helps the company to ensure continuous operation and high-performance rates. Succession planning is not purely data analytics activity, however, data analytics can bring it to a rather high-quality level. Using data analytics the companies get a chance to spot the discrepancies in the employee's ratings, to conduct deep benchmarking and define the staffing risks. Data analytics tools can look at every possible combination of various factors (e.g. job, role, skill, position, etc.)  and look for a dangerously high tenure of the employees.

  • Pay for performance

The issues related to money are the most important to both sides of the bargain, the employer and the employee. Job satisfaction of the employee directly depends on the money earned. The business profitability directly depends on costs and revenues. HR teams usually apply advanced analytics and smart tools to calculate the best financial strategies practically for each employee. Pay for performance tools track the plan-based award metrics, measure incentive plan performance in terms of both realized pay and realizable pay, build charts and dashboards and various types of reports to visualize the insights gained. These metrics reflect the dependence of high performance and financial compensation for it. These insights are translated into actions during the annual review cycle. 

  • Engagement chat 

An engaged employee proves to be a reliable and productive employee. This dependency is well familiar to HR managers. Thus, a good HR would recommend you foster the engagement of your team to increase performance.

Start creating a productive office environment by engaging your team members to communicate. Thousands of apps and tools are solely developed to facilitate communication between team members. Messaging applications allows sending text messages, sharing visual items and files, scheduling calls and live discussions, communicating in groups or individually. 

Conclusion 

Does it mean that HR needs to master statistics, machine learning, and programming?  Not only HR! A recent study shows that 58% of respondents state that their companies make decisions on the basis of a gut feeling. Yet another half is already applying data-driven decision making. These companies are one step ahead. Understanding the capabilities of advanced analytics and machine learning techniques is a must-have skills for every manager in the coming years. 

Deep insights, big data analytical algorithms, tools, platforms won an excellent place in the sphere of Human Resources management. Companies that apply data science developments save time and money, recruit and retain better employees, reduce costs and increase the productivity of their business. As for now, it is reasonable to presume that the importance of data science will only increase. 

In this article, we presented several practical applications of data science in HR. Hopefully, these use cases will prove to you or at least stress the importance of BIg Data. Since it can improve the company HR organization, recruiting and hiring process, employee retention, productivity and success of the business as a whole. 

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Tags: data, learning, machine, science, trends

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