Building a Perfect Workplace: Let Data Science Guide You!

People Analytics helps organizations understand their workforce as a whole, departments or work groups, and individuals. It makes data about employees' characteristics, behavior and performance more accessible, interpretable and actionable.

The war of talent is a real thing. The competition is fierce and it has become quite challenging for companies to hire and retain talent. According to Forbes The 5 Biggest Business Trends In 2023 Everyone Must Get Ready For Now, the trends that will have the biggest impact on the way we work and do business in 2023 are accelerated digital transformation, inflation and supply chain security, immersive customer and employee experiences, the talent challenge, and sustainability. Of the five trends mentioned, the first four are directly related to change affecting the workforce.

 

Employee motivations and expectations are changing

Remote working has become the new norm with work-life balance being top priority when choosing a workplace. Offering a bigger salary doesn’t make you more attractive to new talent, there are many more factors that make a workplace desirable, especially to millennials and Gen-Z.

However, experts suggest that instead of hunting for new employees, companies should focus on the people that are already in their organization. The emphasis should be put on motivating people and developing skills in-house.

People Analytics helps organizations understand their workforce as a whole, departments or work groups, and individuals. It makes data about employees’ characteristics, behaviour and performance more accessible, interpretable and actionable.

Data science for forward thinking companies

Traditional methods of employee analysis have various shortcomings: objectivity in employee surveys, ambiguity when filling out surveys and exit interviews, wrong identification of focus groups, and inability to identify career risk triggers.

Machine learning predictive models in HR analytics can be used in different companies, industries, markets, but also for different processes. In your everyday business, you can use it for:

  1. workforce trend analysis
  2. workforce efficiency analysis
  3. employee satisfaction assessment
  4. performance monitoring
  5. employee potential utilization
  6. career planning support
  7. insight-driven actions, tailored to the needs of the HR department

 

Empowering your HR department

Using predictive analytics has financial, organizational, corporate, cultural benefits for business. Such impacts and benefits are cost reduction, increased revenue, increased productivity, retaining experienced employees, improved employee satisfaction, recruitment and training efficacy, better employee experience, better customer experience, morale improvement and improved culture.

So, how do exactly accurate predictions help you create an effective workforce analysis and planning? Imagine being able to forecast how many people are likely to leave or who should be rewarded at what point exactly, whose productivity has dropped or when to invest in upskilling or reskilling programs. Our Employee Turnover Machine Learning model allows you to be several steps ahead of your employee’s dissatisfaction. To sum it up, the Employee Turnover Machine Learning model helps you deliver growth-oriented processes.

 

How the model works

The introduction of such model helps HR experts with the previously mentioned obstacles of employee analysis, enables them to identify critical employees, and react on time. As mentioned, a Machine Learning model identifies employees who are at risk of leaving, should be considered for a promotion, help companies to avoid unnecessary and unexpected costs, and enables productivity and performance gains.

The methodology is:

– to identify the business problem or opportunity

– collect and prepare dana

–  develop an appropriate data science solution

–  evaluate the performance,

–  deployment

–  monitor and maintain the model.

The input data contains various employee characteristics and labelled data on whether the employee stayed with the company or left the company. Input data is not only related to employees but also to their superiors, for example how their manager or director was evaluated.

The result of the model is a percentage for each employee, which is actually the probability of leaving the company. This result informs HR specialists which employees are likely to be dissatisfied in their jobs or whether there are employees with untapped potential.

It also identifies employees who have responded in surveys that they are satisfied at work, although the data themselves indicate that this may not be the case. The result itself can be interpreted in different ways and depends on the particular type of analysis, the reasons for the analysis, and persons individually.

 

The result of the model

The final product can be an on premise or cloud solution. The model results are shown as an interactive model dashboard that is complemented by visualizations. These qualitative, quantitative and visual data are used for planning and analysis in either a top-down or bottom-up approach – in this case, it is a lookup from company data to employee data, or from specific employee data to larger company groups and patterns.

This is how you get a complete picture or a view from another angle, and all this using the company’s internal data. It was only necessary to utilize them in the right way.

In an increasingly dynamic world and business environment, creating a competitive advantage based on available data is an essential element of success, and leveraging technology is the most sustainable way to achieve this.

 

Source: The 5 Biggest Business Trends In 2023 Everyone Must Get Ready For Now

https://www.forbes.com/sites/bernardmarr/2022/10/03/the-5-biggest-business-trends-for-2023/?sh=251aa76f4217

Pictures source: Canva

 

Author: Mateja Novaković, Consultant at Poslovna inteligencija

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Business & Data Analysis
Planning, OLAP & Reporting & Financial consolidation
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DWH models
XBRL point
ConQ Content Analytics
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I acknowledge that the personal data I submit through this contact form will be used by Poslovna inteligencija d.o.o. to contact me and provide information related to my inquiry/application. I consent to Poslovna inteligencija using my submitted personal data to send newsletters containing information about news, products, and services of Poslovna inteligencija. I also acknowledge that I can unsubscribe from receiving newsletters at any time by clicking the 'Unsubscribe' link in each newsletter. Comprehensive information related to my rights and the use of my personal data can be found in Poslovna inteligencija's Privacy Policy.:
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