Future of work: Combo of human experience and machine learning

Combination of big data analytics with human experience can significantly contribute to the optimization of hiring processes, decreasing attrition rates, increasing employee engagement, identifying skill gaps & growth opportunities, enabling informed business decisions & in improving overall work force planning.

People and processes form the main pillars of success and therefore finding out why teams perform well is as important as tracking productivity data and processes. Team engagement increases significantly once the employees know that the policies have been shaped by their own feedback and on the job learning. They are then able to accept it and relate to it.??

I am sharing the key benefits of using a combo of big data with human behaviour in five points:

1. Optimization of hiring process

Once the people analytics and employee feedback data is mapped with the assessment of best performing teams then hiring of similar talent can improve significantly.?The applicants then can be matched with the profile of the best team’s behaviour patterns and work experience. The best part is that HR talent acquisition software solutions are available which can map thousands of applications in a short period of time. These software solutions provide on demand applicant screening, can access applicant data, schedule interviews and manage the entire process on the move due to the mobility feature in the modern age software solutions for HRM.

Google uses people analytics to make Google a great place to work. Project Oxygen is one of the most well-known people analytics projects of Google. This was a multiyear research initiative for measuring key management behaviours and then using the best practices through communication and training. Employees widely adopted the program and the company showed statistically significant improvements in multiple areas of managerial effectiveness and performance. One of the key findings was that managers hate micromanagement on the technical side but are keen to be managed on the career side. This again highlights the importance of combining human behaviour data with big data for strategic decisions in HRM.

2. Decreasing attrition rates and increasing employee engagement

Mapping of the behaviour pattern of employees with short stints can go a long way in finding out the key reasons for attrition. Employee experience and expectations mismatch have been found to be the key reasons for employee attrition. One of the ways of handling this is to check out the feedback and assessment of employees’ team wise, line of work & project wise and then engaging with the employees on real time basis. In several cases, lack of communication was found to be the key factor of attrition. By improving the communication from hiring stage where expectations are set to ensuring a two way communication based on employee feedback can reduce a lot of issues which cause attrition. HR interventions based on big data analytics comprehensively covering all the qualitative and quantitative aspects for each team is known to reduce attrition in several organizations. One such example is that of Microsoft where the retention rate of freshly hired graduates could be significantly reduced by introducing a system of mentorship, coaching & development plans with transparency. Microsoft has defined its HRM focus as an intersection of people, culture and technology which reinforces the need for using hard data with human experience. Employees do not leave a culture that keeps them engaged and happy with a clear career path.

3. Identifying skill gaps and growth opportunities

Identifying skill gaps and opportunities of career growth needs predictive models & artificial intelligence to discover growth opportunities & the potential of skill crossover from one project to another. Analytics in this area would allow leveraging of skills that already exist in the teams for maximizing business in the areas of organization’s strong areas. This can help in business growth as well as individual’s growth due to better deployment and diverse work opportunities. Specific analytics in this area can significantly bring down the attrition levels and improve skill enhancement in strategic areas of growth. It will also reduce the cost of benching employees due to gaps in project timelines.

4. Improving workforce planning & human resource processes

Data driven scheduling & real time analytics on projects wise teams along with real time feedback can significantly improve the workforce planning and its efficiency. A lot of times too many efficient people may be deployed on a large client which may result in shortage of manpower on other projects. Optimization of work force allotment can reduce such errors in decision making through real time feedback from the HRM team. An analytics based system with quantitative and qualitative data would also highlight the areas of improvement within the HRM teams by identifying the inefficient processes. By using evidence based strategies based on real time feedback and analysis, quick response to emerging internal and external challenges is possible.?

5. Linking employee evaluation metrics with operations for strategic decisions

By linking employee evaluation metrics with business operations, the HRM team can significantly impact the overall strategic business decisions at leadership levels. Big data in HR can easily connect the high performing areas for maximization of business efforts for new clients who may be requiring similar skills. By educating the top leadership with evidence based data on the best performing areas, the management support to those areas will increase from an operational as well as promotional point of view. This can significantly impact the profitability as well the revenue goals for an organization. Recommendations from the HRM team would then be based on hard core business evidence which will not only impact the leadership decisions but will also increase the role and importance of HRM from the profit and loss perspective. This can also lead to higher resource allocation for HRM due to its increased importance in the board rooms.?

Conclusion

Future of work would need a collaborative approach between human experience and machine learning as a dynamic process for success in the rapidly changing work environment. The use of AI and big data in businesses is changing the way business decisions were taken in the past so rapidly that to keep pace with technology human beings need to slow down and collaborate more with software based systems and processes. In the post covid era, employee wellbeing has become a major factor for attrition and retention. By using software solutions, work processes can be aligned with time lines more effectively and thereby reducing the need to overstay in the office or carry work to home.