Leveraging Business Analytics for Boosting Employee Productivity and Performance

While tech-driven intelligence and Business Analytics play a crucial role in the hiring process for many organisations, an increasing number of companies are applying complex HR metrics to make data-driven decisions that will affect employees throughout their career journey. According to a 2017 Deloitte report, 71% of businesses rated people analytics as a high priority for their organisation, with 31% viewing it as very crucial.

Employees are undoubtedly the most precious asset when it comes to developing a successful organisation. As Artificial Intelligence and Machine Learning advance, human resource management departments are turning more and more to Business Analytics and Data Analytics to guide their important personnel decisions. HR practitioners now have access to an even more comprehensive range of data to support these decisions. 

Promotions, wage rates, attrition and retention, as well as training and development decisions, which formerly relied only on human feedback and review, are now increasingly data-driven decisions supported by analytics powered by Artificial Intelligence. These AI-derived indicators may be collected and analysed in real-time to enable on-the-spot choices, which is a critical value differentiator.

Here are 5 ways that talent management and human resources departments are using Business Analytics to foster employee growth and build high-performing organisations. 

Measuring Performance

Organisations can use analytics technologies to create standards for employee performance, and then teach current workers and new hires to comprehend these factors and their effects. Deloitte, along with other businesses, analyses billing hours, travel data, and human performance data to assist people in improving both their personal and professional performance. Data obtained from top-performing teams or people can even be used by organisations to study effective practices and provide benchmarks for other divisions to follow.

Making Promotion and Salary Decisions More Informative

Observing underperforming coworkers obtain promotions is a significant demotivator for many high-performing individuals. Human prejudice and nepotism are two common causes of this, though there may be other contributing aspects as well. Organisational leaders can monitor the rate at which employees are given promotions and raise and what major factors influence these decisions by using a data-based approach. For instance, a recent sales performance by a new employee may have been exceptional, but a peer with more experience may have regularly achieved superior performance over time. What time period is used to measure performance, and which performance indicator is more important? Should tenure be considered in any way? Once artificial intelligence systems have been trained to utilise more types and sources of data, they can assist managers in ensuring performance-generated data is a larger component of the equation and generating less biased judgements.

Increasing Retention by Understanding Attrition

Performance-based analytics can be used to identify which employees would be more likely to quit the company while also illuminating the causes of attrition. According to management consulting firm McKinsey & Co., having good managers and supervisors may be more important than having a lot of money. For instance, McKinsey presents a case study of a significant U.S. insurance company that tried to keep staff by implementing an incentive programme but had little success. The organisation then started using Business Analytics to study at-risk employees, and they discovered a pattern: persons on smaller teams, who waited longer between promotions, and who reported to managers who performed worse all had a higher likelihood of leaving. The business started investing less money in these employees.

To analyse trends and handle unexpected spikes, organisations can also use statistics on their turnover rate (both voluntary and involuntary attrition divided by average staff). For instance, an increase in involuntary attrition can indicate that the hiring and training process needs to be reviewed; an increase in voluntary attrition might necessitate more thorough investigations of particular departments or managers.

Examining Employee Engagement

Employee engagement is a critical statistic for any HR department. Employee engagement surveys, such as those conducted by Gallup, are usually used to collect this data. To get quicker results and to retain ownership of their employees' data, more businesses are realising the value of bringing this in-house to their HR departments. In-house HR teams can use brief, tiny surveys to frequently assess engagement and, with the help of AI tools, acquire immediate data insights. This is an alternative to the lengthy questionnaires that many employees detest (and some don't even complete).

Gamification is another strategy that increases engagement while also providing more employee data. Employees can wager on how their day will go depending on their daily goals in one version of the gamification software offered by GamEffective, a startup that creates gamification apps for organisations. Organisations can choose particular KPIs to measure within the app, which can boost employee engagement and inspire them to reach their personal and team goals.

Measuring Employee Development and Learning Outcomes

Organisations can gain from a dynamic training programme with a more productive team and increased retention. Organisations can track an employee's actual development throughout the training process, shifting the focus from whether or not they were satisfied with the training to whether or not they understood it. Businesses can take it a step further by using predictive analytics to create training materials that are individually tailored to fit the unique learning preferences of each employee. Predictive analytics can evaluate training weaknesses at the organisational level (such as when employee engagement declines). In the end, this data may be used to analyse the patterns that contribute to a training's success and guide businesses towards improving content where it is needed.

Conclusion

While these sophisticated data metrics undoubtedly provide HR professionals with useful information, it is essential for HR to keep the human component of their position to guarantee that these technologies actually offer value to people. One method is to use the analytics underlying these five applications to influence organisational design through a predictive strategy that can assist guide the requirements of future positions, help employees improve their skill sets for these roles, and help the organisation achieve its demands.

A data-rich HR department requires employees who are skilled in analytical competencies to evaluate and exploit the power of data-driven insight, as 40% of businesses worldwide automate their HR departments. HR professionals (and their organisations) can gain a competitive edge by developing their skills and knowledge in data mining and management, machine learning applications, and business analytics.