The Future of Work – Big Data, Data Analytics & Artificial Intelligence (AI)

The Future of Work – Big Data, Data Analytics & Artificial Intelligence (AI)

Human Resources

Editor’s Note: Industry 4.0 is coming. The purpose of our series is to get employers thinking about ways to use this new technology to improve their business; and for HR professionals to understand the impact this might have on the workforce and their own roles requiring greater analytical abilities.

In our second edition of the Future of Work, we look at Big Data, Data Analytics and AI. How is this connected to the Future of Work you ask? It plays directly into what work is done by employees by focusing attention on using tools and software to access data that can be analysed to drive efficiencies in all parts of the business.

Let me give you a few examples:

Example 1 – Time and Attendance:

Time and attendance systems currently perform one of two functions, viz. time recording for payroll and access control. With new technologies however, the next step in time and attendance could be the introduction of facial recognition software to track employee movements around the operation. 

This data could then be analysed to identify a number of key operational issues:

  • At a basic level how much time is spent on non-productive tasks e.g. smoke/tea breaks / walking around finding equipment / setting up equipment etc.
  • At a more sophisticated level top performers could be analysed to see what they do differently. This could be combined with Six Sigma to better design work stations to improve the output per shift.

Note: This example is purely about using data to improve existing work conditions. It does not look at using robotics and AI to perform specific tasks. There are already cameras with this type of functionality. The next step will be building these into applications which can be rolled out to meet specific client requirements. 

Example 2 – Office Based Performance

There are already a number of tools available to operations to track performance and create data points for improving efficiencies e.g. logistics companies have software to track routes, fuel usage, speeds, weather conditions etc.

There are fewer tools available for improving office performance and most look at ways to improve communication between and within teams. 

A potential use of data analytics and AI in the future would be for companies to collect data on employees using their PC’s and then use AI tools to analyse this data and report key outcomes to HR (This type of analysis would be naturally too cumbersome for an individual IT technician to repeat on an economic scale). 

These could include:

  • At a basic level, identifying time spent working vs trawling the internet;
  • Identifying training needs e.g.
    • assuming excel is still used extensively, identifying those employees who would benefit from additional training to improve their output; or
    • checking grammar or tone of emails to identify employees requiring soft skills training; or
    • gauging customer reactions to employee emails to identify ways to improve communication and identify customer relationship issues early on;
  • Improving security by identifying early on any security breaches;
  • Identifying behavioural changes (stress related); or
  • Monitoring key performance metrics real-time;

Example 3 – 5G

5G networks are being rolled out in several countries in 2019. The advantage of these networks is the increased speed through the cellular network enabling downloads of >20MBPS and in some cases up to 100MBPS. While not clear when this might be available in South Africa, the 5G roll-out creates the opportunity for every smartphone or compatible device to be used as a monitor for specific activity. This immediately provides an opportunity to collect data on the workforce and assets even if they are not contained within a smaller operational area. 

The key takeaways for employers with these tools will be:

  • Identifying where value is currently created/lost in the organisation and workforce;
  • Identifying metrics and data linked to these value items;
  • Using tools (Monitors/AI/Data Analytics/Business Intelligence) to collect and analyse data to monitor and report on these metrics to improve these areas.