10x In The Press
Could predictive algorithms boost staff productivity?
14th June 2019
Researchers claim to have developed an algorithm that predicts career peaks, but what effect would this have on employee productivity and engagement if it was used widely? Nick Shaw looks at the benefits predictive analytics can bring.
Academics have created an algorithm that they claim can predict, with 85% accuracy, if an actor has reached their “peak” or whether they are yet to have their most productive year.
While this might cause concern for stars and budding actors looking for a role, predictive analytics — when used effectively — can revolutionise recruitment and support employee development in a wide range of sectors.
Some employees might feel nervous about predictive analytics, but they really shouldn’t be. Fundamentally, predicting an individual’s “peak” is going to help identify their progress — whether they are an actor or an employee in an office.
All too often, it’s hard to know if someone is on their way up or has already reached their potential, which can result in lack of productivity from staff that aren’t feeling fulfilled or demotivation for employees who aren’t being challenged. Technology that can provide this insight will therefore not only help the business, but also inform on wider learning and development strategies for individuals.
If managers can better understand an employee’s trajectory, they can tailor opportunities to them. And if they are yet to reach their ‘peak’, the business can support their development by offering training and tools to help them reach their full potential.
Peaks and troughs
To that end, it’s worth noting that nobody has a single “peak”. Productivity is a mixture of peaks and troughs, so anticipating when these are likely to happen will only help that employee’s career development.
This information will be incredibly helpful with everything from hiring the right candidates to reviewing an employee’s progress in their annual appraisal. Mapping periods of success and demotivation will also help to better structure an employee’s experience and improve management of their progress.
Ultimately, knowing how staff are getting on will help with both retention and motivation. Employees that fully understand their progress will be better able to anticipate their own periods of productivity growth, help work allocation and project planning, and improve their confidence.
In order to get the implementation of predictive analytics right, however, organisations need to establish some clear guidelines with both staff and management. All staff need to be fully aware of any new technology that is being used to inform on their progress and development — not only is this important for compliance with data protection legislation, but it’s also essential for building trust.
Employers also need to ensure staff don’t feel their whole career will be dictated by an algorithm. Explaining exactly how the technology will be used will help them visualise the benefits it will have on their career, rather than something that could risk their future.
These insights will help staff understand how they work, manage their own peaks and troughs, and potentially unlock other ways of working that might boost their productivity or keep them motivated. This approach will alleviate concern and garner greater engagement from staff.
Although there may be some initial reservations around using algorithms to anticipate employee progress, if these insights are shared openly between staff and the wider business, it could provide the answer to numerous productivity challenges and motivation issues.