The data-driven employee - the key to unlocking the holy grail of productivity
In Indiana Jones and the Last Crusade, our hero was on a quest to find the Holy Grail to save the world. For organizations in the 21st century, productivity can take on a similar reverence as they look to do more with the same (or less). It’s clear why – productive employees are more engaged, and companies with engaged employees outperform those without by up to 202%. With evolving ways of working and employees expecting more from their environments, both public and private sector organizations need to give their workforces the right tools, supported by the right structure, to be productive. Where Indy only had his whip and a notebook, modern organizations have data.
Data, data, data
Using data to improve the working environment isn’t anything new – there have been various solutions available from an IT support perspective for some time: journey analytics (the route of an end user incident through the help desk system) or end user computing (EUC) analytics (the use of EUC devices to adapt and improve areas like access, security, troubleshooting and deployment) are two examples.
Now there is an opportunity to use the analytics and their insights to improve the entire working experience of the employee – adding the technology element to the ability to look at an individual’s approach to work, their effectiveness and behavior.
Non-negotiable privacy and ethics
With many employees now using multiple devices to work, huge amounts of data are being collected just through usage behaviors. By analyzing this use in line with roles and outputs, insights can be gleaned to identify potential areas for improvement or renewed focus. There’s even an opportunity to compare working patterns, identifying positive behaviors and areas that need work.
This sort of data must be strictly managed – privacy is critical, and it needs to be dealt with transparently. While workers are used to being monitored for performance and development, analyzing every element of their working life is more intrusive, even if the sole aim is to help them work better.
That’s why non-negotiables are essential – only the user can have full access to their data, and any data that leaves that environment must be anonymous. Also important is the ethical consideration – if comparison is possible, even against an average composite, some users may try to only mimic the perceived best practice, at the cost of equally valuable personal approaches that have been missed by the composite.
Ultimately the aim is to empower the worker, not turn them into a homogenous entity. If we do that, we risk losing the ability to combine different behaviors into a team that is greater than the sum of its parts.
The data-driven salesman
One example of using data to improve productivity is in sales. While sales professionals will be assessed on their wins, understanding how they achieve those wins, not just how quickly but in what manner, helps improve performance to ensure individuals are most productive in approaches that deliver results. For example, a sales executive may be expected to work 40 hours a week, with tasks including travelling to customer meetings, capturing next steps from those meetings, contract negotiations, collateral development and customer service issues. If the data demonstrates that 35 hours a week are spent travelling to and from meetings, and 20 hours are spent on customer service disputes, there will be evidence for a redistribution of tasks, potential recurring service issues (if the pattern is repeated across the team) and reviewing geographical coverage.
It's not just about an individual’s performance – half the time spent on service issues might be navigating systems, bringing in EUC analytics. Device usage on the road versus in the office could inform technology procurement – why provide a personal laptop if all the work is done on a tablet in the car or logging on to a terminal in the office with single sign-on and access tailored to individual profiles?
Leaps, not steps, in the quest for the holy grail
There is more to achieving the holy grail of productivity than capturing, analyzing and deploying such insights – when inspired employees are 125 per cent more productive than merely satisfied ones, the true goal needs to be delivering a positive experience in the workplace. However, the outcomes from the effective use of data analytics will be closely linked to that – updating and improving application access based on usability feedback will improve user experience, for instance. What’s important is that organizations recognize the opportunity, manage it appropriately and deploy it effectively.