Organisations interested in improving their productivity, or even more broadly, willing to optimise how they allocate their resources, are getting increasingly interested in the value that process and productivity mining can bring to their operations but would deployment cause employee privacy issues? This is an important topic that I explore in this blog post.
Balancing the needs
Typically, customers of KYP.ai deploy the productivity mining platform primarily to understand the reality of their processes in order to make informed decisions about how they can be optimised.
Many organisations take an even wider approach and state that their overarching goal is to make sure that their people reach their full potential by enabling them to easily fulfil their basic tasks and to focus on what they like to do best at work.
The central question is therefore not only how to make work more efficient, but also how to make it more meaningful and frictionless – make it more meaningful by eliminating inefficiencies, taking away pain-points, frustrations, and generally reducing mundane, drudge work. The benefit is freeing up the employees’ time, and reallocating it their energies and enthusiasm to more fulfilling activities. Only as a result of identifying the potential for productivity gains can this be achieved leading to frictionless processes that are both efficient and pleasant to execute.
Walking the people-centricity talk
While, it is important to make our organisations more competitive by exploring the potential for productivity improvements, it is essential that organisations take a human-centric approach when doing so. After all, when we say that the workforce is our most important asset, we don’t want it to be merely a cliché and a phrase that simply looks good in our corporate communications. Do we?
Facing the monitoring anxiety
When starting out on productivity and process mining, organisations need to emphasise that it is a data-driven way of improving work and that employee privacy can be safeguarded. The data is “produced” through the process that we could call datafication of employee actions. In others words, everything that a person does on their work computer, gets captured and translated into millions (literally) of data points that are then subject to automated analysis and discovery.
Consequently, almost every implementation of a process mining tool like the one that KYP.ai offers, is likely to lead to a privacy-related discussion. What is actually being captured? How it is being captured? How the conclusions are drawn? Who has access to the raw data and what assumptions are used in the analysis? What decisions are being taken based on the findings? What about each and everyone’s right to privacy? These are all legitimate questions that must be thought out and answered.
Capturing data for the sake of process mining is perceived as employee monitoring, even though the monitoring itself is hardly ever the goal here. Hence the perceived (versus actual) intrusiveness of such monitoring.
Doing it the right way
The question of paramount importance remains: how to maximise the insights from productivity/process mining deployments while minimising the often subjective perception of intrusiveness and the sense of anxiety that it might provoke?
Our recommendation to customers is usually built around five principles that we believe should underpin any successful implementation project:
- 1. Be clear about your objectives: Be explicit as to why you are doing it and what your goals are. How is the organisation going to benefit from the data? How the employees are going to benefit from it?
- 2. Stick to proportionality rule: Capture only the data that you need. Your legitimate business and people interest must outweigh the potential harm to privacy rights.
- 3. Ensure transparency: Involve and inform employees early. A good strategy is to tackle anxiety by overcommunicating about the aims of the deployment, but also to be ready to listen. Let employees voice their fears publicly and for them to feel that they are being heard.
- 4. Adopt a data minimisation mindset: Regularly cull data that is no longer required to meet the pre-defined objectives.
- 5. Be strict about data protection. Last but not least, ensure top-notch technical and administrative means to protect the data that is captured.
It’s worth emphasising the importance of keeping people informed throughout the deployment. It is a great way to address any employee anxiety about loss of privacy.