My cognitive science background predisposes me to endless fascination regarding the effects of technology and privacy on human behavior. So it’s certainly an exciting – and intimidating – world to currently live in.
One thing’s for sure: people do not react kindly when they feel that their privacy has been systematically invaded without consent. And you certainly don’t have to look far to find a company drawing public ire for such violations.
This week, Twitter faces a potential class-action lawsuit for allegedly intercepting, analyzing, and even altering private messages between users. Facebook and Google are more than familiar with backlash, too. If users can sue a company for analysis of the data which was inherently generated within that company’s own platform, how does it bode for businesses looking to strategically analyze internal, employee-generated data?
Choices made by the business today will determine that outcome. How employees react to analysis of their work products will depend on corporate policy and transparency... not necessarily technology.
Unstructured data analytics capabilities are nascent but rapidly advancing. With the average business sitting on millions or billions of data points generated by individuals in their daily work, there is a razor-thin edge separating employee betrayal and corporate benefit. This data set is tantalizing to decision-makers, who are always aiming to cut costs and increase productivity.
On one hand, content must already be managed for reactive requirements such as compliance. On the other hand, it also hold holds immense potential for analysis; possibly revealing subtle personal connections, habits, productivity patterns, interpersonal conflict, preferences, and even emotions.
In the enterprise’s defense, there has always been some latent understanding that the content employees generate at work is not fully their own: not “private” in the personal sense. But this doesn’t mean that workers don’t bristle at the thought of it silently being crunched behind closed doors. Just as the right to free speech does not exonerate one from the repercussions of said speech, enterprise ownership and manipulation of data does not exonerate the enterprise from employee distrust that is generated from such manipulation.
A word of advice to the enterprise: don’t get too carried away with analytical machinery too soon. While analysis holds immense potential for mining work patterns and productivity, it can also backfire. Machine computation follows perfect logic, but even the most logical of individuals don’t. Employee wellbeing and corporate culture need to be considered parallel to the expansion of data initiatives, not just in their wake.
The organization that ignores the human element of business does so at its own peril. And employees that feel scared, cheated, or silently judged will not perform as well – especially in the knowledge sector. It is well-established that people need to have some privacy in order to conduct the trial-and-error and experimental thought processes that lead to innovation. Some even hypothesize that privacy itself is the foundation of democratic thought and critical self-reflection: two things undeniably tied to invention and personal advancement. It’s also demonstrated that that workers need a sense of meaning; individuals who see more meaning in their work have a much higher output, even under identical pay conditions. How will analytics help workers find meaning in their work and self-improvement?
Good policy means implementing good communication and setting objectives. How will analytics of email or other data help the employee learn? Minimize frustration? Advance their career? Will the rankings derived from analysis be tied to punishment, or to supportive opportunities to improve? Will employees see their progress at regular intervals, or will it be accessible only to upper management?
The ability to answer these questions today is important. Large-scale analysis without clear objectives will likely underperform… and likely make workers distrustful of the process.
When beginning to leverage employee-generated data, remember that algorithms only provide the backend machinery of analytics. The “black box” effect will be somewhat inevitable in the world of big data, but employees shouldn’t feel like they’re stuck in one. The policies you begin building today will determine whether your employees perceive internal analytics as purely Orwellian or personally empowering.