Privacy and Collaboration: Will Privacy Kill Evidence-based Performance?
Privacy and Collaboration
Privacy and collaboration: The pandemic created opportunities for collaboration developers to invest more than they have, perhaps since their infancy as start-ups, perhaps more than ever. Acquisitions, some very large ones like SalesForce buying Slack, and Cisco buying IMImobile continue. Most of the investment focuses on creating better, more integrated, and more inclusive work experiences. It also creates more data, and like most data, analyzing work patterns and relationships likely reveals opportunities for performance improvements.
If data remains the possession of only its owner, that will constrain meaningful analysis–even if the analysis reveals to the end-user the deepest insights about their work behavior, it will still miss important context by not allowing visibility for impact on peers, or peer performance impact on the user. Aggregate data may offer some insight, but it will not prove actionable in specific ways. Scattershot approaches to improvements will fall on many who may not require them
Scenarios expose additional influences
Most would look at these collaborative data analytics investments through the lens of technology and economics. Scenario planning, however, teaches that influences for and against innovation arrive from many sources. The most important factor driving the future of privacy and collaboration may not be hybrid work models or the integration of artificial intelligence to interpret gestures and blur out video backgrounds—it may be the movement toward data privacy that will greatly constrain the degrees of freedom where AI can be applied. Microsoft pulled away from the exposure of individuals in their productivity scores. On a recent Cisco industry analyst call, the collaboration product management team discussed its need to be cautious about how it uses identity for features like speaker recognition in transcripts.
There is already a lot of relevant data about who does what and when that can be easily derived from explicit interactions like editing, posting, and uploading files. This data reflects performance and engagement. But vendors remain cautious, even opposed to using it beyond anatomized aggregates because of concerns about personally identifiable information because of the backlash from privacy advocates and individuals. Privacy and collaboration often sit at cross purposes in today’s work experiences.
Rethinking privacy and collaboration
There is nothing wrong with people not wanting to have their personal data used without consent. There is also nothing wrong with using personal data with consent. The problem with the use of personal data is not technology but the non-strategic ways vendors approach the roll-out of features and a lack of clarity on intent. There may well be companies, and individuals, who would like to participate in highly analytical, data-driven collaboration environments that expose how people actually work. What they do with the technology. When they work. Who they work with. How processes need to change as customer, vendor, and peer interactions evolve. It may be frightening to think that a system can know all of those things, but it already does. For the most part, that data remains ignored by an overabundance of caution, along with hidden actionable insights.
Collaboration analytics features should be built in cooperation with people and organizations that have opted-in to a better understanding of their work environment. That means individuals discussing the data collected, how it gets used, and by whom. It means human resources rethinking hiring agreements to include explanatory content about data-driven work environments, the benefits of sharing digital work data, and the ramifications of not sharing it. And likewise, procurement needs to redefine contract agreements to include collaborative data capture as part of the expectation. With so many hybrid employee-contractor work experiences, analytics need to examine the totality of the work experience, not isolated parts of it based on affiliation.
And organizations need to be clear about the process of learning. Let employees know what they are trying to accomplish and by what means. Make the learning experience organizational. It may well be that innovations, breakthroughs in adoption, and new approaches to analysis may come not from the data scientists but also from the end-users facing challenges that aren’t visible to the side or above the actual work. Data can be viewed in aggregate, making broad-brush insights easy and removing the worry about privacy and collaboration. Work improvements, however, take place where the work happens, with individuals and teams. Anonymizing data is like blurring a Picasso and then asking would-be artists to learn by studying the blurred brushstrokes.
Organizations need to make a case for cooperative data sharing one employee, one contractor at a time.
Privacy and Collaboration: Learning by Doing
If personal performance, social networking, influence, and work behavior data privacy retrenchment in enterprises prevails, businesses will lose a unique lens into improving processes and practices, both formal and tacit. If they choose to leverage this data, the path forward will not be a smooth one, and organizations will make mistakes. People will be offended. Data may leak. The results may be underwhelming. But those who don’t engage won’t learn. By understanding the data about how people work, organizations will gain the deepest insights possible about what makes them work well or not. Personal influence, engagement, productivity, innovation. Those are all important topics that remain at the level of hearsay, anecdote, perhaps occasional study—some, notably innovation, even rise to the level of myth because organizations cannot watch the sparks turn to flame. The data can see the ignition, but only if we look.
Collaboration remains unkept and fragmented. But underneath the too many tools, the oversharing, the tenuous ideas hidden in passive-aggressive posts, the flurry of activity before meetings and after, and the long tail of tasks that eventually lead to a meaningful outcome, lies data that documents all of them. And if we look at the data, we can see an organization’s intellectual circulatory system flowing to sustain the business, expand to grow it, and adapt to sustain it through change.
An over-emphasis on privacy will result in vastly imperfect models of how organizations work—models not built on evidence but models built on guesswork, presumption, assumption, and innuendo. That is not how modern organizations profess to work, but it is how they do and will work unless organizations, employees, and contractors embrace the hidden insights in their collaborative work data and leverage it to better manage, innovate, perform, and adapt.
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Daniel W. Rasmus
Daniel W. Rasmus, Founder and Principal Analyst of Serious Insights, is an internationally recognized speaker on the future of work and education. He is the author of several books, including Listening to the Future and Management by Design.