If you’ve ever used a presentation or collaboration tool—whether you’re leading a cross-departmental brainstorm, putting together a client pitch, or walking a remote team through a project update—you’ve probably interacted with data that our platform collects. As a provider built exclusively to power those exact workflows, we sit at the intersection of two competing priorities: helping teams work better, and protecting the privacy and trust of the people who use our tools. Lately, though, we’ve spent a lot of time thinking about the ethical issues that come with analyzing that presentation and collaboration data, because the line between making work easier and crossing a privacy line is way thinner than it looks. Presentation & Collaboration
Let’s start with something most teams don’t even think about: the granularity of our data. When someone uses our tool to build a slide deck, that’s not just a single file to us. We track when a user logs in to edit a slide, how long they spend on each individual slide (not just the entire deck), who they @-mention for feedback, how many times a slide is viewed after it’s shared, and even which parts of the deck a team pauses on during a live presentation. On the surface, that sounds like exactly what teams want—if you’re designing a sales deck, knowing that users spend 2x more time on slide 7 than slide 3 could help you tweak messaging to keep audiences engaged. But that same data can reveal personal, non-work-related details that people never intended to share. For example, we once had a client share a deck with a slide titled “Q3 Team Goals,” and our analytics flagged that the most time spent on that slide was during a presentation held on a weekend, with a clear pattern of one attendee switching back and forth between that slide and a personal calendar tab in their browser. That’s not just work data—that’s evidence of someone balancing personal responsibilities with their job, something that should never be part of a work performance review, right? So the first ethical landmine here is: how do we use that granular data without turning it into a tool for micromanagement?
We’re also facing tricky questions around consent, specifically when it comes to cross-team and cross-company collaboration. A lot of our enterprise clients use our tool to share presentations with external partners, like when a marketing agency builds a campaign deck for their retail client, or when a startup shares a product demo with their investors. Our data captures every interaction across those external users too, and historically, we’ve relied on blanket consent agreements that people click through without reading. But last year, a small nonprofit client came to us with a real problem: they’d shared a grant proposal deck with three funders, and one of the funders tried to use our data to call out that the nonprofit’s board spent 45 minutes editing the slide on program outcomes, implying they were overcomplicating things to avoid answering harder questions about overhead. The nonprofit had no idea that funder could access that granular data, and they’d never agreed to external parties analyzing their internal collaboration patterns. That’s a big ethical gap: when data is shared across organizational lines, who owns the right to analyze it, and who has to consent to that analysis? We’ve started updating our consent prompts to be way more specific—asking users not just “do you agree to data collection” but “do you allow external collaborators to access edit-time data for this deck”—but it’s a work in progress, because even specific prompts can be easy to gloss over.
Then there’s the issue of bias in how we analyze this data, which might not seem like an ethical problem at first, but it can shape how teams are managed in really harmful ways. Let’s say our analytics algorithm flags that a team that works remotely from 9-5 has 20% less “continuous edit time” on decks than a team that works in-office 8-6. If a client uses that to rate team performance, that’s not just a data point—it’s a biased judgment. The remote team might be taking more intentional breaks, or collaborating via asynchronous comments instead of real-time editing, which our algorithm doesn’t count as “productive.” We’ve had a client use our data to adjust performance bonuses based on that edit-time metric, and when we raised concerns, they told us they were just using the data to “improve workflow efficiency.” But that’s a classic case of data bias leading to unfair treatment. As a provider, we have a responsibility to not just build good analytics, but to make sure those analytics don’t penalize people for how they choose to work—whether that’s remote, part-time, neurodivergent, or just people who work better in short, focused bursts instead of long, continuous sessions.
Another angle we don’t talk about enough is data minimization, which is a core tenet of privacy, but it’s surprisingly hard to apply to presentation and collaboration data. Our tools are designed to capture as much context as possible to help teams collaborate, but that means we often hold onto data we don’t need. For example, when a user deletes a slide, we might keep the data about how long they spent on that slide in our analytics database, even though the slide is no longer part of the active deck. Why? Because that data might help another team, six months later, build a better deck. But that data could also reveal that a specific employee spent extra time on a slide that had sensitive information—like a layoff announcement draft that was never used. Keeping that data indefinitely creates unnecessary risk: a data breach could expose that the company drafted layoffs, or a manager could dig up that old data to hold it against an employee for something unrelated. We’re currently testing a feature that automatically anonymizes and archives edit-time data after 90 days, unless a user explicitly opts to keep it for a specific project, but many clients push back because they want access to that historical data for workflow improvements. Balancing utility with data minimization is one of the biggest ethical challenges we face every day.
Wait, let’s also talk about transparency. For a long time, we operated under the assumption that if teams knew we collected data, that was enough transparency. But we’ve learned that most users don’t know what edit-time data is, or that our tool tracks who views a slide after a presentation ends, or that we can pull a report on how many comments a user leaves on other people’s decks. Last year, we ran a survey of 500 regular users of our platform, and only 12% could correctly identify three different types of data we collect for analytics. That’s a problem because transparency isn’t just about telling people you collect data—it’s about explaining what that data actually is, how it’s used, and who it’s shared with. For example, we now show users a simple, one-page dashboard when they first log in that lists exactly what data is collected for their decks, and a slider that lets them turn off specific types of data collection (like edit-time tracking) for individual projects. But even that isn’t perfect: some users turn off edit-time tracking because they don’t want their boss to see how long they spend on slides, but they also lose the ability to get personalized insights on their own work. That trade-off—between individual control and collective benefit— is ethical at its core, and there’s no perfect solution; it’s just something we have to negotiate with every single client and user.
I should also mention that we’re not just talking about big corporate clients here. Small businesses and nonprofits use our tools too, and they often have less resources to manage data ethically. A local coffee shop might use our tool to build a seasonal menu pitch for their landlord, and they might not have a legal team to review our privacy policy, or the budget to hire a data protection officer. That means when we roll out new analytics features, we have to make sure they’re not just compliant with global regulations like GDPR and CCPA, but also accessible to teams that don’t have dedicated compliance staff. For example, we translated our data consent prompts into 12 languages, and built a free guide for small businesses that explains what each data setting actually means, in plain English, not legal jargon. That’s not just a nice thing to do—it’s an ethical obligation, because if a small business can’t understand our data tools, they can’t make informed choices about how to use them.
Now, let’s get real about the risks. We’ve had close calls. A year ago, a mid-sized tech client came to us in a panic because one of their managers used our data analytics to single out a new employee who had low edit-time on a project plan deck. The employee was neurodivergent, and they preferred to collaborate via structured, written comments instead of jumping back and forth editing slides, which our algorithm had misclassified as “low productivity.” The client almost fired the employee before we stepped in and explained the limitations of our analytics. That incident changed how we approach data: now, every client gets a mandatory training session when they sign up for advanced analytics, explaining the biases and limitations of our data, and we’ve built a flag in our tool that alerts a client if their analysis might be overstepping, like if they’re using edit-time data to evaluate individual performance. It’s not a perfect solution, but it’s a start.
So what’s the path forward for us as a provider? We’ve stopped treating data as a byproduct of our tools, and started treating it as a core part of what we offer—like security or customer support. That means investing in ethical data engineers who specialize in bias testing, building tools that put individual control first, and being transparent with our users when we make changes to how we use data. We also work with an independent advisory board made up of privacy advocates, workplace psychologists, and small business owners to help us navigate these tricky decisions; last quarter, they helped us roll out a new “privacy by design” setting that lets users choose whether their data is used for internal analytics at all, not just whether it’s shared with third parties.
At the end of the day, our mission is to make collaboration easier, not to police how people work. The ethical issues around presentation and collaboration data aren’t going away, and there will be more challenges ahead as AI gets woven into our analytics tools. But that’s why we’re committed to getting these things right: because every team that uses our tool is made up of real people, with real responsibilities, real needs, and real privacy that matters. If you’re a team looking for a presentation and collaboration partner that prioritizes trust as much as functionality, we’d welcome the chance to chat through how we can work together. For more information on our ethical data practices and to discuss your team’s needs, feel free to reach out to our sales team to arrange a consultation.
Presentation & Collaboration References
- European Commission. (2018). General Data Protection Regulation (GDPR). Official Journal of the European Union.
- National Labor Relations Board. (2022). Guidance on Employee Monitoring and Surveillance in the Workplace.
- World Economic Forum. (2023). Ethical Data Use in Collaboration Tools: A Framework for Platform Providers.
- American Psychological Association. (2021). Bias in Algorithmic Performance Management. APA Journal of Occupational and Organizational Psychology.
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