Workplace collaboration is always complex. When strategies need to be formulated and opinions are loud and disparate, it certainly helps to use data as a guide. So, today, decisions often hinge on dashboards. Tactics are guided by metrics.
Still, even with all the numbers in the world to advise us, there’s no replacement for human discernment. An honest, productive conversation will always yield better outcomes than any pivot table can alone.
Good conversation—the kind you study while in pursuit of a liberal arts or law degree—is an exercise in humility. It isn’t about being right. It’s not about having all the answers (or anticipating them!). It’s not about winning an argument or swaying a room. It’s about asking the kinds of questions that build understanding, reward curiosity, and spark creativity. That’s the art of asking well.
Opening this dialogue to complement any data digs can help protect your team and clients against confirmation bias, facilitate more accurate and productive outcomes, and get a project started on the right foot—preventing rework and regression down the road.
Against this backdrop, you can see how setting a personal goal to ask more questions is an admirable idea. But I can hear you wondering: “Sam, how do I ask more questions without being a pest or looking like a fool?”
Excellent start, my friend. Let’s talk about it.
Asking Without Assumptions
Active listening habits are essential conversational skills. I’m sure you’ve heard plenty of advice here, including how to check for understanding (by repeating your peers’ instructions or feedback in your own words to confirm you’ve heard and interpreted their perspectives correctly).
These are excellent collaboration and social techniques, and you should leverage them where they are helpful. But they’re not what we’re talking about in this article.
Asking better questions in a data-driven workplace goes a little deeper. This is about engaging, thoughtfully and intentionally, in some investigation, examination, and discussion of the instructions and data you’re given so that the outcomes you reach are holistic, accurate, and just.
To accomplish this goal, you need to be mindful of the biases you bring to the table. What assumptions are you making about the project or data at hand? What assumptions may be influencing the instructions you’re receiving from others on this project?
So before you ask your peers or clients any questions, pause and ask yourself a few things first:
- What do I already know about this subject? How have my past experiences with similar topics panned out? How does this scenario differ from those?
- What is my first instinct about where this project is headed? What are some other possible outcomes?
- Have we already investigated this idea in the past? If so, does the old data we used to inform our choices then still ring true now? Or have things changed?
The point is: question everything.
I don’t mean to suggest you start spiraling into an existential crisis. But it’s good and healthy to take a moment to review your reflexive thoughts about a project or idea and ponder where those reactions are coming from. Once you do, it’ll be easier to pull on some threads and see how (or if) things unravel.
Open-Ended vs. Closed Questions
Another great way to avoid confirmation bias? Stop asking yes/no questions.
Sometimes a binary answer will do the trick. Examples might include: “Do we need to collect social media data?” or “Did we win last time we managed a case like this?”
But more often than not, getting strategic about how to move forward on a project or task requires more back-and-forth. And if you’re interrogating data, you want to make sure your thought process isn’t terminal: give the data room to reveal more than you might already know to be there.
Closed questions are efficient, but they can also be shallow. Open-ended questions, on the other hand, welcome complexity. They can help nudge your team to explore context and nuance. Naturally, this is a lot harder to facilitate via filters in a spreadsheet—but that’s where human conversation and AI can help.
Use trusted AI tools available to your team to have some of this discussion before you bring it back to your team. You might offer some context to your org’s preferred and licensed (and safe! always follow internal guidelines when deciding what tool to use) chatbot, then ask for its insights:
- What patterns does it see?
- Can it predict a few possible outcomes?
- How do other organizations handle similar projects?
- What are current best practices in this function, and how might they inform your approach?
Simply going through these hypotheticals with an AI assistant can really help you get your gears turning, and uncover the more insightful and impactful questions you can take back to your actual team.
Data Questions That Drive Action
You want to frame your inquiries in a way that drives creative thinking, actionable insights, and practical outcomes.
The open-ended questions and hypotheticals mentioned above are great for sparking creativity and helping your team see new possibilities. Next, you’ll need to focus on what is going to facilitate the actual steps you take to deliver real results.
Focus on Outcomes
Instead of asking questions that merely seek to check data points, frame them around the desired outcomes.
For example, instead of asking, “How many documents have we reviewed so far?” try: “Are we reviewing at a pace that will help us meet our deadline?”
This approach ensures that your questions, and the effort required in gathering their answers, will help your team stay aligned with the goals and objectives of the project.
Drive Action
Every ideation and brainstorming phase must come to an end, and maintaining forward momentum is key to the success of any project. So once your team is satisfied with the options you’ve explored and the direction you’ll take, make sure you ask questions that are designed to prompt action and drive progress.
For instance, “What steps can we take to mitigate these risks?” or “How can we leverage this data to strengthen our case?” can help you turn the considerations you’ve uncovered so far into actionable conversations that will guide you from analysis to implementation.
Collaborate Honestly with SMEs
Across the Legal Data Intelligence sphere, professionals like you live at the intersection of technology and the law. This means you’re working, regularly and closely, with SMEs and stakeholders who serve pretty different functions and bring diverse perspectives to the table.
This can be a goldmine of effective collaboration, but it can also make your team susceptible to gaps in communication and understanding. To combat this risk, your questions must be well-informed and comprehensive (with a hefty helping of humility).
Be ready to ask questions like, “I’m not familiar with this concept; can you explain it in greater detail for me?” and “How does this plan line up to your team’s priorities? Please be honest.”
It can be uncomfortable at first, but the more questions you ask, the more you’ll learn—and the greater respect you’ll earn from your colleagues in every corner of the organization.
(Looking for more advice? Read more tips on mastering this dynamic here.)
Bring It Back to the Data
Asking all these creative, action-oriented questions is excellent work. It’s a sure sign your project is off to the right start.
But one more query should always stay top of mind: How are we doing, and what data do we have to prove it?
Whether it’s measuring your matter’s progress in a review platform, surveying employee sentiments after rolling out an internal AI education session, or calculating insights based on deeper details, your team should always be mindful of what “success” looks like by the numbers—and whether you’re meeting those goals.
Having a well-rounded approach to measurement that considers both qualitative and quantitative information will not only help you prove your impact on a given project, but also provide the benchmarking information that will accelerate your work and give you something to measure your progress against next time.
So, just as you should be mindful of balancing artificial intelligence with human intelligence to make the most of your workflows, be sure to balance data intelligence and human instinct as you approach every project—and look back at how you did once they’re wrapped.
Graphics for this article were created by Caroline Patterson.
Recommended for you
