If you work with data regularly, chances are you trust it. You know how it's collected and stored. You know the caveats and the roadblocks you face when analyzing it. But, when you bring your findings to those further removed, you're asking them to take a leap of faith and trust in data they may know very little about.
Multiple times in my career I had to come into organizations and take teams that were not trusted in the organization and help build them into the trusted source of data accuracy and insights. This journey is never easy. It takes patience and requires a lot of persistence to change an organizations perception of the department. But these points are good advice on a roadmap to do this.
Start Small
When trying to get people to believe in your team, it can be tempting to chase the biggest problems first. These problems often take a long time to answer, and can take several tries to get right. It's often better to first establish trust by picking early projects that you know you can win, and win quickly.Try starting with basic arithmetic to answer crucial business and product questions. For startups, some example questions might be:
Often, people don’t judge the answers to these questions on technical rigor, they judge them on business impact. Starting small can open doors to the big questions that you may have wanted to start with; if you've earned credibility along the way, you'll have more time, flexibility—and maybe resources—to tackle them.
I always find it helpful to start answering questions that are not currently being answered. As Derek Steer points out in the article, don't start by trying to solve the worlds problem. If your team tries to tackle tough problems, there will be a much more critical eye on the work and the data produce. Allow your team to get some wins under its belt. Remember, this is a journey, not a sprint. Trust comes with wins, not home runs.
Know your audience
Keep your audience in mind as you begin to craft the story from your data. Add in the appropriate amount of detail your audience needs to focus on decisions rather than methods. What context might they need? Spending a little time thinking about what your audience cares about most also helps you anticipate possible questions and prepare answers in advance. Few things can help establish credibility faster than fielding a question during a presentation and immediately flipping to a slide that answers it.
This point is critical to garner trust. When presenting data, make sure there is a story that is being told along with the data. Guide the audience to the answers that you have found, don't let them have to figure it our themselves. Be sure to explain to the audience what they are seeing and why it matters. Make it simple, quick and insightful.
Don’t be a House
House, a brilliant albeit fictional doctor, routinely diagnosed rare diseases but had abysmal bedside manner. The thing was, House didn’t have to win his patients over—they were so desperate to survive that they would listen to his every word.
It's subtle, but consider your findings a conversation starter. Understand that the non-analysts have valid points too: they have experiences you don't have and they likely know something you don't. These discussions aren't about winning an argument, but making the right decision for the business.
Never use data for evil. This is fairly common in Finance departments, but it is important not to attack decisions, but rather try to initiate conversations to come to the best business decision. Once there is a tone of implication in the analysis, your team will lose the trust of the department that you are creating the analysis for. Those departments made decisions that didn't have data to drive their decisions, so treat them as a partner, not someone that needs help.
Be Transparent
Analytics can feel like a black box to many people—making that leap of faith appear even larger. By showing even just the basics of your process, you can help others believe in it. To increase transparency try:
Making your work simple and understandable. Monica Rogatti would urge you to try division before doing anything harder. As your audience becomes more comfortable, up the game to simple regression models—it's not usually difficult for folks to understand the direction and magnitude of coefficients.
Finding simple ways to convey advanced concepts. For example,confidence intervals and p-values can be confusing for many people, but charts with error bars make these concepts easy to understand.
Using stories. If you're presenting information about feature usage, or events with technical backend names, paint a picture of how a user would see these features, or put events in plain-English names.
Numbers are difficult to interpret at times, taking the complex and being able to tell a story with it is an art. Most analysts are great at finding data and creating insights if they have domain knowledge, however they can be terrible at communicating their findings. Always have available the methodology for coming up with the answers, even if you believe it is a waste of time. The haters in the organization will demand this, but it also humanizes the process for the non-analytical audience members that you want on your side.
The most important part of the journey is to persevere. The beginning of the journey is the hardest part. I remember in my last position, the department I took over was the laughing stock of the organization. It took them weeks to come up with an answer and no one believed in what they were saying because they were just being report monkeys, instead of providing any insights. By the time we got going, we were the defacto data source for the organizations. We created analysis for parts of the organization we didn't have anything to do with, but when the organization needed something done right, it came through our team. That was because we had many wins along a journey.
