How Great Leaders Use Data to Find the Best Solution, Gain Alignment, and Avoid Needless Meetings

テクノロジー   |   Shane Remer   |   2020年12月22日

If you’ve ever struggled to gain alignment, there’s a reason for it, and it has little to do with your ideas and more to do with how you use data and present information.


If you’re like most people, you probably take one of the following courses of action when a problem arises or you’re pitching a new campaign:

  • Explain the problem/expected outcome in an email, have teams come up with ideas, then meet and share ideas
  • Call everyone into a meeting, explain the problem/expected outcome, and ask for ideas
  • Call everyone into a meeting, explain the problem/expected outcome, have everyone brainstorm ideas, meet again and discuss

Even in a collaborative environment, these processes lead to problems. For starters, they can take up a lot of your time, especially if you have to repeat any steps in each process. The reason you’re repeating steps? Because there’s no alignment.


All these processes lead up to and end with one of two scenarios: The solution is chosen by one person (whether a C-level executive or yourself), or the solution is chosen by a group.


This is a poor way to gain alignment.

If you don’t already know it from the hundreds of meetings you’ve attended, once people have an answer for something, they’re not likely to change their minds. By setting everyone up to come with their own answer to a solution, you’re setting them up to naturally disagree with anything that goes against that.


Even if they all agree, whether happily or begrudgingly, there’s still another problem with the entire process. How can you be sure you, someone else, or the group picked the best solution?


This is where we want to introduce the guided, data-driven approach to presentations, meetings, and alignment. In this process, you focus on the outcome first, then use data to discover the best solution(s).


Because people naturally like their ideas best, if they all arrive at the same conclusion through self-discovery, then they’re all more likely to support the decision. What’s more, they’re all more likely to understand the reason behind it.


This guided, data-driven approach is similar to the scientific method.


Experimenting with Data

Depending on where you lived and attended school, you may or may not be familiar with the egg and parachute experiment. The basic premise is this: People are split up into teams and given the same supplies. The goal, or expected outcome, is to drop an egg from two stories up without breaking it.


Naturally, if you send each team off to come up with a solution, they’ll all take a different approach. Sometimes the members of each team will disagree with each other. Some will build parachutes. Some will wrap the eggs up. But each team, and person, will feel their solution is the right one. It’s only after you run the experiment does everyone find out what works and what doesn’t.


However, if you start with a guided, data-driven approach, you can improve the outcomes.


For example, say you started the experiment by stating the outcome, then asked for ideas. When one person suggested wrapping up the egg in the sandwich bag and tossing it, you could share data (maybe a video) showing someone else doing that and the egg breaking. If someone suggested using the sandwich bag as a parachute, and that doesn’t work either, you could share the information with them. This could go on and on until everyone wants to do the same thing.


Sure, it makes for a lousy class experiment, but it makes for great business alignment. Because no one starts off with the right answer, this approach allows others to go from gut instinct to gaining insight into information they might not have had previously. As they receive new information, they can adjust their ideas and solutions. What’s more, through this approach, everyone is not only heard but, more importantly, discovers the answer themselves.


You can see how this would work in a business setting. Imagine you have a problem — declining webinar registrations and attendance. Naturally, there could be multiple solutions to the problem, including:

  • Writing more engaging subject lines or email copy to increase click-through rates (CTRs)
  • Changing your approach to landing page copy to convert more visitors to registrations
  • Developing more engaging webinars to encourage registrations and attendance
  • Adding prizes or giveaways as part of the registration/attendance process

Any one of those solutions could be right, but they could also be wrong. What you want is the best solution. For that, you need to evaluate a lot of data and information.


As a very simplistic example, imagine you wanted to assess the performance of a few contributing factors to webinar registrations and attendance. That would include email, CTRs, landing pages, and more. We’ll stick to email, CTR, and landing page performance for this.


Even exploring whether performance was down, even, or up across those three categories would quickly lead to hundreds of possible paths to explore.


Again, this is meant more for demonstrative purposes, but you can see how exploring the data at your disposal can help you learn what the best solution is. For example, if people are opening emails at the same rate, clicking-through at higher rate, and landing page views are still up, you might try reevaluating your webinar topics and format. On the other hand, if the email open rate is down while CTR is consistent and landing page performance is up, you might need to work on your subject lines.


As you dive deeper into the data, it informs your opinion, leading you to the best solution. The more solutions you explore, the better chance you have of driving a winning outcome. The more everyone sees the data and comes to the same conclusion, the better chance you have of gaining alignment.


This brings us back to the egg and parachute experiment and why many people have trouble gaining alignment in the first place — their teams don’t have enough time to analyze everything.


The more datasets your team incorporates into their analytic processes, and the more routes they explore, the better chance you have of finding the best outcome. But that’s easier said than done, especially when you consider all the decisions you and your company make in any given day, week, quarter, or year.


To accelerate this, companies are using automation, AI, machine learning, and data science. They’re deploying analytic automation platforms to run the numbers and return scoring models with predictive models. They’re empowering teams by enabling analysts and more to perform data science with no-code, low-code analytic platforms.After all, just like the egg and parachute experiment, the more people analyzing data, the higher the probability the solution will be successful.


With this process, you get the information you need to run a meeting or pitch an idea. You can guide your colleagues through your thought process, too. Instead of trying to win support for your idea or determine which of your colleagues’ ideas is the best, all of you naturally choose the best one. From there, you can easily gain alignment on the best solution and deliver the best outcome.


And, who knows? If you repeat this process enough, soon you might trust the process so much that you don’t even need to meet at all.