January 15, 2018 by Dr. Jian Huang

How to present data analytics effectively has been a popular topic during Survature user workshops. Typically the question goes something like, “We’ve got so many findings here, any suggestion on how to present them?” While users and their specific cases for presenting results may vary, there are some things everyone should watch out for and consider when presenting your survey findings. Here are four things we have found that can make or break your presentation.

1. Strike the right attitude.

For many organizations, the things which hold back data analytics are often organizational, not technological. That being the case, your presentation should focus on “seeking agreement” with the audience. Get the organization on the same page. Avoid anything that detracts from that.

Don’t come across as “I know everything”. Don’t alienate the audience or make them feel the need to question the findings. Lead them to the same conclusion you have.

Don’t brag about the data. While It may be true that “this data is so groundbreaking”, don’t let that be your assertion. Let the audience draw that conclusion themselves. In other words, let the data speak for itself.

Have a real conversation with your audience. Ask them for confirmation every step of the way. Some template conversation starters are: “This looks right to me. Does it look right to you?” or “This is in line with my experience, does it fit with your experience too?”

2. Focus on showing the discovery process. Not your discoveries.

“The data itself has no value”. The questions you ask of the data has value. The discoveries you make are the answers for questions you have brought to the data. The questions you bring may or may not be the most valuable questions to your team and your company as a whole.

If the analytics process is valid and the data is valuable, there will be many more discoveries that you and your team will make together. The subtle call to action of your presentation is: “Team, we need to look at this together.”

Show the results through different business perspectives, the goals and constraints you face, the timing of key events, etc. Be brief and to-the-point. Rehearse how to mention different bits and pieces of these business perspectives through different parts of the presentation.

Talk about the logistics of your survey using the language of your business. Start from how the data came about. For feedback analytics specifically, show the participation levels and the distribution of participants. Be objective and terse with adjectives. Instead of “fantastic engagement”, let the data talk. “84% completion rate without using any survey completion incentives”—state facts, not opinion.

Go beyond bar chart or pie charts of one-dimensional data. Show the depth and comprehensiveness of the results. Use crosstabs to combine two or three different dimensions and show how your survey takers distribute. For example, not only show 563 completions from your dealer network broken down by geographic regions, but also show how that distribution relates to each dealer’s volume of sales, and their participation in different marketing programs. The goal is to convince your team that your data is significant and can impact many aspects of the business, and to do so without making that kind of assertion.

3. Allow time for your audience to interact the results.

Take frequent pauses. After showing a new piece of information, ask whether the audience would like to see some variations of the same analytics. Say, you just showed an interactive visualization of how Broadway shows are unique, ask your audience whether they want to compare with Rock concerts. For another example, show a crosstab of different business units’ perception of your internal audit process, ask whether the audience wants to see an extra cut based on the types of audits that were done.

Get confirmations. The value of confirmation is often underappreciated. Someone in the audience may know of something qualitatively. But until there is data to confirm, he/she may never take action solely based on a hunch, or may not be able to get buy-in from their team and colleagues.

Collect as many experiential or heuristic confirmations from the team as possible during your presentation. You will be able to show that your company’s current understanding of your concern fits within the data. The best approach to decision making combines data and manager’s expertise.

Look out for “a-ha moments”. We’re very familiar with how data analytics are used by cross-functional teams in the field. We repeatedly see that an a-ha moment for one person is commonly a confirmation for another person. When that happens, be prepared to let your agenda be hijacked for a few minutes, so that the team can have some real cross-functional conversations.

4. Always bring the presentation back to the strategy level. Don’t get lost in a lengthy “interrogation of the data”.

Don’t settle for being a data oracle. Be a strategist. Don’t let paralysis of analysis kill your strategic initiatives. Again, the value of the question determines the value of the analytics. Without a valuable strategic question, the analytics has no strategic value.

Save the last 10-20 minutes to show how the discovery process and the analytics can feed into strategy development and guide execution. The most valuable questions are invariably: how are we doing (e.g. assessment of current situation), how are things going (e.g. know what our customers and donors want and how will that change), how do we adapt (e.g. know what’s most important, why and what kind of tradeoffs and resource reallocation are required, etc.)

It’s unlikely that this meeting for presenting the data analytics will cover strategy development. Your call-to-action to the audience is that the data analytics and the process are in place to help make better and more confident decisions.

Use summary analytics. For feedback data, the corresponding data product is our Action-Priorities Grid (APG). The APG is not a new concept, it may seem new to people because of the prohibitively high cost to create quantitatively accurate APGs using other tools. The goal here is to demonstrate the full data-to-decision cycle to your whole team, so that your team can develop strategic questions on-the-fly while the serendipity is in the air, hit the data with those questions, and get the APGs they need within seconds.

Watch out for inadvertently leading the discussion into a fog of metrics and trigger group-think.

Jian Huang is the Chief Executive Officer at Survature providing the vision for reinventing the way the world experiences surveys. He is a professor of computer science at the University of Tennessee (UT) researching data analysis, visualization, and human-computer interaction. His research has received funding from the National Science Foundation, the US Department of Energy, the US Department of Interior, Intel, NASA, and UT-Battelle. Jian received his PhD from the Ohio State University.