Our society is inundated with data, but not all data is equal. How do you filter through the noise to uncover meaningful insights and then communicate them in ways that matter?
How to Communicate Data Analysis Effectively
We regularly undertake projects that require synthesizing large amounts of complex data—both quantitative and qualitative—and using it in communications materials to convey key messages clearly and accurately and drive target audiences to act. While the kinds and amount of data, topic areas, and intended audience of each communications product vary, to be successful, our strategy for developing effective products includes some common elements.
1) Gather your sources
Whether you’re mining U.S. Census data, interpreting a 100-page technical report, analyzing user-reported technology demonstration data, or summarizing survey or workshop findings, it’s important to ask questions about your data. Do your sources provide conflicting information or information that is contrary to what you expected? If so, do some additional digging to further evaluate and resolve these issues and exclude any outliers or unreliable sources. Consistent data is critical to develop a final communications product with a convincing argument.
2) Assess the accuracy of quantitative data
While numbers seem like they should be reliable, their credibility depends on many factors. When using quantitative data, assess the source, check the sample size, and double check the calculation if possible. Consider the magnitude or proportional weight of a number—not all big numbers are meaningful. Some simple probing and mental cautioning can help you uncover potential bias or inaccuracies—whether intentional or inadvertent.
3) Analyze what the data says
Now that you know your data is as accurate as possible, it’s time to organize it into logical categories. What are the main buckets of information you are dealing with? Do you have both qualitative and quantitative information? Once you have a better understanding of the depth and breadth of your data, you can more easily summarize the key points from each data grouping.
4) Determine what is most important to your audience
Some pieces of data are more important to your audience than others. Just because you can synthesize and communicate the data doesn’t mean you should. Try to cut your key points in at least half by asking yourself what is meaningful and why each point matters. If you can’t come up with an answer, it’s likely that key point shouldn’t be given emphasis in your communications piece either, or perhaps doesn’t need to be included at all. Work to distill complexity into concise, yet accurate, key takeaways to help you organize your findings. Consider what terminology is critical to maintain for accuracy and how you can better relate the key takeaways from the data to the experiences of your audience. Why should they care about what you are working to communicate?
5) Identify the most meaningful format for presenting your data
To increase the effectiveness of your communications piece, this step should really be on your mind throughout the entire data analysis process. It’s important to determine what communications format will be most useful to your audience so your communications piece inspires action. For example:
- Is your audience likely to access information via a website?
- Are they more likely to watch a video?
- Would they prefer a physical executive summary or fact sheet with an infographic that captures key data points?
- Do they need a high-level summary of key takeaways, along with more in-depth analysis?
- Would visuals communicate the data better than words?
The purpose of the information should determine the format of your piece. Remember, we live in a very visual age and you have a limited amount of time to capture someone’s attention.
Do the Leg Work So Your Audience Doesn’t Have To
Effective data analysis requires doing the leg work so no one else needs to get bogged down by the data. Your audience won’t have the time to struggle to determine what information is most important; it’s your job to interpret the data for them.