What do you do if your non-technical audience struggles to grasp complex data analysis concepts?

The best way to share complex insights is to prepare a story where the data is the key tool to achieving success. Data is useless without the problem it’s trying to solve or the opportunity upon which it's trying to capitalize. Positioning your audience as the protagonist against a challenge is the most effective frame to present any information. Now, the data and insights become the tool/guidance/etc. for the protagonist to complete their journey and achieve their goal. This framing will simplify your message and convey your key points in an easily understood way; all while keeping your audience engaged.

To help non-technical audiences understand complex data analysis, use simple language and avoid technical terms. Show data visually with charts and graphs, and explain why it matters to them. Tell stories or use examples they can relate to, and break down information into smaller parts. Encourage them to ask questions and engage with the data, and provide resources for further learning. Put yourself in their shoes and ask for feedback to improve your communication. By doing these things, you can make data analysis easier to understand for everyone.

Simplicity is important to me. I mean, why make things complicated? As Data Analysts, it's crucial to remember that not everyone we're communicating with understands all the technical details we do, and that's why we're here to help. When we're analyzing data or explaining our findings, it's important to consider our audience. Find ways to explain concepts in simpler terms, use examples they can understand, and avoid overly complex charts (please!). Most of the time, our audience don't need to see the nitty-gritty of our code and calculations. Only share that information if they specifically ask for it.

My favorite way to help non-technical audiences understand complex data analysis concepts is through storytelling. Stories that resonate with them. For example, I teach people with disabilities, so I might share a story about how data analysis helped improve accessibility for individuals who are deaf. Imagine Adam, a college student who is deaf. He faces challenges accessing important information during lectures and meetings due to a lack of sign language interpreters and captions. By collecting and analyzing data on accessibility barriers faced by deaf individuals, we discovered opportunities to improve communication access, such as providing real-time captioning services and training faculty on deaf-friendly teaching techniques.

Below is a checklist for stakeholder-friendly data stories: ? Use concise data column names that are easy to understand and follow a consistent naming convention. ? Provide context for the data slice, including timeframes, geographies, and relevant demographic information. ? Use clear, concise language that avoids technical jargon, and explain complex concepts in plain terms. ? Balance text, numbers, and visuals to provide a well-rounded and engaging story. ? Make the story compact, not a lengthy report.

For non-technical users, examples are a really effective way to simplify concepts and create a narrative around the data. Telling a story helps not only helps convey the message the data is showing but it makes it more memorable as to why it matters. Avoid using technical jargon when using examples and narratives to explain data concepts as it could lead to confusion for the audience. If the audience can't to relate to what the data is telling them and why the data matters then they are less likely to take the necessary follow up actions. If no action is taken the value of the data is dramatically reduced.

Use analogies relevant to them in order to avoid jargon. A. Why use Maxima or minima, when you can convey the message with "peaks and trenches". Why use "random error" when you can just say "signal noise" that is easier to comprehend? B. I have noted that sports analogies are always great to tell. For example, for a low probability event can be equivalent to "RCB winning the IPL (?), Afghanistan winning the world cup cricket", or the lowest seed team winning the march madness tournament. C. The more subtle thing is the body language, especially hand expressions. Presenting does not have to be the slides only it's the way of presenting. A surprising data point should also be explained with expressions that makes audience think.

Utilize data visualization tools like Tableau, Google Charts, or Excel to create visually appealing charts, graphs, and dashboards that simplify complex data into easily understandable visuals. By presenting data in a clear and engaging manner, you can help any non-technical folk comprehend trends, patterns, and insights without getting lost in technical details. Visualizing the results enhances communication and ensures that the message is conveyed accurately to the stakeholders.

When communicating complex data analysis concepts to a non-technical audience, it's important to avoid using jargon. Instead of technical terms, focus on the problem statement and results of your analytics to explain the findings. Break down complex ideas into easy-to-understand concepts and relate them to real-world examples or analogies that your audience can relate to. By steering clear of technical jargon and speaking in plain language, you can make data analysis more accessible and engaging for everyone, regardless of whether they're from a technical background or not.

Jargon is a no-no when explaining data science or any technical topic to a non-technical audience. I don't want to confuse and disengage my listeners. Instead, I opt for plain language and avoid using complex terminology that doesn't require further explanation. As a Nigerian, I could use Pidgin or any local language if the need arose.

Sometimes, the best way to help a non-technical audience understand complex data analysis concepts is by showing them how these concepts directly apply to real-world situations. Provide case studies or anecdotes that demonstrate how similar analyses have led to actionable insights or solved specific problems in the past. For example, when I was presenting data on customer churn rates, I illustrated how implementing targeted marketing strategies based on data analysis helped another company reduce churn and increase customer retention. People often grasp concepts more easily when they can see the relevance and potential impact in their own lives or work environments.