Powerful visualizations are worth 1000 words. However, having the right words and telling the right story is vital. This holds especially true as long as you are clear in your wording, your words inspire action, and your narrative is on point.
Insights must be simple, clear, exciting, and relevant.
Words themselves are critical, and we, as fast-paced professionals, are always in a rush. We tend to sweep through words to find quick hints of value.
Every community uses a code to communicate value, and members of those communities abide by that "secret" code. You need to foster familiarity with "the code" and use the right words to communicate clearly, acknowledge the community, accommodate the flow and generate the right amount of tension. Be mindful that it is more about what your audience is prepared to learn and less about what you want to preach to your audience. Focus on meeting expectations first, then focus on going beyond them.
Here are five key tips for writing insights effectively and showcase the value in your data.
Tip no.1: Make sure you have one
People often mistake observations for insights. Insight is the articulation of the root cause of an observation. Do you have something worth sharing? Does your data answer why something happened, or does it just show what happened? If you don't have one, keep digging!
Tip no.2: Watch your language
Do not make statements or claims that are not 100% backed by your data. Use objective words like “more”, “less”, “greater than” or “less than”. Never use words like “better”, “worse”, “good” or “bad”
Tip no.3: Keep it simple and clear
“If you can’t explain it to a six-year-old, you don’t understand it yourself.” Include all essentials and nothing more. What are the fundamental ideas? How do they connect? If it brings no context or clarity, it doesn't have to be there. Clutter will only do damage.
Tip no.4: Tune into thought patterns
Your readers may find value either in instability or consistency. Generally, data is being produced to address an issue or to reaffirm something. Adapt the language to reflect that. Ask yourself why they are taking the time to listen to you. What do they expect out of this? It’s more about what your audience wants to see than what you want it to see. Don't mess with people's expectations.
Tip no.5: Communicate value
Every community uses a code to secretly communicate value. Think of it as "expert jargon." You need to master that code and use the right words to acknowledge the community, accommodate the flow and generate the right amount of tension. Challenging the community is always a mistake. You will lose connection and won't get your message across or validation of your results.
To acknowledge the community use words like "marketers", "stakeholders", "providers", "widely accepted", "in general", or "typically". This will position you as familiar with the universe, status quo, and ways of working of your audience and will make your audience feel heard and understood.
To accommodate flow, use words like "given", "considering", "in light of" or anything that connects the dots, shows acknowledgment, shows due diligence, or progression. It is important to show where you're coming from, show what you've done and show where you're going. This will get your audience on board.
To generate tension, use words like "however", "in spite of", "nonetheless", "nevertheless", "that being said", or "yet". Think of these as cues for your audience to increase their attention. You come forward and encourage your audience to examine closely what you have to say. You show where you stand and what you have to share. You brought tension. The ultimate tension-generating word is "but". But, I strongly advise you against using "but".
Ultimately, you communicate value with data by showing that you've done something, what your data is and what it is not, and by stating the contributions. Lengthy pages of conclusions and recommendations are not a good way to go about this. Your contributions need to be specific on what was done, how it was done, and for what major benefit.
Key takeaway: Write insights, not observations.
Observations are fundamental elements for data literacy and are all about describing data. Observations are concerned only with extracting information on a specific data set with no reference to causes or broader context that may explain that data. On the other hand, insights are key elements for attaining knowledge and wisdom. Insights focus on gaining deep, accurate, and intuitive understanding.