Storytelling with Data
Welcome to the tenth and final lesson!
Yesterday, we shared the second critical area for data measurement: growth and normalization. We demonstrated that there are three macro-level ways in which growth occurs, there’s more than one right way to calculate change, and change is dependent on the context surrounding it.
Today, we’ll provide our two favorite frameworks for helping others get the most out of your data analysis work! The first framework is for:
Presenting Spreadsheet Data
Step 1. Describe each field, and walk through a single row of data
All too often, presenters of a spreadsheet jump into “the weeds,” details that are important and make sense to them but are typically not important and are confusing to others. Take the time to explain what each field/column represents. For example:
• Column 1 (e.g., date) denotes the date in PST.
• Column 2 (e.g., # of sales) denotes the total number of sales recorded, excluding refunds.
Once you have everyone informed on the fields, walk through a single row of data and tell the story for that row. This will help you solidify understanding of what’s being displayed, but more importantly, it will help you segue to the next two steps.
Step 2. Validate data integrity and/or material annotations.
Skepticism on the validity of data is common, and rightly so: Sometimes it’s hard to get your hands on a perfectly “clean” dataset. One way to ease the tension here is to put your validation efforts on the table:
• Where was the data sourced?
• Was there any clean-up necessary, and if so, what did that consist of?
• What context surrounds the dataset that’s worthy of an annotation?
Pick no more than three of the most important annotations you’d like to make, and share them. If you’re working with a smaller and knowingly “perfect” dataset, then you could possibly skip this step. However, in most cases, we’ve found this to be an incredibly powerful way of building trust and showing that you’re well-attuned to the dataset.
Step 3. Highlight insights and recommendations.
Now that you’ve got everyone on the same page as to what’s in the spreadsheet (Step 1), there’s trust in the data being presented, and all are aware of any material context (Step 2), it’s time to deliver the goods: insights and recommendations. This could be in a variety of ways:
• What’s the most interesting thing you noticed?
• Is there anything particularly great or negative happening?
• Where should we go from here?
When making recommendations, a good approach is to frame it in terms of possible next steps, or hypotheses to test. It’s common for an analysis to generate more questions and a new round of analysis as next steps.
The second framework is for presenting a data visualization.
Presenting a Data Visualization
Step 1. Take the time to explain what’s being visualized
As with spreadsheets, presenters of a data visualization tend to also jump into “the weeds,” details that are important and make sense to them but are typically not important and are confusing to others. Take the time to explain what’s being visualized:
• What’s on the x-axis: at what increment/in what order/starting and ending at?
• What’s on the y-axis: at what increment/in what order/starting and ending at?
• Here a high-level description of what’s being visualized (i.e., “So, what you’re looking at is …”)
Steps 2 and 3 are the same as above! Once you have everyone informed as to what they’re looking at, provide the relevant context and then deliver the most fascinating insights and recommendations!
The above framework can also be used for sending an email. However, we recommend reversing the order (Insights ➡︎ Annotation ➡︎ Guide) and keeping it short.
In summary, there is no right way to present a spreadsheet or data visualization. Possibly, you’d prefer to share context after the insights and recommendations have been shared. Or, maybe you don’t care to receive validation on the integrity of a dataset. The above is simply a cadence we’ve found to help set a strong foundation for the insights and recommendations provided.
Thank you for taking Data Fundamentals!
—Colby and Serge
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