From Case Study to Success Story – Building Trust in the Water Industry
Your Hero saved the day! By picking your solution, your case study candidate was able to solve their problem and save the ‘world’. The next challenge in writing the success story is being able to prove your solution worked. Prove it with your case study results. Prove it in a way that is easy to assess and digest.
By showcasing the case study results graphically, your success story is that much more believable. In this final post in the series: Four Things about Case Studies: From Case Study to Success Story – Building Trust in the Water Industry we examine the importance of data visualization in turning a water industry case study into a brilliant success story.
Thing 4: Let Your Data Sell the Story
In the Results section of a water industry case study you have the opportunity to prove the benefits of your Solution. Your Hero’s testimonial is even more powerful when backed by data. Because people better understand data shown visually, presenting your data in charts and graphs improves the impact and recall of your story. Data visualization is a powerful tool for persuading your audience and engendering trust.
Hubspot defines data visualization as showcasing data, numbers, and statistics through images and charts. Data visualization is most important in:
- identifying trends;
- answering questions;
- proving theories;
and, when used in B2B marketing,
- showcasing your brand.
The Oxford English dictionary defines data as: facts and statistics collected together for reference or analysis. But, having large tables full of numbers, no matter how great, will not help the reader. You need to present the data in a way the allows analysis. You need a graph!
What graph, you may be wondering? That depends on your data. Understanding what type of data and data relationships you have allows you to pick the most effective graph to display the data.
Most data fall into one of two groups: numerical or categorical:
Numerical data, also known as quantitative data, have meaning as a measurement. Numerical data is either discrete or continuous:
- Discrete data can’t be measured but can be counted. Data take on possible values that can be listed out.
- Continuous data can’t be counted but can be measured. Their possible values cannot be counted and can only be described using intervals on the real number line.
Categorical data represent characteristics and can be sorted by group or category.
Before you can pick the best visual for a given data set, you need to understand data relationships. There are seven important data relationships. The table below defines each type of relationship and gives an example of each.
Charts to visualize data types and relationships
Now to pick the chart. Each different data and data relationship can be represented by at least one chart type. The trick is to pick the chart that will optimize analysis. There may be more than one chart that allows you to visualize the data accurately. In this case, consider what you’re trying to achieve, the message you’re communicating, and who you’re trying to reach.
Bar charts are best used to show change over time, compare different categories, or compare parts of a whole. Bars can be shown vertically, effective for chronological data, or horizontally, grouped or stacked, effective for comparing multiple parts-to-whole relationships.
Pie charts are best for making part-to-whole comparisons, with either discrete or continuous data. They work best with small data sets. Limit your slices to 6 at a maximum.
Line charts show time-series relationships with continuous data. Use line charts to illustrate trend, acceleration, deceleration, and volatility.
Area charts also describe time-series relationships, but they differ in that they can represent volume as well. A standard area chart is used to show or compare progression over time. A stacked area chart visualizes part-to-whole relationships, helping show how each category contributes to the cumulative total.
Scatter plots are used to show the relationship between items based on two sets of variables. They demonstrate correlation in a large amount of data.
Bubble charts are excellent for displaying nominal comparisons or ranking relationships. The bubble plot is basically a scatter plot with bubbles, good for displaying an additional variable. A bubble map is used to visualize values over specific geographic regions.
Heat maps display categorical data. The intensity of color represents values of geographical areas or data tables.
Once you have determined which type of chart best visualizes your data set, there are some formatting tips that improve the impact and comprehensibility of your chart.
Tip #1: Label intuitively
Labels help your reader to interpret the data. Double-check every chart to make sure the labels are there and correct, but don’t overdo it. Label data points directly so the reader doesn’t have to search for the legend. Keep labels on the x-axis horizontal not tilted.
Tip # 2: Call out or highlight important information
Rather than relying on a legend alone, use arrows and text, circles or rectangles, or use a contrasting color to aid interpretation. Use callouts to highlight relevant information or provide additional context.
Tip #3: Choose attractive and consistent colors
Choosing the right color scheme is very important. There are lots of rules about using color in data visualization. A couple worth noting here include:
- Use a single color to represent the same type of data. For instance, if you are depicting a single water quality parameter month by month with a bar chart, use a single color. If you are comparing values between years in a grouped bar chart, use a different color for each year.
- Make sure there is enough contrast between colors. If the colors are too similar it can be hard to tell the difference.
- Avoid patterns. Patterns can be incredibly distracting. Instead, if, for instance, you are trying to differentiate values on a heatmap, use different saturations of the same color. In the same way, use solid lines rather than dashed lines.
- As a rule, don’t use more than 6 colors in a single layout.
Tip # 4: Order the data set
A visualization is much easier to understand when the data is ordered intuitively. In a bar chart, for example, make sure the larger values are at the top for horizontal bars, and from left-to-right for vertical bars.
- Order data intuitively. There will be a logical hierarchy in the data. Order categories alphabetically, sequentially, or by value.
- Order consistently. The ordering of items in your legend should reflect the order of your chart.
- Order evenly. Use natural increments on your axes (0, 5, 10, 15, 20) instead of awkward or uneven increments (0, 3, 5, 16, 50).
Tip #5: Avoid 3D graphs
The nature of these graphs makes them hard to assess. The tilt required to create the effect skews the reader’s view of the data.
Tip #6: Choose appropriate data ranges
The range of your data set is the difference between the highest and the lowest values. In visualizing data, you may need to consolidate data into groups. When grouping data, be sure to use consistent ranges. Select three to five numerical ranges that allow an even distribution of data between them and use +/- signs to extend the high and low ranges.
Data visualization allows you to showcase your case study results and prove your solution. By understanding your data and data relationships you can pick the chart that will let your data sell the story.
This series has highlighted four things about case studies that help you tell your success story. By turning a case study into a success story, you build trust in the water industry: