The data drought has passed; almost every company has the data that they need to understand what’s happening with their business. However, presenting that data is a complex task that often receives little attention. Ugly, rushed and incoherent visualizations abound! Luckily, even a small amount of time spent on presentation can significantly increase the value and impact of the data you are presenting… and make you look like an all-star in the process.
5 tips to engagingly present data and avoid report vomit
With the advent of powerful and easy to use databases and cloud reporting tools, now even the smallest companies are able to collect the information that they need to make the right decisions. However, large and diverse data can present its own problems when organizations attempt to share and analyze it. For one, companies have to find an analytics tool that connects to their databases, is easy to use and can allow them to share their findings.
Effectively using big data isn’t just a technical problem though, it’s also a design problem. Even with the best technology, at the end of the day someone has to create a report, send it to other people – and crucially – those people have to pay attention to it. Luckily, creating an interesting, valuable and engaging visualization can be an easy and even fun process… you just have to know where to start.
One question at a time
Having big data at your fingertips can be intoxicating. The ability to answer a series of questions as fast as you can think of them can leave you with some great findings, and the urge to share them with other people. After a good exploration session, the temptation can be to combine all of your findings together into a single dashboard. However, this can lead to clutter and confusion. In other words, people will be more likely to pay attention to reports that answer a single question (or set of closely related questions). This means that it’s best to create separate dashboards for your sales pipeline (“How are we doing?”) and your closed sales (“How did we do?”).
Combining multiple data sources to answer a single question is fine, though. Someone may ask what parts of your product or website are the most difficult to use. For that you might combine a user survey database with product usage data… but they are working together to answer the same question.
Adding interactivity can also be a great way to extend an analysis without confusing it. Enable users to filter back to the data for last year, or drill into a separate region. Changing the context of a question within a single dashboard is helpful as long as you don’t try to answer more than one question with a single dashboard.
Use the right tool for the job
This is simple but important. There are so many different types of visualizations available now that it is often extremely difficult to know what the right way to display information is… and very easy to create a confusing presentation. Luckily, 95% of the data in the world can be presented with these five commonly used visualization types.
- For comparing one measure: Bar chart. They are simple but they can be beautiful, and they utilize our natural predisposition to recognize differences in length. Our eyes (and brains) have much more difficulty with discerning area, which is why pie charts often get a bad wrap, although they have occasional use cases.
- For comparing two measures (or more): Scatterplot. One axis is one metric (sales), and the other is the second metric (profit). The marks on the plot denote the groups or individuals (salespeople).
- For data over time: Time series. Also known as a line chart, this is an extremely effective way to use that same perceptive ability to distinguish between relative length and position that bar charts and scatterplots take advantage of.
- For geographic data: A map. Surprise! This one is relatively self-explanatory. Combine with a bar chart or time series for added context on the same data.
- For detailed information: A table. Yes, boring, I know. But a table used in conjunction with any of the visualization types above can enable very powerful guided analysis. Click California in the map so you can see the customers in California in the table below.
Avoid subtracting or hiding data from view
I will keep this short and sweet. It’s tempting to shorten an axis to increase the relative change in a measure, or zoom in on the portion of the data that is the most complementary. It’s not really blatantly untruthful, but that doesn’t mean it isn’t a little dirty.
… but don’t be afraid to tell a story
By all means, however, call out that big sales win with an annotation. Or, point out how complaints have increased in aggregate, but declined per customer. Adding more context to your analysis makes it more valuable and can enable you to show off successes with more ease. The difference is that additive changes often make a viz more accurate, where subtractive changes can be sneaky.
Take some time to let your inner designer out
The person viewing your visualization should be able to understand what the topic and most important finding is within 10 seconds. If it takes any longer than that many will simply glance at it, ignore it, or worse, send it back for another revision.
Make your viz easier to understand by reducing non-essential elements on the dashboard, aligning common elements into columns and rows and grouping interactive elements like filters together. Don’t be afraid to spend time on color, fonts and formatting. No one is going to complain if your visualization is beautiful and analytically useful. Just make sure not to forsake your findings for added fluff.
Even the smallest amount of time and attention spent on your dashboards can significantly increase the value you get from your data. What are your own guidelines for creating a great visualization?
Ross Perez is a marketing manager at Tableau Software. Tableau makes business intelligence and analytics software that makes it easy for people to create useful and beautiful dashboards and share them with people in their organization.