What does Joe really need? Where do you start? For anyone in a business environment who collects or manages some kind of raw data, tasks that are becoming more pervasive, the need to process that data into a human-usable form is increasingly common.
Visualizations, like the chart Joe asked for, are a great way to accomplish this, but they can be difficult to do properly, as anyone who has sat through a slide show presentation with an unreadable pie chart or vague growth projection graph can attest. As available data becomes more complex and extensive, weaving it into a visualization that invites engagement, understanding and decision-making is a bigger challenge, with a bigger opportunity for payoff.
Some of the traditional business standbys, like a one-off pie chart or simple line graph, even if done well, may not offer enough data to answer multi-faceted questions like Joe's. (See Figures 1 and 2, at left.) How can we take visualizations to the next level, so they can take on the challenge of today's business complexity?
Get the Fundamentals Right
The first step is to back up and focus on the basics. If you have ever played a team sport with a good coach, you may recall that he or she spent a lot of time working on fundamentals. Trick plays or advanced moves don’t win a game without solid fundamentals supporting them, and data visualization is no different. The most complex, data-rich graphic is useless unless it follows basic principles of good visualization:
1. Understand the problem domain. If you are producing visualization for your own use or that of your department, chances are good you already understand the area you will be working in. But if, as in our scenario with Joe, the visualization is for another department, or even an external stakeholder such as a customer or partner, you may need to ask questions and do more research to understand what is involved. In this case, you should investigate when these initiatives started, whether any others are in progress at the same time and what metrics the executive team will use to determine success.
2. Get sound data. This may seem obvious, but good data is at the heart of any effective visualization. Make sure the data you select is as accurate as possible, and that you have a sense of how it was gathered and what errors or inadequacies may exist. For example, maybe our store sales data for Joe is only current as of the last close of business, thanks to an older cash register system. Make sure you get relevant data and enough of it. We probably want not only sales data after these changes, but also the month or quarter before and even the same period in past years for comparison purposes. Above all, to create an effective visualization, you need to understand the meaning of the data you are working with. This can be a challenge if it has been stored as raw numbers. In this case, we may need to determine the store visitor counting method being used to know what those numeric tallies mean.
3. Show the data and show comparisons. Picking the best type of visualization is an art and science; however, the basic rule of thumb is to choose a spatial metaphor that will show your data and the relationships within it, with minimum distractions or effort on the part of the viewer. As Eddie Breidenbach explains, most graphic arrangements fall into one of four categories or metaphors (see Figure 3, at left):
- Network - to show connections, sometimes in a radial layout.
- Linear - to show how something varies over time or in relation to another factor, often on an X/Y space.
- Hierarchical - to show groupings and importance; these can come in many different layouts.
- Parallel - to show reach, frequency or shares of a whole; these can come in many different layouts.
For Joe's chart, we can start with a well-labeled, linear line graph since we want to see how sales have been affected since introducing these new initiatives. (See Figure 4, at left.)
4. Incorporate visual design principles. Using sound visual design elements, like line, form, shape, value and color, with principles like balance and variety, make a visualization both more inviting and easier to read for trends and comparisons. (See Figure 5, at left.) This will become particularly important as we take our linear metaphor visualization to the next level.
Bring in More Dimensions
Once we have good data and a sound underlying spatial metaphor (in this case, a linear metaphor), it is time to take account of the complexity at play. Though it might seem like we have satisfied the initial question at face value (“Sales are up since changing the store layout and starting the new promo”), this answer is likely to spur more questions.