“Context,” according to the Merriam Webster dictionary, is “the interrelated conditions in which something exists or occurs.” It might sound complicated, but when broken down, it’s actually quite simple: context puts things into perspective. That’s why context is hugely important to the success of your data visualization efforts. Think about it like this: You wouldn’t expect to drop into a meeting halfway and know what it’s about; similarly, you wouldn’t pick up a book, flip to a random page and understand the story.
In the same way, you can’t grasp the full implications of your data without knowing the context first.
Data storytelling needs context. It requires an understanding of the circumstances that surround each metric. These circumstances (usually in the form of related data sets or events) shed light on information that would otherwise be nothing more than rows of numbers in a spreadsheet. In data visualization, context turns facts into actionable information and – in the end – decisions that have a positive impact on your business or company.
Be Environmentally (Data) Friendly
Simply put, the context of your data environment is the situation that created the data. Let’s say you’re in the education sector. If you’re drawing enrollment data from multiple universities, each campus is its own data environment. If one university increases funding for scholarships while others don’t, that school might see increased student enrollment and retention. When comparing that particular school with others, it’s important to take those environmental factors into consideration to gain a better understanding of your data and its implications. Creating contextual labels can provide guidance for the viewer of your dashboard or data visualization.
How Temporal Context Impacts Data
Temporal context (time) influences the meaning of your data too. In a way, temporal context is similar to environmental context; it’s another factor that can change the way you view certain metrics.
In some cases, temporal context is predictable because it creates a pattern. In the housing market, for example, sales tend to peak in the summer months – particularly June and July. If you’re in the business of real estate, understanding when the market tends to peak and when it tends to fall is imperative. Thus, comparing the winter housing market to the summer housing market might not make sense. Instead, you could give your data the right context by comparing sales and revenue on a year-over-year basis. Instead of making assumptions based on how January sales compared to June, compare June’s sales from the revenue generated from last June – not January. By simply understanding temporal patterns, you’ve given your data the right context.
Time and context aren’t always related to consumer patterns, though. In some cases, unique events can change the way you look at your data. Let’s say your organization’s website experiences a serious spike in traffic. That’s great! But then it tapers off. What happened? Before you make assumptions about the effectiveness of the website, consider specific events that could correlate with your data. Did you redesign the website recently? Run a campaign to drive traffic from a specific channel? Get a mention in a popular publication? Any of these events could influence the number people who visit your site. This is why annotations are so critical to effective data analysis.You can’t grasp the full implications of your data without knowing the context first. #dataincontext Click To Tweet
Data Visualization and Context
Data visualization allows users to look at data from different perspectives, placing that data into the context the visualization creator devises. Creating context, therefore, is the responsibility of the person who is making the data visualization. There are many methods to create context (labeling, chart choice, color, etc.). After all, a line graph and a scatter chart can hold the same data but communicate different ideas. The key, therefore, is to understand what you need to communicate and then finding the right visual elements to convey it.
Ask yourself: what is the message, and what base information does my audience need to understand that message? If, for example, you need viewers to understand why website traffic suddenly increased, consider a month-over-month graph and an area chart to help them understand. By illuminating different angles of the same information, you give your data context and – most importantly – help your audience see the context.
Data Context and Your Audience
As a dashboarder, your goal is to present the right context of data to the right audience. The information you provide to stakeholders, for instance, is probably different from the data you want consumers or the public to see. While the data itself might be the same, the context will be different because separate implications matter to each party. When setting up a dashboard for a specific set of users, take their goals and priorities into consideration. Ask yourself the following questions:
- What is important to this particular audience?
- What charts and graphs can I use to show the data they need to see?
- What context do I need to provide to each audience?
- How should context differ between audiences?
- Which visual elements can help me highlight contextual snippets most important to this audience?
Location, Location, Location
When it comes to presenting your data, location is also important. Where are you going to present your dashboard? In the shop floor? In the break room? In a board meeting? Each of these scenarios requires a different level of context and background information. Much like your audience, your data’s location will influence not only how it will be interpreted, but also how you should provide it to users.
Data displayed in a board meeting will carry different weight than information displayed in a break room. Think about your audience and where they’ll be when they see the dashboard. If you’re presenting information in a more casual context, such as a break room, you probably won’t need the same level of detail and information as you would in a boardroom.
On the other hand, data on the shop floor should provide viewers with a completely different context. If employees are focused on meeting daily goals and quotas, provide charts and metrics that support those intentions. If the data will be used in high-level meetings about the overall success of the department or organization, give it to users in a way that helps them interpret it in quickly as it relates to that particular context.
Are you ready to help your audience understand their data?
Context should help users understand data. After all, understanding and interpreting data are the first steps toward using it to benefit your organization. Don’t be afraid to find new ways to present your data and truly reach your audience – to cater the context of your dashboard to the unique people, environments, and situations that accompany it. Then, get feedback from users to understand how you can hone, refine, and improve your dashboard to meet their unique needs moving forward.
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