If you work with data regularly, you know context matters. As American writer Dan Simmons said, “Context is to data what water is to a dolphin.” Essentially, data can’t survive without it. Context can be defined as the facts or circumstances that surround an event or situation. In the case of data, context represents the added details and setting that can help explain an observation or finding in the data.
At each and every step in the analytics process, you can see how context plays an instrumental role in shaping and enhancing data. To say, having proper context is important in the data collection phase because you want to ensure you’re capturing the right data that aligns with your business strategy. Without adequate context into how the data is going to be used, the data preparation phase will lack purpose and focus. During the analysis phase, if you don’t have adequate background information or in other words – domain knowledge, it will be difficult to interpret and make sense of the numbers.
When it comes to data storytelling, context is equally crucial. During the communication stage, putting your numbers in context will help them resonate more deeply with decision-makers. Tableau’s President Mark Nelson said, “Not everyone can speak the language of data, so you need to make data speak the language of everyone else. Putting data in context is the key to making it accessible.”
Unfortunately, for many people, ‘putting data in context’ simply means adding annotations to their data visualizations. While annotating your data charts can be helpful and make your information more accessible, context is much more than just descriptive commentary. As American novelist Michael Ventura said, “Without context, a piece of information is just a dot. It floats in your brain with a lot of other dots and doesn’t mean a damn thing. Knowledge is information-in-context — connecting the dots.” If you use context properly, it can help your audience see the big picture and gain a richer appreciation of why your insight matters.
Whenever you fail to properly contextualize your insights, you put them at risk of being misunderstood, overlooked, and ignored. While it’s generally accepted that it’s important to contextualize insights, here are a couple of ways in which you can add meaningful context to your insights and inspire audiences to act on them.
How to contextualize your key insights?
Each insight is unique and will require a slightly different form of context to enhance its meaning and significance. In some cases, you may discover that an insight may benefit from more than one approach. In addition, your intended audience may influence how much context is needed and which approach to use. We recommend exploring each of the following tactics and then prioritizing the approaches that are most appropriate for your insight and audience.
Direct: To add more perspective, it’s very common to share an item with other similar items. For example, you could compare the monthly sales of one product to those of other related products. With this form of context, you can either emphasize how similar or dissimilar the item’s results are in comparison to those of the other items. In addition, you may compare the result to a goal/target or benchmark.
Indirect: In some cases, you may want to compare items of different sizes. While you can’t rely on a direct comparison of the absolute values, you can utilize an indirect comparison with a rate or ratio. For example, you could compare the win rate of a single salesperson with the average win rate for an entire sales team. Providing a benchmark can reveal meaningful parallels or discrepancies.
Relative: It may be beneficial to show what portion a figure represents of a total or a whole amount. For example, you could clarify what percentage of total worldwide new hires occurred in a specific country. In this case, the relative context can highlight how significant or insignificant the portion is in relation to the whole.
Another crucial form of context is to show how something has performed over time. For example, in the case of product sales, there may be a variety of factors that contribute to differences between products (e.g., diverse target markets, product life cycles, marketing activities, etc.). Rather than using an unfair comparison between two dissimilar products, it may be better to compare a product with its own past performance. With a historical perspective, you can stress how things have improved or degraded over time.
When you compare time periods, you’ll want to be mindful of potential seasonal effects or other cyclical behaviors that can skew the results for a particular time period. For example, it would be better to compare September’s results with those of the same month from the previous year. In addition, you want to avoid comparing partial time periods with complete ones (e.g., month-to-date vs. past month).
3. Scaled (Up/Down)
When you look at a value in terms of a single time period, it may not seem that meaningful in isolation. However, when you extrapolate out the combined effect over a longer timeframe, people gain a deeper appreciation of the item’s total positive or negative impact.
For example, if you identified a way your company could save $50K in IT costs each month, it may not sound that significant. However, if you aggregated the savings for a reasonable time horizon such as the next 12 months, the benefit becomes more significant ($600K).
In reverse, if you already have a large figure for a longer time duration, you may consider breaking its impact down for a shorter time period. Instead of only emphasizing the annual benefit of a new program, it may be helpful to isolate what the monthly or weekly gains could be. Smaller amounts for shorter timeframes may be more relatable for your audience.
Another major source of context comes from the situational information surrounding a pattern, trend, or anomaly. A variety of events may have coincided or potentially contributed to your finding. The audience can gain a richer perspective by understanding the circumstantial details.
This background information could include internal factors that overlapped with the result. For example, a spike in product sales may have coincided with a promotional discount, the increased availability of inventory, or the launch of a new product. In addition, external factors could also be flagged as being potentially influential. For example, the same sales increase could be tied to a competitor’s product recall announcement, a favorable review by a major influencer, or new government regulations.
With informational context, you must be careful to not assume or imply correlation means causation as there may or may not be a causal relationship between the two events.
A result may be hard to comprehend for audiences because it’s unfamiliar or unrelatable. For example, the metric in question may be less common, or the size of the number may be beyond the typical levels that people are accustomed to seeing. To help them understand the data, you may need to bridge the information gap with an equivalent example that’s more recognizable or approachable.
When Apple launched the iPod in 2001, its marketing campaign didn’t focus on the device’s enhanced storage capacity (5 GB). Instead, Apple’s CEO Steve Jobs positioned the device as “1,000 songs in your pocket”, which is a more consumer-friendly approach.
In the case of very large or small figures, it can be hard for people to fathom the magnitude or smallness of the numbers. For example, the Mariana Trench is the deepest point in the ocean at 36,201 feet (11,034 meters). By simply converting the unit of measurement from feet to miles (or kilometers), you make the depth more relatable (6.8 miles or 11 km). However, if you happened to be sharing this information with an audience in New York, you could highlight the Mariana Trench’s depth is the same distance as that between the Empire State Building and Yankee Stadium. Connecting a number to something more relatable will increase audience comprehension.
In some cases, you may anticipate a figure will be hard to believe or accept because it is a significant anomaly or outlier. Rather than letting your audience question or dismiss the finding, you may need to provide confirmative context so the audience can better understand why it is reliable and accurate.
Several factors can influence the perception of how credible a finding is. An audience’s trust in a statistic can be enhanced or eroded by its source, data collection methodology, freshness, and other factors. If you anticipate questions or concerns about a number (“that sounds too good to be true”), you may need to highlight how it was verified or validated to ensure it is viewed as being credible.
Strengthen your insights by putting them in context
Insights should always be contextualised to increase their impact, even if these contextual aspects can be applied to every data observation or finding. They are less likely to be completely understood, appreciated, and acted upon without sufficient context. You don’t want to reveal insights that you’ve spent a lot of time and effort uncovering “naked” without sufficient context.
When you contextualize your insights, you also put them through a valuable vetting process. Some of your insights will become stronger when they are combined with context. While others may develop cracks and fall apart. Regardless of what happens to each insight, adding the right context to them is a critical step that will enhance the power of your data storytelling.