If there's one metric that best describes the digital era, it's likely the massive amount of available data. Indeed, from data analytics to articles on subjects of everyday interest, the internet provides troves upon troves of data. In part, this has driven the need for data visualization, conveying information in more digestible forms. However, merely creating bar charts, graphs, data maps, or other data visualizations is only the first step. For maximum effectiveness, you'll also need to make your data visualization more engaging. Fortunately, many common ways to do so are reasonably straightforward.
Initially, let us briefly discuss why data visualization is worth your consideration.
As outlined above, data visualization offers an easier way to convey information. Humans are highly visual by nature and are keen on spotting patterns. They also have natural limitations to how much data they can reasonably extract from the written content. In today's digital landscape, this factor alone is reason enough for data visualization to boom as a market. Statista highlights this boom in no uncertain terms, and the reason is simple; data visualization makes data more accessible.
Before considering ways to make your data visualization more engaging, we must make a crucial point. Data quality is the primary factor determining the final product's quality, and it's absolutely imperative.
In much the same way that search engine optimization (SEO) hinges on content quality, data visualization hinges on data quality. Insightful, accurate data will make the final product both substantive and engaging, while poor or dishonest data will undermine it. What's more, flawed data will often reduce audiences' trust, which has both short-term and long-term consequences. As such, the very first step should always be to ensure data quality before all other concerns.
Having stressed the significance of data quality, let us now explore how to make your data visualization more engaging. In no particular order of importance, consider the six tips below.
As a fundamental step of visualizing data, choosing the right format for your purposes should always take priority. Common formats include:
Understandably, these and other formats all serve the same purpose; to make data more digestible. However, each facilitates different types of data and various visualization purposes in its own way. For example, heat maps are a natural fit for geographical data, while bar charts are ideal for comparing data sets within a single measure. Conversely, heat maps would be distinctly unfit for comparing sales figures, and bar charts can't effectively filter data across measures. Such analyses would be nigh unreadable and would likely discourage viewers. As such, the format you choose can have a massive impact on how engaging your final product will be.
An equally important choice lies in color choices. Your visualization's readability depends in no small part on how clearly the colors define and delineate data points.
The basics of color theory for design apply here as well. Using a single color, or only shades of one color, will diminish readability. Similarly, using similar warm or cold colors will confuse and dismay. Using too many colors will also create a visual overload, reducing your visualization's effectiveness. Thus, you'll need to use color smartly;
Another factor that can make your data visualization more engaging is size. Datapoint and shape size can help illustrate data size difference as a readily understandable visual cue.
Keeping size relative with values is a very simple yet effective way to ensure readability. It follows an intuitive understanding of data presentation, where proportionality is conveyed easily. Naturally, this method can't apply to all formats equally or at all. Data maps facilitate it best, while line charts don't need it by definition. Such designs as pie charts also use the concept of size proportionality by definition. Still, using it where applicable can be very beneficial.
Labels and hierarchy are equally crucial elements to consider, as they directly affect readability. Confusing labels can make visualizations undecipherable while missing hierarchies can obscure intended messages.
Thus, it doesn't suffice to provide labels and establish hierarchies. Labels need to be clear and simple, or intuitive and productive when many are required. They need to resonate with your chosen format and help convey the message without cluttering the visualization. Similarly, hierarchies – where applicable – should be clear and understandable, using color and size choices in intuitive ways.
On the subject of keeping visualizations clear, this factor should be noted by itself. To make your data visualization more engaging, you need to ensure it's readable and straightforward.
The functional benefits of simple designs and data illustrations are too many to catalog thoroughly. At its core, simplicity ensures readability, which should be one's most fundamental concern. For a practical example of these merits, consider the booming Customer Relationship Management (CRM) software industry. MoversTech CRM and other prominent CRM solutions store and process troves of data, which requires impeccable, yet clear and simple visualization. This core concept applies to all visualizations; less is more, and simplicity drives engagement.
Finally, a crucial matter lies in data honesty. Unlike data quality, which one might excuse, data dishonesty can bludgeon trust and dismay viewers.
Audiences are increasingly tech-savvy and critical; they care for framing devices, selective data choices, deliberate data obfuscation, and other tactics. Even if these are not within your intentions, using such tactics can inadvertently damage your reputation and discourage engagement. As such, after ensuring data quality, you should always strive to be as honest with your data as possible.
To summarize, there are multiple ways to make your data visualization more engaging and compelling. From picking the proper format to strategically using colors, shapes, labels, and hierarchy, options abound. In all cases, clarity and readability should be your primary goal, after ensuring data quality and honesty.
