Data Visualization Mistakes to Avoid

Data visualization is the graphic representation of data. The portrayal means revealing detailed information at a glance. Just think of the map of the New York City subway system or maybe the diagram of a human brain. A successful visualization is not only known for the aesthetic design but the sophisticated layers of unique insights and new perspectives.

Learning how to blend your data and visualization successfully is like knowing how to tell a captivating story.

The Significance of Data Visualization

Data visualization gives business leaders an upper hand in this evolving landscape. It brings better clarity, increased engagement, and unexpectedly essential insights. 

Dealing with vast volumes of siloed data can often lead to misinterpretations between departments. Visually grouping several data points improves the quality of analytics and insights while providing a seamless way for business leaders to make strategic and informed decisions.

Here are some of the top reasons why data visualization is valued so much:

• Identifying New Opportunities
• Making Schedules for Deliverables
• Highlighting Risks
• Processing Huge Networks
• Quantifying Change and Frequency Happening Overtime

Making data-driven decisions has always been important, but disruption has forced businesses to make critical decisions with narrowed information. The business models have changed, like the way we work has changed. Leaders at all levels have the urgency to make the right decisions faster. In today's business environment, organizational speed is becoming a competitive advantage and differentiator as:

• It can reduce the time it takes to process information.
• It enables business leaders to understand how the business data can be interpreted for decision making
• Leads the target groups to focus on valuable insights to find areas that need attention
• Reveals plenty of unnoticed key points to compose a perfect data analysis report
• Visualizes data to manage growth by making sense of the information

Navigating Through the Challenges of Data Visualization

While analysts with data visualization expertise have the power to unleash the data stories, creating insightful graphs isn't easy, as there are some critical challenges. A lack of design in the charts and maps will mislead the audience and distract them from important information. A lot can happen with data visualization. For example, there is a unique feature called "Data Blending" in data visualization. This feature enables businesses to capture data from multiple sources together in a single view. Over time, data blending has garnered significant attention amongst analytic companies as it is a fast way to extract value from several data sources. It can also help to deliver meaningful insights without additional time or resources. However, there are a few challenges that can make visualizations problematic, such as -

1. Lack Of Better Data Visualization Literacy 

A lack of proper knowledge of data visualization can do more harm than good. First, it confuses the audience, or worse, it misleads them. Additionally, a poorly structured data visualization denotes a loss of time and effort, which delays the decision-making process in deadline-driven projects. Moreover, some data visualization tools do not explain what they are meant to explain. Hence, the viewers often understand whatever comes into their comprehension, and sometimes viewers understand more than what they do. Such results often happen due to reasons like:

• Using the wrong chart type
• Poor use of a 3D chart
• Presentation of misleading or insufficient data
• Inconsistent scale across the data represented
• A visually cluttered graph

Organizations' lack of data literacy blocks their efforts to become data-driven and creates a significant barrier between merely gathering data and utilizing it to make informed decisions. For instance, a new report called "The Human Impact of Data Literacy" conducted by The Data Literacy Project says that 87% of employees find enterprise data as a valuable asset. Still, only 37% can create meaningful insights from data to become more productive. The fact that only 37% of employees find the data more effective for extracting meaningful insights is the major challenge that most firms face in their journey to translate data into long-term business success.

2. The Oversimplification of Data

Another challenge is making the data visualization overly simple. This is as harmful as making it more complex. Your clients or the decision-makers won't have sufficient data to make well-rounded decisions if critical points are being left out.

Data visualization must showcase the data in a way that is easy to grasp. However, if the crucial parts are incomprehensive and insufficient, the audience won't understand the essential pain point of the presentation. Therefore, instead of oversimplifying data, it is better to put all the critical context points together and structure them so that anyone can quickly grasp the true aspects.

Without a clear context, your audience will take away the wrong message from the data or may not understand it. Moreover, sometimes oversimplification can cause viewers to draw different conclusions than intended. Such confusion between other individuals will cause disruptions in work that often result in employee disbalance and disagreements. Thus, to avoid such situations, ensure to put as much relevant data as possible and showcase it in an approach that is easy to understand for both non-technical and technical viewers. On the other hand, you also don't want to miss out on the reader's attention with oversimplification; thus, do proper measures of including enough information that reflects the true meaning. After all, it's all a balancing act.

3. A Need to Balance Beauty and Understanding

Beauty in the context of data visualization is a worthy goal to pursue. For a data visualization to qualify as beautiful, it must be aesthetically pleasing, but it must also be novel, informative, and efficient. Several design components can make a significant difference in data visualization.For example - color is known as one of the critical design components. It is often observed that too much color is being used in visualizations to make it look beautiful. To avoid this, it is crucial to understand that picking the right ones requires knowing how our intended audience perceives colors.

Consider selecting colors that go well together. Make sure to use only two or three colors throughout the visualization to keep the pictures clear and concise. Include the same iconography and typography in every image so your reader can quickly understand it. The graphical aspects of the design must focus on serving the goal of presenting the information. Any facet that doesn't aid with the presentation is a potential road blocker as it can reduce the success of a data visualization. Proper usage of these elements is crucial for guiding the audience, conveying meaning, accentuating conclusions, and visual appeal.

4. Implicit Bias

The responsible, managing the data visualizations, often induce bias in it no matter how small it is. The preference can be of different types like sampling, algorithmic, cognitive, and intergroup. However, the most common bias in data visualization is cognitive bias due to one's personal interceptions and interpretations of the data.

Thus, whenever you or some team is working with data to design its infographics and visualizations, familiarize yourself with all possible types of bias that may incur because of your personal perceptions. Familiarizing yourself with types of bias is also mandatory to identify all those crucial elements and factors that affect your comprehension and perception of the data. This way, the bias can be avoided no matter how big or small it may occur in the visualization. This way, the results can be interpreted more efficiently.

5. The Inevitability of Visualization

Data Visualization has garnered a lot of attention already as several tools are available to help know complicated data sets, including charts, illustrations, and visual diagrams. We are on a short journey to take it in multiple industries, and there is no going back. This may not look like an issue; however, this may create an overreliance on visuals since companies are developing products that offer visualization, and consumers are looking for products that provide visualization. These feed into overemphasis on visuals and increase the potential of human errors in development.

Tips to Overcome Data Visualization Challenges

Data visualization isn't perfect. It isn't a magic bullet to make data more interpretable. Here are a few things to consider while depending on data visualization -

Graphs Don't Tell the Entire Story

It is essential to realize that charts don't always tell the whole story. For example, think about the fact that an interactive map may showcase that the average salaries are higher in Los Angeles, California than in Cleveland, Ohio. While that is based on accurate depiction can create a misleading verdict. According to My Live Elsewhere, the living expenses are much higher in Los Angeles than in Cleveland, so you don't have similar buying power even with an increased salary. This underscores that graphs and charts can only take you so far.

Explanation is Still Needed

Several analysts and marketers utilize data visualization products and tools to explain their work and results. For instance, an analyst may use a chart to show the ROI of a recent strategy. However, it is not enough to influence or fully educate an audience. You would still need to conduct the interpretation and articulation of the results.

Sources Matter

Data visualization can work with what they're given. This means the reliability of the data is still paramount. For example, if wrong data is included in a visualization, you're only going to receive a misleading chart even if the visuals are aesthetically pleasing. This emphasizes that none of the chart, diagram, or infographic should be relied upon until you know where its stats and numbers came from.

Data Visualization to the Rescue

Data visualization when done correctly allows you to explore hidden metrics that could bring more in revenue for your business. It is not wrong to say that data itself is crucial and challenging to explore both for non-technical and technical analysts, but, when you get clear visualizations out of big chunks of data, you will get the true findings such as trends, clusters, and figures that you can leverage later for the best of your business.

Curious to know whether data visualization could be the solution to your business goals? Take your digital transformation to the next level with Icreon's data engineering and consulting services.