Data Visualization 101: Presenting Insights in a Compelling and Informative Way
Human beings are visual and sensory consumers. Everything we touch, see and feel initiates a reaction in our minds, helping us create an attachment to the object of interest.
Throughout history, people have visualized data via various mediums, going as far back as cave walls, clay tablets, early maps depicting land masses, celestial maps showing stars, planets, the moon, and even human body diagrams by early medical practitioners.
Data visualization has taken on an entirely new meaning in our present business environment. Any given business generates data daily from its operations, employees, customers, production, and other processes.
Understanding what it means can take a while without utilizing aids that help you visualize it for easier consumption. Today, everyone can use various online tools that have made visualization accessible and easy to understand.
Let us closely examine what data visualization means, why it helps make sense of everything, and the best techniques one can employ to optimize visuals.
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Data Visualization Today
Visual communication is most effective when you acknowledge that the data comes not just from one action but many, and each step requires a unique plan, resource, and skill set to decipher.
Business intelligence describes visualization as one of its key processes. It involves accumulating raw data for modeling and delivering to establish conclusions for future action.
The Harvard Business Review writes* that you must consider two types of questions to start thinking visually.
- Is the information conceptual or data-driven?
- Am I declaring something or exploring something?
Conceptual data focuses on the overall message, trends, patterns, and insights. You are showing information that will assist in establishing a comprehensive, exploratory, and actionable view.
Data is used strictly in its original form to present it as accurately as possible, as it deals with quantitative data.
You are simply communicating what the data says without any assumptions otherwise. For instance, your app had 100,000 downloads last month.
Using the same data, you are now trying to understand why you had a surge in downloads in Week 3 compared to the other weeks, which showed similar activity.
Recently, visualization trends have expanded to include interactive and dynamic elements. These elements allow for data shifting, such as incorporating a drop-down menu with filter options to rearrange or focus on specific data at a granular level, enabling more in-depth analysis.
Data Visualization Methods
Some of the techniques used to visualize today include:
- Heat maps
- Area charts and graphs
- Tables, Pie charts, and Bar charts
- Data journalism
Data can also tell stories; combined with data visuals and contextual information, it can impact a broader audience because you want the data to assist in better decision-making so that you can use the insights gained to progress to the next step.
Making Sense of Insights
The main advantage of data visualization is not that it makes the data more beautiful but that it provides insight into complex datasets by communicating their vital aspects in more intuitive and meaningful ways.
In the 21st century, researchers have identified data visualization as a necessary skill for research.
The Path of Visualization and Insights
The job of data is to reveal information, and drawing meaningful conclusions comes with analysis and interpretation.
Data must navigate through processes to establish the studied subject and make effective, informed decisions based on the insights below.
- Collecting: This includes defining, finding, and gathering.
- Cleaning: Exploring, scrubbing, and describing.
- Describing: Analyzing and modeling to be presentable.
- Communication: Telling the story the data contains is the final stage.
Businesses seek to improve performance and processes, identify opportunities, and gain an advantage.
Some of the necessary insights they would look for are:
- Market Analysis: Data that allows a business to understand its products' performance, trends, opportunities, and customer sentiments. For instance, a clothing brand can assess that its women's wear collection has performed well this past season. Understanding why other segments see fewer sales can help them track customer preferences.
- Customer Experience: By looking at how customers interact with their products by bringing together feedback, social reviews, and interactions, businesses can identify how satisfied or not their customers are. The same clothing brand also noticed that their men's wear saw increased interaction during a recent campaign but didn't result in an increase in sales. Assessing customer feedback helps identify negative sentiments to guide future products and marketing.
- Performance Analysis: Visualizations of key performance indicators (KPIs) tell the business if they are seeing results in their primary focus areas. These include revenue targets, customer acquisition, satisfaction levels, etc.
- Operational Efficiency: The operations of any business eventually run into some bottlenecks. With the correct data, companies can initiate early implementation of optimization procedures to identify and address any production, logistics, and resource management issues, thereby enhancing operational efficiency. For example, men's wear didn't see an increase in sales because the marketing department released the campaign too early, as preparations for production were still underway. The data showed that the production and marketing departments needed to sync.
These are just a few examples of how data insights can help steer a business to grasp trends and patterns, enabling it to make data-driven decisions and identify opportunities.
Best Practices for Visualization
#1 - Choose The Right Visualization
The right chart type will help define and tell the story of your data. It will reveal patterns and trends to understand the significance of the data set you visualize instantly.
The most commonly used chart types include:
- Comparison Charts: You can utilize these charts to compare values across different categories or time-based data. Types include Column charts, Bar charts, Clustered charts, Layered charts, Radar, Line charts, Step charts, Multi-line charts, and Area charts.
- Static Composition: This chart illustrates part-to-whole relationships between data, such as product sales broken down by region. Types include Stacked Bar, Stacked Area, Percentage Bar, Funnel, and Pie charts.
- Correlation Charts: When you need to understand relationships between two or more data points. Types include Scatter plots, Bubble charts, Heat grids, Circle grids, and Event charts.
- Distribution Charts: Used when you want to understand the frequency of values in the data. Types include Histograms, Scatter plot charts, Box and Whisker charts.
- Location Charts: These charts are great for showing where things happen and what happened. Types include Thematic map charts, Bubble map charts, Heat maps, and Raster map charts.
- KPI Charts: One of the most important types, as it will tell you how the business is performing at a glance. Types include Big number charts, Bullet layered charts, Dial charts, Meter charts, and Thermometer charts.
#2 - Format and Style
Spending time selecting the correct elements to prepare a chart for viewing will help get the data across more quickly, allowing for more accurate decision-making.
- Colors: Use color in a divergent, sequential, or categorical method from low to high with a critical midpoint. You can show single colors for each category as low to high or with different colors. If necessary, add legends to support your color scheme.
- Labels and grid lines: Adding label data to bar charts can help explain the data more efficiently. Grid lines help in communicating key threshold areas.
#3 - Clear and Simple
What do your charts represent? If you know it offhand, your audience must know it instantly by looking at it.
- Titles: Your chart titles can be either descriptive, i.e., Q1 sales were a specific dollar amount, or explanatory, i.e., Q1 sales were 20% up Y.O.Y.
- Sorting: You can sort data alphabetically, in ascending or descending order.
#4 - Feature
Your data visualization can display and communicate a lot of information. However, if you are not getting the critical insights across, this affects the strategies a business would need to adopt. Featuring and highlighting the essential elements will show your audience what's important.
- Conditional Formatting: Use color to communicate data above or below thresholds.
- Reference Lines: Show targets on charts through lines, showing where you are with your goals.
- Highlight Trends: These help understand any patterns emerging from your data.
- Project Forecasts: Show projections for the future based on present data
#5 - Collaboration
Your data visualization is at its best when shared with all your business or organization's key departments and stakeholders. Their insights can help drive decisions more effectively.
- Dashboards: Best used to display KPIs, allowing users to examine performance and explore reports on a granular level.
- Broadcasts: Scheduling alerts when reports are ready for users. These have proven effective for teams that need to act upon data.
#6 - Iterate and Improve
Updating your visualizations as new data becomes available is part of the process. If data changes, so must the visuals. When done right, visualizations tell a story and reveal hidden details that aren't apparent in a spreadsheet.
#7 - Don't forget Mobile
As our phones advance with feature sets, the requirement for all business information and data to be viewed and interacted with on mobile becomes even more necessary. If your visualization showcases big datasets and charts, designing an option optimized for mobile, with just the highlights, for decision-makers on the go helps keep the collaborative aspect ongoing.
Data visualization is a valuable tool that can help businesses improve their decision-making, communication, engagement, and efficiency and gain a competitive advantage to achieve their goals.
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* - HBR source
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