Techniques for Data Visualization and Reporting

Data analytics is the science by which experts analyze massive amounts of raw data in order to make conclusions about information. It has exploded in popularity as businesses seek new ways to understand their clients and optimize performance. According to Precedence Research, the global market for data analytics is predicted to surge from $30 billion in 2022 to $393 billion in 2032.¹ As this market grows, effective data visualization–”the presentation of data in a pictorial or graphical format”²–will become increasingly important.

While the definition can vary depending on context and application, the term ‘data’ usually refers to “raw, unprocessed information that is not recognized as having any meaning.”² Data can be organized in many forms, including name, numbers, age, signs, characters, symbols, files, reports, and graphs.

Through data visualization, people present complex data in graphical representations: interactive charts, dashboards, pie charts, and so on. These displays enable business analysts to tell interesting stories with data and share their findings with diverse audiences.² However, creating clear and engaging data visualizations requires the right skills and tools. This article explores data visualization best practices for business and analytics professionals.

Understanding Data Visualization

Data visualization uses digital tools to present substantial amounts of information in a graphic format. Business analysts manipulate these visual representations to explore data sets and identify patterns. They also use visualizations to explain their findings to company leaders, stakeholders, and other audiences.²

Common types of data visualization include:²

• Diagrams
• Maps
• Graphs
• Scatter-plots: Graphs with at least two variables plotted along an x and y axis
• Simulations
• Tables
• Timelines
• Trees: Graphics that draw hierarchical connections between ideas
• Waveforms: Graphs showing waves that represent change over time

Good data visualization allows business analysts to identify and correct problems in data sets quickly, so they don’t draw incorrect conclusions, and to process large amounts of information and gain insights more efficiently.²

Choosing the Right Data Presentation and Reporting Techniques

As a business analyst, you can use many data visualization techniques to report and represent information in a visual format. It’s best to determine which methods to use based on the kind of data you’re examining and the goals of the analysis.²

Common data presentation methods and their uses include:²

• Maps represent relationships between geographic locations or objects
• Tables use columns and rows to display quantitative or qualitative data
• Tree maps depict hierarchical relationships between different categories of data

After you select a format, you’ll also need to consider other visual elements such as color and labels. Common color-related data visualization methods include:³

• Sequential: Colors go from light to dark to show increasing values
• Diverging: Two contrasting colors represent two extremes
• Qualitative: Colors have no symbolic meaning but highlight different categories of information

Additionally, it's especially helpful in presenting data to use detailed captions and labels to explain the information in greater detail.³

Data Visualization Tools

A data visualization tool is the software that generates the desired presentations.² Business analysts use a variety of data reporting and visualization tools to represent and analyze information. Popular software includes:

Power BI

Microsoft Power BI allows analysts to gather, clean, and look at structured and unstructured data. It also features artificial intelligence (AI) tools, which use complex algorithms to mimic human thought and help users derive fresh insights from data.⁴

Tableau

Tableau is a platform that offers data management, analytics, and visualization tools. The company’s products enable users to transform data into bar charts, graphs, heat maps, and other representations. Tableau tools also include features for collaboration so that analysts can work together to analyze and visualize data.⁵

Strategies for Effective Data Storytelling

Data visualizations can help audiences better understand data sets, such as a company’s financial records and survey responses from clients. However, most people need additional context to get the full picture. Business analysts use data storytelling to create an engaging story about the data, explain the meaning of patterns in data, and communicate important insights.⁶

Analysts can use many techniques to craft stories about data, such as:⁶

• Explaining the origins and significance of data
• Comparing different types of charts
• Adding captions or labels to explain each part of the graphic representation
• Suggesting future actions based on the findings

Dashboard Design Best Practices

Dashboards are reporting tools that consolidate data sets and key metrics into interfaces, which are visual displays or webpages that showcase important information. They feature presentation tools such as charts and tables that users can explore to understand data. They also highlight key performance indicators, such as revenue and website traffic.⁷

Principles for designing an engaging dashboard include:⁷

• Create an accessible and easy-to-navigate user interface
• Clearly label data points
• Use color and size to spotlight the most significant data points and insights
• Ensure the dashboard adapts to different devices, such as computers and smartphones
• Limit the amount of data included to avoid overwhelming users

Infographics and Visual Data Communication

Infographics allow business analysts to communicate large amounts of data in concise and visually appealing formats. They typically blend images and text to share information. For instance, the United States Census Bureau uses interactive bar graphs and maps to convey information about population demographics.⁸ These infographics allow people without backgrounds in business analytics to grasp patterns and insights from complex data sets quickly.

Guidelines for creating effective infographics include:⁹

• Use color, shape, and size to organize data into a visual hierarchy
• Incorporate three to five colors, including at least one light, one dark, and one for emphasis
• Pair a light background with dark text and images
• Include graphic elements such as charts and timelines to display data

Data Visualization Best Practices

Data analysis experts have developed many best practices to ensure accessibility and accuracy in data visualizations, such as:³

• Identify essential information that you want to include
• Pick a suitable geometry–or type of graphic–for the dataset
• Choose colors strategically to strengthen your message
• Use resources such as ColorBrewer to choose accessible color schemes that people with color blindness and other visual challenges can see
• Acknowledge uncertainty in data analysis
• Balance simplistic visuals with more comprehensive captions to convey the full story
• Ask other business analysts for feedback

Prepare for Leadership in Data Analytics

Fine-tune your data visualization abilities in the Online Master of Science in Business Analytics program from Santa Clara University. Taught by world-class faculty, you’ll gain industry-leading expertise through a robust curriculum that emphasizes data science and machine learning, fintech, natural language processing, and other technologies. The industry practicum experience will enrich your skillset and expand your professional network.

Don’t wait to advance your career. Schedule a call with one of our admissions outreach advisors today.