What is Data Visualization and its Importance in Business Intelligence?

Like dashboards, data visualization is synonymous with business intelligence, but what does it mean exactly? As a core capability of analytics solutions, it’s important to know its role in the BI process today, to understand just how much it can transform your reporting.

Data visualization is the practice of representing information graphically to communicate and contextualize data. It highlights important changes, patterns and trends using a visual format, such as charts, graphs, maps and plots, to help make data easier to understand.

So, what is data visualization best at? When used correctly, visualizations help business people interpret complex data-sets, draw conclusions, and make informed decisions faster, and identify correlations they may not see in static tables or text-based reports alone. Finding the right BI solution with data visualization built-in, then, is a critical need.

What is data visualization in the context of BI?

The aim of business intelligence is to collect and turn data into actionable information that fulfill operational and strategic objectives, and support a person's decision-making.

After business data is collected, curated and modeled, organizations are keenly aware they need to provide their users and decision-makers numerous ways to easily interpret and drill down into data without requiring technical expertise to get started - otherwise, they may remain reliant on experts to provide analysis, or under-utilize BI tools altogether.

Data visualization tools is one such method. They are provided as a self-service BI reporting capability in modern analytics solutions, enabling companies to display and share quantitative data in a much more data driven, digestible and user-friendly manner for their customers and end-users to get up to speed on the most important figures fast.

Organizations can deliver day-to-day key performance indicators (KPIs) and business metrics in a more interactive way using data visualization tools, which are increasingly combined with data storytelling narratives to provide deeper context and meaning.

Enterprise and software companies across multiple industries, including retail, science, finance and healthcare, ultimately adopt BI solutions to break down their data and provide a clearer understanding of business performance, objectives and potential insights. Data visualization tools are one such way of realizing this important goal.

What types of data visualization are in BI tools?

Historically, the earliest forms of data visualization - simple pie charts, line graphs and tables - were used to supplement text-based operational reports and spreadsheets. As BI has become more of a priority in the past decade, analytics solutions increasingly support newer options to visualize complex sets of data and realize effective data visualization.

Today, there are many types of data visualization to show and represent data in a more engaging way, with the exact output dependent on the analytics solution in question.

For example, Yellowfin provides the following types of tabular and chart visualizations out of the box for data analysis, just to name a few of its 50+ options:

• Area chart

• Bar chart

• Column chart

• Image map

• Meter chart

• Numeric display

• Pie chart

• Scatter plots

• Stacked bar

• Treemap

Selecting the appropriate visualization that best communicates the intent of your data is key if you want to ensure end-users will be able to interpret, understand and act on it, such as store sales by region across several states.

What are the categories of data visualizations?

Most types of data visualizations today fall into six distinct categories:

1. Comparison – enables comparison of metrics over time or for one or more dimensions

2. Composition – describes the makeup of your numbers like pie or bar charts

3. Distribution – shows how values are distributed by time, value of dimensionality

4. KPI - Highlights the current status of your metric

5. Relationships – shows the relationship between metrics such as a scatter plot.

6. Location – show your data on a map, floor plan, and so on

The type of data visualization you choose to use in a report or dashboard is dependent on what the visualization needs to display, and what your end-user needs to analyze.

Do you need to show a relationship between a set of metrics?

Do you need to compare two data-sets closely together?

These considerations should ideally be mapped out prior to using any visualization, so you, your team and end-users understand just what data visualization is best used for.

Some BI solutions assist organizations in selecting the best chart category, such as Yellowfin, which has an Autochart builder that automatically selects the best chart for your data selections, as well as provides a short explanation of what the chart is and what it is best intended for.

What is data visualization best practice for BI reporting?

There are many general best practice principles to adhere to when designing your visualizations.

• Define your audience: Keep the intended audience front-of-mind when designing your data visualization to ensure it can address their needs and motivate them to take action on a data-point.

• Keep colors and fonts consistent: Color is a valuable method of drawing attention to data the users need to see, in the order you need them to see it, highlighting changes to figures and charts, while fonts help guide the users. Pick a complementary color theme and font, and stick with it to maintain consistency. 

• Context is key: Showing and telling your audience why the data in your visualization matters is crucial in ensuring to care enough to act on it. A compelling graphic combined with a data story narrative can help drive action better than just a pretty chart on its own.

• Use formatting and sorting: Organize data in a logical order (chronological, sequential) and use bars, icons, labels, and lines to keep things simple and help users interpret the data.

• Know when to hero tables: Visualizations will typically be the attention-drawer in any dashboard, but tables are still useful for dense visual design depending on the data-set - take advantage of sparklines and conditional formatting to make them just as impactful as your charts and graphs.

• Present values accurately: Connect values where a relationship exists in the data, not just for the sake of it - using a line chart to join dimensions, for example, isn’t needed, and is better represented using columns or bars.

You can learn data visualization best practices in more detail in our video guide, which we’ve embedded below for your convenience.