Business Intelligence Data Visualization

Business intelligence (BI) combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations to make mores IT glossary, “business analytics includes data mining, predictive analytics, applied analytics, and statistics.” In short, organizations conduct business analytics as part of their larger business intelligence strategy. BI is designed to answer specific queries and provide at-a-glance analysis for decisions or planning. However, companies can use the processes of analytics to continually improve follow-up questions and iteration.

Business analytics shouldn’t be a linear process because answering one question will likely lead to follow-up questions and iteration. Rather, think of the process as a cycle of data access, discovery, exploration, and information sharing. This is called the cycle of analytics, a modern term explaining how businesses use analytics to react to changing questions and expectations.

Business Intelligence is More than Painting a Picture on Top of Data. Data visualization is superb for the function it performs, that is, to communicate information efficiently for fast and easy understanding. But it should only be one element of your Business Intelligence strategy, not the full extent of it.

The difference between traditional BI and modern BI

Business Intelligence Business data is a goldmine, but finding a nugget can be a challenge. When done right, BI enhances your decisions, improves operational efficiency, drives revenue growth and gives you a competitive edge. At Wissen we believe that business intelligence and visualization are an art – art of understanding the data and unlocking the true value of their data. Business intelligence (BI) leverages software and services to transform data into actionable intelligence that informs an organization’s strategic and tactical business decisions.

Modern BI prioritizes self-service analytics and speed to insight.

Historically, business intelligence tools were based on a traditional business intelligence model. This was a top-down approach where business intelligence was driven by the IT organization and most, if not all, analytics questions were answered through static reports. This meant that if someone had a follow-up question about the report they received, their request would go to the bottom of the reporting queue and they would have to start the process over again. This led to slow, frustrating reporting cycles and people weren’t able to leverage current data to make decisions. Traditional business intelligence is still a common approach for regular reporting and answering static queries.

However, modern business intelligence is interactive and approachable. While IT departments are still an important part of managing access to data, multiple levels of users can customize dashboards and create reports on little notice. With the proper software, users are empowered to visualize data and answer their own questions.

How major industries use business intelligence

Example of an economic indicators dashboard, showing the long-term drivers of the U.S. economy.

Many disparate industries have adopted BI ahead of the curve, including healthcare, information technology, and education. All organizations can use data to transform operations.

Financial services firm Charles Schwab used business intelligence to see a comprehensive view of all of their branches across the United States to understand performance metrics and identify areas of opportunity. Access to a central business intelligence platform allowed Schwab to bring all of their branch data into one view.

Now branch managers can identify clients that may have a change in investment needs. And leadership can track if a region's performance is above or below average and click in to see the branches that are driving that region's performance. This leads to more opportunities for optimization along with better customer service for clients.

Business intelligence tools and platforms

Many self-service business intelligence tools and platforms streamline the analysis process. This makes it easier for people to see and understand their data without the technical know-how to dig into the data themselves. There are many BI platforms available for ad hoc reporting, data visualization, and creating customized dashboards for multiple levels of users.

We have outlined our recommendations for evaluating modern BI platforms so you can choose the right one for your organization. One of the more common ways to present business intelligence is through data visualization.

Benefits of visual analytics and data visualization

Visual analytics keeps you in the flow of data analysis.

One of the more common ways to present business intelligence is through data visualization. Humans are visual creatures and very in tune with patterns or differences in colors. Data visualizations show data in a way that is more accessible and understandable.

Visualizations compiled into dashboards can quickly tell a story and highlight trends or patterns that may not be discovered easily when manually analyzing the raw data. This accessibility also enables more conversations around the data, leading to broader business impact.

Using Self-Service Business Intelligence (SSBI) for your company

Today, more organizations are moving to a modern business intelligence model, characterized by a self-service approach to data. IT manages the data (security, accuracy, and access), allowing users to interact with their data directly.

Modern analytics platforms like Tableau help organizations address every step in the cycle of analytics—data preparation in Tableau Prep, analysis and discovery in Tableau Desktop, and sharing and governance in Tableau Server or Tableau Online. This means that IT can govern data access while empowering more people to visually explore their data and share their insights.

The future role of business intelligence

Business intelligence is continually evolving according to business needs and technology, so each year, we identify current trends to keep users up-to-date on innovations. Realize that artificial intelligence and machine learning will continue to grow, and businesses can integrate the insights from AI into a broader BI strategy. As companies strive to be more data-driven, efforts to share data, and collaborate will increase. Data visualization will be even more essential to work together across teams and departments.

This article is just an introduction to the world of business intelligence. BI offers capabilities for near real-time sales tracking, allows users to discover insights into customer behavior, forecast profits, and more. Diverse industries like retail, insurance, and oil have adopted BI and more are joining each year. BI platforms adapt to new technology and the innovation of its users. Stay up-to-date with all of the trends and changes in business intelligence as we list the top 10 current trends in BI.

What is Data Visualization?

Data visualization is the illustration of information in charts, bars, graphs, and other iage-based mediums. Data visualization serves as a way to translate dense, complex values into simple to understand (and share) graphics and charts.

Why does data visualization matter?

Data visualization helps simplify complex information so that every person who sees it can comprehend what's happening in the data. It helps illustrate trends and variations in a way that appeals to visually-oriented people as well as those who are comfortable working in spreadsheets and databases.

In other words, data visualization helps teams align on what's happening in a particular data set, as well as helping them understand why those things are happening. This of course makes it easier to find and agree upon a course of action to correct mistakes, build off positive trends, and predict problems before they arise.

Which Data Visualizations Matter to Your Business?

Having an easy selection of data visualizations is easily one of the chief benefits of business intelligence software. They can help you discover new insights and provide an at-a-glance view of your success metrics. It's up to you to choose what visualizations will best tell your data story.

Here are some of our favorite data visualizations for building out solid dashboards, comparing trends, and gaining a greater understanding of the inner workings of your company.

Unique Visualizations for Unique Intents

When building your dashboard, the first point to keep in mind is that the visualizations you choose needs to correspond with the dashboard’s primary use. For example, a sales leaderboard will look very different from a dashboard highlighting business forecasting.

A sales leaderboard will use visuals like an area chart that shows profits over time, or a bar graph that displays which salespeople have made the most sales so far that quarter. A forecasting dashboard, on the other hand, will use a stacked column/line combo graph to compare previous forecasts with actual profits, or a funnel chart that easily displays the stages of the forecasting process. (You can see examples of different dashboards on our Dashboards We Love page.)

Dissecting Data Visualizations

How do you decide which visuals will help you the most?

First, you need a solid understanding of each type of visualization so you can figure out how to best bring your data to life. Here’s a list of the most important types, including some specific examples of how they’re commonly used in a corporate dashboard:

Types of Data Visualization

  • Area
  • Bar
  • Bar/Line Combo
  • Bubble
  • Bullet
  • Donut
  • Funnel
  • Gauge
  • Line
  • Map
  • Pareto
  • Pie
  • Radar
  • Stacked Column
  • Stack Column 100%
  • Stacked Column / Line Combo
  • Stacked Bar
  • Table


An area chart is a line chart with the area below the line filled with color. Often, several area charts are mapped together for comparison.

Example:MachMotion uses an area chart in their sales projection dashboard to compare given quotes to booked orders.


On a bar chart, numerical values are represented by horizontal bars and compared by length.

Example: Displaying the results of a “yes/no” customer survey question.

Bar/Line Combo

A bar/line combo combines a bar graph with a line graph to display a trend or compare multiple data sets. It is horizontally oriented.

Example: Comparing targeted sales with actual sales.


Bubble charts are used to display data points with three numerical dimensions represented by the x-axis, y-axis, and area of the bubble.

Example: Comparing overall value with cost and profits of a group of projects.



A bullet graph is a variation of a bar graph that measures values on a qualitative range.

Example: Comparing monthly budget allocations from one year to the next.


A donut chart is a hollow circle divided into sections that each represent a portion of the whole.

Example: Comparing budgets for different departments.


Funnel charts represent stages in a sales, marketing, or other “funnel” process.

Example:Sococo uses a funnel chart to gauge the success of their sales in comparison to the success of their marketing efforts.


Gauge charts use needles to show change in a single value on a dial.

Example: Noting current customer satisfaction levels.


A line graph displays information as a series of data points connected by straight lines.


Business Intelligence Data Visualization Definition

Example: Tracking unique site visitors over time.


A map chart displays geolocational data.

Example: Displaying common sales locations.

Business Intelligence Data Visualization


A Pareto chart contains columns and a line, where columns are presented in descending order and the line shows the cumulative total.

Example: Determining the most significant problem in a company’s customer service process.


A pie chart is a circle divided into sections that each represent a portion of the whole.

Example: Breaking down audience demographics.


A radar chart displays multivariate data on multiple axes starting from the same point.

Example:Command and conquer generals keyfreeband. Comparing three products based on eight different characteristics.

Stacked Column

A stacked column chart breaks down and compares parts of a whole. Columns represents the whole, and segments represent different parts of the whole.

Example:Spread the Vote uses a stacked column graph to track the number of monthly voters added per state each month.


Similar to a stacked column chart, but each column segment represents a percentage of the whole, rather than the actual value.

Example: Tracking the percentage of monthly newsletter signups by region.

Stacked Column/ Line Combo

Similar to a column/line combo, a stacked column/line combo overlays a line graph on a stacked column graph.

Example: Charting budget funds spent on specific projects.

Stacked Bar

A stacked bar chart breaks down and compares parts of a whole. Bars represent the whole, and segments represent different parts of the whole.

Example: Comparing the cost of a marketing strategy by product.


Business Intelligence Software Vs Data Visualization

A table is a set of data systematically displayed in rows and columns.

Example: Displaying scores from employee competency surveys.

Business Intelligence Data Visualization

Different Visualizations for Different Data

There isn’t a set format for building out your KPI dashboard (although Grow does offer templates if you're new to BI software)—what determines the visualizations you choose is the goal that you’re driving toward. The purpose of visualizations is to bring data to life, moving it from a static spreadsheet to a more dynamic layout that your team can make sense of.

Business Intelligence Visualization Tools

Ready to get started building your own dashboards? Grow can help. We offer a variety of powerful visualizations that can help you tell your story in the most impactful way possible. Demo Grow for yourself.