The concept of data visualization in today’s modern business world has been expanding. High-profile businesses such as JFrog can use visual representations of enormous datasets to make sense of their data, spot patterns and relationships that would be hard to unearth using numerical representations alone and get vital insights into consumer behavior and market possibilities.
Thus, in this article, we’ll discuss data visualization along with recommended practices for developing visually appealing material that may help your company make more informed decisions.
What Is Data Visualization?
As a picture is worth a thousand words, it is easier to absorb information for company managers and other team members who prefer to dismiss data. Simply put, data visualization is the process of displaying data graphically. It is an effective approach for presenting meticulous mapping via the use of visually appealing charts, maps, and graphs. This is not only a straightforward solution to the issue of data analytics, but it is also a good approach for easing data exchange throughout your organization.
Key Practices to Visualize Your Business Data
Here are the crucial strategies for making the most of your data visualization process to help your company make better decisions.
- Define Your Success Criteria and Objectives
You should set goals that are demanding but not impossible to attain. Do you want to increase your customer base, boost your market share, enter a new market sector, or conduct research and development? Make your objectives as clear as possible, and make sure they can be assessed.
And make regular use of your dashboard to keep track of the facts about these various domains.
Since corporate goals are sometimes extremely generic, you should use very specific language to demonstrate that your company’s aim has been met.
- Keep Things Simple
Being concise and presenting your story with the fewest charts feasible while still having the most impact is almost always the best way to proceed.
The most important reason to visually represent facts is to make a point. For example, if your company’s sales increased over the previous month, a graph indicating an increase in sales flowing in an upward manner from the left side of the screen to the right side of the screen would make sense.
There is no need for you to provide any non-related content, as long as the information in question is not addressed on another page within another subject.
It is critical to keep each data visualization clear and simple so that people can easily understand what the data is trying to show.
- Determine Decision Metrics
You most likely have access to a large quantity of data, but only a subset of that data will be relevant for making your decision. Before being overwhelmed by the data sets, consider which specific parts of the data would be most relevant to answering your choice question.
When you’ve found the right metrics, the next step is to see whether you can collect them with any level of accuracy. Certain data points may likely be unavailable or offer incorrect information. In such cases, you usually have two options: launch a project to collect the data, or return to the first step and amend your question.
- Tell a Story
One of the advantages of data visualization is the ability to tell a story with data. A successful visualization will properly portray the most significant points from the data in an easy-to-understand manner, thus enabling effective communication.
- Include Teams for Data Sources
Involvement from all levels of the organization is required to grasp both the short-term and long-term objectives. These inputs might be used to direct the questions that people ask in their analyses, as well as how you prioritize the sources of verified data.
All parts of the organization, including roles, responsibilities, architecture, processes, and performance indicators, will contribute to the success of your analytics deployment and future state.
- Use Shapes and Colors Properly
Colors and shapes can significantly improve processing data display as they may be used to distinguish, label, and highlight process data elements. If you have a bar chart that illustrates the frequency of process issues, use various colors to identify crucial spots that must be handled effectively in a certain business cycle.
For example, in a correlation matrix that shows the relationship between numerous process variables, use different shapes to represent positive, negative, or zero correlations. Using colors and shapes to visualize process data may result in contrast, harmony, or hierarchy.
