Data Visualization Best Practices for Enterprises

An effective visualization must display complex data connections without clutter. This is crucial for business users to grasp the critical insights communicated through visualization quickly.

Data points are the building blocks of visualized data. As a best practice, choose the data points most relevant to the “story” being presented.

Data quality is essential for visualization. Low-quality data can lead to inaccurate conclusions and interpretations, impacting decision-making.

Therefore, enterprises must clean and preprocess data to eliminate errors or anomalies. Additionally, data cleansing should include normalizing data and removing any duplicate values.

Enterprises must also use recently updated data to make informed business decisions. Otherwise, outdated data can mislead business users with inaccurate insights in an utterly dynamic market environment.

Talking about data quality, a good database schema design is necessary for eliminating (or reducing) redundant data. Schema designing refers to the best practices used to construct a database schema.

Enterprises can only efficiently run their database and retrieve information using schema design. Besides consuming energy and time, disorganized databases are difficult to maintain and cannot provide valuable enterprise data. Here are some of the best practices for schema designing:

• Use the right naming conventions
• Encrypt sensitive data, including passwords and personally identifiable information (or PII)
• Understand every data element and its attributes for an effective schema design

With data visualization, enterprises have an efficient tool to identify market trends and improve their performance. Automation can streamline this by automatically providing data from connected applications. Automating their reporting process means enterprises no longer have to spend valuable time transferring data between applications.

Organizations can quickly share reports among stakeholders through automation – with minimum effort and time. Similarly, automation can quickly facilitate data input into visualized elements used in bars and charts.

This way, sales reports, for instance, can provide real-time data about new sales leads, prospects, and customers.

Business intelligence transforms data visualization from a visual tool to an effective business analysis tool. BI technology enables users to analyze business information to improve their decision-making process.

It can enable users to interact with visual data from different angles for their specific use cases. Here are some capabilities of a BI-enabled dashboard:

• Connecting with the third-party system through APIs
• Adding multiple visual elements on the BI dashboard
• Using interactive visual elements to manipulate the presented data
• Automating the visualization process

Enterprises must ensure that visualized data is readable in any format and supported on multiple devices. For example, interactive charts must be easy to follow on desktop and mobile devices. Here are some valuable tips to ensure readable data on any device:

• Use CSS to present consistent images on any mobile device
• Optimize high-quality images for consistent speed and fast loading
• Tweak background colors to highlight vital information on any device