Introduction

Data visualization tools enable you to see and understand trends, patterns in data, and outliers. 

It can turn granular data into business information that is useful, visually compelling, and – easy to understand.

For BI users, data visualization tools are essential to make data-driven decisions.

But a quick search of “data visualization tools” results in hundreds of different solutions.

Critical criteria such as security, dashboard & reporting should be taken into consideration while find searching for the best fit for your organization.

Here we list the top factors you should consider, to find the best BI data visualization tool. 

10 Critical Criteria for Evaluating BI Data Visualization Tools

1. Data connectors 

Regardless of where your data lives, you should pick a data visualization tool that can connect directly to your data sources. 

Your data source can be anything from Google Analytics, an internal SQL database, or a simple spreadsheet. 

Data connectors allow you to integrate data from different sources into the data visualization tool.

This provides a seamless flow of information that enhances the data management process.

2. Visual Analytics

Visual analytics enables you to analyze large datasets with data representations.

There are various types of data representations. The most common are bar charts, line charts, pie charts, scatter plots, heat maps, funnel charts, bubble charts, and histograms.

Select a data visualization tool that will best represent your BI data in a few clicks. Writing complex codes for visualizing data is a thing of the past!

Tableau and Power BI provides built-in visualizations for most use cases.

3. Ease of Use

The data visualization software you choose must have easy-to-use features and a user-friendly interface. 

An easy-of-use data visualization tool allows non-technical users to get started quickly. 

You must be able to build interactive data presentations, reports, and dashboards with ease.

This also helps avoid the additional costs of training your employees on software usage. 

4. Dashboards 

A good data visualization dashboard should be clear, with a compelling data story and adequate whitespace. 

Ensure the data visualization tool allows complete customizability to support different teams with different priorities.

It must allow you to customize which datasets appear prominently on the dashboard. 

You must be able to create a collection of visual components that function together to cover a broader array of information.

5. Data security

Many data visualization tools have in-built security mechanisms. 

Depending on the security level, the visual report needs authentication and authorization of different levels.

This enables different individuals or teams to see certain parts of the data and not all of it.

Security levels offered by a data visualization tool:

• Row-Level Security (RLS) is a key feature that helps you implement restrictions on data row access.
• Column-level security (CLS) ensures that only specific users can see the content of specific columns in database tables. It enables granular access to specific data within the table.

• Object-level security (OLS) secures specific tables or columns from report viewers. In addition, it also restricts object names and metadata.

When searching for the right data visualization tool, it’s critical to ensure the tool you select helps you restrict the permissions for datasets at the user/role level.

6. Template creation

A template in data visualization is a set of widgets that form a report with a predefined layout for data.

These widgets are the placeholders for certain types of data, shapes, and icons.

Ensure the data visualization tool you choose allows you to create a custom template. 

This helps you create reports that appeal to your audience visually.

7. In-place filtering

In-place filtering in visualization tools helps you focus on the data you’re interested in.

It reduces the amount of redundant data shown. 

For example, you might filter by Year and visualize data only for the recent 5 years.

The Range, List, Date, and Expression filter types are specific to every visualization tool.

Filter types are automatically determined based on the data elements you choose as filters.

8. Big Data Processing

Big data processing is the ability of software to collect, process, and analyzes a large volume of data.

It is among the utmost important components of a data visualization tool. 

The tools for Big Data visualization should provide a certain set of features:

• Presenting complex data as graphs, charts, tables, diagrams, or other visuals.
• Converting large data quickly into easy-to-comprehend visual formats. 
• Capability to process multiple types of incoming data.

9. Cost

One of the biggest factors involved in choosing a tool is weighing the benefits with the cost.

Fortunately, not every data visualization tool will totally undermine your budget.

• Power BI – The Pro plan starts at $9.99 per month per user.
• Tableau – The basic creator bundle starts at $70 per month per user.

Analyzing business requirements will help balance the budget with maximizing the return on investment in data visualization tools.

10. Embeddability

The data visualization tool you choose must have a provision to let you embed reports to external portals, such as websites or customer portals.

Some companies send reports to investors/customers/other stakeholders. In such cases, it’s critical to ensure the data visualization tool you use allows you to embed the reports or dashboards into external sources seamlessly.

Summary

When choosing a data visualization tool focus on the key areas mentioned above to help your organization objectively find the best fit.

Tableau and Power BI are the top data visualization tools in 2022. 

Tableau is capable of handling large datasets. It is best known for its visually attractive data visualizations. 

Microsoft Power BI is more user-friendly. It has the ability to work with multiple data points and is suitable for small teams with limited resources.

Get a detailed comparison of Tableau vs. Power BI.