The best data visualization tools & software compared

Data Visualization Tool Definition: What is data visualization and why is data visualization software needed?

As the flood of data in companies increases, it becomes more and more difficult for those whose work is strongly tied to it to apply relevant information efficiently and effectively - in other words to actually derive truly useful decisions from it. Machines and their algorithms have no problem capturing, reading, and optimally utilizing big data in the respective context. However, humans are different. They need specific support to make extensive data tangible and ultimately useful for them.

This is where data visualization comes in. Through this process, huge data sets are precisely aligned with the respective usage context using common visualization methods such as diagrams, plots, infographics, animations, etc., and made understandable. Such presentations convey complex data relationships and data-based insights in a relatively easy-to-understand way.

Of course, creating data visualizations is not easy. It is hardly possible for designers to simply take a data set, with perhaps hundreds of thousands of entries, and manually create a visualization from the ground up. Such an approach would mean dozens or hundreds of hours of work. Data visualization tools take over these tasks and deliver prompt results.

Software for visualizing data – often also called database visualization tools, big-data visualization tools, or big-data visualization software – thus enables a largely automatic conversion of textual and numerical information into visual diagrams, tables or other representations required in the respective context.

How do Data Visualization Tools Work?

Software for visualizing data is usually integrated into the underlying system. This means it works directly with another software or tool collection. The data visualization software can capture, analyze, and ultimately report on defined information from the respective system.

The collected information is usually presented in a preconfigured visual data mode. For example, the monthly number of visitors to a website over the course of a year can be visualized in bar charts, each bar representing the number of visitors in a given month.

The data visualization software depends on input data from the system to be analyzed or a software used within it. Information can only be queried and visually represented through an appropriate linkage or integration. While standalone applications generally have specific interfaces, there are also data visualization tools that are directly part of a specific software system (software package) and therefore do not need to be connected manually.

What are the Advantages and Disadvantages of Data Visualization Tools?

• Data-based decisions: At the core, data visualization software has the advantage of making it easy to track data, metrics, goals, and KPIs (in real time). Setting specific goals and working towards them are important economic factors in companies. Corresponding progress should of course be tracked as differentiated as possible. Data visualizations for the underlying KPIs can make relevant processes tangible for people or decision-makers in companies. Data visualization tools are widely applicable. All relevant business metrics, such as web traffic, revenue, or customer acquisition, can be represented using them.

• Data understanding and usability for humans: Furthermore, data visualization software helps its users or the people in the company who need to accept information for decisions to easily – and in many cases only at all – understand this. Through the use of data visualization solutions, relevant employees often only get the opportunity to draw real value from a large flood of information. An overview and a purposeful use of this data would hardly be possible without the work of the software.

• Holistic view: In companies, there are a lot of important business data that should be included in economically relevant decisions. Corresponding information is ideally compiled from different sources and processed holistically. A data visualization software provides the necessary overview here. This is relevant for the management, but also for teams that need to understand critical company data.

• Strategy efficiency: With the help of data visualization software, trends in business metrics can be quickly and easily identified. If certain values do not meet the goals by the end of the month, quarter or year, companies can determine this with data visualization tools. Depending on how the KPIs develop, strategies and methods for achieving the goals can then be adapted. The ability to react quickly and data-based is not least an important prerequisite for digital transformation.

There are hardly any direct disadvantages in relation to data visualization tools. When personal information is displayed, those responsible should of course ensure that their software complies with the applicable data protection guidelines. If this is not the case, there is a considerable risk of serious legal problems.

Otherwise, the most common challenge is the right - or really purposeful in the own company's context - use of software for data visualization. The central questions here are:

• Are the right pieces of information really being considered?

• Is the representation comprehensive for the respective purpose?

• Are the visualizations going to the right people?

• Are the data being interpreted advantageously?

Whether and how these important points are followed up, of course, lies hardly within the responsibility of the data visualization tool itself.

How to Choose the Right Data Visualization Tool or What to Pay Special Attention To?

With data visualization tools, it's the same as with all digital helpers: Not every visualization software fits all corporate requirements.

Some data visualization solutions are oriented towards specific industries or teams within a company, thus naturally bringing with them more specialized functions than general data visualization software. For example, a marketing team is usually well-advised to opt for a specific marketing solution. This should not resort to an extensive enterprise suite, since it only needs to visualize and interpret the progress of certain marketing metrics. There are also tools for sales analysis, finance, and several other business areas.

In addition, many other software products have controlling and reporting functions, which are then occasionally connected with data visualization. A CRM or a helpdesk tool, for example, can have data visualization functions. The difference is that these are typically rather rudimentary features and not the main function of the product.

As can be easily read out from the lines, those interested in searching for a suitable data visualization tool should first ask themselves whether a stand-alone software for the visualization of general data is really needed or perhaps a product that brings along important visualization functions only for its application area? Closely linked to this, one should deal with the core functions that are likely to be needed. The following possible components should always be considered.

• Data compatibility: The main feature and the most important component that potential buyers should focus on when looking at data visualization software is that the tool can be integrated with the required data sources and can retrieve exactly the right information. Many visualization software can access SaaS applications, cloud and on-premise databases, or even self-developed solutions. However, restrictions are possible. For example, if one of the main reasons for purchasing a data visualization tool is to track marketing campaigns, but the selected program cannot be integrated into the existing marketing automation system, the purpose is defeated.

• Real-time tracking: The tracking of real-time data is another often important function of data visualization tools. This offers the opportunity to immediately observe progress and trends in a company's KPIs. Thus, the tool ensures that those responsible can react quickly and adjust their strategies, as long as it provides significant benefits or avoids disadvantages.

• Features to support decision-making: A visualization tool provides data in a meaningful format. It can analyze previously recorded information and even predict future patterns. However, corresponding data only proves to be really meaningful if the end users are also able to make the necessary decisions based on the information uncovered. To make this more likely, data visualization tools sometimes have features to support trends recognition, advanced analytics, and other help functions for assessing metrics.

• Scalable/flexible infrastructure: Another important factor contributing to the success of a big data visualization tool is the right infrastructure. The more dynamic it is, the more efficiently the solution can be used. The amount of stored, managed, and analyzed data is usually immense and growing constantly. Therefore, cloud computing or serverless computing often come into play. Here, the infrastructure can be easily managed and configured for larger workloads without incurring high additional costs. This not only saves money but also time and effort for the employees and makes the process more flexible as a whole.

• Integration ability: Visualizations from big data only help users if they can be seamlessly integrated into the respective operational business systems. Decision-makers can easily investigate the reasons for successes or failures, view the associated metrics, predict upcoming events, and derive methods or procedures to achieve the desired result. For this purpose, good data visualization tools have effective API support that enables exactly such smooth processes in connection with programs already used successfully within the team.

• Collaboration possibilities: There are many cases where team members need to communicate remotely to exchange their insights or have a meaningful discussion about currently visualized data. Real-time interaction is of course much more efficient and ultimately more effective in drawing advantageous conclusions than just forwarding collected data. Direct collaboration features are thus indeed a very helpful element of data visualization software. They allow particularly fast and productive results.

• Usability and performance features: If visualization tools are difficult to use or do not work smoothly or produce unsound processes, they are likely used less. A delay of a few seconds may not matter for some use cases. Users who need to visualize data all day or make corresponding decisions can be significantly disturbed in their workflow by this. Typical functions that contribute to performance improvement include prompts, data optimization settings, dynamic loading options, or KI.

• KI integration: Visualization tools for big data increasingly contain machine learning, deep learning, and natural language processing to make analyses and predictions easier and more effective.

• Mobility: Visualization tool providers today often provide mobile features so that users can observe or represent business metrics regardless of their location via smartphones or tablets. In today's business world, employees are often on the move or generally work remotely at home. The ability to view data visualization dashboards from mobile phones or tablets is therefore often indispensable.

What do Data Visualization Tools cost?

Data visualization tools are basically available in two different deployment versions:

• on-premise (installed on local computers)

• or in the cloud as Software-as-a-Service (SaaS).

Systems that use the cloud delivery model are also referred to as "web-based software". In these versions, the full application resides on the provider's servers and is maintained by them. Users can then access it from virtually any device connected to the Internet. Access is via a conventional browser or a special app. Corresponding tools are usually charged as a subscription. Companies pay a certain amount monthly or annually. Depending on the scope of the application, this ranges between about ten and €300 per month.

With the on-premise deployment, the data visualization software is hosted internally on your own servers. The data in the databases and all affiliated applications are stored and executed locally. Users can also remotely access the system here - but only if it comes with a remote function. Such programs are often bought once for a larger sum or also rented and can then be used relatively freely. Companies with on-premise open-source visualization tools are particularly flexible.

On-premise solutions generally require more (personnel) IT resources, which entail additional costs. Smaller companies often do not have sufficient (financial) resources to operate such software. For them, cloud data visualization tools are usually the cheaper option. They can usually be implemented faster and easier and do not call for any additional hosting and maintenance effort.

Those looking for a data visualization tool as a free solution will also find what they are looking for. However, a data visualization software freeware only comes with certain basic functions. Therefore, such data visualization toolkits are only suitable for business purposes to a limited extent.