Data Visualization Tools – A Feature Analysis


data visualization tool

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If you are using big data analytics for business, the use of data visualization tools can help you optimize the data processing and analysis of the captured information. Having too much data available at your disposal can result in the mismanagement in the processing of the data that can result in a more complex analysis results. For better and improved business intelligence, using data visualization tools offer digital marketers better clarity in their data and interpretation. Using the tools allows you to understand the data in a clearer and more targeted view based on your business analytics needs.

What Is A Data Visualization Tool

Visualization tools now go beyond the regular use of graphs and spreadsheet presentation. Tools that help visualize data more intricately and in detail are available that can help capture big data for better analytics. They come in different forms such as dials and gauges, heat maps, detailed pies and charts, beautiful infographics, as well as interactive features that gives data analysts more accurate indicators in extracting the data they need for analysis. Data visualization tools are considered to improve business intelligence and big data analytics.

Data Visualization Tools – Benefits and Drawbacks

Here are some of the data visualization tools available in the market and learn what beneficial features they provide for your business, as well as the common drawbacks from using them.

  • Open Layers

This operates as a Java Script library that displays map information. It comes with an open source code that opens bigger opportunities for improvement for developers to improve its functionality. The tool works together with OpenStreetMap, as well as the other maps available for data visualization. This is a most beneficial among data analysts who like to work on JavaScript. However, the tool is yet to be improved as it is being continuously undergoing rapid development, but provides a promising data visualization tool for map-based data collection.

  • Google Refine

This provides data collection in a spreadsheet presentation that provides a clear text and numeric data for analysis. It is easy to extract the data using varieties of formats such as JSON files, XML, text files or Excel. The user can exercise control on which data to accept or reject when importing them and the program is easier to control in terms of the strength of its suggestion functions when grouping data together. The moment learns to feel at ease in using its platform, this data visualization tool provides a combination of ease of use and functionality for data manipulation and analysis. A drawback from this tool, however, is the lack of calculating features that are commonly available for spreadsheets. Thus, it may take a while before one can finally carve out a large data set using this tool.

  • The R Project for statistical computing

This tool offers a platform mainly for statistical analysis. Running on a command line, the user can use the tool for organizing means, standard deviations, median and correlations in their data. It uses charts and graphs and accommodates add-on for open source projects. The beneficial feature of R is allowing the user to enjoy wider spatial analysis on their numerical data. It is not recommended, however, for users who do not have a skill in command prompts. At some point, some users also report some barriers in using the tool for larger data sets.

  • Exhibit

This is a data visualization tool for users who like working on coding. The tool can create web pages that come with advanced filtering and text search functionalities. It is also empowered with timelines and interactive maps, including calendars and customized lenses. You can easily sort out data and present them any way you like. A drawback of using this tool lies on the fact that the user is not used to coding visualizations and is unfamiliar with visual syntax and coding.

  • Google Fusion Table

One of the best features of the Google Fusion Table is its ability to turn your data into a map or a chart. It is possible to create a heat map, line chart, tables, scatter plot, timeline, pie chart or story line with animation to display the data. You can enjoy more customizations on how you want to view the data according to your choice. It is easy to join data tables and to filter, add or sort the columns. You can collaborate your data presentation with others where comments on the data can be displayed with it. By using the tool you can opt to display the data as private or public. A downside of using the tool is that your data sit on Google servers. However, this can be an advantage to users who prefer to save from paying for server bandwidth, but can be a drawback to those who value data privacy. While you feel more comfortable about analyzing data according to your preferred form of data visualization, using the Google Fusion Table imposes some limitations on the data capacity on its server.

  • JavaScript InfoVis Toolkit

This is a data visualization tool that offers an interesting interactive visualization library with high polished graphics to use for data analysis. The beautiful graphics are made of codes which is a drawback of the tool when its user is unfamiliar with using visualization coding. Additionally, the visualization types are also quite limited and the user is required to use JSON formatted data to work well with the tool.

Choosing Data Visualization Tool for Data Analytics

Choosing the right data visualization tools will require the exercise of a sound judgment on whether it can address your data analytics needs. More advanced tools for data visualization have promising features but you should be able to look for a tool that can provide you features like animation, personalization, dynamic data management, and multi-dimensional visualization that can adapt to the changing course of data influx to your business. These essential features from a data visualization tool can help you plot your data for better clarity and efficient data analysis.

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