Data visualization is a crucial aspect of many firms' business strategies due to the ever-increasing volume of data and its value for the business.

In this post, we look at some of the most popular data visualization tools and discuss the aspects that influence how people choose visualization techniques and tools. We'll go over the most popular data visualization tools and offer some pointers on how to integrate data visualizations into useful dashboards.

This article includes the following sections:

·        Data visualization approaches are determined by a number of factors.

·        The most common data visualization approaches and how they're used.

·        Data visualization, exploration, and analytics tools.

·        Tips for making data dashboards that are both efficient and responsive.

·        Real-world applications and potential collaborations

What factors influence data visualization decisions?

The first step in making sense of data is to visualize it. Data analysts employ various data visualization approaches, including charts, diagrams, and maps, to translate and present complicated data and relationships in a straightforward manner. The right technique and its implementation are frequently the only ways to make data intelligible. Poorly chosen strategies, on the other hand, may not allow data to reach its full potential, and may even render it obsolete.

The following are five elements that influence data visualization decisions:

Audience. It's critical to tailor data visualization to the target audience. Users of fitness mobile apps, for example, may easily work with simple representations to track their progress. If data insights are intended for researchers and experienced decision-makers who work with data on a regular basis, however, you can and should go beyond simple charts.

The tactics will be determined by the type of data you're dealing with. When it comes to time-series metrics, for example, line charts are frequently used to depict the dynamics. Scatter plots are frequently used to depict the relationship between two factors. Bar charts, on the other hand, are ideal for comparative analysis.

Context. Depending on the situation, you can utilize various data visualization techniques and read data. You can use the shades of one hue on the chart to emphasize a certain statistic, such as considerable profit growth, and highlight the greatest value with the brightest one. Contrast colors, on the other hand, can be used to distinguish items.

Dynamics. There are many different sorts of data, each with its own rate of change. Financial outcomes, for example, can be measured monthly or annually, whereas time series and tracking data are always changing. In data mining, you can use dynamic representation (steaming) or static data visualization approaches, depending on the rate of change.

Purpose. The purpose of data visualization has an impact on how it is implemented. Visualizations are compiled into dynamic and controlled dashboards packed with various tools for visual data analytics in order to make a sophisticated analysis (comparison, formatting, filtering, etc.).