Data visualizations are impactful ways of communicating complex statistical information in an intuitive and interactive manner. Data visualizations are used by business analysts, data analysts, data scientists etc. to understand business and derive actionable insights.
Here are three most common ways of building data visualizations:
Data Visualizations for Business Intelligence
Enterprise data visualization tools like tableau and qliksense enable users within the enterprise to access enterprise data in an intuitive and interactive manner. These tools have connectors that can connect directly to data sources. These tools are available on cloud on subscription based models.
These are self service visualization tools, and business users can build their own visualizations to analyze data with very little training. Business users can build visualizations very quickly using these tools, but there are tool specific limitations in building the visualizations.
This method of building visualization is adopted by companies for internal use, mainly for business intelligence and data analytics applications.
Data Visualizations on Internet for public consumption
This method of building visualizations is adopted by companies when they need to communicate with external parties like customers or vendors etc. or public in general.
Data Visualizations for research and data mining
Researchers and data scientists generally build data visualizations to understand the data sets, identify patterns within data and derive insights that would normally go unnoticed. They typically use open source programming languages like (R and Python) or proprietary tools SAS that are specifically designed to do complex data analysis.
The data visualizations built by data scientists are generally for their own consumption, or for presenting their findings to a selected set of audience. The data visualizations are built using the visualization libraries of the programming languages or tools of their choice.
These visualizations are quite basic in nature, but very powerful for identifying hidden patterns and anomalies within data.