You're an Operations Research analyst. Which data visualization tools should you use?

Understanding the nature of your data is crucial. Consider where your data is coming from and in what format. .Some Popular Options can be Tableau:Its User-friendly, diverse visualization options, good for exploratory analysis. Cost, might have limitations with extremely large datasets.Power BI:Integration with Microsoft ecosystem, easy to create interactive dashboards.Might have a learning curve for advanced features.Python with Matplotlib/Seaborn or R:Customization, scripting for automation, extensive libraries for statistical visualizations.Requires coding skills, steeper learning curve.d.QlikView/Qlik Sense:Associative data modeling, powerful for complex data relationships.Can be resource-intensive, may require additional training.

In my opinion, txt, csv, Excel, are the popular file formats used in BI solution development. There are other direct database connection formats like MySQL, SQL Server, Sybase, Oracle etc which are available through ODBC and JDBC connectors. Since some of the databases like Oracle, MySQL, Sybase can run on Linux/Unix platforms mostly JDBC connectors are used widely as an industry standard. JDBC/ODBC connectors can also support txt, csv and Excel data formats so that the developers need to use appropriate drivers to load the data into the DW.

The origins and structure of your data dictates which tools are up to the task, given their strengths and limitations with different data types and complexities. This ensures you pick a tool that meshes well with what you’re working with, be it handling large datasets or connecting smoothly to various data sources.

Understanding the audience for your operational research findings is critical to developing the right analytics and visualization output. For instance, in health programming, analyzing seasonal trends would be most useful particularly given that addressing population health needs requires understanding of the time-based and characteristic-based differences. Line graphs such as epicurves are handy in communicate and can be produced using a number of tools such as Tableau or PowerBI. If the operational research goals demand reproducible data visualizations, then opting for programming-intensive tools like R would be more relevant.

An aspect I've found beneficial is assessing how well the tool can handle large datasets and complex visualizations efficiently. Take into consideration factors such as rendering speed, responsiveness, and scalability, especially when dealing with expanding data volumes or intricate analytical tasks. Tools that come with features like in-memory processing, parallel computing, or cloud-based solutions may provide improved performance tailored to your needs.

I like to keep it simple: If there's an audience who is not very technical, surface the insights through a BI tool like Power BI or Tableau. If they are tech savvy or you yourself are going to visualize the data, work with the platform/language they/you are most comfortable with. Whether it's Python, R or Java, there's plenty of visualization libraries and packages out there to meet and exceed one's needs.