How can you work more efficiently with data analysis and visualization?

Business needs are one of the most helpful things to keep in mind when you are in the planning phase. When you have a clear idea of the business challenge(s) or question(s) your analysis will adress you are better able to stay focused when planning how to approach your analysis. If you aren't sure about the precise questions or challenges - ask! It is crucial in this planning phase to connect with your "customer". Ask questions about their challanges, about what has and hasn't worked in the past, and about their ideas for future strategies. The more curious you are, the more you will learn about the issues, and the better and more focused your analysis will be. This can also save you time by enabling you to edit out useless analysis.

I think we sometimes underestimate the value of keeping things simple with data visualization. Of course there are times when a complex or intricate visualization is appropriate. However, when and where possible, clear, clean, and simple visualizations can be most effective - particularly when you are preparing an analysis for a busy colleague who will appreciate being able to look at your work product quickly to understand the bottom line and act upon it.

Firstly, one needs to identify the business problem that needs to be solved. What existing data do we have? Is the data source reliable? Depending on the raw data, we should choose the most suitable programming language and visualisation tool. Ensure that the visualisation is simple and conveys the important data points and findings in the most effective way.

In my experience as a data analyst who recently migrated from academic research to another area, we have the ability to have insights, learn tools, and be careful when manipulating data in comparison to other different experiences or data. Scientific research follows a methodology that has a set of rules formatted so as not to invalidate all investigative and experimental work, guaranteeing results without bias and meeting pre-established goals. The same happens with data analysis, so this allows me (and other good researchers) to contact the data area in a careful and safe way, even if full mastery of the tools is under development.

Perspective-taking is crucial when you are presenting information or analysis to another person. The more successful you are at putting yourself in their shoes (adopting their perspective), the more successful you will be in getting your message accross and delivering real value to that person or team.