Episode 35: Key Questions to Ask When Choosing a Data Visualization Tool
In this episode, we explore the process of choosing the right data visualization tools for data professionals. We discuss key questions to consider. By answering these questions, you can make an informed decision that aligns with your specific needs.
What You’ll Learn in this Episode
- The importance of choosing the right data visualization tools
- Questions to ask when selecting a data visualization tool:
- The evolving nature of data visualization needs
- Making an informed decision based on individual requirements
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Data Visualizaton Tools
Data Visualizaton Tools
[00:00:00] Hana: In today’s episode, we will be diving into an important topic for data professionals, and that’s choosing the right tools for data visualizations. As a data professional, you may find yourself faced with a lot of options when it comes to selecting the right data vis tools for creating your impactful visualizations.
[00:00:20] So how do you decide which one is the best one in this episode? I’ll explore the key questions you should ask yourself when making this decision, and I’ll provide some recommendations to help you make an informed choice. So let’s get started.
[00:00:35] First, let me address a question I get a lot related to this topic, which is what’s the best data viz tool?
[00:00:42] And this is hard to answer because it will vary depending on the person, the situation, and the data you’re working with. So in this episode, I’ll share questions you should ask yourself to help you choose the right data viz tool.
[00:00:56] And the first question is, what are my data visualization objectives?
[00:01:02] So when you’re choosing your tool, it’s important to first define your objectives. Are you trying to explore the data for insights, create interactive visualizations for your stakeholders, or convey specific messages to your audience? All of this will help you figure out and narrow down your options in choosing a tool that aligns with your goals.
[00:01:25] Something else you wanna keep in mind is who you’re making these visualizations for. So if you’re doing some analysis in Python and you need to make some quick exploratory visualizations that you don’t plan on sharing widely with others, you know, these are mostly for yourself, then making a quick viz using a Python library would be better for you.
[00:01:44] But if you plan on creating visualizations for other people, maybe for a client to keep track of their KPIs, then you’ll need to use a different database software.
[00:01:54] The second question is, what type of data do I have? So you wanna consider the type of data you’re working with. Is it structured or unstructured?
[00:02:03] Is it numerical or categorical? There are different tools designed for different types of data. You wanna consider the type of data you’re working with and which visualization tools are compatible with it? So, an example is, I have a geography background, so I used to work with geographic data a lot.
[00:02:20] A tool like a GIS software. Where like QGIS or an Esri product would be more suitable for me. So keep that in mind. And you also wanna consider where the data is stored and which tools support connecting to what data sources easily. For instance, if most of your data is stored in a Postgres SQL database, you wanna make sure that it’s easy for that tool to connect to that, and you’re not spending a lot of time trying to export the data into a format that can be easily plugged into your tool.
[00:02:50] The third question is, what is my level of technical expertise? You wanna consider your skills and experience? Because some tools do require coding skills while others are more user friendly and they don’t require you to have coding knowledge.
[00:03:06] So think about what your comfort level is with different tools, and choose the one that matches your technical expertise. If you’re willing to learn and expand your technical expertise, that’s something you can consider as well. Additionally, you wanna consider the learning curve associated with each tool and the availability of resources like tutorials or documentations online that can help you with this learning process.
[00:03:29] For instance, this wasn’t for particular data visualization, but back when I was doing data analysis with geographic data, I was encouraged to learn a programming language that very small group of scientists in the world actually use.
[00:03:42] I started to learn Python and I found a big community of people in the field, particularly the atmospheric science field, that were using this programming language to do their analysis and visualizations.
[00:03:55] And I found a lot of support for that. And plus, Python in general has a lot of documentation and support and tutorials online versus the very niche language that I was using at the beginning.
[00:04:03] Question number four is what is my budget? Budget is an important factor to consider because some of these data visualization tools do cost a pretty penny.
[00:04:13] They can be a substantial investment if you are going to be personally funding these. So if you have a tight budget, you can consider some free or more budget friendly tools and you can also wait against the features and capabilities of the tools in order to make a decision that isn’t gonna break your wallet. If you are an employee or student, you can see what licenses your university or employer offers for data visualization tools and make your decision based on that.
[00:04:40] If you are convinced that a different tool that they don’t have a license for is going to work better for you. I would recommend preparing a proposal to help convince them to get you a license for that. They don’t have to buy everyone a license. They could possibly just get you one, or it could be a temporary one.
[00:04:55] Question number five is what are the scalability and collaboration options? If you anticipate working with large data sets, or you need to collaborate with a team, so you could be working in a larger data team.
[00:05:07] It’s not just you. You wanna ensure that the tool can handle those requirements.
[00:05:12] And there you have it. Those are some of the questions you should ask yourself when choosing your data viz tools. By considering your objectives, the type of data you have, your technical expertise, your budget, and the collaboration options, you can make an informed decision that aligns with your specific needs.
[00:05:29] So really the best data viz tool is the one that will align with your needs the most. And it’s possible that as time goes on or you change jobs or you work on a different project, those needs will change. So the best tool will actually change as well. So I hope you found these insights valuable. Let me know if you ended up using any of these questions to help you choose your data