Data visualization is a key aspect of data science work as it illustrates insights and findings very efficiently and effortlessly. It is an intriguing methodology that’s used in every data science and data analytics project at one point or another. However, many people today, particularly those in the data analytics field, entertain the idea that it’s best to perform this sort of task using Tableau, a paid data visualization tool. Note that there are several options when it comes to data visualization, many of which are either free or better-priced than Tableau. The latter appears to be popular as it was one of the first such software to become available. However, this doesn’t mean that it’s a good option, particularly for data scientists.
So, what other options are there for data visualization tasks? For starters, every programming language (and even a math platform) has a set of visualization libraries. These make it possible to create plots on the fly, customizing them to your heart's content, and being able to replicate the whole process easily if needed through the corresponding script. Also, they are regularly updated and have a community around them, making it easy to seek help and advice on your data visualization tasks. Also, there are other data visualization programs, much more affordable than Tableau, which are also compatible with Linux-based operating systems. Tableau may be fine for Windows and macOS, but when it comes to GNU/Linux, it leaves you hanging.
Let's shift gears a bit and look at the business aspect of data science work. In a data science team, there are various costs that can diminish its chances of being successful. After all, just like other parts of the organization, a data science team has a budget to work with. This budget has to cover a variety of tasks, from data governance costs (e.g. a big data system for storing and querying data), data analytics costs (e.g. cloud computing resources), and of course the salaries and bonuses of the people involved. Adding yet another cost to all this, for a Tableau subscription, doesn’t make much sense, especially considering how challenging it can be for a data science project to yield profits in the beginning. Also, considering that there are free alternatives for data visualization tasks, it makes more sense to invest in them (i.e. learn them instead).
So what are some better ways to invest money for data science work? For starters, you can invest in the education of your team (e.g. through a course or a good book). Even if they are all adept in the essentials of data science work, they can always get up to speed on some new technology or methodology that can be used in some of their projects. Also, you can invest in additional equipment, upgrading the computers involved, and even getting more cloud resources. Finally, you can always invest in specialized software that is related to your domain or hire consultants to help out when needed.
A few years ago, as I was writing the Data Science Mindset, Methodologies, and Misconceptions book, I mentioned Tableau as a data visualization alternative. However, I didn't look at the bigger picture of data science work from the organization's perspective. The latter is something my co-author and I did in our book Data Scientist Bedside Manner, which I'd encourage you to buy. In it we cover a variety of topics related to data science work and how there are better ways to invest resources for it, building towards a more successful pipeline. Cheers!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.