Your R&D team needs data visualization tools. What’s the most affordable option?

To determine the most affordable option, first, define your goals and requirements for data visualization. Consider factors like the type of data, interactivity needs, and integration with your existing tech stack. Once you have a clear understanding of your needs, you can explore options like Matplotlib, Plotly, or Chart.js that align with your objectives and budget constraints.

It's completely dependent on the availability of project funding. Researchers can use both options, but I strongly believe open source should be the way to go. As research progresses, we might need multiple functions or options that may not be available in commercial tools. Open source tools are highly customizable, but researchers might lose some perks that commercial software solutions provide, such as bug or error solving, etc. However, I have seen great progress in the adoption of open source software in research labs, but it definitely requires the skill set to handle it.

I personally favour open-source solutions, but company preferences may vary. I opt not to use anything by Google due to past incidents and would not recommend it for any scenario where privacy is paramount. Python and R both offer excellent visualization libraries, albeit with a learning curve. Most of the time, I use Excel or Minitab, as these are the approved tools within my company. Both of them are highly effective.

Comparing commercial and open-source versions when selecting a data visualization tool is crucial to balance features, costs, and project-specific needs. While commercial solutions may offer robust support and user-friendliness, open-source tools provide flexibility, customization, and cost-effectiveness. Thoughtful evaluation allows aligning the tool choice with project requirements, considering aspects such as scalability, support community, and integration with other technologies.

Minitab is great user friendly tool which can be used partially for free trial There is no need for coding There is help menu which explains behind of sub tools

I work in the pharmaceutical industry, so I'm rather paranoid about data protection. This means that I don't like sharing data with anyone who might be insecure. While the global company's data security doesn't solely depend on me (of course!), every small detail matters. In other words, if the company were mine, I would choose safe and secure partners or utilize in-house tools.

In my experience this is not that easy to do because in R&D department we always depend on innovation curve to mesure the return of investment. Sometimes the market oscillates turning too difficult to predict it. When it comes to solving problems it is more accurate and easy to predict because problems and costs are there already

I have virtually no idea how anyone can measure the return on investment in visualization. However, most tools serve purposes beyond visualization. For example, if you're purchasing a commercial package, there are numerous other enhancements to consider. And I find it hard to quantify...