What do you do if you need to choose the most sought-after data visualization tools in the industry?

Begin by assessing your specific needs and requirements for data visualization. Consider factors such as your data sources, project objectives, team size, and technical expertise. Understanding your needs will help you narrow down your options and focus on tools that best align with your goals.

This could be the most critical step in choosing the best dataviz tool for your needs. Although most tools have very similar functionality, not all have the same list of features. Your first step should be to document what functionality the tool MUST have along with a list of NICE to have features. Think about your current AND future needs. With this list in hand, you can commence your tool research.Short list your potential software candidates using your list. Once you have narrowed down your options to 3 or 4 tools, this is the time to start chatting with the vendor in order to dig deeper into their tools. Eventually a tool should present itself as the prime candidate for your needs.

I think that assess needs is related to see not your actual needs, but what you could need in the middle time and in the long time. What is the strategic roadmap? To where are we going? Look just for your actual needs could led to oversimplified solution that could be change in few months/years.

With your needs in mind, explore the wide array of data visualization tools available in the market. Research popular tools such as Tableau, Power BI, Google Data Studio, and Python libraries like Matplotlib and Seaborn. Explore their features, capabilities, and pricing models to identify potential candidates for your toolkit.

In certain instances, pre-existing commercial agreements with one company or budget constraints urge the team to utilise a particular tool. In such scenarios, it is crucial to emphasise that the effectiveness of the outcome primarily hinges on the adept handling, curation, and presentation of data, rather than solely relying on the tool itself.

I think that first of all, you have to talk with your team, and discuss with them the options and best solution for your work. With so many options available, it is important to understand the learning curve of each one, and if you have in your team someone with skills and abilities in some of them, which could increase the use of the tool.

Conduct a comprehensive comparison of the features and functionalities offered by each tool. Evaluate factors such as data connectivity, visualization types, interactivity, customization options, and collaboration features. Look for tools that offer the flexibility and scalability to meet your evolving needs over time.

Before making a final decision, test your shortlisted tools thoroughly to ensure they meet your requirements. Take advantage of free trials, demo versions, or sandbox environments to get hands-on experience with each tool. Pay attention to usability, performance, and compatibility with your existing systems and workflows.

I think that this could be step two/three. It is easier talk with someone that already use the tool instead spend hours searching in the internet. So, in my opinion, steps 2, 3 and 5 is just step 2.

Capacitation: is essential that you keep up to date about the tools and data visualization theory and evolution. So, spending time reading and studying is also very very important.

Scalability: Choose tools that can scale with your business as your data visualization needs grow and evolve. Training and Support: Evaluate the availability of training resources, documentation, and customer support offered by the tool vendors to ensure you have the necessary support to succeed. Integration: Consider how well the tools integrate with your existing systems, data sources, and workflows to streamline your data visualization processes.