🔍 Assess Project Requirements: Start by identifying the specific data types and visualization complexity needed for the project, ensuring the new tool can handle these demands effectively. 🧑💻 Evaluate User-Friendliness: Choose a tool that is intuitive for the team, reducing the learning curve and minimizing the need for extensive training to accelerate adoption. 🔗 Check Integration Capabilities: Confirm the tool can integrate smoothly with existing data sources and software, ensuring a seamless flow of data without compatibility issues. 📈 Prioritize Scalability: Select a tool that can grow with project needs, accommodating larger datasets or more sophisticated visualizations as requirements evolve.

If your data visualization tool isn’t working for you, start by thinking about what you need it to do. Look for something that’s simple to use, so your team can pick it up quickly without extra training. It’s also important to make sure the tool works well with the software and data sources you already use. Many people find it helpful to try out a few options, read reviews, and get advice from others who’ve faced the same challenges. The key is finding a tool that feels like a natural fit for your team and your project.

With advancements in GenAI, tools are also evolving. Try a modern tool like Tower by Codygon. Tower generates dashboards using AI but you can still customize visuals by chatting with your charts.

In the process of converting data to info to knowledge, along with visualisation, one might need to integrate more data sources and data platforms to bring higher intelligence to the analysis. For example, going to an extra step to create spatial visualisations using d3 or any other js, we need to add Google Maps or such base Layer to create more intuition and information to enable businesses and product teams identify actual patterns from real world. We also might add more APIs from more platforms or systems which are not by default in BI platforms. Here we need to create APIs and BI modules to arrive at the requirement which is not just a task but fun and also a great value addition to the BI platform, either existing one or custom one.

When choosing a new data visualization tool, I applied several approaches. First, I carefully identified the visualization requirements and the complexity of the data that we needed to work with. This helped to immediately filter out tools with limited capabilities. Secondly, I organized testing with the team to evaluate the usability of each tool. It was important that the tool be intuitive, as learning can take a long time. Finally, I evaluated the compatibility of each tool with our existing systems, especially with the data sources that we already use. Integration with our stack would help to avoid additional costs and simplify the transition.

A data visualisation tool is just a tool. You wouldn’t clean dishes with a pressure hose - it might work, but it’s likely to ruin your dishes. Similarly, any tool you use needs to suit the task. To assess the right tool, consider: - Who are the users, and what are their roles (e.g., data engineers, analysts, marketers)? -What data sources need connecting, and what purpose do they serve? - How comfortable are users with designing dashboards? However, dashboards fail less because of the tool and more due to poor design. To succeed, understand your users’ needs (not wants) and design dashboards around them. When you put your users at the centre of your dashboard designs and decisions, you’ll create a tool they trust, use, and value.

When choosing a new data visualization tool, consider these steps: Know your needs: For example, if you're analyzing trends in women in leadership roles, a tool like Tableau can help visualize progress across industries or regions. Pick an easy tool: For teams new to data, Power BI or Google Data Studio can simplify visualizing data on topics like gender diversity or AI's role in leadership development. Check if it works with your data: Ensure the tool connects to sources like LinkedIn datasets or AI research files for smooth integration. For insights into women in leadership and AI, you can use these tools to showcase how AI is influencing leadership opportunities, highlighting gaps or progress in gender equality within tech sectors.

1. Pinpoint the gaps: Identify what your current tool can’t do—missing features, performance issues, or poor compatibility. 2. Define your needs: Clarify your project’s specific requirements, like interactivity, scalability, or integration with your data sources. 3. Explore alternatives: Research tools with strong reviews and features that align with your goals—Tableau, Power BI, or even open-source options like D3.js. 4. Test before committing: Use free trials or demos to see if the tool actually delivers. Hands-on beats promises. 5. Get team input: Your colleagues might have great insights or experience with other tools. 6. Think long-term: Ensure the solution can scale and handle future projects without constant upgrades.