You're starting a new AI project. How do you pick the best data visualization tools?

The following considerations should guide your choice of data visualization tools: ? 1- Compatibility with Data Sources: ?Some visualization tools directly integrate specific data sources (e.g., SQL DB, cloud storage like AWS S3, and APIs). ? 2- Support for Native Data Formats:?Different visualization tools have specific data formats that they work best with??. ? For example:? - Tableau: CSV, JSON, Excel, PDF, Spatial files, SQL databases, custom SQL queries? - D3.js: JSON, CSV, XML, HTML (Being a JavaScript library, it can handle almost any web-accessible data format). ? 3- Frequency & Real-Time Requirements:?Not all viz. tools are optimized for handling high volumes of data, and not all tools have provisions for real-time data analytics.

Select data visualization tools based on project needs. Identify data sources and formats first. If dealing with structured data, tools like Tableau excel; for unstructured, use Python libraries like Matplotlib or Seaborn. Consider scalability, ease of use, and integration capabilities for seamless data representation.

Embarking on a new AI project involves selecting optimal data visualization tools to effectively convey insights. Consider the following points: User-Friendly Tools: Consider Tableau or Power BI for their intuitive interfaces. Python Integration: Choose Matplotlib or Seaborn for seamless integration with Python. Interactive Visualizations: Opt for Plotly for dynamic, interactive visuals. Project Requirements: Assess specific needs, data complexity, and preferred programming language. Flexibility: Evaluate tools' flexibility to adapt to evolving project requirements. Seamless Integration: Ensure chosen tools integrate smoothly with the existing tech stack.

Pay special attention to data cleansing and integration capabilities of the visualization tool. In large organizations, data resides in different formats in different data sources. For data visualization tools to work as desired, they should be able to integrate will the data sources and harmonize the data.

You can use python libraries like pandas, numpy, matplotlib, plotly when you have unstructured, semi-structured data and if your team includes data scientist/analysts. For structured data PowerBI/Tableau is better option, as it provide interactive user-friendly interface. It's better option if your audience are non-technical people.

Understanding your data types and dimensions is ?? to selecting the right visualization tool. Consider if your data is numerical, categorical, temporal, spatial, or relational. Each type requires specific visualization techniques. Tools vary in their strengths: some excel in charts and graphs, while others shine in maps, networks, or interactive dashboards. Look for versatility to handle multidimensional data like time series, geospatial, or hierarchical. Customization options are crucial for tailoring visuals to your audience and goals. Choose a tool that aligns with your data's characteristics and your visualization needs.

After addressing data source considerations, the next pivotal aspect is understanding the nature of your data and determining the dimensions you aim to visualize. Distinct data visualization tools possess varying strengths and limitations in presenting diverse data types, including numerical, categorical, temporal, spatial, or relational data. The ability to support complex and multidimensional data, such as time series, geospatial, or hierarchical data, is another differentiating factor. Choosing an apt tool involves aligning it with the specific data types and dimensions you wish to visualize. It should empower you to create visually compelling representations tailored to your audience and purpose.

Choosing the right visualization tool is like picking the perfect pair of glasses – you want the most transparent, accurate view of your data. If your project involves different types of data – like numerical or spatial – your visualization tool should be able to handle them effectively. The tool you choose should match the type of data you have and be capable of digging deeper. Look for tools that can do more than show data; they should be able to analyze trends, spot correlations, and segment data in insightful ways. And don’t forget about interactivity – it lets you explore your data more dynamically, making it easier to spot those key insights and share them with others in a way that’s easy to grasp.

It's crucial to align the tool's capabilities with the specific nature and complexity of your data. AI datasets often involve multidimensional, dynamic, or unstructured elements, requiring a tool that not only presents data accurately but also reveals deeper patterns and insights. Look for tools that offer flexibility in handling various data types, the key is to choose a solution that not only displays data effectively but also enhances interpretability and actionable insights for both AI experts and stakeholders. This approach ensures that the visualization tool is not just a presentation instrument but a bridge between complex AI algorithms and practical, understandable insights.

? Different tools may have different learning curves and user interfaces, and some may require more technical or coding skills than others. You should choose a tool that matches your level of expertise and comfort, and that allows you to create visualizations that meet your standards and expectations.

Define your goals: Clearly articulate your objectives for data analysis and storytelling using visualization tools. Choose the right features: Assess tools based on features like statistical functions, calculations, machine learning, presentation, interactivity, animations, annotations, and filters. Consider analytical capabilities: Prioritize tools that align with your analytical needs, whether it's basic statistical analysis or advanced machine learning models. Evaluate presentation and interactivity: Look for tools that enhance storytelling through animations, annotations, and interactive elements, fostering audience engagement.

Different tools encompass varying features and functions tailored to diverse forms of data analysis and storytelling. For instance, certain tools may shine in analytical capabilities, offering statistical functions, calculations, or even integration with machine learning models. The level of guidance and inspiration provided—via templates, examples, or tutorials—varies across tools, with some requiring more creative input and coding skills. Choosing the right tool entails aligning it with your specific goals for data analysis and storytelling. It should complement your expertise level and preferred style of expression, facilitating a seamless translation of data into meaningful narratives.

Depending on your visualization goals, you may want software that supports animations or interactivity. In some cases, you may not; you may want a static interface that just produces simple charts and nothing too fancy. Whatever your goals may be, ensure you pick the right visualization software that supports your needs. It is very easy to find out what a visualization software does by reading the documentation and going through example demonstrations therein.

Your end goal with data visualization significantly influences tool selection. If your aim is to unearth and communicate complex data narratives, you'll need tools that offer not just analytical capabilities but also strong storytelling elements like annotations and interactive elements. For analytical depth, I prefer tools that integrate statistical functions or machine learning models, aiding in more insightful data interpretation. The key is to balance analytical rigor with engaging and understandable visual storytelling.

? Depending on what you want to achieve and communicate with your visualization, you may need different features and functionalities from the tool. For example, some tools may be more suitable for exploratory analysis, while others may be more focused on presentation and storytelling. You should also consider the level of interactivity, customization, and collaboration that the tool offers, and how easy it is to share and export your visualizations.

The two most popular Data Visualization tools are Tableau and Power BI. These are also the two rated as leaders in the Gartner magic quadrant for Data virtualization technologies. Tableau was the first company to democratize and revolutionize the Data Visualization market. Power BI has made giant leaps in the recent past and emerging as a strong contender. If you are primarily on the Microsoft platform, take a close look at Power BI.

I love using Matplotlib, Seaborn, and Altair packages in Python for creating really nice plots. I was able to create interactive and colorful 3D plots using these tools. I also find Microsoft's PowerBI software to be a really good tool for data visualization, as it supports a wide range of data sources and data types.

Tableau is a leading data visualization software that enables users to create interactive and insightful visualizations from various data sources, facilitating data exploration, analysis, and decision-making. Qlik's visualization tools empower users to explore and interpret complex data through interactive visualizations, driving insights and informed decision-making and it is trending one.

Matplotlib: A Python library that provides a low-level interface for creating 2D plots of various kinds, such as line charts, bar charts, histograms, scatter plots, etc. Plotly: A web-based platform that allows users to create interactive and responsive charts and dashboards using Python, R, JavaScript or Julia. Tableau: A business intelligence and analytics tool that enables users to connect to various data sources, such as databases, spreadsheets, web services, etc., and create interactive and insightful visualizations. Tableau supports a wide range of chart types, such as pie charts, treemaps, gauges, etc., and also provides features such as filters, calculations, annotations, etc.

Popular data visualization tools for AI projects include Matplotlib, Seaborn, Plotly, and Tableau. Matplotlib and Seaborn are Python libraries offering versatile interfaces for creating various types of plots and charts, with Seaborn specializing in statistical and relational visualizations. Plotly, another Python library, provides interactive and web-based visualizations. Tableau offers a user-friendly interface for creating and exploring data visualizations, catering to both simple data exploration and complex visualizations with coding. These tools support data analysis, inference, presentation, communication, discovery, and storytelling in AI projects.

Before you do anything with data in an AI project, you have to understand what the business wants, what the business needs, and anticipate what they might not realize they need. Ask questions about goals, business requirements, how will the product be used, and so on. Often times, the product requested isn’t what is really needed.

Consider the compatibility of data visualization tools with your data sources and formats, ensuring seamless integration. Evaluate how well the tools handle your data types and dimensions, including structured, unstructured, and multidimensional data. Align the features of the tools with your data analysis and storytelling goals, prioritizing interactivity, customization, and storytelling capabilities. Explore popular data visualization tools such as Tableau, Power BI, matplotlib, seaborn, Plotly, and ggplot2, comparing their functionalities and suitability for your project. Additionally, assess factors like scalability, ease of use, community support, and cost to make an informed decision.

In addition to knowing what your needs are, spend time evaluating the data visualization tool for -->Compatibility: Ensure the tool supports the data formats you're working with and can be integrated into your existing data analysis ecosystem. -->Interactivity: Interactive visualizations allow users to explore data in-depth. Look for tools that offer dynamic filtering, drilling down, and other interactive features. -->Scalability: Choose tools that can handle your data volume and complexity. -->Customization: The ability to customize visualizations is key for tailoring analyses to specific project needs. -->Collaboration and Sharing: Look for tools that facilitate easy sharing and collaboration.

One of the most important things to consider when it comes to data visualization, is that visualization is for *people* not for machines. The purpose of visualization is to give people insights into data that they couldn't otherwise get from simply looking at rows of numbers and columns of data. This means that the right visualization is the one that provides the most clear understanding of the data that tells the compelling story that you want to communicate. Visualization is about communication first and foremost. And if you don't communicate what the data is trying to tell you, or even worse, if you can't understand what the data is trying to tell you, than the visualization is not successful.