What are the most innovative AI data visualization tools available today?

1/ Google Data Studio: A powerful tool that transforms complex data analytics into clear and concise reports, complete with customizable dashboards. 2/ Stretch: An AI-powered data visualization tool that provides natural language processing (NLP)-powered tools to make data accessible to a wider audience. PlotGPT: A tool that generates interactive visualizations based on user input. 3/ Graphy.app: A tool that allows users to create interactive visualizations and dashboards. 4/ Findly: A tool that helps users discover and organize data. 5/ Columns: A tool that organizes data into columns for easy analysis. 6/ Julius AI: A tool that performs advanced analysis like linear regression, modeling, projections, and even creates data animations.

Passionnant de voir comment l'IA peut révolutionner la visualisation des données et optimiser nos processus décisionnels. Cependant, il est crucial de rester vigilant quant à l'éthique et à la transparence dans son utilisation pour assurer des avancées positives et durables.

Para trabalhar com IA é preciso aprofundar os conhecimentos na área de programação . Dessa forma as melhores ferramentas para visualização dos dados são a partir da linguagem python. Bibliotecas como "pandas" e "matplot" são as mais indicadas

The most innovative AI data visualization tools available today that you might find useful include: ThoughtSpot, Tableau, Power BI, Qlik Sense, Zoho Analytics. Each tool offers unique features and advantages, so the best choice will depend on your specific needs, such as the ease of use, data connectivity, visualization options, interactivity, customization, and collaboration features. Consider these factors carefully to select the most suitable tool for your data visualization needs.

Tableau: While not strictly an AI tool, Tableau integrates AI features like natural language processing (NLP) to enable users to ask questions and get insights from data using conversational language. Power BI: Similar to Tableau, Power BI offers AI-powered features like natural language query, which allows users to ask questions and get visualizations as answers. DataRobot: DataRobot is an AI platform that helps organizations build and deploy machine learning models. It can also be used for data visualization and analysis.

Three of the most popular AI-driven data visualization tools are: - Tableau - Polymer - Sisense Some considerations for choosing the right one are as follows: ease of use and ability to map data to the visualization; type and quality of visualization output; cost considerations, data import options; and availability of tools to run on a laptop, tablet, etc.

I really love Carto! It’s a Spanish company founded many years ago and how they visualize geospatial data is unique! It provides many insights and many customers worldwide are using their services.

Another example of a data visualization tool is Tableau, which allows users to create interactive and shareable dashboards and reports from various data sources. It offers a wide range of visualization options and features, including drag-and-drop functionality, data blending, and real-time analytics. Tableau's AI-powered features include smart recommendations for visualization types and data preparation tasks, as well as natural language processing capabilities for querying and interpreting data.

Additionally, Power BI by Microsoft is another popular tool for data visualization and business intelligence. It enables users to transform raw data into visually compelling insights through interactive dashboards, reports, and self-service analytics. Power BI leverages AI to enhance data exploration, automate report generation, and detect anomalies or trends within datasets, empowering users to make informed decisions quickly and effectively.

Tableau, Power BI, Looker, Databricks, Google Data Studio, and QlikView are prominent data visualization tools used by data scientists and analysts to create compelling visualizations, comprehensive dashboards, and interactive reports. Tableau- It can create compelling visualizations and comprehensive dashboards. Power BI- Provided by Microsoft, enhances the efficiency of data visualization and analysis. It allows users to create interactive data visualization reports and share them across the organization. Google Data Studio- A product from Google, allows users to turn data into customizable dashboards and reports. Like the others, it also integrates with multiple data sources, offering AI-driven insights from the examined data.

Data visualization tools are key to first understand the story behinds the scenes and then to tell it to others. I’m a big fan os visualization tools as they have a unique capability making easy and obvious to understand what’s it’s hidden within mountains of data.

The big cross-business line benefit is using natural language to interact and ask questions about your data visualizations to expand understanding and refine how the data is presented to suite the audience.

From my perspective, AI-powered data visualization tools present numerous advantages for both experts and non-experts alike. They streamline the process of creating and refining visualizations, thereby saving time and effort while ensuring higher quality and accuracy through the application of best practices and standards. Moreover, these tools enhance user engagement by offering interactive and personalized visualizations tailored to different contexts and user needs. Additionally, AI capabilities enable the generation of clear and concise explanations, insights, and recommendations, facilitating better understanding and actionable decision-making based on the data presented.

AI data visualization tools can facilitate the discovery of hidden patterns, trends, and correlations within large and complex datasets, enabling users to uncover valuable insights and make informed decisions. By leveraging machine learning algorithms, these tools can automatically analyze data and suggest meaningful visualizations, reducing cognitive overload and enhancing decision-making processes. Furthermore, AI-powered data visualization tools can adapt to changing data environments and user preferences, providing dynamic and responsive visualizations that evolve with the data and user interactions.

AI data visualization tools offer numerous benefits. They allow users to process large amounts of data quickly and in real time, enabling faster decision-making. They present complex data in a simple, easy-to-understand manner which enhances data comprehension and interpretation. AI tools also help in identifying trends, correlations, and patterns within the data that might go unnoticed in traditional analysis. Moreover, they improve predictive capabilities as AI and machine learning algorithms can forecast outcomes based on historical data patterns. Additionally, they enhance data accuracy and reduce human error by automating data analysis. Lastly, these tools offer interactive visualization that can be customized.

Challenges of AI data visualization tools: 1. Lack of control and transparency 2. Limited creativity and diversity 3. Ethical and social issues Addressing these challenges is crucial for maximizing benefits.

Understanding complex visualizations, especially those showing unusual relationships, requires specialized knowledge. If this domain expertise is missing the generated visualizations may be irrational or worse lead to incorrect inferences.

Additionally, scalability and compatibility can present challenges for data visualization tools, particularly when dealing with large volumes of data or integrating with diverse data sources and platforms. Scaling up to handle big data requirements while maintaining performance and responsiveness can be a significant hurdle for AI-driven visualization tools. Moreover, ensuring compatibility with various data formats, systems, and technologies across different environments and devices can be complex and time-consuming. Interoperability issues may arise when integrating AI data visualization tools with existing workflows, databases, or analytics platforms, hindering seamless collaboration and data exchange.

One of the most notable challanges. Is getting tangled in the technicality of the tools and losing sight of why these tools are there. For example I have seen many people trying show of their skills in using these tool and tend to over complicate dashboards or visualization. This defies the whole purpose of using the visualization. Which is used to simplify a message

AI data visualization tools can face challenges such as handling large data sets, ensuring real-time data interpretation, and maintaining data security. Data complexity can make it difficult to design intuitive and informative visualizations. Additionally, there may be lack of standardization or integration capabilities with existing systems. Lastly, there are also concerns about AI interpretability and the potential for algorithmic bias.

Make sure you have connectors to your data and you have a way to define roles and policies, all this should be inherited from your current data lake. Then choose the one that gives you flexibility and integrates good with current systems. Finally it’s key to be super visual and intuitive

When it comes to data visualization, rather than trying to be innovative, it's more important to be appropriate, pertinent. Too many people try to impress the crowd with data visualization... And end up confusing the audience...😓... Mission failed❌ DM me if you want, to get introductions to SME (LinkedIn is not allowing me to tag them)

It depends on what is your need . For most uses cases XLSX is fine. For large scale timeseries - Grafana For google like text - Thoughtspot AI driven - Amazon Quicksigh Simple and Easy to use - Power BI For most of usescase (enterprise ) - Qlik, Tableau, DOMO Want to embed - use Open source like Jasper to save money. If you have a good data model , all BI tools will work. if not a good model, most of them give performance issue. Unlike data compute , BI tools can not scale out memory, so focus on data model.

Choosing an AI data visualization tool involves considering the size and complexity of your data, the tool's integration capabilities with your existing systems, and its ease of use. The tool should also come with options for customization and scalability. Additionally, verify the tool's credibility, read reviews, and check if the provider offers adequate support. You might also want to consider your team's proficiency level and whether the tool offers features like real-time data visualization, the ability to handle multiple data sources, and generate intuitive and interactive visual representations. Always opt for a tool that ensures data security and privacy.

I call it, Ask D.A.V.I.S: Data Story What are you trying to uncover or communicate with your data? Audience Who needs to understand the insights? Visual Type What is the most appropriate way to represent your data (charts, maps, networks, etc.)? Interface How easy is the tool to use, and does it fit your workflow? Shareability Do you need collaborative features or easy ways to export your visuals?

A piece of paper and a pencil. Yep. That’s right. Old school drawing something on a piece of paper or even a napkin. I can’t think of a better way to visualize something than this classic. Flexible. Cheap. Tailored to your exact needs. The perfect visualization tool. How does this relate to AI and visualization? Simple. Take a picture of your masterpiece and upload it to chatGPT or another LLM. Now you can ask the chatbot to do something with it. From a website design to D3 JavaScript code. The best visualizations will always start with a piece of paper and a pencil.

There are many innovative tools that you can use today for data visualization, but I particularly love the Python Seaborn package. It makes it easy to create attractive statistical visuals and allows me the flexibility of customizing it the exact way I want, leveraging code. It is also Open-Source and free for anyone to use!

Hay que tener en consideración la importancia de la IA, el rol fundamental que está jugando y jugará en las relaciones personales e intrapersonales de cada persona de aquí al futuro. Pero nunca olvidar que es un complemento a las habilidades humanas, siempre hay que buscar que sea una simple ayuda, un conducto para concluir mucha información, etc. Pero el trabajo que realmente se da, es desde las personas. También me gustaría comentar que al menos en Chile, las Universidades ya están comenzando a integrar IA en sus mallas o clases, pero no a la velocidad con que se está integrando en el mundo laboral... Por ende es de suma importancia que los próximos profesionales sean muy entendidos en el tema, nativos.

I think we must understand that data visualization is a tool for communication. The most effective way to communicate is most often the most simple way to do so. AI can help us understand complex patterns in data, but if it cant convey that information in a way that is easily understood, then it does not provide value. It is not uncommon that we see graphs that are difficult to understand, with too much information or with an unclear conclusion. If we use AI to understand much more complex patterns in our data, we can not visualize them with even more cluttered graphs full of information. AI visualization might be a more efficient way to do this, but if the conclusion can be simplified by humans, we should probably do that!