Top 7 Tools for AI-Driven Data Analytics
AI's contribution to the global economy by 2030 is expected to be $15.7 trillion (Source). Data analytics is one of the core parts of any business that wants to grow in the market. Companies can extract essential information and useful insights from raw data that can drive better decision-making processes.
With the rise in artificial intelligence (AI), data analysis has changed to be more productive, precise, and perceptive than ever. AI-driven data analysis tools help you visualize, analyze, and track data so you can derive insights needed to achieve your business goals. Here is a list of the top 7 AI-driven tools for data analytics:
- Tableau
- Supports complex computations, data blending, and dashboarding
- Allow users to ask questions in plain language and receive visual responses
- Ease of implementation
- Handles large amounts of data
- RapidMiner
- Offers a variety of built-in tools for data preprocessing, analysis, and machine learning model building
- Automation capabilities streamline the data analysis process, enabling faster insights and decision-making
- Suitable for both data scientists and business users
- Polymer
- Robust AI tool that transforms data into a database
- Doesn’t require any coding
- Analyzes data and improves users’ understanding
- Makes spreadsheets searchable and interactive
- Microsoft Power BI
- Integrates seamlessly with existing applications
- Creates personalized dashboards
- Helps publish secure reports
- No memory and speed constraints
- MonkeyLearn
- Classifies text in labels in a simple way
- Makes it easy to clean, organize, and visualize feedback
- No code AI-driven platform
- Saves hours by automating business workflows and analyzing text
- Akkio
- No-code machine learning platform
- Great for beginners looking to get started with data
- Build neural networks around selected variables
- Accuracy rating for the models
- Qlik
- Allows for interactive exploration and analysis
- Enable users to navigate data intuitively
- Provide users with valuable suggestions and guide them toward relevant findings
- Allow users to visualize and present data in a personalized and engaging manner
Tableau is an analytics and data visualization platform that enables users to interact with their data. One of the top-selling points of Tableau is that it doesn't require any coding knowledge. With Tableau, users can create reports and share them across desktop and mobile platforms.
Tableau runs on the query language platform VizQL, which translates drag-and-drop dashboard and visualization components into back-end queries. 8,841 live websites are using Tableau and an additional 16,819 sites that used Tableau historically (Source).
Advantages:
RapidMiner is a robust data science platform that provides a drag-and-drop interface for building and deploying machine learning models. The platform has a user-friendly interface with a drag-and-drop workflow builder that simplifies the data analysis process for users with varying technical expertise.
The automation of data preparation and predictive model building streamlines the workflow, saving time and effort. The extensive library of pre-built AI models allows users to leverage established algorithms and methodologies for efficient analysis.
Advantages:
Polymer is a powerful AI analytics tool that simplifies data transformation into a dynamic and accessible database, eliminating the need for coding. The platform enhances data comprehension and enables immediate data investigation for insights by transforming uploaded spreadsheets into an interactive database.
Polymer powers your spreadsheet with a robust AI, to help you analyze your data and improve your understanding of it, all with a few clicks. The solution offers intuitive visualization capabilities and almost limitless ways of interacting with your data.
Advantages:
Microsoft Power BI is a famous business intelligence platform that allows users to sort through their data and visualize it to search for insight. Users can build machine learning models and access other AI-powered features to help them analyze their data.
The platform is available with multiple integrations, including a native Excel integration, and a UI faster and more responsive than some other solutions, although often at a higher price tag. Appy Pie allows users to integrate various apps to automate tasks with its AI-driven no-code platform.
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MonkeyLearn is a no-coding platform that uses AI data analysis features to help users visualize and rearrange their data. The platform includes multiple AI-powered text analysis tools that instantly analyze and visualize data to the user’s needs.
The tool can also be used to set up text classifiers and text extractors, which help automatically sort data according to topic or intent and extract product features or user data. MonkeyLearn can save you hours of manual data processing with its reliance on machine learning to automate business workflows and analyze text.
Advantages:
Akkio is aimed at beginners and is ideal for users wanting to get started with their data. The AI tool enables users to upload their dataset and select the variable that they want to predict, which helps Akkio build a neural network around that variable.
The platform is highly useful for predictive analysis, marketing, and sales. Like many of the other top tools on this list, Akkio doesn’t require any prior coding experience. Rather than predicting results, the AI solution offers an accuracy rating for the models and pulls out false positives.
Advantages:
Qlik is a business intelligence and data visualization solution that allows users to tap into AI technology to analyze their data. The platform has multiple data exploration features and a versatile platform that’s friendly to both technical and non-technical users.
Teams can seamlessly collaborate on the platform using simple drag-and-drop editors and workflows to manipulate their data to their liking. The high cost and low AI feature set make Qlik a better choice only for users who can use the full extent of the platform.
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Conclusion
The utilization of AI for data analysis had changed the way organizations approach deriving information and drawing insights from heaps of unorganized data. By using AI-driven tools, businesses can bring AI to their use by automating tasks, uncovering hidden patterns, and making data-driven decisions. These AI-driven data analysis tools have helped organizations come closer to unfolding the full potential of their data, gaining a competitive edge, and thriving in today’s fast-paced business landscape.