TOP best AI tools for data analysis

On average, a business uses data from around 400 different sources to power its business intelligence (BI) and analytics systems. Therefore, aggregating data, building custom dashboards, and generating analytical reports is becoming increasingly complex.

AI tools simplify these processes, supporting businesses in making data-driven decisions and optimizing operational efficiency. Below are some of the top AI tools for business data analytics that businesses should consider adopting.

Top AI tools to include in your business strategy.

In many respects, the future of business analytics is tied to AI. Below is an overview of the most prominent tools currently available.

Tableau

Tableau is a data analytics and visualization platform that allows users to create and share reports without needing programming knowledge. Tableau utilizes AI to generate data for:

1. Automate data management tasks.
2. Suggest analytical questions.
3. Provide detailed information and explain the data in easily understandable language.

Therefore, Tableau is suitable for both business leaders who want an overview of their business operations and non-technical employees who want to leverage data more effectively.

ChatGPT

ChatGPT is a useful AI assistant for business analysts. This tool can analyze datasets, identify trends, predict business outcomes and customer behavior, and support strategic decision-making.

Additionally, users can use the prompt to request ChatGPT to perform risk analysis, explain data in simple language, write SQL queries, and summarize reports. However, data security should be considered, and the accuracy of the results should always be verified.

Polymer

Polymer is a business intelligence tool that requires no technical skills. Users simply upload spreadsheets from Microsoft Excel or Google Sheets. Polymer's AI then automatically visualizes the data, builds interactive dashboards, and creates search tools directly within the reports.

MonkeyLearn

MonkeyLearn is an AI-powered text analysis tool that combines data visualization capabilities. The platform uses Machine Learning to analyze customer reviews, surveys, meeting transcripts, and chat messages.

MonkeyLearn can analyze sentiment, find trends, categorize data, create visual charts, and its strength is that it doesn't require programming knowledge.

Microsoft Power BI

Microsoft Power BI is one of the most popular enterprise data analytics tools available today. It supports data visualization, analysis and reporting, and connects multiple data sources.

Thanks to its deep integration with the Microsoft ecosystem, including Excel, Teams, and Azure, Power BI becomes a unified data center for businesses.

Sense

Sisense is a data analytics platform for both analysts and developers. Key features include:

1. Low-code and No-code.
2. Drag-and-drop interface.
3. SDK and API set for developers.
4. Integrate data analytics directly into the product.

Similar to many other tools on this list, Sisense supports data visualization, analysis, reporting, and natural language data querying through AI.

Akio

Akkio is marketed as an AI assistant for data. It's a beginner-friendly tool that supports business forecasting, data analysis, and data visualization.

Thanks to its powerful predictive capabilities, Akkio is particularly well-suited for revenue, marketing, and sales forecasting. Users can create predictive reports and models without requiring technical skills.

Google Looker

Google Looker is part of the Google Cloud ecosystem. This platform provides data modeling, data analytics, and application development with integrated dashboards.

Because it operates on a cloud platform, Looker can handle large volumes of data and work directly in the browser. However, reports are not generated automatically like some other AI platforms.

Qlik Sense

Qlik Sense is a cloud-based data visualization and analytics platform. Key features include interactive dashboards, predictive analytics, and AI-powered data preparation.

Users can ask questions in natural language to explore data and collaborate with colleagues through notes or discussions.

Split

While not strictly a Business Intelligence tool, Splunk is still a very powerful data analytics solution. Splunk allows businesses to centrally manage data, monitor real-time data, and search and analyze data from any source.

It is particularly useful in cybersecurity, system monitoring, and incident management. Its ability to handle massive amounts of data allows Splunk to support efficient real-time analysis.

TIBCO Spotfire

TIBCO Spotfire is a data analytics tool for business analysts, data scientists, and data-driven managers.

This platform brings together structured and unstructured data, creating a unified data source and supporting data exploration with drag-and-drop functionality.

Additionally, Spotfire can apply Machine Learning models to real-time data to uncover immediate insights.

RapidMiner

RapidMiner is a well-known data science platform in the field of data mining. Its main functions include:

1. Text mining.
2. Prepare the data.
3. Data analysis.
4. Visualization.
5. Forecast.
6. Process automation.

RapidMiner supports both no-code for general users and custom programming for data professionals.

SAS Business Analytics

SAS Business Analytics helps businesses collect and analyze data from various sources.

The platform utilizes both low-code and no-code interfaces. AI automatically generates insights, interprets data, and detects trends. This allows users to discover important data patterns more quickly.

KNIME

KNIME is an open-source data science platform suitable for a wide range of users, including data professionals, managers, and MLOs engineers.

KNIME supports data preparation and analysis, system monitoring, and team collaboration. Additionally, users can create reusable workflows for various analytical tasks.

Domo

Domo is an integrated data experience platform: interactive dashboards; data storytelling; reporting; and AI-powered analytics.

Users can utilize low-code or no-code tools to build custom applications, create AI/ML models, and integrate data and AI into various parts of their business.