Top 15 AI Tools our analysts use for definition, documentation, analysis and data platforms

The world of data analytics is undergoing a profound transformation, driven by the rapid advancement of artificial intelligence (AI) technologies. As businesses grapple with ever-increasing volumes of data (clean or not!), AI-powered tools are emerging as game-changers, democratizing insights and enabling organizations to make data-driven decisions with unprecedented speed and accuracy. With many things in the information age however, we can’t be so quick when falling in love with all the new shiny objects...and we certainly can't rest and let robots (or agents) hallucinate all over us.

In this series, we'll explore some of the most innovative analytics tools using AI to increase efficiencies within the analytics industry and discuss how they are empowering businesses to unlock the full potential of their data.  We will also discuss some of the cons and why you need to be cautious when utilizing “artificial” information output by AI. 

Spoiler Alert:  Artificial intelligence doesn’t care as much as you do about data quality!

Considering each tool is meant to perform different tasks, we have split them into four categories.
See a detailed description of each tool below

AI Tools used for Data Definition - you have hundreds of metrics and dimensions:  Does your organization know what it all means and what data sets they should be using?

• Atlan
• Databricks
• Collibra

AI Tools used for Data Documentation - From ETL tables to defining your tech stack, data documentation is paramount to a mature analytics program.  If someone new came onboard, would they know how to reconstruct your dashboards?

• Napkin
• Eraser
• Gliffy

‍AI Tools used for Data Analysis - Analysis should be used lightly here, because we believe a true analysis necessitates too much context about the organization, its competitors and changes being made to come up with any reasonable results, but, AI can help save you some time in the process.

• Tableau
• Polymor
• Julius
• Microstrategy

‍AI Platforms - If you are truly going to take advantage of Artificial Intelligence, then, you must have a means of hosting it.  These systems will save you both time and money.  We have also included AI platforms that perform specific tasks for you marketers!  

• Google ML \ Gemini
• Thoughtspot
• Akkio
• Coefficient
• ClickVoyant

Detailed Breakdown of Each Tool

Atlan - https://atlan.com/ai

📌 General Summary:

‍Atlan is designed to democratize data access, enabling both technical and non-technical users to interact with data seamlessly. Its platform fosters a collaborative environment where data teams can efficiently manage and analyze data assets.

🤖 AI Features:

• AI Copilot: Atlan introduces an AI Copilot that simplifies complex data queries through natural language processing.
• SQL Autogeneration: Users can generate SQL queries using natural language inputs, reducing the need for deep technical expertise.
• Auto Documentation: The platform automatically creates comprehensive documentation for data assets and processes, ensuring consistency and ease of understanding.
• Organization-wide Business Glossary: Atlan promotes a consistent understanding of data terms across the enterprise by providing a centralized business glossary.

✅ Pros:

• Automated Documentation: Saves time by automatically generating detailed records for data processes.
• User-Friendly SQL Generation: Simplifies complex queries with natural language inputs, making data analysis more accessible.
• ‍Comprehensive Business Glossary: Ensures consistent understanding of data terminology across the organization.
• Democratizes Data Access: Empowers users across various skill levels to interact with and analyze data effectively.

❌ Cons:

• ‍Learning Curve: Non-technical users might require initial training to fully utilize all features.
• ‍Dependency on AI Accuracy: Reliance on natural language processing might lead to occasional misinterpretations of queries.

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Databricks AI/BI - https://www.databricks.com/product/ai-bi

📌 General Summary:

Databricks offers a unified data and analytics platform designed to simplify and accelerate data processing, analysis, and utilization for organizations of all sizes. Built on Apache Spark, it provides a collaborative environment for data engineers, data scientists, and business analysts to work seamlessly on data projects.

🤖 AI Features:

• Dashboards: A low-code experience for analysts to quickly build interactive data visualizations using natural language.
• Genie: Allows business users to converse with their data, asking questions and self-serving their analytics needs.

✅ Pros:

• Interactive Dashboards: Offers an intuitive low-code interface for creating dynamic visualizations.
• Conversational Analytics: Genie allows users to explore data through natural language questions, reducing reliance on analysts.
• Seamless Integration: Works natively with Databricks, providing unified governance and security.

❌ Cons:

• Platform Dependency: Requires familiarity with the Databricks ecosystem.
• Scalability Costs: Advanced features at enterprise scale may involve high costs.

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Collibra Data Catalog - https://www.collibra.com

📌 General Summary:

Collibra provides a comprehensive data intelligence platform that focuses on data governance, data quality, and data cataloging. It aims to empower organizations to find, understand, and trust their data, facilitating better decision-making processes.

🤖 AI Features:

• Automated Data Discovery: Utilizes machine learning to automatically discover and profile data assets across the enterprise.
• Data Lineage Tracking: Employs AI to map data lineage, providing insights into data origins, movements, and transformations.
• Intelligent Data Matching: Leverages AI to match and link related data entities, enhancing data consistency and quality.

✅ Pros:

• Robust Data Governance: Offers comprehensive tools to manage data policies, stewardship, and compliance.
• Enhanced Data Quality: Provides automated data quality checks and monitoring to ensure data reliability.
• Scalable Architecture: Designed to scale with organizational growth, accommodating increasing data volumes and complexity.

❌ Cons:

• Complex Implementation: May require significant time and resources to implement and customize according to organizational needs.
• Steep Learning Curve: Users might need extensive training to effectively navigate and utilize all features.

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Napkin - https://www.napkin.ai

📌 General Summary:

Napkin is an AI-powered tool that quickly converts text-based information into structured visuals such as diagrams, infographics, and flowcharts. It is built for marketers, presenters, and content creators who need a fast, no-code solution to transform ideas into compelling visuals.

🤖 AI Features:

1. Automatic text-to-visual generation – Instantly creates diagrams from raw text.
2. Customizable design elements – Icons, fonts, colors, and decorators.
3. Export flexibility – Supports PNG, PDF, SVG formats for multi-platform sharing.

✅ Pros:

• AI-driven automation speeds up the process.
• Simple, no-code tool for non-technical users.
• Great for content-heavy workflows (e.g., marketing, blogging, presentations).

❌ Cons:

• Limited version control and real-time collaboration features.
• Not suitable for technical teams needing code-driven diagrams.

🔎 Conclusion:

Napkin is best suited for small to medium-sized teams that need fast, AI-powered visuals for documentation and presentations.

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Eraser - https://www.eraser.io

📌 General Summary:

Eraser is a developer-friendly documentation tool that supports diagram-as-code, making it ideal for engineering, DevOps, and data teams. It focuses on precision, version control, and GitHub integration, ensuring maintainable, structured diagrams.

🤖 AI Features:

1. Diagram-as-code functionality – Create diagrams using structured syntax.
2. Version history & GitHub integration – Ensures consistency across revisions.
3. High-performance UI – Designed for technical teams needing focus and flow.

✅ Pros:

• Best for code-driven diagrams (e.g., system architecture, cloud workflows).
• Deep version control with GitHub integration.
• Ideal for DevOps and technical documentation teams.

❌ Cons:

• Steep learning curve for non-technical users.
• Not visually appealing for content creators or general business users.

🔎 Conclusion:

Eraser is the best choice for large enterprises and technical teams that need structured, maintainable documentation.

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Gliffy - https://www.gliffy.com

📌 General Summary:

Gliffy is a cloud-based diagramming tool designed for cross-functional teams, enabling easy drag-and-drop diagram creation with enterprise integrations.

🤖 AI Features:

1. Smart shapes & auto-alignment – Helps structure diagrams efficiently.
2. Real-time collaboration – Allows team commenting & change tracking.
3. Enterprise integrations – Works seamlessly with Atlassian, Confluence, Jira.

✅ Pros:

• Easy-to-use drag-and-drop interface.
• Great for collaboration across teams.
• Flexible design with enterprise-level security.

❌ Cons:

• Lacks diagram-as-code functionality.
• Customization is moderate compared to Napkin.

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Tableau AI - https://www.tableau.com/products/artificial-intelligence

📌 General Summary:

‍Tableau is a leading data visualization and business intelligence (BI) tool designed to help organizations explore, analyze, and present their data visually. Its AI capabilities enhance usability by providing automated insights, recommendations, and improved user guidance.

🤖 AI Features:

• Smart Recommendations: AI-powered suggestions help users create dashboards and optimize visualizations.
• Einstein Discovery Integration: Predictive analytics capabilities allow users to surface trends and anomalies.
• Natural Language Processing: Users can ask data-related questions using natural language queries.
• AI-Driven Forecasting: Automates time-series forecasting to predict future trends.

✅ Pros:

• Intuitive AI Assistance: Reduces manual effort by offering guided insights.
• Predictive Capabilities: Uses AI to identify patterns and provide data-driven forecasts.
• User-Friendly Natural Language Queries: Makes data analysis more accessible to non-technical users.
• Seamless Integration: Works with various data sources and ecosystems.

❌ Cons:

• Complex Licensing Structure: Accessing advanced AI features may require additional costs.
• Learning Curve for Advanced Features: While easy for basic use, deeper functionality requires training.

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‍ Polymer - https://www.polymersearch.com/AI-dashboard-generator

📌 General Summary:

Polymer is an AI-powered tool designed to simplify data analysis and visualization by enabling users to create interactive dashboards with minimal technical expertise. It is particularly useful for organizations looking to make data accessible and actionable for non-technical users, thereby democratizing data-driven decision-making.

🤖 AI Features:

Polymer offers an AI-powered dashboard generator designed to simplify data analysis and visualization. Key features include:

• AI-Suggested Visualizations: Generates charts and dashboards based on data patterns.
• Natural Language Querying: Allows users to ask questions and receive data insights.
• Automatic Chart Explanations: Provides AI-generated descriptions for visualizations.
• Data Categorization & Organization: Uses AI to structure data efficiently for better analysis.

✅ Pros:

• Ease of Use: Simple setup and AI-suggested visualizations make it beginner-friendly.
• Democratized Access: Non-technical users can quickly create interactive dashboards.
• Automatic Chart Explanations: Helps users interpret visualizations without external help.

❌Cons:

• Limited Customization: May not provide the depth of control needed for complex analyses.
• Feature ConstrAInts: Advanced users may find the tool less versatile compared to other platforms.

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Julius - https://julius.AI/

📌 General Summary:

 Julius is an AI-powered data analysis tool designed to simplify the way users interact with and derive insights from data. It caters to a broad audience, ranging from students and educators to professionals and organizations, offering a versatile platform for both basic and advanced data needs.

🤖AI Features: 

Julius was built on top of AI technology, but, here are some of it’s mAIn 

• Natural Language Querying: Users can analyze data using plain language questions.
• Automated Data Cleaning & Preparation: AI-driven optimization of raw data.
• Dynamic Visuals: AI generates interactive charts, graphs, and GIFs.
• Predictive Analysis: Provides forecasting and advanced statistical modeling.

✅Pros:

• Broad Capabilities: Supports everything from natural language querying to advanced predictive modeling.
• Versatile Exports: Compatible with widely used formats like CSV and Excel.
• Dynamic Visuals: Offers engaging visual outputs, including animated charts.
• Automation: Reduces time spent on routine tasks like data cleaning and reporting.

❌Cons:

• Wide Scope: Might overwhelm users with too many features.
• ‍Performance Variability: Heavy reliance on AI can lead to inconsistencies in results.

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MicroStrategy - https://www.microstrategy.com/  

📌 General Summary:

MicroStrategy is a robust enterprise analytics and business intelligence (BI) platform designed to empower organizations with actionable insights through advanced data analysis, visualization, and reporting. It is renowned for its ability to handle large-scale datasets and provide decision-making tools across organizational levels.

🤖AI Features: 

• Conversational AI for Data Queries: Users can ask questions using natural language.
• AI-Powered Data Suggestions: Provides insights and predictions based on historical trends.
• Automated Visualization Generation: Uses AI to suggest optimal chart types.
• Predictive Modeling: Allows forecasting and advanced analytics.

✅Pros:

• Conversational Analytics: Allows users to interact with data using natural language.
• Predictive Insights: AI-powered suggestions enhance decision-making.
• Streamlined Visualization: Simplifies the process of creating dashboards and reports.
• User Accessibility: Designed to be intuitive for non-technical users.

❌Cons:

• Steep Learning Curve: Advanced features may require significant trAIning.
• Cost-Intensive: Enterprise-level implementation can be expensive.

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BigQuery (Google) ML - https://cloud.google.com/bigquery/docs/bqml-introduction

📌 General Summary:

BigQuery is Google Cloud's fully managed, serverless, and highly scalable data warehouse designed for fast and efficient analysis of large datasets. It enables organizations to store, query, and analyze their data using standard SQL without the need for infrastructure management.

🤖AI Features: 

• SQL-Based Machine Learning: Enables ML model creation within BigQuery.
• Integration with Vertex AI: Provides access to Google’s advanced AI tools.
• Automated Model Training & Deployment: Simplifies ML workflows.
• Support for Multiple Model Types: Includes classification, regression, and forecasting.

✅Pros:

• SQL-Based ML: Enables analysts to create machine learning models without coding expertise.
• Integration with Vertex AI: Offers access to advanced AI functionalities.
• Efficiency: Eliminates the need to export data to separate ML platforms.

❌Cons:

• Technical Dependency: Requires users to be proficient in SQL for effective utilization.
• Infrastructure Costs: Scaling operations may lead to high costs.

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Gemini in BigQuery

https://cloud.google.com/gemini/docs/bigquery/overview

📌 General Summary:

Gemini in BigQuery is an AI-powered assistant integrated within Google’s BigQuery, designed to streamline data analysis and management through automated insights and enhanced user experience.

🤖AI Features: 

Gemini in BigQuery is Google's AI assistant integrated into their BigQuery data warehouse, offering a range of AI-powered features to enhance data analysis and management:

• Automated Data Insights: Uses AI to discover patterns and trends.
• BigQuery Data Canvas: Offers a natural language interface for querying and visualization.
• Assisted SQL & Python Analysis: AI-powered code generation, completion, and explanation.
• Performance Optimization: AI-driven recommendations for improving query efficiency.

✅Pros:

• Automated Insights: Uses AI to identify patterns and trends effortlessly.
• Natural Language Interface: Simplifies querying and visualization tasks.
• Performance Optimization: Provides actionable recommendations for infrastructure improvements.

❌Cons:

• Tool-Specific Expertise: Users must be familiar with the Google Cloud ecosystem.
• Complexity for Beginners: Advanced features might intimidate less experienced users.

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ThoughtSpot - https://www.thoughtspot.com/

📌 General Summary:

‍ThoughtSpot is a modern analytics and business intelligence platform designed to empower users with instant insights through intuitive, AI-driven tools. It simplifies data exploration and decision-making by enabling natural language querying and delivering interactive visualizations tAIlored to users' needs.

🤖AI Features: 

ThoughtSpot provides an AI-driven analytics platform with two mAIn offerings:

• Search-Driven Analytics: Enables users to ask questions in plain language.
• Automated Data Visualization: AI generates dynamic dashboards based on user queries.
• Predictive Insights: Identifies trends and patterns automatically.
• Embedded Analytics SDK: Allows integration with other applications.

✅Pros:

• Search-Driven Analytics: Allows quick querying and visualization using natural language.
• Customizable Visuals: Offers tAIlored analytics outputs.
• Ease of Integration: Can embed analytics into other platforms seamlessly.

❌Cons:

• Limited Advanced Features: Might lack the depth needed for complex data scenarios.
• User Limitations: May not scale well for extremely large datasets or intricate queries.

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Akkio - https://www.akkio.com/

📌 General Summary:

Akkio is a user-friendly, AI-powered analytics platform designed to simplify machine learning (ML) for businesses, particularly in marketing and sales. It enables users to harness the power of artificial intelligence without requiring coding or data science expertise. Akkio focuses on delivering actionable insights quickly, making it ideal for organizations seeking to leverage data for decision-making.

🤖AI Features: 

Akkio positions itself as an AI analytics platform specifically designed for advertising agencies. Its key features and benefits include:

• LLM-Powered Analytics for Marketing: AI designed for ad optimization.
• Automated Data Processing: Simplifies workflows from data ingestion to visualization.
• Predictive Modeling & Forecasting: Provides AI-driven marketing insights.
• No-Code ML Implementation: Enables ML use without programming knowledge.

✅Pros:

• Advertising Focus: TAIlored to meet the specific needs of marketing agencies.
• Performance Forecasting: Provides quick, actionable campAIgn evaluations.
• Simplified Optimization: AI helps refine audience targeting and media strategies.

❌Cons:

• Niche Focus: Limited utility outside the advertising industry.
• Restricted Features: Broader analytics needs might not be fully addressed.

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Unique Marketing Industry Platforms

ClickVoyant - https://clickvoyant.com

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📌 General Summary:

ClickVoyant is an AI-powered analytics platform designed to help marketers quickly identify trends, uncover insights, and optimize campaigns. Unlike traditional analytics tools that require deep expertise, ClickVoyant leverages AI-driven automation to provide instant, actionable recommendations.

Built specifically for marketing and conversion optimization, ClickVoyant eliminates the need for manual data crunching by automating data exploration, anomaly detection, and performance diagnostics. This makes it an ideal solution for marketing teams, agencies, and business leaders looking for faster, data-driven decision-making.

🤖 AI Features:

• Automated Data Analysis: Identifies key trends and anomalies.
• Predictive Marketing Analytics: Forecasts campaign performance.
• Conversion Optimization Insights: AI pinpoints improvement areas.
• Cross-Channel Attribution: Evaluates marketing effectiveness across channels.

✅ Pros:

• Marketing-Specific AI: Optimized for ad performance analysis.
• Automated Reports & Insights: Saves time for marketers.
• Predictive Forecasting: Helps optimize campaigns proactively.

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❌ Cons:

• Limited for Non-Marketers: Best suited for digital marketing use cases.
• Requires Sufficient Data: AI insights improve with larger datasets.

ClickVoyant is an excellent choice for marketers and agencies looking to enhance their data-driven decision-making with AI-powered automation. Its ability to provide instant insights, optimize campaigns, and improve conversion rates makes it a valuable addition to any AI-powered marketing analytics stack. 🚀

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Coefficient - https://coefficient.io/

📌 General Summary:

Coefficient is a tool that enhances the functionality of spreadsheets like Google Sheets and Excel by integrating them with various business systems and automating workflows. While it incorporates AI features, many of its core functionalities do not rely on AI and are designed to improve efficiency and connectivity in spreadsheet-driven environments.

🤖 AI Features:

Coefficient focuses on enhancing spreadsheet capabilities with AI and integration features:

• Live Data Syncing: Connects spreadsheets with over 60 business systems.
• Automated Data Refresh: Ensures spreadsheets stay up-to-date.
• AI-Powered Alerts: Sends notifications based on data changes.
• Two-Way Data Integration: Syncs updates between spreadsheets and databases.

✅ Pros:

‍Spreadsheet Integration: Enhances familiar tools like Google Sheets and Excel with AI features.

• Real-Time Updates: Ensures live data connections and automatic refreshes.
• Proactive Monitoring: Sends alerts powered by AI for better data oversight.

❌ Cons:

• Spreadsheet Dependency: Relies on users’ comfort with spreadsheet-based workflows.
• Scaling Challenges: May struggle to handle extremely large datasets efficiently.