What are the best ai tools for data analytics?

AI tools are transforming data analytics, helping businesses make faster, smarter decisions. Here’s a quick rundown of the top tools:

• Querio: Simplifies data queries with natural language processing, making insights accessible to non-technical users.

• Tableau AI: Combines AI with advanced visualizations for intuitive, interactive dashboards.

• Looker AI: Offers robust data modeling and seamless Google Cloud integration for in-depth analytics.

• Microsoft Power BI: Affordable, user-friendly, and tightly integrated with Microsoft’s ecosystem, ideal for real-time insights.

• IBM Watson Analytics: Though discontinued, it pioneered AI-driven analytics and influenced modern tools.

Quick Comparison:

Need to choose the right tool? Consider your budget, technical needs, and whether you prioritize ease of use, advanced features, or integration with existing systems.

17 Generative AI Data Analytics Tools Everyone Should Know About

1. Querio

Querio makes interacting with data simpler by using AI to handle natural language queries. Instead of navigating technical barriers, business users can ask questions in plain English and get instant insights, bridging the gap between complex data analysis and everyday decision-making.

AI-Driven Querying and Insights

Querio’s AI engine translates plain English questions into precise database queries, making it invaluable for marketing managers, sales directors, and executives. No need to learn SQL or depend heavily on data teams - accessing critical insights is now straightforward.

Users have reported impressive results, including 20x faster reporting, 100% accuracy, and noticeable cost reductions [1]. Jennifer Leidich, Co-Founder & CEO, highlights how the platform boosts both decision-making and operational efficiency [1].

Visualization and Dashboard Tools

Querio goes beyond basic charts and graphs to offer dynamic, customizable dashboards tailored for various stakeholders. Aparna K S from Aufait UX sums it up perfectly:

"A dashboard that frustrates is a dashboard that fails. Make it engaging, and users will never look away." - Aparna K S, Aufait UX

With Querio, users can design KPI tracking systems and customize views to fit their specific needs. Its chart-building tools even suggest the best visualization types based on the data, ensuring insights are displayed in a format that’s easy to understand.

Database Integration

Querio connects directly to databases, eliminating the need to write SQL [2]. It supports popular systems like PostgreSQL, MySQL, and MariaDB [3][4], covering a wide range of business database environments. Since data stays in its original location, Querio acts as an interface layer for analysis and reporting. To get the most accurate results, users may need to provide additional details or context when asking questions [4].

Collaboration Features

Querio fosters collaboration between business and data teams by offering tools that work for all skill levels. Its notebooks allow data professionals to perform advanced analytics while making the results accessible to non-technical users. The natural language interface serves as a shared platform, enabling teams to explore data together. This approach ensures that data-driven decisions become a collective effort, rather than being limited to technical specialists.

Next, we’ll explore how other platforms are using AI to revolutionize data analytics.

2. Tableau AI

Tableau AI builds on the strengths of the Tableau platform, adding advanced AI capabilities to make data analysis more intuitive and accessible. By combining artificial intelligence, machine learning, and natural language processing, Tableau AI helps businesses uncover insights and engage with their data in new and meaningful ways [7].

AI-Driven Querying and Insights

Tableau AI simplifies the way users interact with data by breaking down technical barriers. At the core of this transformation is Tableau Agent, an AI-powered assistant that helps users prepare data sources, create visualizations, and craft compelling data stories [8]. With Tableau Agent, users can rely on plain English to generate insights, thanks to features like smart suggestions, automated tasks, and tailored recommendations [6]. The platform's "Ask" Q&A tool uses natural language processing to make querying effortless, while machine learning uncovers trends, correlations, and outliers in real time [6][7].

For example, a regional bank saw a 40% increase in self-service analytics adoption after implementing Tableau Agent. Branch managers were able to quickly generate insights without needing extensive technical training [9]. Similarly, a marketing analyst at a consumer goods company used Tableau Agent to analyze customer feedback by sentiment. Instead of navigating complex syntax, the analyst simply asked, "Classify these survey comments by sentiment", and immediately received text analysis and visualizations showing positive and negative feedback trends [9].

Visualization and Dashboard Capabilities

Tableau AI takes traditional dashboards to the next level with intelligent features that make insights easier to understand and act on. The Data Stories feature adds plain-language explanations directly to dashboards, while Explain Data uses dynamic visualizations to reveal the reasoning behind key insights [5][13]. Tableau Pulse delivers automated analytics in everyday language and even anticipates user questions before they’re asked [5][13]. The mobile experience is equally robust, offering a redesigned homepage where users can compare metrics, drill down into dimensions, and customize views - all while on the go [11].

These visualization tools have driven measurable results for organizations. For instance, Lufthansa Group improved efficiency by 30%, and Providence St. Joseph Health achieved better quality measures while cutting costs [12].

"Without our visual analytics solution, we would be stuck analyzing enormous amounts of data in spreadsheets. Instead, our dashboards provide clear actionable insights that drive the business forward."

• Donald Lay, Senior Business Intelligence Manager at Charles Schwab Corporation [12]

Database Integration

One of Tableau AI's strengths is its seamless integration with a wide variety of data sources, including spreadsheets, big data systems, and relational databases. Native connectors and integration with Data Cloud allow users to access enterprise data with just one click, processing over 24 trillion records each month [14]. The platform also supports tools for data storage, preparation, transformation, cataloging, and metadata management. Built on the Einstein Trust Layer, Tableau AI ensures data security and privacy throughout the analytics process [5][15]. With support for multiple languages, the platform is well-suited for global teams working with diverse datasets [17].

In one example, EMD Serono used Tableau Pulse in September 2024 to deliver personalized, AI-driven insights that improved patient care. This demonstrates how Tableau AI can bring innovative analytics solutions to industries such as healthcare [16].

Collaboration Features

Tableau AI also enhances collaboration, making it easier for teams with varying technical expertise to work together. Integration with Slack and an upgraded Q&A feature allow teams to track insights and share findings more effectively, leading to a 36% boost in productivity [11][14]. Tableau Pulse enables users to group and sort metrics, link related content, and monitor performance directly from their data source - all within Slack [11].

With 85% of business leaders needing insights within 30 minutes for critical decisions - and many struggling to analyze data with confidence - Tableau AI addresses an essential business challenge [10]. Its collaborative tools ensure that teams can access and act on insights when they need them most.

3. Looker AI

Looker AI takes data analysis to the next level with its integration into Google Cloud, offering a unique approach to data exploration. Unlike other tools that emphasize visual insights, Looker AI focuses on conversational queries and seamless cloud integration. By combining Google Cloud's AI capabilities with business intelligence, it provides conversational analytics, automated insights, and intuitive data exploration - making it accessible for users across all levels of technical expertise.

AI-Driven Querying and Insights

Looker AI simplifies data querying through its integration with Gemini, enabling advanced AI-powered business intelligence. With natural language queries, users can bypass the need for complex SQL and receive automatic insights. Developers can use the platform's Conversational Analytics API to embed natural language query features directly into custom applications, expanding analytics beyond traditional dashboards. At the heart of Looker AI is its semantic layer, which provides a single source of truth. Paired with AI, it automates the detection of patterns, trends, and anomalies, while also offering formula assistance, LookML code guidance, and automated slide generation for presentations [18].

Visualization and Dashboard Capabilities

Looker AI excels in creating real-time, interactive dashboards that are both user-friendly and governed. With its drag-and-drop interface, users can explore data tiles, apply dynamic filters, ask new questions, and drill down into detailed insights. Looker Studio complements this by enhancing interactive reporting, offering access to over 800 data sources and connectors. AI-powered visualizations make data exploration more intuitive and interactive, striking a balance between self-service analytics and strong governance [19][20].

Database Integration

When it comes to database connectivity, Looker AI stands out by supporting over 700 partner connectors, while Looker Studio offers 21 connectors for commonly used data sources [21]. Its universal semantic modeling layer unifies data from multiple sources, simplifying analysis. For instance, integration with BigQuery enables users to access data quickly and translates complex data structures into clear, actionable business logic [22]. Looker AI also incorporates Google Cloud IAM for secure single sign-on and private networking, while its API-first design allows for the creation of custom data applications [19].

Collaboration Features

Collaboration is a key strength of Looker AI, enabling teams to work together in real-time on dashboards and reports. The platform caters to diverse user needs through two approaches: enterprise-grade governed analytics via Looker and flexible, ad hoc reporting through Looker Studio. This ensures data quality and governance are maintained while offering flexibility for various reporting requirements [19].

4. Microsoft Power BI with AI Features

Microsoft Power BI brings AI capabilities to the forefront of data analytics, delivering a comprehensive solution as part of Microsoft Fabric. This integration provides businesses with a unified analytics experience, reshaping how they interact with and understand their data. While many platforms incorporate AI, Power BI stands out with its seamless integration into Microsoft Fabric and its extensive database connectivity. Microsoft's recognition as a leader in Gartner's June 2024 Magic Quadrant for Analytics and Business Intelligence Platforms further underscores its advancements in this space [26].

AI-Driven Querying and Insights

Power BI's AI features are anchored by Copilot, a tool designed to simplify query creation by writing DAX queries based on user-defined hierarchies [24]. Using large language models, the platform enhances language understanding, interpreting synonyms, phrases, and specific values from user input. Copilot also supports ad hoc calculations, enabling the generation of new DAX queries on demand to answer questions instantly [24].

The AI toolkit includes a variety of features such as natural language querying, anomaly detection, sentiment analysis, key influencers analysis, decomposition tree visualizations, and forecasting tools [25]. Additionally, data agents allow users to query OneLake for real-time answers while maintaining role-based access controls. Microsoft Fabric's Real-Time Intelligence further enhances the experience by processing and analyzing streaming data instantly, offering live dashboards, AI-powered anomaly detection, and automated alerts [23].

Visualization and Dashboard Capabilities

Power BI makes creating interactive dashboards simple with its drag-and-drop interface. Real-time analytics empower organizations to monitor performance as it happens, enabling quicker decision-making [27]. The platform’s AI-powered visual tools, like Key Influencers and Decomposition Tree, uncover insights that might otherwise go unnoticed. Its natural language Q&A feature allows users to ask questions in plain English, while anomaly detection highlights unexpected trends in the data [29].

Real-world examples illustrate Power BI's impact. Peak Tech, a software firm, transitioned from SAP BusinessObjects to Power BI, enabling managers to create their own dashboards and freeing IT teams to focus on strategic tasks [28]. Similarly, Global Freight Co. replaced IBM Cognos with Power BI, moving from daily data updates to real-time insights, improving decisions around routes, inventory, and shipping schedules [28].

"Power BI democratizes data, giving decision-makers at all levels the tools they need." – John Doe, Senior BI Consultant, Gartner [28]

Database Integration

Power BI excels in connecting to a wide range of internal and external data sources. It includes pre-built connectors for popular services and integrates effortlessly with Excel, Azure cloud services, and SQL databases [31]. Power Query, the platform’s data transformation engine, provides over 300 options to clean, reshape, and merge data for analysis [31]. In April 2025, Microsoft introduced enhanced SQL database integration for Power BI Desktop, offering streamlined and precise access to both SQL and Fabric SQL databases [31]. Organizations can also schedule regular data refreshes, and the REST API supports administration, embedding, and managing large datasets and reports [30][31].

Collaboration Features

Power BI's collaboration tools are tightly woven into the Microsoft ecosystem, particularly through Microsoft 365. For example, EcoSolutions, an energy management firm, adopted Power BI to share real-time dashboards within Teams, significantly speeding up decision-making [28]. AI agents within Fabric automate data processes - such as ingestion, transformation, analysis, and visualization - by working seamlessly with tools like Data Factory, Real-Time Intelligence, and Power BI. Features like Copilot grounding enhance reporting by incorporating corporate-specific terminology, calendars, and reports for greater context during report creation [23].

These collaborative features not only improve efficiency but also scale to meet diverse business needs, all while maintaining cost-effective pricing.

Scalability and Pricing

Power BI offers flexibility with its free version and budget-friendly subscription plans. For instance, SwiftMart, a medium-sized e-commerce business, switched from Oracle BI to Power BI, cutting its BI costs by 50% while gaining more flexible reporting capabilities as it grew [28]. A Forrester Consulting study found that using Power BI alongside Microsoft 365 yielded a 321% return on investment over three years [26]. Similarly, Forrester's Total Economic Impact study reported a 238% ROI within the same timeframe [28].

Microsoft's AI for Business Impact Report highlights that 85% of Power BI users experienced faster and more efficient decision-making thanks to its AI tools [28]. TechnoVista, a SaaS company, leveraged these AI features to analyze customer behavior, predict churn rates, refine marketing strategies, and boost conversions - all without needing to expand its data science team [28].

5. IBM Watson Analytics

IBM Watson Analytics was a cloud-based platform designed to simplify data discovery and analytics using AI. While the platform has not been available for new purchases since July 31, 2018 [33], its groundbreaking features continue to shape modern AI-driven solutions.

The platform gained widespread acclaim for its innovative approach to business intelligence. PCMag awarded Watson Analytics an impressive 4.5/5 rating, with reviewer Pam Baker highlighting its standout features.

"IBM Watson Analytics is an exceptional business intelligence (BI) app that offers a strong analytics engine along with an excellent natural language querying tool. This is one of the best BI platforms you'll find and easily takes our Editors' Choice honor."

• Pam Baker, PCMag UK [33]

AI-Driven Querying and Insights

Watson Analytics stood out for its ability to leverage natural language processing, allowing users to ask straightforward questions and receive meaningful insights. The platform automatically analyzed datasets to uncover patterns and trends, streamlining tasks like data preparation, model creation, feature engineering, and hyperparameter tuning. Its "Starting Points" feature provided users with immediate insights as soon as data was uploaded, making analytics approachable even for non-technical users.

Visualization and Dashboard Features

One of Watson Analytics' strengths was its ability to turn complex datasets into easy-to-understand visuals. Using a drag-and-drop interface, users could create charts, graphs, and interactive dashboards effortlessly. These visuals could be shared or exported in formats like Microsoft PowerPoint or Adobe Acrobat. Dynamic dashboards added another layer of functionality, updating visuals in real time to reflect user selections and offering clear insights at every step.

Seamless Database Integration

The platform supported data integration from a variety of sources. The basic version allowed users to upload Excel files, while the Enterprise edition extended functionality to include connections with databases, ERP systems, and reporting platforms. Supported databases included IBM Db2, MySQL, and Oracle. This integration allowed organizations to combine data from both cloud-based and on-premise sources without hassle, ensuring smooth workflows.

Scalability and Pricing

Watson Analytics was available in three pricing tiers: a free trial, the Plus plan at $30 per user/month, and the Professional plan at $80 per user/month [33]. While it earned praise for its intuitive interface and guided analytics, it faced limitations in real-time streaming capabilities. Despite its discontinuation, the platform's innovations have left a lasting legacy.

For example, insights from Watson Analytics have contributed to the development of IBM's newer AI technologies. Research suggests that IBM's watsonx.data technology could improve AI accuracy by up to 40% compared to traditional retrieval-augmented generation methods [32]. This demonstrates how Watson Analytics continues to influence advancements in AI and analytics.

Advantages and Disadvantages

Each tool brings its own strengths and weaknesses to the table, which can significantly influence your decision-making process. Knowing these trade-offs ensures you choose a solution that aligns with your needs and budget.

Querio stands out for its natural language query capabilities, making data accessible to all users. However, as a newer platform, its features are still evolving.

Tableau AI is often praised for its ease of use compared to alternatives like Python, Business Objects, and Domo [34]. Its AI-driven features help users uncover actionable insights quickly. That said, its pricing can be a hurdle: Tableau Creator licenses cost $115 per user per month [35], and Tableau Server can run up to $175,000 for an eight-core system, with additional fees of about $35 per user [34].

Looker AI excels in data modeling and integrates seamlessly with Google Cloud. However, it comes with a steep learning curve, a less flexible interface, and higher costs for embedded use. Pricing starts at $5,000 per month for the Standard edition [38].

Microsoft Power BI offers cost-effective analytics with native Microsoft integration. However, performance can dip with extremely large datasets, and integrating with non-Microsoft platforms may pose challenges [36][39]. Power BI Pro supports up to 10 GB of data at $14 per user per month [40], while Power BI Premium handles capacities up to 100 TB [37].

IBM Watson Analytics, although no longer available for new purchases since July 31, 2018, was a trailblazer in natural language querying. Its influence is still seen in IBM’s newer technologies, such as watsonx.data, which research suggests could improve AI accuracy by up to 40% compared to traditional methods [32].

When evaluating these tools, consider the balance between cost, capabilities, and your specific business needs. The table below provides a quick comparison:

These tools also differ in performance and usability. For example, Microsoft Power BI is ideal for those seeking affordable analytics within the Microsoft ecosystem. Tableau AI shines for teams needing advanced, interactive dashboards, while Looker AI caters to users who require robust data modeling capabilities. Querio, on the other hand, offers a user-friendly solution for universal data access, making it accessible even to non-technical users.

However, keep in mind that the advanced visualizations offered by Tableau AI often come with a higher price tag.

Conclusion

When selecting an AI analytics tool, take into account your organization's size, budget, and specific needs. These tools are designed to serve a wide range of users, from small businesses to large enterprises, making it essential to find the one that aligns with your goals.

For small businesses, affordability and simplicity are often top priorities. Research indicates that 54% of businesses have successfully reduced operational costs by leveraging AI tools for data analysis [42]. Many solutions in this category are crafted to be budget-friendly and easy to use, ensuring accessibility for teams with limited technical expertise.

"For most SMBs, the easiest way to use AI - and probably the safest and most productive way - is as part of the applications you already use every day, so that it's a seamless experience." - Laurie McCabe, cofounder and partner at SMB Group [41]

Enterprise organizations, on the other hand, require tools that can handle extensive datasets and support intricate analytics workflows. For example, Looker AI excels in data modeling with its deep cloud integration, while Tableau AI stands out for its advanced visualization features, making insights easier to interpret and act upon.

It's also crucial to align your tool choice with your primary objectives. If creating interactive dashboards and visually rich reports is a priority, Tableau AI is a strong contender. For organizations that value robust data modeling and semantic layers, Looker AI provides a sophisticated solution. Teams aiming to make data accessible to users of all skill levels may find Querio's AI-driven natural language querying particularly appealing.

Data-driven insights can accelerate decision-making by up to five times [43]. To make the most of your investment, choose a tool that fits your technical capabilities, budget, and long-term goals. Start by identifying your most pressing data challenges, and then select the solution that directly addresses them.

Ultimately, the best tool strikes a balance between functionality, affordability, and ease of use. Even the most advanced technology will fall short if your team struggles to adopt it, while a simpler, more user-friendly option can deliver significant value through consistent, everyday use.

FAQs

How can I select the best AI tool for data analytics that fits my organization's goals and budget?

How to Choose the Right AI Tool for Data Analytics

Start by pinpointing your organization's specific goals and the challenges you're aiming to tackle. Whether it's enhancing data visualization, automating reporting processes, or digging into actionable insights, having a clear focus will help you zero in on tools that meet your needs.

Budget is another key factor. Compare the value each tool brings to the table. Pay attention to features like ease of use, compatibility with your existing systems, and scalability to accommodate future growth. Many tools offer free trials or demos - take advantage of these to evaluate their functionality before making a commitment. Aim for a solution that strikes the right balance between affordability and long-term value for your team and business.

How do Tableau AI and Looker AI differ in their data analytics capabilities?

Tableau AI vs. Looker AI: A Quick Comparison

Tableau AI and Looker AI each offer distinct advantages when it comes to data analytics, catering to different needs and priorities.

Tableau AI is all about making data exploration and visualization more intuitive. With tools like the Tableau Agent and Einstein Copilot, it uses natural language processing to let users interact with their data conversationally. Whether you're creating visualizations or diving into automated insights, Tableau AI simplifies the process, making it a go-to for users who prefer hands-on data analysis.

Meanwhile, Looker AI takes a slightly different approach. It focuses on delivering consistent and accessible data interpretation through its robust semantic layer. Users can ask questions in natural language and receive AI-driven insights, which are particularly helpful for automated reporting and data storytelling. This makes Looker AI a strong choice for organizations that prioritize clarity and seamless access to insights across teams.

In essence, Tableau AI shines when it comes to interactive data manipulation and visualization, while Looker AI stands out for its ability to make data accessible and tell compelling stories across an entire organization.

How can Querio's natural language querying help non-technical users gain insights from data?

Querio's natural language querying feature simplifies data interaction for everyone, even those without technical expertise. Instead of grappling with complicated query languages or waiting on technical teams, users can simply ask questions like, "What were last month's sales figures?" or "Which products are performing best this quarter?" and get immediate, clear answers.

By making data easy to access and understand, Querio helps teams make quicker decisions and encourages a data-focused mindset - no specialized skills required.

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