What tools are used in business analytics?

Business analytics tools help turn raw data into insights that drive smarter decisions. Here’s a quick rundown of seven popular options:

• Querio: AI-powered tool with natural language queries, predictive analytics, and real-time insights.

• Tableau: Advanced data visualization platform with drag-and-drop features and AI integration.

• Microsoft Power BI: Affordable, user-friendly tool with strong Microsoft ecosystem integration.

• Looker: Google-backed platform with governed data modeling and AI-driven features.

• SAS Business Analytics: Combines statistical analysis with AI for real-time data processing and predictions.

• ThoughtSpot: Search-driven analytics with AI-powered dashboards and natural language queries.

• Sisense: Focuses on embedded analytics, real-time updates, and AI-driven insights.

Quick Comparison

Choose a tool based on your needs, budget, and technical environment. For affordable options, Power BI is ideal. For advanced visualizations, Tableau shines. If you need AI-driven insights, Querio or Looker might be the best fit.

BI Analysis of BI Tools (The Most In-Depth Comparison of BI Tools)

1. Querio

Querio stands out as a platform that demonstrates how AI is transforming the way we approach analytics.

This AI-driven business intelligence tool allows users to interact with their data through natural language, making complex queries feel as simple as having a conversation.

AI/ML Integration

Querio’s AI data agent is designed to make analytics accessible to everyone. Instead of requiring knowledge of SQL or other technical skills, users can simply pose questions in plain English. On top of that, it offers predictive analytics, helping businesses anticipate trends and uncover patterns based on historical data - making it a valuable tool across various industries [2].

Data Visualization Features

One of Querio’s strengths lies in its ability to transform raw data into interactive dashboards. These dashboards are not only visually appealing but also easy to create, enabling teams to quickly identify trends and anomalies without sifting through endless spreadsheets [1]. Plus, they’re fully customizable, allowing different departments or roles to focus on the metrics that matter most to them.

Seamless Database Connectivity

Querio integrates directly with major databases, ensuring that its analytics are always based on the most up-to-date information. It also features a flexible notebook environment that caters to both technical users and non-technical teams. This setup encourages collaboration between data experts and business stakeholders, ensuring everyone is on the same page.

Real-Time Data Processing

Querio’s real-time analytics capabilities set it apart, providing instant insights into trends and anomalies. This is especially valuable for fast-paced industries like finance, eCommerce, gaming, and logistics, where quick decisions can have a big impact. By connecting directly to live data streams, Querio enables immediate action, unlike traditional analytics platforms that rely on slower batch processing methods [3] [4]. This not only improves decision-making but also helps reduce operational costs by streamlining processes.

Next, we’ll take a look at other top analytics platforms to see how they measure up.

2. Tableau

Tableau, launched in 2003 as a project from Stanford, is a leader in visual analytics. Its foundation lies in VizQL, a technology that transforms drag-and-drop actions into complex data queries [6].

Data Visualization Capabilities

Tableau's drag-and-drop interface makes it easy for users of all skill levels to create insightful visualizations. From basic charts to advanced geographical plots, the platform offers a wide range of options. Importantly, this simplicity doesn’t sacrifice depth - users can customize visualizations extensively to meet specific needs [7].

The tool also includes advanced features like cross-database joins, nested sorting, and seamless drag-and-drop functionality, making it a versatile choice for data analysis [7]. With accessibility in mind, Tableau encourages best practices like limiting visualizations to eight colors or fewer and ensuring key information is easy to interpret [5].

The impact of Tableau is clear: users report a 33% increase in insights-driven decisions, 32% higher productivity, and 27% better IT flexibility [7].

AI/ML Integration

Tableau incorporates artificial intelligence through Tableau AI, which is powered by the Einstein Trust Layer. This integration offers features like Tableau Pulse and Tableau Agent, enabling natural language–assisted data exploration and delivering smart insights [8]. Additional tools, such as Data Stories, Explain Data, and Einstein Discovery, further enhance the platform’s AI capabilities [8].

For predictive analytics, Tableau supports extensions like Aible, which allows users to create predictive AI models directly within the platform [9]. For instance, banks have leveraged Tableau dashboards with Aible to optimize marketing campaigns for term deposits, using "what-if" scenarios to refine strategies and boost conversion rates [9].

"Tableau AI is a fully integrated, next-generation portfolio of AI-plus-analytics capabilities that enables everyone in an organization to drive tangible business outcomes. It is trusted AI across the entire Tableau platform, simplifying and democratizing data analysis and insight consumption at scale." - Dr. Nate Nichols, Head of Generative AI, Tableau [10]

Database Connectivity

Tableau connects to hundreds of data sources, from files and databases to cloud services, offering both live connections and data extracts. This flexibility ensures compatibility with a wide range of systems while allowing organizations to prioritize performance or security as needed [11][12]. Whether working with on-premises databases or cloud-based platforms, Tableau integrates seamlessly with third-party applications [7].

For organizations with specialized data needs, Tableau provides additional connectors through the Tableau Exchange. This ensures that even proprietary or niche systems can be included in the analytics workflow [12].

Up next, we’ll explore Microsoft Power BI and its distinct approach to refining data analysis.

3. Microsoft Power BI

Microsoft Power BI has become a go-to tool for business intelligence, trusted by 97% of Fortune 500 companies for their analytics needs [16]. Developed by Microsoft, this platform redefines how organizations visualize data and make informed decisions with its robust set of features.

Data Visualization Capabilities

Power BI excels at turning complex datasets into clear, actionable insights through its advanced visualization tools. It offers a rich library of custom visualizations - like ribbon charts, stacked area charts, and interactive maps - that help businesses go beyond static spreadsheets to create dynamic, impactful dashboards [13]. These visualizations provide a deeper understanding of data, setting it apart from traditional tools like Excel [14].

One standout feature is the ability to create interactive reports. These reports include interconnected components, tooltips, and drill-through capabilities, making it easy to navigate between related data points and uncover hidden insights [14].

"Data visualization helps you turn all that granular data into easily understood, visually compelling - and useful - business information. When you look at your data presented in a visual format, patterns, connections, and other 'a-ha' insights emerge that would otherwise remain out of sight." - Microsoft [15]

Additional features like Quick Insights automatically surface trends, while the Q&A functionality lets users type natural language queries to get answers instantly [16]. Reports are sourced from multiple dashboards, offering structured views that emphasize key findings [13].

AI/ML Integration

Power BI integrates cutting-edge AI and machine learning capabilities to automate analysis, predict trends, and deliver real-time insights [19]. Key AI-driven features include predictive analytics, natural language processing, anomaly detection, and AI-powered image recognition [19]. These tools enable organizations to improve prediction accuracy by an average of 20% and make decisions up to 25% faster [17][20].

Through Microsoft Fabric, Power BI simplifies the process of incorporating machine learning models for predictive analytics, creating a seamless data pipeline [18]. Automated pattern detection can boost analytical efficiency by up to 25%, while businesses that leverage machine learning in their analytics often see a 30% improvement in customer retention [20].

Database Connectivity

Power BI's versatility shines in its ability to connect to a wide range of data sources, making it adaptable to various data ecosystems. Using Power Query as its connection engine, it links to files, databases, Microsoft Fabric, Power Platform, Azure services, and numerous online services [21][22].

This extensive connectivity enables users to aggregate and analyze data from multiple sources, creating comprehensive dashboards that provide a holistic view of their business [23]. Both local and cloud-based data sources are supported, offering flexibility in how data is accessed and managed [23].

While Power BI Desktop offers a broader range of data source options compared to Power BI Service, both versions provide powerful tools for creating detailed reports and dashboards [23]. For large, dynamic datasets, Power BI supports DirectQuery to maintain live connections, ensuring up-to-date insights [24]. Additionally, PBIDS files simplify the data connection process by allowing users to share connection details in a standardized format [21].

Up next, we’ll dive into Looker and how it approaches modern business intelligence and data exploration.

4. Looker

Looker is a powerful business intelligence (BI) platform that combines data modeling with Google's advanced AI tools to offer both governed and self-service analytics.

Data Visualization Capabilities

Looker delivers two distinct visualization options: an enterprise-grade platform and Looker Studio. The enterprise platform features drag-and-drop dashboards built on governed, real-time data, along with a broad library of customizable visualizations. It also supports real-time collaboration, making it a versatile tool for teams. On the other hand, Looker Studio is a free tool designed for interactive, ad hoc reporting, offering access to over 800 data sources. For more specialized needs, Looker supports custom applications tailored to unique visualization requirements [25][26][27][28].

AI/ML Integration

By incorporating Google's advanced AI models, including the Gemini models, Looker redefines how users interact with data. In April 2025, Looker introduced several AI-driven features such as Conversational Analytics, Visualization and Formula Assistants, Automated Slide Generation, and LookML Code Assistant [27]. These tools simplify complex tasks like data modeling, creating charts, and generating presentations.

The Conversational Analytics API takes it a step further by enabling developers to embed natural language query capabilities into custom apps. This allows users to ask questions in plain English and receive instant, actionable insights. As Peter Bailis, VP of Engineering, aptly noted:

"Modern organizations require more than just accurate insights; they need AI to uncover hidden patterns, predict trends, and drive intelligent action." [27]

Additionally, Looker's semantic layer ensures that everyone works from a unified dataset, streamlining analysis and accelerating decision-making processes.

Database Connectivity

Looker offers extensive connectivity options, making it compatible with a wide variety of data ecosystems. It integrates with both transactional and analytical databases like Oracle, MySQL, BigQuery, Snowflake, and Redshift. It also connects to business applications such as Google Analytics, Salesforce, and Marketo [30][31]. With pre-built connectors and robust APIs, Looker provides access to over 1,000 data sources, ensuring a seamless flow of information between systems. This unified approach allows users to blend data from multiple sources, generating deeper and more comprehensive insights [29][32][33].

Next, we’ll take a closer look at SAS Business Analytics and its approach to advanced statistical analysis.

5. SAS Business Analytics

SAS Business Analytics blends traditional statistical methods with modern AI tools, making it a powerful solution for tackling complex analytical problems across industries. With a user rating of 4.4 out of 5 from 252 reviews, it's trusted by enterprises worldwide for its advanced capabilities [34].

Data Visualization Capabilities

SAS Visual Analytics simplifies the creation of interactive dashboards and reports. Its intelligent autocharting feature automatically selects the best visuals for your data, making the visualization process more efficient [35][36]. The platform also shines in geographical data visualization, combining business data with location metrics - an asset for industries like retail and logistics. Plus, SAS integrates seamlessly with Microsoft 365, allowing users to embed insights into familiar Microsoft applications and access reports directly from tools like Excel and PowerPoint [35].

"Data visualisation is going to change the way our analysts work with data. They're going to be expected to respond to issues more rapidly. And they'll need to be able to dig for more insights – look at data differently, more imaginatively. Data visualisation will promote that creative data exploration." - Simon Samuel, Head of Customer Value Modeling for a large bank in the UK [36]

AI/ML Integration

SAS has heavily invested in AI, with 91 of the top 100 companies on the 2023 Fortune 500 list using its platform for analytics [39]. Its AI-driven automation reduces manual work, streamlines workflows, and delivers real-time insights for forecasting. For example, Lenovo used SAS predictive analytics to cut warranty costs by 10–15%, while Express Scripts saved between $1,500 and $9,000 per patient by identifying non-adherence to treatments [37]. SAS also combats bias by relying on data-driven analysis, automates repetitive tasks, and personalizes customer experiences [38].

Real-Time Data Processing

SAS stands out for its real-time data processing capabilities, powered by its In-Database Technologies. These tools allow models to run directly within databases, minimizing data movement and cutting down processing time [40]. SAS Viya, for instance, is 30 times faster than many competitors, resulting in an 86% reduction in cloud operating costs [41]. The platform also integrates with Azure IoT Hub and Azure IoT Edge, enabling real-time data streaming from IoT devices for faster decision-making. For instance, CNG Holdings uses SAS machine learning to improve fraud detection, swiftly adapting strategies to reduce costs and enhance investigations [42].

"Our $1 billion investment in industry solutions includes the integration of trustworthy generative AI capabilities that are accurate, explainable and defendable." - Bryan Harris, SAS Chief Technology Officer [43]

Database Connectivity

SAS Business Analytics supports seamless integration with various database systems through its In-Database Technologies. This approach allows analytics to occur directly where the data resides, keeping it fresh and consistent while significantly improving processing speed. The platform also enables automated model deployment with in-database processing and scoring accelerators, enhancing productivity for data science teams [40].

Next, we'll dive into ThoughtSpot.

6. ThoughtSpot

ThoughtSpot makes business analytics more accessible by turning data exploration into a simple, search-driven process. With this platform, users can bypass technical hurdles and get instant insights using straightforward queries.

Data Visualization Capabilities

ThoughtSpot shines when it comes to interactive visualizations. Users can ask questions in plain English, and the platform instantly generates charts, graphs, and dashboards tailored to their queries. This eliminates the need to manually choose the right visualization, making the process quick and intuitive.

The platform’s AI-powered dashboards provide real-time, interactive insights that users can explore in depth. These dynamic visuals adapt to user interactions, revealing patterns and relationships within the data. Additionally, teams can embed these visualizations directly into other applications, ensuring decision-makers have access to insights right where they need them.

What sets ThoughtSpot apart is how its AI capabilities enhance the visualization process, making data interpretation faster and more accurate.

AI/ML Integration

ThoughtSpot combines its search functionality with AI to deliver deeper insights. Users can type business questions in everyday language, and the platform responds with actionable visual results. According to an ISG Market Lens AI Study, 99% of participants reported positive outcomes from using natural language search queries, while 97% saw benefits from AI-driven data interpretation [44]. These findings highlight the value AI integration brings to improving business decisions.

Next, we’ll dive into Sisense’s distinct approach to business analytics.

7. Sisense

Sisense combines traditional business intelligence with embedded analytics, making it a go-to solution for internal reporting and integrating data from multiple sources. It’s especially well-suited for embedding analytics into existing workflows.

Data Visualization Capabilities

Sisense transforms complex data from various sources into easy-to-understand dashboards and customizable reports. Its visualization tools range from standard charts to more advanced options like rich text widgets, calendar heatmaps, and forecasting charts.

With its Compose SDK, Sisense simplifies code generation and supports dashboard co-authoring, letting teams collaborate on updates in real time. Users can also interact with data conversationally and receive personalized visualizations, no technical background required. In Q1 2025, Sisense introduced dashboard co-authoring, allowing multiple users to edit and republish shared dashboards simultaneously [45].

A practical example of Sisense's impact comes from Measuremen, a workplace analytics company. They transformed their client reporting process from a two-week manual effort to same-day delivery. By replacing static PDFs with interactive dashboards and automating data validation, they reduced reporting time from 16 hours to just 30 minutes per project. Clients now benefit from real-time, self-service insights [45].

These visualization tools lay the groundwork for Sisense's advanced AI capabilities, which are explored in the next section.

AI/ML Integration

Sisense integrates generative AI and machine learning to provide predictive and prescriptive insights that go beyond traditional analytics. Its AI Knowledge Graph captures user queries and patterns, enabling the platform to deliver highly relevant, context-aware insights [50].

Key features powered by AI include Sisense Forecast, AI Trends, Natural Language Query, Sisense Explanations, and Sisense Pulse. These tools help businesses gain actionable insights, contributing to a 21% year-over-year organic revenue growth for companies leveraging predictive analytics - compared to the industry average of 12% [49].

Sisense is also testing its composable GenAI Analytics Chatbot, which allows development teams to integrate conversational analytics directly into their products [48][50].

Real-Time Data Processing

Sisense excels in real-time data processing, enabling businesses to make immediate decisions based on current information. Dashboards can be updated and adjusted instantly, ensuring decision-makers always have the latest insights.

This capability has been transformative for companies like Barrios, a tech solutions provider. Devin Vyain, Senior Solutions Architect, shared:

"We went from custom application development to the ability to rapidly change something on the dashboard and then publish it again in real-time within the production environment - without sacrificing any of the security. That really helped us level up." [47]

Beyond simple updates, teams can reconfigure dashboards, add visualizations, and republish changes instantly. This ensures businesses can adapt quickly to shifting conditions without missing a beat.

Database Connectivity

Sisense connects seamlessly to a wide range of data sources, creating unified dashboards that cater to industries like SaaS, logistics, healthcare, and finance.

Its robust connectivity spans databases, cloud services, and applications, delivering measurable ROI and operational efficiencies for clients [47]. Sisense’s ability to manage diverse data sources while maintaining performance makes it a versatile choice across industries [46].

USA Swimming is a standout example. Patrick Murphy, Senior Director of Data and Product at USA Swimming, explained how Sisense has reshaped their approach to data:

"We're trying to enable our teams and coaches around the country to think in new ways and to look at data in different ways, and Sisense is helping us to do that." [47]

Strengths and Weaknesses

After exploring the features of each tool, let’s break down their core strengths and limitations. Every business analytics tool has its own set of advantages and challenges that can shape your organization's data strategy. Knowing these trade-offs allows you to choose the tool that best fits your needs, budget, and technical environment.

Querio stands out with its AI-driven natural language interface, making data querying accessible for all users. It also supports direct database connectivity and includes notebook features for smooth collaboration. While its community and integration options are still growing, it shows promise in these areas.

Tableau shines with its highly customizable data visualizations and strong community support. It handles large datasets effectively, though it comes with a higher price tag and limited data preparation features. The Creator version costs about $70 per month, with annual subscriptions reaching up to $1,000 [52].

Microsoft Power BI offers exceptional value, combining a user-friendly interface with seamless integration into the Microsoft ecosystem. With over 115 million users [51], the Professional version is priced at less than $10 per month per user [52]. However, it struggles with very large datasets and offers limited options for visual customization.

Looker is known for its governed data modeling and excels in embedded analytics with its scalable, cloud-native architecture. On the downside, it requires technical skills (like SQL knowledge) and is more expensive than Tableau or Power BI [53].

Here’s a quick comparison of the key strengths, weaknesses, and ideal use cases for each tool:

Choosing the right tool depends on balancing technical features, budget, and specific use cases. For instance, Power BI offers an affordable option with broad scalability, while Tableau provides a strong return on investment for detailed data analysis [55]. If your organization spends 30+ hours weekly on ad hoc reporting, self-service interfaces might be the way to go [54].

Conclusion

Choosing the right analytics tool boils down to understanding your specific needs, technical expertise, and budget. As Info-Tech Research Group puts it, "Determine what exactly you require from your BI and analytics solution; platform selection must be driven by your major use cases and usage patterns" [58]. With 54% of enterprises identifying BI and data-driven tools as critical to their operations now and in the future [53], this decision plays a key role in staying competitive.

If you're looking for an AI-powered tool that's easy to use and doesn't demand advanced technical skills, Querio is a compelling option. Its natural language interface makes data accessible to everyone, while features like direct database connectivity and collaboration tools simplify workflows for modern teams.

For businesses already using Microsoft products, Power BI offers excellent value at just $10 per user per month [60]. It's particularly appealing for small to medium-sized enterprises that need affordable but effective analytics solutions [59]. On the other hand, if your focus is on creating interactive and visually engaging reports without diving into complex data modeling, Tableau delivers exceptional visualization capabilities, starting at $15 per user per month [60].

Organizations leveraging Google Cloud or handling large-scale data may find Looker to be a standout choice. Its seamless integration with Google Cloud and robust LookML data modeling make it ideal for those prioritizing data governance and scalability [56]. However, it does require a higher upfront investment.

Timely insights are critical - data loses half its value within just 8 hours [57]. Opt for a platform that not only provides real-time analytics but also grows with your business. Factors like integration, scalability, and ease of adoption should weigh as heavily as cost when making your decision.

Ultimately, the best tool is the one that fits seamlessly into your existing workflows without creating inefficiencies or redundancy [58].

FAQs

How can I select the best business analytics tool for my organization?

Choosing the right business analytics tool begins with a clear understanding of your organization's goals and the specific challenges you aim to tackle using data. When you define these priorities upfront, it becomes easier to find a solution that aligns with your strategy and delivers actionable insights.

After that, take a close look at essential factors like ease of use, compatibility with existing systems, and advanced features such as real-time data analysis and predictive modeling. A tool with a simple, intuitive interface and smooth integration with your current setup can minimize disruptions and streamline adoption.

Lastly, don't forget to assess the total cost of ownership. This includes not just subscription fees but also any hidden or additional costs. Keeping your budget in mind ensures you select a solution that not only meets your needs but also makes financial sense. By focusing on these criteria, you can equip your team with the right tools to confidently make data-driven decisions.

How do AI-powered analytics tools like Querio differ from traditional analytics platforms?

AI-powered analytics tools, like Querio, offer a fresh approach compared to traditional platforms. While older systems often depend on static dashboards and predefined queries, AI-driven tools work dynamically, processing data in real time. They let users ask complex questions using natural language, making it possible to uncover insights quickly - no technical know-how required.

What sets these tools apart is their ability to go beyond simply showing "what happened." They can dig deeper, explaining the "why" and "how" by spotting patterns and offering richer contextual insights. This feature becomes a game-changer in fast-moving business environments where making smart, timely decisions is non-negotiable.

How can businesses make the most of real-time data insights from analytics tools?

To get the most out of real-time data insights, businesses should focus on a few essential strategies. First, make sure your analytics tools are capable of processing and delivering real-time data. This allows you to respond immediately to shifts in customer behavior or operational trends. Whether it’s tweaking a marketing campaign, streamlining a process, or resolving an issue, acting quickly can make a huge difference.

Another key step is creating a data-driven culture within your organization. Train your teams to understand and act on real-time insights effectively. Use tools like alerts for critical metrics and clear, easy-to-read visualizations to present data in a way that’s actionable. When teams can make fast, informed decisions, it not only boosts customer satisfaction but also enhances operational efficiency.

By following these steps, businesses can tap into the full potential of real-time analytics, staying flexible and competitive in today’s fast-paced market.

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