# Best Analytics Platforms *By [Tian Lin](https://research.g2.com/insights/author/tian-lin)* Analytics platforms provide a tool set for businesses to transform raw data into meaningful, actionable insights. They enable organizations to explore data, uncover trends, forecast future outcomes, and support informed decision making. Unlike tools limited to reporting on past performance, analytics platforms often include advanced capabilities such as predictive modeling, statistical analysis, and machine learning (ML). These platforms are designed to be flexible and scalable, supporting a wide range of use cases across the business. These platforms are used in nearly every business function, from marketing and sales to finance, operations, and HR, supporting both strategic planning and day-to-day performance monitoring. From data analysts and scientists to business stakeholders and executives, analytics platforms are used by a wide range of personas. While analysts focus on exploring data and generating insights, self-service tools now enable non-technical users to interact directly with data. IT teams support platform integration and security, reflecting the growing push to democratize data access and embed analytics into daily decision-making across the organization. Analytics platforms support critical functions such as data blending and modeling, enabling users to combine data from diverse sources and build robust, interconnected data models. The visual outputs — dashboards, reports, and interactive charts — help users explore trends, drill down into granular details, and communicate insights clearly. Unlike standalone data visualization tools, which are limited to presenting information, analytics platforms encompass the full analytical workflow. Many also offer advanced capabilities such as embedded analytics, natural language query, and augmented analytics, which leverage ML to automate insight discovery and make data exploration more accessible to a broader audience. Analytics platforms and [business intelligence (BI) software](https://www.g2.com/categories/business-intelligence) often work in tandem to support data-driven organizations. While BI tools focus on tracking and reporting historical performance through dashboards and key performance indicators (KPI), analytics platforms provide broader capabilities that support exploratory analysis and strategic planning. BI answers "what happened," while analytics platforms help users understand why it happened and what might happen next. Rather than replacing BI, analytics platforms complement it by enabling deeper insights and empowering a wider range of users across the organization. To qualify for inclusion in the Analytics Platforms category, a product must: - Ingest and integrate data from a wide range of structured and semi-structured sources - Prepare and transform data using built-in tools for cleaning, enrichment, and formatting - Support connections to diverse data sources, including file uploads, databases, application programming interfaces (API), and SaaS apps - Enable users to model data relationships, join datasets, and explore data interactively - Offer tools to build meaningful business reports, dashboards, and visualizations - Allow creation and sharing of internal analytics applications or embedded insights across teams ## Top Analytics Platforms at a Glance | # | Product | Rating | Best For | What Users Say | |---|---------|--------|----------|----------------| | 1 | [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) | 4.5/5.0 (1,586 reviews) | Microsoft-connected interactive dashboards | "[Zero-Code Dashboards with Easy Data Transformations and One-Click Publishing](https://www.g2.com/survey_responses/microsoft-power-bi-review-12864007)" | | 2 | [Tableau](https://www.g2.com/products/tableau/reviews) | 4.4/5.0 (3,574 reviews) | Flexible visual dashboard exploration | "[Tableau Makes Data Visualization Easy with Strong Integrations](https://www.g2.com/survey_responses/tableau-review-12975791)" | | 3 | [Databricks](https://www.g2.com/products/databricks/reviews) | 4.6/5.0 (1,277 reviews) | Governed lakehouse analytics and ML workflows | "[Powerful Lakehouse for Big Data, Collaboration, and Efficient Pipelines](https://www.g2.com/survey_responses/databricks-review-12946286)" | | 4 | [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) | 4.3/5.0 (758 reviews) | Cloud analytics for governed data science | "[Powerful & Transforming Data into Decisions—Effortlessly and Intelligently.](https://www.g2.com/survey_responses/sas-viya-review-12682824)" | | 5 | [Alteryx](https://www.g2.com/products/alteryx/reviews) | 4.6/5.0 (789 reviews) | No-code data preparation and automation | "[Easy, Time-Saving Data Automation with Alteryx’s Drag-and-Drop Workflows](https://www.g2.com/survey_responses/alteryx-review-12594796)" | | 6 | [Looker](https://www.g2.com/products/looker/reviews) | 4.4/5.0 (1,583 reviews) | Governed shared BI metrics | "[Transforms Data, But Challenging for Beginners](https://www.g2.com/survey_responses/looker-review-12784757)" | | 7 | [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) | 4.8/5.0 (261 reviews) | Semantic-layer acceleration for enterprise BI | "[Kyvos Unified Our Business Logic with a Single Semantic Model](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-12797024)" | | 8 | [Domo](https://www.g2.com/products/domo/reviews) | 4.3/5.0 (989 reviews) | Centralized self-service business dashboards | "[All-in-One Platform for Real-Time Analytics and Dashboards](https://www.g2.com/survey_responses/domo-review-12676104)" | | 9 | [Amazon QuickSight](https://www.g2.com/products/amazon-quicksight/reviews) | 4.3/5.0 (675 reviews) | AWS-native serverless BI dashboards | "[Turns Raw Data into Interactive Dashboards for Better Trend Monitoring](https://www.g2.com/survey_responses/amazon-quicksight-review-12740199)" | | 10 | [Sigma](https://www.g2.com/products/sigma-computing-sigma/reviews) | 4.4/5.0 (543 reviews) | Warehouse-native spreadsheet-style analytics | "[Easiest BI Tool: Live Snowflake Data in a Spreadsheet-Like Experience](https://www.g2.com/survey_responses/sigma-review-12573150)" | --- ## What Are the Most Common Questions About Analytics Platforms? *AI-generated · Last updated: May 26, 2026* ### What analytics platforms with intuitive UI that non-technical users adopt without extensive training? Based on G2 reviews, buyers looking for analytics platforms with an intuitive interface often prioritize drag-and-drop workflows, easy report creation, and minimal training overhead. According to verified users, Microsoft Power BI is frequently described as easy to use for dashboard creation, with non-technical teams able to navigate dashboards and build simple reports. Tableau reviewers also mention beginner-friendly dashboarding and drag-and-drop analysis, especially for quick visualization work. G2 reviewers mention that Looker can support self-service access once models are set up, though some users note a steeper learning curve for report creation. Across recent reviews, ease of use is strongest when teams need straightforward dashboard access, fast setup, and familiar visual exploration. ### What most trusted analytics solutions by data teams based on user reviews for teams with similar? Based on G2 reviews, trust among data teams in this category often comes from reliability in handling large datasets, flexible workflows, and strong collaboration between technical and business users. According to verified users, Databricks is repeatedly trusted for unifying data engineering, analytics, and machine learning in one environment, which reduces tool sprawl. G2 reviewers mention Microsoft Power BI as dependable for centralizing reporting and sharing metrics across teams, especially in Microsoft-heavy environments. Tableau is also trusted for visual exploration and dashboard creation, particularly when teams need strong storytelling and flexible analysis. Recent reviews show that data teams value tools they can rely on for scaling workflows, consolidating sources, and keeping metrics accessible to stakeholders. ### Which analytics solutions integrate with data warehouses like snowflake, BigQuery, and Redshift seamlessly? Based on G2 reviews, Databricks stands out for teams that need seamless integration with modern data warehouse and cloud data environments. According to verified users, Databricks brings data engineering, analytics, and machine learning into one platform while connecting well with tools such as Snowflake, BigQuery, and related cloud ecosystems. G2 reviewers mention native connectors, shared notebooks, workflow orchestration, and unified data handling as key reasons it simplifies work across warehouse-based stacks. Reviews also describe easier collaboration and fewer handoffs between teams because data processing and analysis stay in one environment. For buyers prioritizing warehouse-centric analytics, recent feedback most consistently points to Databricks as the strongest fit in this dataset. ### What top analytics platforms for mid-market companies building self-service dashboards across departments that scales with team? Based on G2 reviews, mid-market teams building self-service dashboards across departments often look for tools that centralize reporting, support business users, and reduce dependence on manual spreadsheet work. G2 reviewers mention Microsoft Power BI as a strong fit for cross-functional KPI tracking, automated dashboards, and broad access across business users. According to verified users, Databricks supports scalable analytics workflows when teams need broader data engineering and analytics in one place. Tableau is also cited for interactive dashboards and accessible visual exploration, especially when organizations want polished reporting for multiple stakeholders. Across recent reviews, these platforms are described as helpful for scaling visibility across finance, operations, sales, and marketing without constantly rebuilding reports. **Here are some of the top-rated products on G2:** - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) – used to centralize reporting and give business teams self-service dashboards across departments - [Databricks](https://www.g2.com/products/databricks/reviews) – supports scalable data workflows that combine analytics, reporting, and shared access to large datasets - [Tableau](https://www.g2.com/products/tableau/reviews) – helps teams build interactive dashboards and share visual insights across business functions ### Which analytics solutions provide fast query response and drill-down capability for ad-hoc exploration? Based on G2 reviews, Microsoft Power BI is a strong choice when teams need fast query response and flexible drill-down for ad-hoc exploration. According to verified users, Power BI makes it easy to drill into numbers, filter data, and explore trends without writing repeated custom queries. G2 reviewers mention cross-filtering, interactive dashboards, and quick access to KPIs as key strengths for day-to-day analysis. Reviews also highlight its ability to centralize data from multiple sources and let business users navigate details on their own. While some reviewers mention performance slowdowns on especially large or complex datasets, recent feedback still shows Power BI as the most consistently referenced option here for accessible drill-down analysis. ### What analytics platforms supporting collaborative dashboards, annotations, and mobile access for on-the-go insights? Based on G2 reviews, collaboration and mobile accessibility are recurring strengths for several analytics platforms. According to verified users, Domo supports sharing dashboards, discussing insights, and enabling collaboration across departments, with reviewers also highlighting its mobile experience. G2 reviewers mention Yellowfin BI for collaboration through comments, annotations, and shared reporting workflows that help teams discuss data in context. Microsoft Power BI is also noted for mobile and web access, along with easy dashboard sharing and centralized reporting. Recent reviews suggest these tools are most relevant when teams need analytics that travel beyond a desktop, allowing leaders and frontline users to access dashboards, review updates, and align on performance while moving between meetings or locations. ### Which analytics platforms prevent incorrect conclusions by enforcing data governance and preventing metric manipulation? Based on G2 reviews, Looker is one of the strongest options for preventing incorrect conclusions through governed metrics and centralized definitions. According to verified users, its modeling approach helps define metrics once and apply them consistently across dashboards, reducing disagreements over what a number means. G2 reviewers mention that this centralized layer helps stop teams from pulling conflicting versions of the same metric and creates a more trusted reporting environment. Reviews also describe faster decision-making because teams are working from one shared definition instead of reconciling spreadsheets. For organizations focused on governance, consistency, and reducing metric drift, recent review language most directly supports Looker in this area. ### What is the best analytics platforms for business teams accessing insights without requiring SQL or data science skills? Based on G2 reviews, Microsoft Power BI is the best-supported answer in this dataset for business teams that need insights without requiring SQL or data science skills. According to verified users, it is easy to use, supports drag-and-drop reporting, and helps non-technical users access dashboards and explore data without heavy training. G2 reviewers mention self-service dashboards, natural language features, and accessible visualizations that help staff make informed decisions without depending on technical teams for every question. Reviews also note strong integration with common business tools and data sources, which helps teams get started faster. For non-technical business users, Power BI is the most consistently referenced option in recent reviews. **Here are some of the top-rated products on G2:** - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) – helps non-technical teams build and use dashboards with drag-and-drop reporting and familiar workflows - [Tableau](https://www.g2.com/products/tableau/reviews) – supports quick visual exploration with an intuitive drag-and-drop interface for broad business use - [Looker](https://www.g2.com/products/looker/reviews) – enables self-service exploration and shared dashboards once models are set up for business teams ### What highest rated analytics platform for enterprises democratizing data access across business functions? Based on G2 reviews, enterprises democratizing data access across business functions often choose platforms that centralize metrics and make dashboards accessible to non-technical users. G2 reviewers mention Microsoft Power BI for unifying reporting across departments and helping business users explore dashboards directly. According to verified users, Looker supports centralized metric definitions and broader self-service analytics across organizations, reducing inconsistencies between teams. Tableau is also frequently cited for making complex datasets easier to communicate through interactive dashboards and visual storytelling. Across recent reviews, these platforms are associated with giving finance, operations, marketing, and leadership a common view of performance so teams can act without waiting on repeated manual reporting. **Here are some of the top-rated products on G2:** - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) – centralizes dashboards and gives business teams direct access to live metrics across functions - [Looker](https://www.g2.com/products/looker/reviews) – provides a centralized modeling layer that helps teams work from shared definitions across the organization - [Tableau](https://www.g2.com/products/tableau/reviews) – makes cross-functional insights easier to share through interactive dashboards and visual exploration ### What analytics solutions with robust caching and performance optimization to maintain fast speeds at scale? Based on G2 reviews, several analytics solutions stand out for maintaining speed at scale through performance optimization. According to verified users, Databricks is frequently praised for handling large-scale data processing efficiently and supporting complex analytics without major slowdowns when workflows are managed well. G2 reviewers mention Kyvos Semantic Layer for accelerating query response on very large datasets and improving dashboard responsiveness, especially in BI environments. Tableau and Microsoft Power BI are also noted for strong performance in many day-to-day scenarios, though reviewers sometimes mention tuning is important as complexity grows. Across recent reviews, the strongest performance-focused language centers on Databricks and Kyvos when buyers need fast access to large volumes of data. ## How Many Analytics Platforms Products Does G2 Track? **Total Products under this Category:** 348 ### Category Stats (Jun 2026) - **Average Rating**: 4.49/5 (↑0.01 vs May 2026) The average rating of products in this category, based on all submitted ratings - **New Reviews This Quarter**: 820 - **Buyer Segments**: Mid-Market 40% │ Enterprise 33% │ Small-Business 27% Represents the distribution of reviewers across all products in this category. - **Top Trending Product**: Diver Platform (+3.71%) - Among all products in this category, Diver Platform recorded the largest rating increase compared to last month *Last updated: June 09, 2026* ## How Does G2 Rank Analytics Platforms Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 28,300+ Authentic Reviews - 348+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## Which Analytics Platforms Is Best for Your Use Case? - **Leader:** [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) - **Highest Performer:** [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) - **Easiest to Use:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Top Trending:** [Hex](https://www.g2.com/products/hex-tech-hex/reviews) - **Best Free Software:** [Tableau](https://www.g2.com/products/tableau/reviews) --- **Sponsored** ### AgencyAnalytics AgencyAnalytics is the only reporting platform designed specifically for marketing agencies. 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[Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) Power BI Desktop puts visual analytics at your fingertips. With this powerful authoring tool, you can create interactive data visualizations and reports. Connect, mash up, model, and visualize your data. Place visuals exactly where you want them, analyze and explore your data, and share content with others by publishing to the Power BI web service. Power BI Desktop is part of the Power BI product suite. To monitor key data and share dashboards and reports, use the Power BI web service. To view and interact with your data on any mobile device, get the Power BI Mobile app on the AppStore, Google Play or the Microsoft Store. To embed stunning, fully interactive reports and visuals into your applications use Power BI Embedded. **Average Rating:** 4.5/5.0 **Total Reviews:** 1,586 **How Do G2 Users Rate Microsoft Power BI?** - **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 9.1/10) - **Steps to Answer:** 8.4/10 (Category avg: 8.3/10) - **Reports Interface:** 8.9/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.6/10 (Category avg: 8.5/10) **Who Is the Company Behind Microsoft Power BI?** - **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft) - **Year Founded:** 1975 - **HQ Location:** Redmond, Washington - **Twitter:** @microsoft (13,091,954 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (231,632 employees on LinkedIn®) - **Ownership:** MSFT **Who Uses This Product?** - **Who Uses This:** Data Analyst, Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 42% Enterprise, 37% Mid-Market #### What Are Microsoft Power BI's Pros and Cons? **Pros:** - Ease of Use (137 reviews) - Data Visualization (125 reviews) - Integrations (66 reviews) - Powerful BI (63 reviews) - Charting Features (49 reviews) **Cons:** - Learning Curve (75 reviews) - Slow Performance (65 reviews) - Performance Issues (29 reviews) - Complex Data Modeling (27 reviews) - Limited Customization (25 reviews) ### 2. [Tableau](https://www.g2.com/products/tableau/reviews) Tableau is the world’s leading AI-powered analytics platform. Whether you are a business user or an analyst, Tableau turns trusted data into actionable insights. With our flexible, interoperable platform, you can: Turn data into action at scale with human and agent collaboration. Tableau Next delivers agentic AI for faster data-insight-action workflows. It surfaces insights, provides proactive recommendations, and helps you take action in the flow of work. Scale data-driven insights with complete operational confidence. Tableau Cloud enables fully managed analytics at scale. It accelerates your time to value and gives you access to the latest AI-powered innovations. Deploy visual, self-service analytics with unmatched control and flexibility. Tableau Server meets your organization's governance and security needs. It provides enterprise-grade, self-service analytics on-premise or in your private cloud. **Average Rating:** 4.4/5.0 **Total Reviews:** 3,574 **How Do G2 Users Rate Tableau?** - **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 9.1/10) - **Steps to Answer:** 8.3/10 (Category avg: 8.3/10) - **Reports Interface:** 8.7/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.5/10 (Category avg: 8.5/10) **Who Is the Company Behind Tableau?** - **Seller:** [Salesforce](https://www.g2.com/sellers/salesforce) - **Company Website:** https://www.salesforce.com/ - **Year Founded:** 1999 - **HQ Location:** San Francisco, CA - **Twitter:** @salesforce (579,511 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3185/ (83,223 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Analyst, Business Analyst - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 41% Enterprise, 36% Mid-Market #### What Are Tableau's Pros and Cons? **Pros:** - Ease of Use (634 reviews) - Data Visualization (563 reviews) - Visualization (424 reviews) - Features (351 reviews) - Intuitive (317 reviews) **Cons:** - Learning Curve (282 reviews) - Learning Difficulty (240 reviews) - Expensive (225 reviews) - Slow Performance (155 reviews) - Difficulty (139 reviews) ### 3. [Databricks](https://www.g2.com/products/databricks/reviews) Databricks is a unified data and AI platform that helps organizations build, govern and scale data pipelines, analytics, machine learning, AI applications and agents. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on Databricks to work with enterprise data and AI at scale. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Agent Bricks, Lakeflow, Lakehouse, Lakebase, Genie and Unity Catalog. Founded in 2013 by the original creators of Apache Spark™, Delta Lake, MLflow and Unity Catalog, Databricks is built on an open lakehouse architecture that brings data, analytics and AI together. The platform is used by data engineers, data scientists, analysts, developers, machine learning teams, AI teams and business users to collaborate across the full data and AI lifecycle. Key Databricks capabilities include: - Data engineering: Build, automate and manage reliable batch, streaming and real-time data pipelines. - Analytics and business intelligence: Run SQL analytics, create dashboards and enable business teams to explore data. - Data governance: Discover, secure and manage data and AI assets across teams, clouds and workloads. - Machine learning and AI: Develop models, build generative AI applications and create production-grade AI agents. - Data applications: Build and deploy data-driven applications using governed enterprise data. Available across AWS, Azure and Google Cloud, Databricks helps organizations work across clouds, reduce data silos and simplify collaboration across teams and tools. Customers use Databricks for use cases such as customer personalization, fraud detection, predictive maintenance, real-time analytics, cybersecurity, healthcare research, financial risk management, supply chain optimization and AI-powered decision-making. Databricks is used across industries including financial services, healthcare and life sciences, retail, manufacturing, energy and the public sector. Organizations use the platform to modernize data infrastructure, accelerate AI adoption and turn enterprise data into business value. **Average Rating:** 4.6/5.0 **Total Reviews:** 1,277 **How Do G2 Users Rate Databricks?** - **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.1/10) - **Steps to Answer:** 8.3/10 (Category avg: 8.3/10) - **Reports Interface:** 9.1/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.6/10 (Category avg: 8.5/10) **Who Is the Company Behind Databricks?** - **Seller:** [Databricks Inc.](https://www.g2.com/sellers/databricks-inc) - **Company Website:** https://databricks.com - **Year Founded:** 2013 - **HQ Location:** San Francisco, CA - **Twitter:** @databricks (91,542 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3477522/ (15,627 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Engineer, Data Analyst - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 48% Enterprise, 38% Mid-Market #### What Are Databricks's Pros and Cons? **Pros:** - Features (288 reviews) - Ease of Use (278 reviews) - Integrations (189 reviews) - Collaboration (150 reviews) - Data Management (150 reviews) **Cons:** - Learning Curve (112 reviews) - Expensive (97 reviews) - Steep Learning Curve (96 reviews) - Missing Features (69 reviews) - Complexity (64 reviews) ### 4. [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) SAS Viya is a cloud-native data and AI platform that enables teams to build, deploy and scale explainable AI that drives trusted, confident decisions. It unites the entire data and AI life cycle and empowers teams to innovate quickly while balancing speed, automation and governance by design. Viya unifies data management, advanced analytics and decisioning in a single platform, so organizations can move from experimentation to production with confidence, delivering measurable business impact that is secure, explainable and scalable across any environment. Key capabilities required to deliver trusted decisions include: • End-to-end clarity across the data and AI life cycle, with built-in lineage, auditability and continuous monitoring to support defensible decisions. • Governance by design, enabling consistent oversight across data, models and decisions to reduce risk and accelerate adoption. • Explainable AI at scale, so insights and outcomes can be understood, validated and trusted by business and regulators alike. • Operationalized analytics, ensuring value continues beyond deployment through monitoring, retraining and life cycle management. • Flexible, cloud-native deployment, allowing organizations to start anywhere and scale everywhere while maintaining control. **Average Rating:** 4.3/5.0 **Total Reviews:** 758 **How Do G2 Users Rate SAS Viya?** - **Has the product been a good partner in doing business?:** 8.2/10 (Category avg: 9.1/10) - **Steps to Answer:** 8.1/10 (Category avg: 8.3/10) - **Reports Interface:** 8.4/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind SAS Viya?** - **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2) - **Company Website:** https://www.sas.com/ - **Year Founded:** 1976 - **HQ Location:** Cary, NC - **Twitter:** @SASsoftware (60,863 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,638 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Student, Statistical Programmer - **Top Industries:** Pharmaceuticals, Banking - **Company Size:** 33% Enterprise, 33% Small-Business #### What Are SAS Viya's Pros and Cons? **Pros:** - Ease of Use (234 reviews) - Features (218 reviews) - Analytics (196 reviews) - Data Analysis (166 reviews) - Intuitive (145 reviews) **Cons:** - Learning Difficulty (151 reviews) - Learning Curve (144 reviews) - Complexity (143 reviews) - Difficult Learning (117 reviews) - Expensive (108 reviews) ### 5. [Alteryx](https://www.g2.com/products/alteryx/reviews) Alteryx, through it's Alteryx One platform, helps enterprises transform complex, disconnected data into a clean, AI-ready state. Whether you’re creating financial forecasts, analyzing supplier performance, segmenting customer data, analyzing employee retention, or building competitive AI applications from your proprietary data, Alteryx One makes it easy to cleanse, blend, and analyze data to unlock the unique insights that drive impactful decisions. AI-Guided Analytics Alteryx automates and simplifies every stage of data preparation and analysis, from validation and enrichment to predictive analytics and automated insights. Incorporate generative AI directly into your workflows to streamline complex data tasks and generate insights faster. Unmatched flexibility, whether you prefer code-free workflows, natural language commands, or low-code options, Alteryx adapts to your needs. Trusted. Secure. Enterprise-Ready. Alteryx is trusted by over half of the Global 2000 and 19 of the top 20 global banks. With built-in automation, governance, and security, your workflows can scale and maintain compliance while delivering consistent results. And it doesn’t matter if your systems are on-premises, hybrid, or in the cloud; Alteryx fits effortlessly into your infrastructure. Easy to Use. Deeply Connected. What truly sets Alteryx apart is our focus on efficiency and ease of use for analysts and our active community of 700,000 Alteryx users to support you at every step of your journey. With seamless integration to data everywhere including platforms like Databricks, Snowflake, AWS, Google, SAP, and Salesforce, our platform helps unify siloed data and accelerate getting to insights. Visit Alteryx.com for more information, and to start your free trial. **Average Rating:** 4.6/5.0 **Total Reviews:** 789 **How Do G2 Users Rate Alteryx?** - **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.1/10) - **Steps to Answer:** 8.5/10 (Category avg: 8.3/10) - **Reports Interface:** 7.9/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.9/10 (Category avg: 8.5/10) **Who Is the Company Behind Alteryx?** - **Seller:** [Alteryx](https://www.g2.com/sellers/alteryx) - **Company Website:** https://www.alteryx.com - **Year Founded:** 1997 - **HQ Location:** Irvine, CA - **Twitter:** @alteryx (26,153 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/903031/ (2,304 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Analyst, Analyst - **Top Industries:** Financial Services, Accounting - **Company Size:** 64% Enterprise, 21% Mid-Market #### What Are Alteryx's Pros and Cons? **Pros:** - Ease of Use (333 reviews) - Automation (148 reviews) - Intuitive (132 reviews) - Easy Learning (102 reviews) - Efficiency (102 reviews) **Cons:** - Expensive (88 reviews) - Learning Curve (80 reviews) - Missing Features (62 reviews) - Learning Difficulty (55 reviews) - Slow Performance (41 reviews) ### 6. [Looker](https://www.g2.com/products/looker/reviews) Looker, Google Cloud’s business intelligence platform, enables you to chat with your data. Organizations turn to Looker for self-service and governed BI, to build custom applications with trusted metrics, or to bring Looker modeling to their existing environment. The result is improved data engineering efficiency and true business transformation. **Average Rating:** 4.4/5.0 **Total Reviews:** 1,583 **How Do G2 Users Rate Looker?** - **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.1/10) - **Steps to Answer:** 8.2/10 (Category avg: 8.3/10) - **Reports Interface:** 8.6/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.4/10 (Category avg: 8.5/10) **Who Is the Company Behind Looker?** - **Seller:** [Google](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google (31,901,456 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (341,888 employees on LinkedIn®) - **Ownership:** NASDAQ:GOOG **Who Uses This Product?** - **Who Uses This:** Data Analyst, Data Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 60% Mid-Market, 20% Enterprise #### What Are Looker's Pros and Cons? **Pros:** - Ease of Use (108 reviews) - Insights (64 reviews) - Easy Integrations (57 reviews) - Integrations (54 reviews) - Analytics (51 reviews) **Cons:** - Learning Curve (52 reviews) - Learning Difficulty (38 reviews) - Slow Loading (29 reviews) - Slow Performance (29 reviews) - Complexity (27 reviews) ### 7. [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) Kyvos is a semantic layer for AI and BI. It gives organizations a single, consistent, business-friendly view of their entire data estate. By standardizing how data is defined and understood, Kyvos eliminates metric drift across BI tools and ensures that LLMs and AI agents work with governed business semantics rather than raw tables. Kyvos also delivers lightning-fast analytics at massive scale and high concurrency — including granular multidimensional analysis on the cloud — without the sluggish query times and escalating cloud costs that typically come with it. Why Organizations Use Kyvos Unified Semantic Foundation for AI and BI Kyvos semantic layer standardizes how metrics, KPIs, dimensions, hierarchies, relationships, calculations, and business rules are modelled across the enterprise — so that dashboards, analytics tools, notebooks, and AI systems all operate on the same understanding of the business. Kyvos enables: - Shared semantics — one common data language across every tool, team, and system - Governed access — data exploration within defined security, role, and permission boundaries - Platform interoperability — consistent semantic context across diverse platforms and environments - AI readiness — LLMs and agents work with governed business semantics rather than raw tables or ambiguous schema AI Grounded in Business Context Kyvos grounds AI systems in the governed semantic model, ensuring they operate on established business context rather than raw schemas — improving the accuracy, traceability, and reliability of AI-generated insights. Consistent Metrics Across BI Tools Kyvos centralizes metric and KPI definitions in the semantic layer and applies them consistently across every analytics interface — eliminating metric drift and improving trust in analytics. High-Performance Analytics at Scale Kyvos delivers high-performance analytics that scale with demand, enabling: - Sub-second query performance across massive datasets - High concurrency across thousands of users and workloads - Consistent response times regardless of data volume or concurrency - No performance degradation as adoption grows - Multidimensional Analytics on the Cloud Kyvos enables deep multidimensional analytics, supporting: - Granular analysis across billions of rows - Thousands of measures and dimensions in a single model - Fast drill-down across complex hierarchies - Full analytical depth without sacrificing query speed Cloud Cost Efficiency Kyvos serves analytics through its semantic layer rather than routing every query to the warehouse — reducing compute consumption across analytics and AI workloads. As adoption grows, organizations can scale users, workloads, and analytical complexity without a corresponding rise in warehouse compute costs. **Average Rating:** 4.8/5.0 **Total Reviews:** 261 **How Do G2 Users Rate Kyvos Semantic Layer?** - **Has the product been a good partner in doing business?:** 9.6/10 (Category avg: 9.1/10) - **Steps to Answer:** 9.3/10 (Category avg: 8.3/10) - **Reports Interface:** 9.6/10 (Category avg: 8.7/10) - **Calculated Fields:** 9.4/10 (Category avg: 8.5/10) **Who Is the Company Behind Kyvos Semantic Layer?** - **Seller:** [Kyvos Insights](https://www.g2.com/sellers/kyvos-insights) - **Company Website:** https://www.kyvosinsights.com - **Year Founded:** 2014 - **HQ Location:** Los Gatos, CA - **Twitter:** @KyvosInsights (689 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/kyvos-insights-inc-/ (152 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Software Engineer, Senior Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 57% Mid-Market, 39% Enterprise #### What Are Kyvos Semantic Layer's Pros and Cons? **Pros:** - Ease of Use (120 reviews) - Speed (88 reviews) - Performance (54 reviews) - Analytics (53 reviews) - Fast Querying (50 reviews) **Cons:** - Learning Curve (34 reviews) - Difficult Setup (33 reviews) - Complexity (9 reviews) - Feature Limitations (7 reviews) - Connectivity Issues (6 reviews) ### 8. [Domo](https://www.g2.com/products/domo/reviews) Domo's AI and Data Products Platform empowers organizations to turn data into actionable insights and solutions. It allows users to seamlessly connect diverse data sources, prepare data for use, and generate dynamic reports and visualizations—all within a single interface. With built-in AI and automation capabilities, teams can easily build and use AI agents, streamline workflows, and create tailored solutions. **Average Rating:** 4.3/5.0 **Total Reviews:** 989 **How Do G2 Users Rate Domo?** - **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 9.1/10) - **Steps to Answer:** 7.9/10 (Category avg: 8.3/10) - **Reports Interface:** 8.5/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.2/10 (Category avg: 8.5/10) **Who Is the Company Behind Domo?** - **Seller:** [Domo](https://www.g2.com/sellers/domo) - **Company Website:** https://www.domo.com - **Year Founded:** 2010 - **HQ Location:** American Fork, UT - **Twitter:** @Domotalk (63,526 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/25237/ (1,305 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Analyst, Business Analyst - **Top Industries:** Computer Software, Marketing and Advertising - **Company Size:** 49% Mid-Market, 29% Enterprise #### What Are Domo's Pros and Cons? **Pros:** - Ease of Use (248 reviews) - Data Visualization (116 reviews) - Intuitive (95 reviews) - Easy Integrations (93 reviews) - Integrations (88 reviews) **Cons:** - Learning Curve (66 reviews) - Missing Features (59 reviews) - Data Management Issues (55 reviews) - Expensive (45 reviews) - Complexity (43 reviews) ### 9. [Amazon QuickSight](https://www.g2.com/products/amazon-quicksight/reviews) Amazon QuickSight is a cloud-based unified business intelligence (BI) service at hyperscale. With QuickSight, all users can meet varying analytic needs from the same source of truth through modern interactive dashboards, paginated reports, natural language queries and embedded analytics. With Amazon Q in QuickSight, business analysts and business users can use natural language to build, discover, and share meaningful insights in seconds, turning insights into impact faster. Over 100,000 customers use Amazon QuickSight. Learn more at https://quicksight.aws **Average Rating:** 4.3/5.0 **Total Reviews:** 675 **How Do G2 Users Rate Amazon QuickSight?** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 9.1/10) - **Steps to Answer:** 8.0/10 (Category avg: 8.3/10) - **Reports Interface:** 8.2/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Amazon QuickSight?** - **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd) - **Year Founded:** 2006 - **HQ Location:** Seattle, WA - **Twitter:** @awscloud (2,231,239 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®) - **Ownership:** NASDAQ: AMZN **Who Uses This Product?** - **Who Uses This:** Data Analyst, Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 40% Small-Business, 37% Mid-Market #### What Are Amazon QuickSight's Pros and Cons? **Pros:** - Integrations (69 reviews) - Ease of Use (62 reviews) - Easy Integrations (57 reviews) - Data Visualization (41 reviews) - Dashboard Management (39 reviews) **Cons:** - Limited Customization (62 reviews) - Learning Curve (36 reviews) - Limited Visualization (26 reviews) - Missing Features (20 reviews) - Poor Interface Design (20 reviews) ### 10. [Sigma](https://www.g2.com/products/sigma-computing-sigma/reviews) Sigma is the AI apps and analytics platform connected to the cloud data warehouse. Using Sigma, business and technical teams can build intelligent, production-ready AI apps that accelerate and automate operational workflows. Sigma provides a spreadsheet interface, SQL and Python editors, visual builders, and native AI to help teams turn live data into interactive applications, analysis, reports, and embedded experiences. **Average Rating:** 4.4/5.0 **Total Reviews:** 543 **How Do G2 Users Rate Sigma?** - **Has the product been a good partner in doing business?:** 9.1/10 (Category avg: 9.1/10) - **Steps to Answer:** 8.4/10 (Category avg: 8.3/10) - **Reports Interface:** 8.6/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.7/10 (Category avg: 8.5/10) **Who Is the Company Behind Sigma?** - **Seller:** [Sigma Computing](https://www.g2.com/sellers/sigma-computing) - **Company Website:** https://www.sigmacomputing.com/ - **Year Founded:** 2014 - **HQ Location:** San Francisco, California - **Twitter:** @sigmacomputing (1,556 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/7801411/ (1,415 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Analyst, Customer Success Manager - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 58% Mid-Market, 21% Enterprise #### What Are Sigma's Pros and Cons? **Pros:** - Ease of Use (73 reviews) - User Interface (29 reviews) - Customer Support (27 reviews) - Data Visualization (27 reviews) - Data Handling (26 reviews) **Cons:** - Slow Loading (23 reviews) - Slow Performance (22 reviews) - Limited Customization (20 reviews) - Learning Curve (17 reviews) - Missing Features (14 reviews) ### 11. [Hex](https://www.g2.com/products/hex-tech-hex/reviews) Hex is the world’s favorite AI Analytics platform. With Hex, anyone can explore data using natural language, with or without code, all on trusted context, in one AI-powered platform. Get started now \> https://app.hex.tech/signup?source=g2 Get a demo \> https://hex.tech/request-a-demo/?source=g2 **Average Rating:** 4.5/5.0 **Total Reviews:** 397 **How Do G2 Users Rate Hex?** - **Has the product been a good partner in doing business?:** 9.1/10 (Category avg: 9.1/10) - **Steps to Answer:** 7.6/10 (Category avg: 8.3/10) - **Reports Interface:** 8.3/10 (Category avg: 8.7/10) - **Calculated Fields:** 7.7/10 (Category avg: 8.5/10) **Who Is the Company Behind Hex?** - **Seller:** [Hex Tech](https://www.g2.com/sellers/hex-tech) - **Company Website:** https://hex.tech/ - **Year Founded:** 2019 - **HQ Location:** San Francisco, US - **Twitter:** @_hex_tech (6,952 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/hex-technologies/ (249 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Analyst, Data Scientist - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 54% Mid-Market, 22% Small-Business #### What Are Hex's Pros and Cons? **Pros:** - Ease of Use (130 reviews) - SQL Queries (81 reviews) - Data Management (79 reviews) - SQL Querying (74 reviews) - Data Analysis (62 reviews) **Cons:** - Limited Features (45 reviews) - Missing Features (41 reviews) - Lacking Features (38 reviews) - Limited Visualization (30 reviews) - Data Management Issues (29 reviews) ### 12. [IBM Cognos Analytics](https://www.g2.com/products/ibm-cognos-analytics/reviews) IBM Cognos Analytics is a business intelligence and analytics solution that uses agentic AI to help teams transform trusted data into actionable insights, build governed analytical applications, and make better decisions. Teams can explore data, monitor KPIs, analyze performance, forecast trends, and share insights across the business. The solution is built for business leaders, analysts, report authors, IT teams, and data governance teams that need governed reporting, self-service analytics, data modeling, and flexible deployment options. Common use cases include enterprise reporting, operational reporting, financial reporting, dashboarding, performance management, forecasting, and governed self-service analytics. It supports both centralized BI teams and distributed users who need consistent access to trusted analytics. Key capabilities: 1. Create governed reports and dashboards: Build, schedule, distribute, and manage reports, dashboards, and visualizations for teams, executives, and stakeholders. Support routine reporting, business reviews, and purpose-built analytical applications with consistent information. 2. Explore data with control: Use self-service analytics, certified data models, governed metrics, access controls, and auditability to keep reporting consistent across teams and departments. 3. Analyze and forecast faster: Use natural-language assistance, automated insights, and forecasting to help users understand data faster in supported versions and deployments. 4. Put Reporting Agents to work: Use agentic AI capabilities in supported versions and deployments to find reports, summarize results, share insights, and create or refine reports using natural language. 5. Deploy where the business needs it: Run Cognos Analytics in on-premises, IBM-hosted, hybrid, or certified container environments to align with infrastructure, security, and governance requirements. Cognos Analytics helps organizations reduce repetitive reporting work, improve consistency across metrics and dashboards, and make governed data analytics easier to access across the business. **Average Rating:** 4.1/5.0 **Total Reviews:** 435 **How Do G2 Users Rate IBM Cognos Analytics?** - **Has the product been a good partner in doing business?:** 7.8/10 (Category avg: 9.1/10) - **Steps to Answer:** 7.6/10 (Category avg: 8.3/10) - **Reports Interface:** 8.0/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.1/10 (Category avg: 8.5/10) **Who Is the Company Behind IBM Cognos Analytics?** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Company Website:** https://www.ibm.com - **Year Founded:** 1911 - **HQ Location:** Armonk, New York, United States - **Twitter:** @IBMSecurity (74,679 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (328,202 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Software Engineer, Analyst - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 58% Enterprise, 26% Mid-Market #### What Are IBM Cognos Analytics's Pros and Cons? **Pros:** - Ease of Use (36 reviews) - Report Generation (17 reviews) - Analytics (15 reviews) - Data Visualization (15 reviews) - User Interface (12 reviews) **Cons:** - Learning Curve (18 reviews) - Learning Difficulty (10 reviews) - Slow Performance (9 reviews) - Complexity (8 reviews) - Complex Usage (6 reviews) ### 13. [IBM Business Analytics Enterprise](https://www.g2.com/products/ibm-business-analytics-enterprise/reviews) IBM Business Analytics Enterprise is a comprehensive suite designed to unify and streamline business intelligence, planning, budgeting, reporting, and forecasting processes across organizations. By integrating data from multiple sources and vendors into a single, no-code content hub, it empowers users to make informed, data-driven decisions efficiently. Key Features and Functionality: - Composite Dashboards: Consolidate content assets from various business intelligence tools into a unified, integrated view accessible to all users. - Insightful Decision-Making: Leverage real metrics and insights to make confident business decisions, eliminating guesswork. - Easy Collaboration: Facilitate seamless collaboration across the organization, allowing teams to scale and adjust business objectives without overhauling existing processes. - Enhanced Customer Service: Optimize resource allocation and manufacturing decisions to provide more streamlined delivery for customers. - Data Management: Integrate multiple assets from different data sources into a single dashboard for easy access and faster decision-making. - Visualization Layer: Discover, access, personalize, and recommend content across multiple BI vendors and solutions from a centralized hub. - Personalization: Utilize AI to recommend content to users and allow for customized searches, aligning the platform with organizational branding and customer experience. - Forecast Optimization: Integrate operational, profitability, and financial planning with automated tools to optimize decision-making, using predictive analytics to identify trends and seasonal patterns. - Integrated Planning: Adjust organizational plans and forecasts in real time, adapting to changing demands swiftly with AI-infused extended planning and analysis. - Enterprise Reporting: Provide scalable reporting to enhance the data analytics culture, delivering the right data to the right people at the right time. Primary Value and Solutions Provided: IBM Business Analytics Enterprise addresses the challenge of data silos by offering a unified platform that integrates various analytics and planning tools. This consolidation enables organizations to: - Break Down Data Silos: Provide a single point of entry for users to access the data they need, enhancing collaboration and data consistency. - Enhance Decision-Making: Equip teams with comprehensive insights, allowing for informed decisions that drive business performance. - Improve Operational Efficiency: Streamline planning and forecasting processes, enabling organizations to respond swiftly to market changes and operational demands. By integrating analytics tools into a cohesive environment, IBM Business Analytics Enterprise empowers organizations to harness the full potential of their data, fostering a culture of informed decision-making and strategic agility. **Average Rating:** 4.4/5.0 **Total Reviews:** 21 **How Do G2 Users Rate IBM Business Analytics Enterprise?** - **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.1/10) - **Steps to Answer:** 8.6/10 (Category avg: 8.3/10) - **Reports Interface:** 8.9/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.8/10 (Category avg: 8.5/10) **Who Is the Company Behind IBM Business Analytics Enterprise?** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Year Founded:** 1911 - **HQ Location:** Armonk, New York, United States - **Twitter:** @IBMSecurity (74,679 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (328,202 employees on LinkedIn®) - **Ownership:** SWX:IBM **Who Uses This Product?** - **Company Size:** 43% Small-Business, 29% Enterprise #### What Are IBM Business Analytics Enterprise's Pros and Cons? **Pros:** - Ease of Use (5 reviews) - Data Visualization (3 reviews) - Scalability (3 reviews) - Customer Support (2 reviews) - Flexibility (2 reviews) **Cons:** - Complexity (3 reviews) - Complex Usage (2 reviews) - Learning Curve (2 reviews) - Bugs (1 reviews) - Dependency Issues (1 reviews) ### 14. [GoodData.AI](https://www.g2.com/products/gooddata-ai/reviews) GoodData is the full-stack, AI-native decision intelligence platform that helps businesses turn data into actionable, enterprise-grade insights. Designed for governed, scalable analytics, GoodData enables organizations to build, operationalize, and embed decisions, workflows, and AI agents directly within products and business workflows. The platform combines Analytics as Code, a governed semantic and metrics layer, APIs, SDKs, and open AI interoperability to help teams create composable analytics and AI experiences across products, workflows, and customer environments. From embedded analytics and dashboards to assistants, AI workflows, and interoperable agents, GoodData gives teams the foundation to move from insight to action with governance, performance, and deployment flexibility built in. Today, GoodData serves over 140,000 companies and 3.2 million users worldwide. **Average Rating:** 4.3/5.0 **Total Reviews:** 589 **How Do G2 Users Rate GoodData.AI?** - **Has the product been a good partner in doing business?:** 8.4/10 (Category avg: 9.1/10) - **Steps to Answer:** 8.3/10 (Category avg: 8.3/10) - **Reports Interface:** 8.6/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.2/10 (Category avg: 8.5/10) **Who Is the Company Behind GoodData.AI?** - **Seller:** [GoodData.AI](https://www.g2.com/sellers/gooddata-ai) - **Company Website:** https://www.gooddata.ai/ - **Year Founded:** 2007 - **HQ Location:** San Francisco, CA - **Twitter:** @gooddata - **LinkedIn® Page:** https://www.linkedin.com/company/202760/ (282 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Analyst, Product Manager - **Top Industries:** Computer Software, Consumer Services - **Company Size:** 44% Mid-Market, 40% Small-Business #### What Are GoodData.AI's Pros and Cons? **Pros:** - Ease of Use (52 reviews) - Data Visualization (34 reviews) - Integrations (34 reviews) - Intuitive (30 reviews) - Customization (28 reviews) **Cons:** - Learning Curve (28 reviews) - Learning Difficulty (19 reviews) - Missing Features (19 reviews) - Complexity (13 reviews) - Limited Customization (12 reviews) ### 15. [Yellowfin BI](https://www.g2.com/products/yellowfin-bi/reviews) Yellowfin is the only analytics suite that successfully combines action based dashboards with industry-leading automated analysis and data storytelling. By delivering the best analytical experience, Yellowfin provides your users with unique ways to engage with and act on their data, and addresses the needs of data analysts, business users, customers and developers who want to build, deploy or use amazing analytical experiences. Analytics for software companies Integrate and embed analytics with a difference into your app, your way \* Replace legacy or home grown reporting tools \* Embed a modern self-service analytics suite \* Deliver an exceptional customer experience Analytics for enterprise Get more value from your data in new and innovative ways \* Migrate from spreadsheets to a modern analytics platform \* Replace legacy BI applications \* Embed analytics into operational workflows Analytical Application Builders Leverage your domain expertise to create data products that delight \* Create unique data driven applications \* Close the loop on analytics \* Deliver insights as a service **Average Rating:** 4.4/5.0 **Total Reviews:** 418 **How Do G2 Users Rate Yellowfin BI?** - **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.1/10) - **Steps to Answer:** 8.0/10 (Category avg: 8.3/10) - **Reports Interface:** 8.6/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.2/10 (Category avg: 8.5/10) **Who Is the Company Behind Yellowfin BI?** - **Seller:** [Yellowfin](https://www.g2.com/sellers/yellowfin) - **Year Founded:** 2003 - **HQ Location:** Austin, Texas - **Twitter:** @YellowfinBI (5,783 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/358856/ (63 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** General Manager, Business Analyst - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 46% Small-Business, 35% Mid-Market #### What Are Yellowfin BI's Pros and Cons? **Pros:** - Ease of Use (69 reviews) - Dashboard Customization (35 reviews) - Data Visualization (34 reviews) - Intuitive (32 reviews) - User Interface (26 reviews) **Cons:** - Learning Curve (33 reviews) - Large Data Handling (26 reviews) - Slow Performance (23 reviews) - Performance Issues (19 reviews) - Limited Customization (17 reviews) ### 16. [Oracle Analytics Cloud](https://www.g2.com/products/oracle-analytics-cloud/reviews) Oracle Analytics Cloud is a comprehensive cloud analytics platform that empowers you to fundamentally change how you analyze and act on information. Empower leaders, analysts, and IT to access data from wherever they are, even using mobile devices. Oracle Analytics Cloud helps organizations discover unique insights faster with machine learning. With augmented analytics, combine data from across your organization with third-party data and automate important and time-consuming tasks such as data preparation, visualization, forecasting, and reporting. **Average Rating:** 4.1/5.0 **Total Reviews:** 293 **How Do G2 Users Rate Oracle Analytics Cloud?** - **Has the product been a good partner in doing business?:** 7.8/10 (Category avg: 9.1/10) - **Steps to Answer:** 7.9/10 (Category avg: 8.3/10) - **Reports Interface:** 8.4/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.2/10 (Category avg: 8.5/10) **Who Is the Company Behind Oracle Analytics Cloud?** - **Seller:** [Oracle](https://www.g2.com/sellers/oracle) - **Year Founded:** 1977 - **HQ Location:** Austin, TX - **Twitter:** @Oracle (828,032 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1028/ (208,078 employees on LinkedIn®) - **Ownership:** NYSE:ORCL **Who Uses This Product?** - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 61% Enterprise, 27% Mid-Market #### What Are Oracle Analytics Cloud's Pros and Cons? **Pros:** - Analytics (3 reviews) - Data Visualization (3 reviews) - Ease of Use (3 reviews) - Scalability (3 reviews) - Business Improvement (2 reviews) **Cons:** - Learning Curve (4 reviews) - Complexity (2 reviews) - Complex Usage (2 reviews) - Limited Customization (2 reviews) - Bugs (1 reviews) ### 17. [FICO Analytics Workbench™](https://www.g2.com/products/fico-analytics-workbencha/reviews) FICO® Analytics Workbench™ is a comprehensive predictive analytics tool designed to empower businesses in developing and deploying explainable machine learning models. It caters to both business users and data scientists, facilitating data exploration, visual data wrangling, decision strategy design, and machine learning within a unified environment. The platform is built on the high-performance FICO® Decision Management Platform, ensuring scalability and integration with real-time business operations. Key Features and Functionality: - Explainable AI Toolkit: Provides transparency in AI-derived decisions, enabling users to validate and interpret machine learning models effectively. - Integrated Development Environment: Combines decision trees, scorecards, and machine learning techniques, offering a versatile toolkit for model development. - User-Friendly Interface: Designed for users with varying skill sets, from business analysts to data scientists, promoting collaboration and productivity. - Cloud-Based Deployment: Offers a cloud-ready solution, allowing for scalable and flexible deployment options. - Regulatory Compliance Support: Automates the production of necessary documentation to meet internal review and external regulatory requirements. Primary Value and Problem Solving: FICO® Analytics Workbench™ addresses the growing need for transparent and interpretable AI models in business decision-making. By providing tools that make machine learning models explainable, it helps organizations comply with regulatory standards and build trust in AI-driven decisions. The platform's intuitive design and comprehensive features enable faster time-to-value, enhanced productivity, and improved business outcomes through analytically powered decisions. **Average Rating:** 4.3/5.0 **Total Reviews:** 11 **How Do G2 Users Rate FICO Analytics Workbench™?** - **Has the product been a good partner in doing business?:** 8.7/10 (Category avg: 9.1/10) - **Steps to Answer:** 8.8/10 (Category avg: 8.3/10) - **Reports Interface:** 8.8/10 (Category avg: 8.7/10) - **Calculated Fields:** 9.2/10 (Category avg: 8.5/10) **Who Is the Company Behind FICO Analytics Workbench™?** - **Seller:** [FICO](https://www.g2.com/sellers/fico) - **Year Founded:** 1956 - **HQ Location:** Bozeman, Montana - **LinkedIn® Page:** https://www.linkedin.com/company/fico/ (3,806 employees on LinkedIn®) - **Ownership:** NYSE:FICO - **Total Revenue (USD mm):** $1,294 **Who Uses This Product?** - **Company Size:** 45% Enterprise, 27% Mid-Market #### What Are FICO Analytics Workbench™'s Pros and Cons? **Pros:** - Ease of Use (1 reviews) ### 18. [Count](https://www.g2.com/products/count/reviews) Count is a modern data collaboration platform that helps data teams actually work together. It combines a data notebook with a real-time collaborative canvas for analysis and visualisation, so everyone can query, debug, and explore data in one place. Count replaces the mess of disconnected tools with a single workspace where analysts, engineers, and stakeholders can write SQL or Python, build visualisations, and share insights instantly. You can import and debug dbt models, see live results from connected CTEs, and export back to dbt Cloud, GitHub, or full SQL scripts. Used by over 500 data teams, including Accenture, Cleo AI, and Too Good To Go, Count helps teams move beyond static dashboards and focus on solving real business problems. It’s data collaboration that actually works. **Average Rating:** 4.8/5.0 **Total Reviews:** 102 **How Do G2 Users Rate Count?** - **Has the product been a good partner in doing business?:** 9.5/10 (Category avg: 9.1/10) - **Steps to Answer:** 8.5/10 (Category avg: 8.3/10) - **Reports Interface:** 8.5/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.8/10 (Category avg: 8.5/10) **Who Is the Company Behind Count?** - **Seller:** [Count Technologies](https://www.g2.com/sellers/count-technologies) - **Company Website:** https://count.co/ - **Year Founded:** 2016 - **HQ Location:** London, United Kingdom - **Twitter:** @counthq (2,104 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/counthq/ (26 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Analyst, Analytics Engineer - **Top Industries:** Financial Services, Information Technology and Services - **Company Size:** 79% Mid-Market, 10% Enterprise #### What Are Count's Pros and Cons? **Pros:** - Team Collaboration (34 reviews) - Ease of Use (28 reviews) - Customer Support (20 reviews) - Flexibility (20 reviews) - Data Visualization (19 reviews) **Cons:** - Learning Curve (18 reviews) - Slow Performance (9 reviews) - Limited Customization (7 reviews) - Missing Features (7 reviews) - Layout Issues (5 reviews) ### 19. [Deepnote](https://www.g2.com/products/deepnote/reviews) Deepnote is a data workspace where agents and humans work together. It's designed to simplify data exploration, accelerate analysis, and quickly deliver actionable insights for you and your team. Unlike outdated tools such as Jupyter, Deepnote is built with the next decade in mind. Deepnote gives anyone working with data superpowers. It unifies your data workflow through an integrated semantic layer, preparing your data for advanced AI applications. You can also leverage our AI data copilot to chat with your data, create charts, write code, or turn your AI notebooks into fully-fledged data dashboards or apps. Combine data, SQL or Python code, and visualizations side-by-side on a flexible canvas - enhanced with cutting-edge AI reasoning models. 🤖 Analyze with AI • Generate code and visualizations by describing your goal. • Auto-write, run, and debug code with AI. • Move faster with context-aware AI suggestions. 🔗 Unify • Connect to 60+ data sources like BigQuery, Snowflake, and PostgreSQL. • Combine Python and SQL in one notebook. • Build reusable ETL, analytics, and metric modules. • Create a semantic layer with shared definitions and trusted metrics. ⚖️ Scale • Instantly boost compute power, more included than Colab. • Schedule jobs and get notified with fresh results. • Organize work in projects and folders for team clarity. • Manage workflows via REST API. 🚀 Launch • Turn notebooks into dashboards or data apps, natively or with Streamlit. • Let users explore data with interactive inputs. • Share secure, live apps in one click. **Average Rating:** 4.5/5.0 **Total Reviews:** 377 **How Do G2 Users Rate Deepnote?** - **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 9.1/10) - **Steps to Answer:** 7.7/10 (Category avg: 8.3/10) - **Reports Interface:** 8.0/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.2/10 (Category avg: 8.5/10) **Who Is the Company Behind Deepnote?** - **Seller:** [Deepnote](https://www.g2.com/sellers/deepnote) - **Company Website:** https://www.deepnote.com - **Year Founded:** 2019 - **HQ Location:** San Francisco , US - **Twitter:** @DeepnoteHQ (5,241 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/deepnote (17 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Student, Data Analyst - **Top Industries:** Computer Software, Higher Education - **Company Size:** 68% Small-Business, 24% Mid-Market #### What Are Deepnote's Pros and Cons? **Pros:** - Ease of Use (170 reviews) - Collaboration (120 reviews) - Easy Integrations (76 reviews) - Team Collaboration (76 reviews) - Data Management (67 reviews) **Cons:** - Slow Performance (61 reviews) - Limited Features (32 reviews) - Data Management Issues (29 reviews) - Missing Features (26 reviews) - Lacking Features (25 reviews) ### 20. [Incorta](https://www.g2.com/products/incorta/reviews) Incorta is the first and only open data delivery platform that enables real-time analysis of live, detailed data across all systems of record—without the need for complex ETL processes. By enabling direct analysis on raw, source-identical data, Incorta provides faster, more accurate insights while removing barriers to exploration. With intuitive low-code/no-code tools, AI-powered querying through Nexus, and prebuilt business data applications, enterprise teams can quickly surface insights, break down technical roadblocks, and make smarter decisions without heavy engineering effort. For more information, please visit www.incorta.com. **Average Rating:** 4.4/5.0 **Total Reviews:** 55 **How Do G2 Users Rate Incorta?** - **Has the product been a good partner in doing business?:** 9.4/10 (Category avg: 9.1/10) - **Steps to Answer:** 9.2/10 (Category avg: 8.3/10) - **Reports Interface:** 9.3/10 (Category avg: 8.7/10) - **Calculated Fields:** 9.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Incorta?** - **Seller:** [Incorta](https://www.g2.com/sellers/incorta) - **Company Website:** https://www.incorta.com/ - **Year Founded:** 2013 - **HQ Location:** San Mateo, CA - **LinkedIn® Page:** https://www.linkedin.com/company/incorta/ (348 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 56% Enterprise, 29% Mid-Market #### What Are Incorta's Pros and Cons? **Pros:** - Data Integration (1 reviews) - Easy Integrations (1 reviews) - Integrations (1 reviews) **Cons:** - Bugs (1 reviews) ### 21. [Dataiku](https://www.g2.com/products/dataiku/reviews) Dataiku is the Platform for AI Success: the AI orchestration layer where enterprises build, deploy, and govern analytics, models, and agents at scale. It sits on top of the data platforms, clouds, and AI services you already use, working across all of them without locking you into any one. Dataiku expands who can build production AI, putting the right tools in the hands of data scientists and domain experts alike, from fraud analysts to demand planners. It orchestrates machine learning, rules, LLMs, and agents as one governed system, built on more than a decade of running production AI. Governance is part of the build rather than something bolted on afterward, so teams ship faster while keeping performance, cost, and risk under control. The result: AI that moves from experimentation to trusted, measurable execution now, not in 18 months. **Average Rating:** 4.4/5.0 **Total Reviews:** 196 **How Do G2 Users Rate Dataiku?** - **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 9.1/10) - **Steps to Answer:** 7.7/10 (Category avg: 8.3/10) - **Reports Interface:** 7.8/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.2/10 (Category avg: 8.5/10) **Who Is the Company Behind Dataiku?** - **Seller:** [Dataiku](https://www.g2.com/sellers/dataiku) - **Company Website:** https://Dataiku.com - **Year Founded:** 2013 - **HQ Location:** New York, NY - **Twitter:** @dataiku (22,922 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/dataiku/ (1,619 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Scientist, Data Analyst - **Top Industries:** Financial Services, Pharmaceuticals - **Company Size:** 58% Enterprise, 23% Mid-Market #### What Are Dataiku's Pros and Cons? **Pros:** - Ease of Use (82 reviews) - Features (82 reviews) - Usability (46 reviews) - Easy Integrations (43 reviews) - Productivity Improvement (42 reviews) **Cons:** - Learning Curve (45 reviews) - Steep Learning Curve (26 reviews) - Slow Performance (24 reviews) - Difficult Learning (23 reviews) - Expensive (22 reviews) ### 22. [SAP Analytics Cloud](https://www.g2.com/products/sap-analytics-cloud/reviews) With the SAP Analytics Cloud solution, you can bring together analytics and planning with unique integration to SAP applications and smooth access to heterogenous data sources. As the analytics and planning solution within SAP Business Technology Platform, SAP Analytics Cloud supports trusted insights and integrated planning processes enterprise-wide to help you make decisions without doubt. **Average Rating:** 4.2/5.0 **Total Reviews:** 750 **How Do G2 Users Rate SAP Analytics Cloud?** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 9.1/10) - **Steps to Answer:** 8.0/10 (Category avg: 8.3/10) - **Reports Interface:** 8.3/10 (Category avg: 8.7/10) - **Calculated Fields:** 7.9/10 (Category avg: 8.5/10) **Who Is the Company Behind SAP Analytics Cloud?** - **Seller:** [SAP](https://www.g2.com/sellers/sap) - **Company Website:** https://www.sap.com/ - **Year Founded:** 1972 - **HQ Location:** Walldorf - **Twitter:** @SAP (297,052 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,955 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Senior Consultant, Consultant - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 49% Enterprise, 28% Mid-Market #### What Are SAP Analytics Cloud's Pros and Cons? **Pros:** - Ease of Use (68 reviews) - Data Analysis (52 reviews) - Data Visualization (51 reviews) - Easy Integrations (40 reviews) - Analytics (39 reviews) **Cons:** - Slow Performance (36 reviews) - Learning Curve (35 reviews) - Learning Difficulty (33 reviews) - Performance Issues (32 reviews) - Large Dataset Handling (30 reviews) ### 23. [Coefficient](https://www.g2.com/products/coefficient/reviews) Coefficient is a new way to work with your company data better, faster, and more accurately without ever leaving your spreadsheet, integrating with the tools you already use. Install the Coefficient Excel or Google Sheets extension and use it in a new or existing sheet in seconds. Once installed, Coefficient lives as a sidebar companion so your company data is only a couple of clicks away at any time. Any data source that you work with is available directly in your Coefficient sidebar – such as Salesforce, HubSpot, Snowflake, NetSuite, QuickBooks, MySQL, and Looker – with the ability to consolidate your data from multiple systems into one spreadsheet. Use Coefficient filters to easily customize your imports to only work with the data you need, keeping your spreadsheets performant. Quickly go back anytime to add more data in the same report. Never rebuild the same analysis twice by keeping your data up to date with scheduled updates. And, use Coefficient alerts to trigger Slack or email messages anytime your spreadsheet updates. Now, you can turn your spreadsheet into the most flexible, powerful monitoring system across all of your company data. Say “goodbye” to manual data workflows and “hello” to connected spreadsheets. **Average Rating:** 4.7/5.0 **Total Reviews:** 188 **How Do G2 Users Rate Coefficient?** - **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 9.1/10) **Who Is the Company Behind Coefficient?** - **Seller:** [Coefficient](https://www.g2.com/sellers/coefficient) - **Company Website:** https://coefficient.io/ - **Year Founded:** 2020 - **HQ Location:** Palo Alto, CA - **Twitter:** @coefficient_io (345 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/coefficientworks/ (71 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 47% Mid-Market, 36% Small-Business #### What Are Coefficient's Pros and Cons? **Pros:** - Ease of Use (72 reviews) - Automation (42 reviews) - Integrations (42 reviews) - Time-saving (36 reviews) - Easy Integrations (31 reviews) **Cons:** - Limited Features (18 reviews) - Feature Limitations (17 reviews) - Limitations (13 reviews) - Missing Features (12 reviews) - Integration Issues (11 reviews) ### 24. [Teradata Autonomous Knowledge Platform](https://www.g2.com/products/teradata-autonomous-knowledge-platform/reviews) Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI. Learn more at Teradata.com. **Average Rating:** 4.3/5.0 **Total Reviews:** 355 **How Do G2 Users Rate Teradata Autonomous Knowledge Platform?** - **Has the product been a good partner in doing business?:** 8.2/10 (Category avg: 9.1/10) - **Steps to Answer:** 8.1/10 (Category avg: 8.3/10) - **Reports Interface:** 7.7/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.4/10 (Category avg: 8.5/10) **Who Is the Company Behind Teradata Autonomous Knowledge Platform?** - **Seller:** [Teradata Autonomous Knowledge Platform](https://www.g2.com/sellers/teradata-autonomous-knowledge-platform) - **Company Website:** https://www.teradata.com - **Year Founded:** 1979 - **HQ Location:** San Diego, CA - **Twitter:** @Teradata (93,113 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1466/ (9,880 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Engineer, Software Engineer - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 69% Enterprise, 22% Mid-Market #### What Are Teradata Autonomous Knowledge Platform's Pros and Cons? **Pros:** - Performance (14 reviews) - Analytics (11 reviews) - Scalability (11 reviews) - Speed (11 reviews) - Large Datasets (9 reviews) **Cons:** - Learning Curve (9 reviews) - Steep Learning Curve (5 reviews) - Complexity (4 reviews) - Cost (3 reviews) - Expensive (3 reviews) ### 25. [SAS Enterprise Guide](https://www.g2.com/products/sas-enterprise-guide/reviews) SAS Enterprise Guide is a Windows-based client application that provides a user-friendly, point-and-click interface to the powerful analytics capabilities of SAS software. Designed to cater to both novice and experienced users, it facilitates data access, management, analysis, and reporting without the need for extensive programming knowledge. By integrating a wide array of analytical tasks with an intuitive graphical interface, SAS Enterprise Guide empowers users to efficiently conduct complex analyses and share results across their organization. Key Features and Functionality: - Intuitive Interface and Wizards: Offers guided access to SAS capabilities, from basic reporting to advanced analyses, through flexible wizards and an intuitive process flow diagram facility. - Comprehensive Analytical Tasks: Includes over 100 prebuilt tasks for descriptive statistics, predictive modeling, regression analysis, and more, enabling users to perform complex analyses without writing code. - Data Management: Provides a powerful graphical query builder for accessing and manipulating various data types, including SAS datasets and native Windows data types, without requiring SQL expertise. - OLAP Access and Visualization: Supports dynamic slicing, drilling, and pivoting of data for exploration, with integration capabilities for SAS OLAP Server and other third-party vendors supporting OLE DB for OLAP. - Result Distribution and Sharing: Facilitates the distribution of results through multiple channels, including SAS BI report/content repository, Microsoft Office documents, and email, ensuring seamless sharing and collaboration. - High-Performance Computing and Grid Enablement: Automatically detects grid environments for efficient processing, analyzes SAS programs to optimize performance, and enables parallel execution of tasks on the same server. Primary Value and User Solutions: SAS Enterprise Guide addresses the need for a self-service analytics environment that empowers business analysts and other users to perform sophisticated data analyses without relying heavily on IT departments. By providing guided access to data integration, preparation, analytics, and reporting, it enables users to quickly access data, conduct analyses, and distribute results, thereby accelerating decision-making processes. The integration with SAS Viya further enhances its capabilities, allowing users to leverage modern, cloud-based platforms for scalable and efficient analytics. This comprehensive toolset ultimately helps organizations harness their data effectively, leading to more informed business decisions and improved operational efficiency. **Average Rating:** 4.3/5.0 **Total Reviews:** 112 **How Do G2 Users Rate SAS Enterprise Guide?** - **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 9.1/10) - **Steps to Answer:** 7.9/10 (Category avg: 8.3/10) - **Reports Interface:** 8.4/10 (Category avg: 8.7/10) - **Calculated Fields:** 8.1/10 (Category avg: 8.5/10) **Who Is the Company Behind SAS Enterprise Guide?** - **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2) - **Year Founded:** 1976 - **HQ Location:** Cary, NC - **Twitter:** @SASsoftware (60,863 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,638 employees on LinkedIn®) - **Phone:** 1-800-727-0025 **Who Uses This Product?** - **Top Industries:** Banking, Hospital & Health Care - **Company Size:** 57% Enterprise, 26% Mid-Market #### What Are SAS Enterprise Guide's Pros and Cons? **Pros:** - Ease of Use (8 reviews) - User Interface (4 reviews) - Data Analysis (3 reviews) - Data Visualization (2 reviews) - Ease of Learning (2 reviews) **Cons:** - Slow Performance (3 reviews) - Complex Usage (2 reviews) - Learning Curve (2 reviews) - Bugs (1 reviews) - Integration Issues (1 reviews) ## What Is Analytics Platforms? [Analytics Tools & Software](https://www.g2.com/categories/analytics-tools-software) ## What Software Categories Are Similar to Analytics Platforms? - [Data Visualization Tools](https://www.g2.com/categories/data-visualization-tools) - [Predictive Analytics Software](https://www.g2.com/categories/predictive-analytics) - [Embedded Business Intelligence Software](https://www.g2.com/categories/embedded-business-intelligence) - [Marketing Analytics Software](https://www.g2.com/categories/marketing-analytics) - [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) - [ETL Tools](https://www.g2.com/categories/etl-tools) - [Data Preparation Software](https://www.g2.com/categories/data-preparation) --- ## How Do You Choose the Right Analytics Platforms? ### What You Should Know About Analytics Platforms ### What are analytics software platforms? Analytics platforms, also known as business intelligence (BI) platforms, enable companies to gain visibility into their data through data integration, cleansing, blending, enrichment, discovery, and more. These tools are robust systems that sometimes require IT and data science skills to access and decipher company data through custom queries.  Analytics platforms offer a comprehensive look into a company’s data by pulling from structured and unstructured data sources through detailed queries. Casual business users also benefit from analytics platforms, which offer customizable dashboards and the ability to drill into particular data points and trends. ### What types of analytics tools and platforms exist? #### **All-in-one software** ##### **Self-service analytics platforms** Self-service analytics platforms do not require coding knowledge, so business end users can use them for data needs. Cloud-based business analytics software often provides drag-and-drop functionality for building dashboards, prebuilt templates for querying data, and, occasionally, natural language querying for data discovery.  ##### **Embedded BI software** Embedded BI software can integrate proprietary analytics functionality within other business applications. Businesses may choose an embedded product to promote user adoption; by placing the analytics inside regularly used software, companies enable employees to take advantage of available data. These solutions provide self-service functionality so average business end users can use data for improved decision-making. #### **Point solutions** ##### **Root cause analysis** Companies of all sizes produce vast amounts of data from a host of different sources. It can be difficult to keep track of the ebbs and flows of data and to spot outliers and trends across tens if not hundreds (sometimes even thousands) of data sources. Some solutions provide the user with a bird' s-eye view of their data and intelligently alert them to changes in real time. Once alerted, they are able to dive in to evaluate the situation and solve it. ### What are the common features of analytics solutions? Analytics software platforms are a great aid to any organization needing timely data visualization of high-level analytics. The following are some core features within analytics platforms that can help users make the most of them: **Data preparation:**  Although standalone [data preparation software](https://www.g2.com/categories/data-preparation) exists that assists in discovering, blending, combining, cleansing, and enriching data—so large datasets can be easily integrated, consumed, and analyzed—analytics platforms must incorporate these functionalities into their core offering. In particular, analytics platforms must support data blending and modeling, allowing the end user to combine data across different databases and other data sources and to develop robust data models of this data. This is a critical step in making meaning out of the chaos by combining data from various sources. **Data management:** Once the data is properly integrated, it must be managed. This includes restricting data access to certain users, for example. Although some companies opt for a standalone data management solution, such as a data warehouse, analytics platforms must, by definition, provide some level of data management. **Data modeling and blending:** As mentioned, it is not efficient and often not effective to examine data when it is sprawled across many systems. As a business cloud, analytics platforms help businesses consolidate data and combine data points to understand the relationship between data and derive deep insights. **Reports and dashboards:** Multilayered, real-time dashboards are a central feature of analytics platforms. Users can program their analytics software to display metrics of their choice and create multiple dashboards that show analytics related to specific teams or initiatives. From predictive website traffic analytics to customer conversion rates over a specified period, users can choose their preferred metrics to feature in dashboards and create as many dashboards as necessary.  Administrators can adjust the permissions of different dashboards so they are accessible to the users in the company who need them the most. Users can share specific dashboards on office monitors or take screengrabs of dashboards to save and share as needed. Some analytics platform products may allow users to explore dashboards on their mobile devices. [**Self service**](https://www.g2.com/categories/analytics-platforms/f/self-service) **:** Organizations use these tools to build interactive dashboards for discovering actionable insights. This enables business users like sales representatives, human resource managers, marketers, and other non-data team members to make decisions based on relevant business data. **Advanced analytics:** Many analytics solutions are incorporating advanced features, sometimes called augmented analytics, to better understand a business’s data, even without IT support. These can include predictive analytics capabilities and data discovery, which includes intelligent suggestions for data visualization and machine learning-powered suggestions for deeper insights. Other features include [Anomaly detection](https://www.g2.com/categories/analytics-platforms/f/anomaly-detection), [Query based](https://www.g2.com/categories/analytics-platforms/f/query-based), [Search](https://www.g2.com/categories/analytics-platforms/f/search), [Traditional](https://www.g2.com/categories/analytics-platforms/f/traditional) ### What are the benefits of using analytics platforms? **Replace old or disparate software:** Businesses can replace outdated data storage solutions and reporting tools and migrate to an all-inclusive business cloud as an analytics platform. However, data migration is not essential for deploying an analytics solution, as businesses may not have the time or resources to do so. Therefore, it should be noted that these platforms can integrate with a whole host of solutions, such as [enterprise resource planning (ERP)](https://www.g2.com/categories/erp-systems) and [customer relationship management (CRM) software](https://www.g2.com/categories/crm). **Improve productivity:** The days of sorting through tens, if not hundreds, of systems and needing immense support from IT have passed. With analytics platforms (especially those that are self-service and have features such as natural language search), anyone looking for data and data analysis, including average business users, can derive insights from their data. **Save time (automation):** For most analytics platforms, users no longer need a strong background in query languages. Instead, data discovery and root cause analysis allow users to automatically receive alerts and insights into their data and get notified if the data has changed meaningfully. **Reduce errors:** Although standalone data preparation tools may be the right solution for businesses with particularly complex data, analytics platforms allow users to clean and prepare their data through data mapping and deduplication methods. **Consolidate data:** In this data-driven era, essentially every program and device a business has produces massive data. To understand this diverse data in the best way possible, combining it through methods such as data blending, which allows users to integrate data from multiple sources into a functioning dataset, is often necessary. **Improve processes:** Without an analytics platform to be used across a business, processes can be slow and inefficient as interested parties seek data from disparate sources and request data from various people. Analytics platforms can help a business user quickly access data and data analysis and share it with internal and external stakeholders. ### **Who uses analytics tools?** Analytics platforms can have both internal and external users.  #### **Internal users** **Data analysts and data scientists:** These employees are generally the power users of analytics tools, creating complex queries inside the platforms to gather a deeper understanding of business-critical data. These teams may also be tasked with building self-service dashboards to distribute to other teams. **Sales teams:** Sales teams use self-service analytics tools and embedded analytics solutions to obtain insights into prospective accounts, sales performance, and pipeline forecasting, among many other use cases. Using analytics tools in a sales team can help businesses optimize their sales processes and influence revenue. **Marketing teams:** Marketing teams often run different types of campaigns, including email marketing, digital advertising, or even traditional advertising campaigns. Analytics tools allow marketing teams to track the performance of those campaigns in one central location. **Finance teams:** Finance teams leverage analytics software to gain insight into the factors impacting an organization's bottom line. By integrating financial data with sales, marketing, and other operations data, accounting and finance teams pull actionable insights that might not have been uncovered using traditional tools. **Operations and supply chain teams:** Analytics solutions often utilize a company's ERP system as a data source. These applications track everything from accounting to supply chain and distribution; supply chain managers can optimize several processes to save time and resources by inputting supply chain data into an analytics platform.  #### **External users** **Consultants:** Businesses, especially larger ones, do not always understand the breadth and depth of their data, perhaps not even knowing where to begin. An external consultant wielding a powerful analytics platform can help businesses better understand their data and, as a result, make more informed business decisions.  Users may consider contacting [BI consulting partners](https://www.g2.com/categories/business-intelligence-bi-consulting) to help determine the most relevant analytics and data to capture about their company’s overall success. Following a proper consultation, these agencies may offer assistance with setting up or choosing BI tools. A number of these agencies can assist businesses with the entire BI process, from complete data analysis to the shaping of processes or protocols related to data collection. A relationship with these consultants can prove highly beneficial for users who have never performed data analysis before or want to optimize their company’s reporting. **Partners:** Partnerships between companies often involve data sharing and cross-company collaboration. As a result, a centralized repository of data, which would allow for data management, data querying, and data insights, can provide an essential tool for these businesses to succeed together, providing them with a birds-eye view of their data. ### **What are the alternatives to analytics platforms?** Alternatives to analytics platforms can replace this type of software, either partially or completely: [**Marketing analytics software**](https://www.g2.com/categories/marketing-analytics) **:** Businesses looking for tools geared toward marketing use cases and marketing data (e.g., related to targeting prospects) should look at marketing analytics solutions that are purpose-built for this. [**Sales analytics software**](https://www.g2.com/categories/sales-analytics) **:** Although sales data such as revenue forecasts and closed deals can be imported and analyzed in general-purpose analytics platforms, sales analytics platforms can provide a more granular analysis of sales-related data and might have better integrations with sales tools such as CRMs.  [**Log analysis software**](https://www.g2.com/categories/log-analysis) **:**  If a business wants to focus on analyzing its log data from applications and systems, it could benefit from log analysis software, which helps enable the documentation of application log files for records and analytics. [**Predictive analytics software**](https://www.g2.com/categories/predictive-analytics) **:** Broad-purpose analytics platforms allow businesses to conduct various forms of analysis, such as prescriptive, descriptive, and predictive. Since analytics platforms allow for these different types of analyses, they might not provide the most robust features for any type. Therefore, businesses focused on looking at past and present data to predict future outcomes can use predictive analytics software for a more fine-tuned solution.  [**Text analysis software**](https://www.g2.com/categories/text-analysis) **:** Analytics platforms are focused on structured or numerical data, allowing users to drill down and dig into numbers to inform business decisions. Text analysis solutions are the best bet if the user is looking to focus on unstructured or text data. These tools help users quickly understand and pull sentiment analysis, key phrases, themes, and other insights from unstructured text data. [**Data visualization software**](https://www.g2.com/categories/data-visualization) **:** Data visualization tools can be an excellent place for businesses to start when looking to better understand their data. With capabilities including dashboards and reporting, data visualization software can often be quick and easy to set up and is frequently cheaper than more robust analytics platforms.  However, it is essential to recognize their limitations. Data visualization solutions do what they say on the box: visualization. They do not give the user an end-to-end analytics solution from data preparation to data insights, nor do they provide significant data management capabilities. ### **Software and services related to analytics platforms** Related solutions that can be used together with analytics platforms include: [**Embedded business intelligence software**](https://www.g2.com/categories/embedded-business-intelligence) **:** Analytics platforms are standalone platforms that help companies analyze data. Businesses who want to build analytics capabilities into applications, whether that be for internal or external use, can use embedded BI software to accomplish this goal. [**Database software**](https://www.g2.com/categories/database-software) **:** There are a plethora of solutions for storing, organizing, and sharing large amounts of data that can later be accessed and analyzed by analytics tools. Database software includes everything from [big data software](https://www.g2.com/categories/big-data) to traditional table-based [relational databases](https://www.g2.com/categories/relational-databases). Businesses should research and implement whichever database tools make the most sense for their particular data types or analytical needs.  When considering an analytics solution, users should investigate which databases can integrate with the tool to make the most logical product choice for their situation. Analytics products would not serve much purpose without one or more company databases to pull data from when the time comes. ### Challenges with analytics platforms **Configuration:** Analytics solutions may have a highly technical setup process, requiring IT or developmental expertise. When trying to implement one of these platforms without an in-house data scientist or IT professional, users may struggle with getting the technology off the ground, integrating it with the appropriate solutions, and creating queries for data collection. This could mean a significant loss of resources and an inability to use the tool as intended. Users can contact BI consulting providers for assistance setting up a program or, in some cases, for handling the entirety of BI reporting. **Overreliance:** Focusing too much on data and analytics can also be problematic. Data-driven decisions are critical to a business’s success, but data-only decisions ignore the various voices from within and without the organization. Successful companies combine rigorous analytics with anecdotal storytelling and thoughtful conversations about the business's success and components. **Integrations:** If the analytics tool does not fully integrate with existing software, getting a complete view of a business’s operational performance becomes challenging. Similarly, if an integration experiences a communication error or other issue during a data query, it causes an incorrect or incomplete reading. Users should make a point to monitor these connections and any potential performance issues throughout their software stack to ensure that correct, complete, and up-to-date information is being processed and displayed on dashboards. **Data security:** Companies must consider security options to ensure the right users see the correct data and guarantee strict data security. Effective analytics solutions should offer security options that enable administrators to assign verified users different levels of access to the platform based on their security clearance or level of seniority. ### How to choose the best analytics tools #### Requirements Gathering (RFI/RFP) for Analytics Platforms If a company is just starting and looking to purchase the first analytics platform, or maybe an organization needs to update a legacy system--wherever a business is in its buying process, g2.com can help select the best analytics platform. The particular business pain points might be related to all the manual work that must be completed. If the company has amassed a lot of data, it needs to look for a solution that can grow with the organization. Users should think about the pain points and jot them down; these should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees needing this software, as this drives the number of licenses they will likely buy. Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist is a detailed guide with necessary and nice-to-have features, including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more. Depending on the deployment scope, producing an RFI, a one-page list with a few bullet points describing what is needed from an analytics platform might be helpful. #### Compare Analytics Platforms Products **Create a long list** From meeting the business functionality needs to implementation, vendor evaluations are essential to the software buying process. For ease of comparison, after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor. **Create a short list** From the long list of vendors, it is helpful to narrow the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list, businesses can produce a matrix to compare the features and pricing of the various solutions. **Conduct demos** To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.  #### Selection of analytics platforms **Choose a selection team** Before getting started, creating a winning team that will work together throughout the process, from identifying pain points to implementation, is crucial. The software selection team should consist of organization members with the right interests, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the primary decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. The vendor selection team may be more minor in smaller companies, with fewer participants, multitasking, and taking on more responsibilities. **Analyze the data** As analytics platforms are all about the data, the user must ensure that the selection process is also data-driven. The selection team should compare notes and facts and figures that they noted during the process, such as time to insight, number of visualizations, and availability of advanced analytics capabilities. **Negotiation** Just because something is written on a company’s pricing page does not mean it is gospel (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to discount multiyear contracts or recommend the product to others. **Final decision** After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and received, the buyer can be confident that the selection was correct. If not, it might be time to return to the drawing board. ### How much do analytics software platforms cost? As mentioned above, analytics platforms come as both on-premises and cloud solutions. Pricing between the two might differ, with the former often coming with more upfront costs for setting up the infrastructure.  As with any software, analytics platforms are frequently available in different tiers, with the more entry-level solutions costing less than the enterprise-scale ones. The former will often not have as many features and may have caps on usage. Vendors may have tiered pricing, in which the price is tailored to the users’ company size, the number of users, or both. This pricing strategy may come with some support, which might be unlimited or capped at a certain number of hours per billing cycle. Once set up, analytics platforms, especially those deployed in the cloud, do not often require significant maintenance costs. As these platforms often come with many additional features, businesses looking to maximize the value of their software can contract third-party consultants to help them derive insights from their data and get the most out of the software. #### Return on Investment (ROI) Businesses deploy analytics platforms to derive a return on investment (ROI). As they are looking to recoup the losses they spent on the software, it is critical to understand its costs. As mentioned above, analytics platforms are typically billed per user, sometimes tiered, depending on the company size. More users will generally translate into more licenses, which means more money. Users must consider how much is spent and compare that to what is gained in terms of efficiency and revenue. Therefore, businesses can compare processes between pre- and post-deployment software to understand better how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from using an analytics tool. ### Implementation of analytics software solutions **How are analytics software Implemented?** Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether an implementation specialist from the vendor or a third-party consultancy. With vast experience under their belts, they can help businesses understand how to connect and consolidate their data sources and use the software efficiently and effectively. **Who is responsible for analytics platform implementation?** Properly deploying an analytics platform may require many people or teams. This is because, as mentioned, data can cut across teams and functions. As a result, one person or even one team rarely has a complete understanding of all of a company’s data assets. With a cross-functional team, a business can begin to piece together its data and begin the analytics journey, starting with proper data preparation and management. ### Emerging trends related to analytics platforms **Increase data accessibility** Business data is no longer locked up in silos. With analytics platforms, more users across a business can find, access, and analyze this data. In addition, [artificial intelligence (AI) tools](https://www.g2.com/categories/artificial-intelligence) such as [natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) help make searching through and for data more accessible and powerful, providing more accurate results. With the amount of data accessible to businesses today, it is a near necessity that they implement some type of analytics software to understand and act on that data better. Implementing analytics software has been a significant initiative for companies undergoing digital transformation, as these tools offer deeper visibility into an organization's data. Companies adopt these solutions to make sense of large datasets collected from various sources. **Shift from on-premises to cloud** The move from on-premises data analytics to the cloud has been underway for several years, with more and more businesses moving their data and data insights into the cloud. This is taking place for various reasons, such as time to insight. Moving away from on-premises infrastructure has helped many companies enable data work anywhere one has access to the cloud—anywhere with internet access. However, not all data users have the luxury of working in the cloud for several reasons, including data security and issues related to latency. In industries such as health care, strict regulations such as the [Health Insurance Portability and Accountability Act (HIPAA)](https://learn.g2.com/health-insurance-portability-and-accountability-act) require data to be secure. Although it is possible to ensure this security in the cloud, it can be more complicated. **Conversational AI** Historically, to query data within an analytics solution, users needed to master a query language like SQL. With the rise of conversational interfaces, users uncover the data and insights they seek using intuitive language. Intuitive methods of querying data enable a larger user base to access and make sense of company data. **Machine learning** AI is quickly becoming a promising feature of analytics solutions throughout the data journey, from ingestion to insights. From AI-powered data preparation to smart insights, in which the platform suggests visualizations to the end user, analytics platforms are quickly becoming more powerful. Machine learning is helping end users discover hidden insights, allowing them to make sense of data and understand what they are seeing. ### Analytics Platforms FAQs #### **Which analytics platforms have an intuitive UI that non-technical users adopt without extensive training?** I looked for analytics platforms that make it easy to find what you need also enable easy collaboration. - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) **:** Has a drag-and-drop chart building without SQL or code that makes it user-friendly. Users can connect Excel, SharePoint, and emails without technical help.  - [Tableau](https://www.g2.com/products/tableau/reviews) **:** Users can create dashboards with limited use and no deep technical background. Onboarding is as simple as sharing the links and giving the right access. - [Sigma](https://www.g2.com/products/sigma-computing-sigma/reviews) **:** Spreadsheet-like UI provides the most distinctive non-technical adoption story. Users can pull in data and work with it in complicated ways that don't require coding. - [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) **:** The semantic layer approach means business users interact with pre-defined, plain-language metrics rather than raw tables or SQL, which is the foundational mechanism for non-technical adoption.  #### **What are the best analytics platforms for business teams accessing insights without SQL or data science skills?** SQL-free access means business users can explore, filter, and create their own views without analyst dependency.  - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) **:** Non-technical users can build powerful dashboards quickly without SQL. Power BI's Power Query Editor handles data transformations through button clicks. The DAX layer exists for advanced users but is not required for standard self-service reporting. - [Sigma](https://www.g2.com/products/sigma-computing-sigma/reviews) **:** Sigma's specific differentiator for SQL-free access is its spreadsheet-like interface on live warehouse data. For teams already on Snowflake or cloud warehouses, Sigma eliminates the SQL barrier entirely at the analysis layer. - [Domo](https://www.g2.com/products/domo/reviews) **:** The no-code platform is easy to use for non-technical teams. Users can apply filtering and date range changes without any coding. The 1000+ connector ecosystem means data arrives automatically, so business users interact with dashboards rather than queries.  - [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) **:** The semantic model defines metrics in plain language; business users query pre-built dimensions and measures without writing a single line of SQL.  #### **Which analytics platforms support collaborative dashboards, annotations, and mobile access for on-the-go insights?** I looked for tools with collaborative analytics, shared dashboards, in-platform discussion, annotations, and mobile-ready access. - [Domo](https://www.g2.com/products/domo/reviews) **:** Sales teams can access data and contacts in real-time on their phones. Has platform-native discussion, report sharing, and insight annotation as part of the daily workflow.  - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) **:** Web and mobile access are daily-use features. The platform enables collaboration so everyone can see the same report at the same time, updated in real time. - [Yellowfin BI](https://www.g2.com/products/yellowfin-bi/reviews) **:** Designed around collaborative BI with built-in story, annotation, and broadcast features for sharing insights with business audiences.  - [Looker](https://www.g2.com/products/looker/reviews) **:** Comes with scheduled report delivery, so automated weekly numbers land in inboxes without anyone manually running anything. Email reports and metric notifications can be set up as daily workflow features.  #### **Which analytics solutions provide fast query response and drill-down capability for ad-hoc exploration?** Fast ad-hoc exploration means users can drill down, pivot, and filter without waiting and without writing a new query every time. - [Tableau](https://www.g2.com/products/tableau/reviews) **:** Users can drill down on data without writing queries. Extracted datasets perform significantly better for ad-hoc work. - [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) **:** The semantic layer pre-aggregates at the warehouse layer so ad-hoc queries against massive datasets return fast without full table scans.  - [Incorta](https://www.g2.com/products/incorta/reviews) **:** Its direct data mapping approach eliminates the aggregation layer that slows most BI platforms during ad-hoc queries. For organizations where query latency on complex, multi-source datasets is the primary pain, Incorta is a good choice. - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) **:** Provides intuitive filtering across countries, teams, and time periods within existing dashboards without analyst involvement. The Direct Lake connection mode specifically reduces ad-hoc query latency. #### **Which analytics solutions integrate with Snowflake, BigQuery, and Redshift seamlessly?** Native, live connections to modern data warehouses — where queries run in the warehouse rather than in the BI tool are what seamless integration actually means for data teams. - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) **:** Integration with Active Directory, SharePoint, and the Microsoft Fabric ecosystem is described as genuinely seamless for organizations already in the Microsoft stack. - [Sigma](https://www.g2.com/products/sigma-computing-sigma/reviews) **:** Built to run directly on Snowflake (and BigQuery/Redshift) without extracting data. The architecture means live warehouse queries are the default mode, not an optional feature. - [Databricks](https://www.g2.com/products/databricks/reviews) **:** It is the warehouse-layer itself for many organizations, not a BI tool connecting to a warehouse, which means integration is inherently native. For organizations that treat Databricks as the processing layer and BI tools as the visualization layer on top, Databricks' own analytics features (via SQL Warehouses and notebooks) eliminate the need for a separate integration layer. - [Looker](https://www.g2.com/products/looker/reviews) **:** BigQuery is the most-named data warehouse in Looker's review base. LookML's push-down SQL architecture means all queries run in the warehouse (Snowflake, BigQuery, Redshift) rather than being extracted into Looker. #### **Which analytics solutions come with robust caching and performance optimization to maintain fast speeds at scale?** Caching and performance optimization matter when datasets are large, dashboards are complex, and business users can't wait for queries to resolve. - [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) **:** The semantic layer pre-aggregates at the warehouse layer specifically to make large dataset queries fast. For organizations where query latency on multi-billion row datasets is the blocking problem, Kyvos is the most purpose-built and best-validated option in the category on these dimensions. - [Databricks](https://www.g2.com/products/databricks/reviews) **:** Photon engine, Delta Lake caching, and auto-scaling compute architecture are the performance mechanisms at scale.  - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) **:** Row Level Security, Premium capacity, and aggregation tables are the scale optimization toolkit. Performance at scale in Power BI is achievable but requires deliberate architecture, not automatic. - [Incorta](https://www.g2.com/products/incorta/reviews) **:** Direct data mapping eliminates the pre-aggregation step that creates scale bottlenecks in most BI tools, allowing ad-hoc queries against large transactional datasets to run without a separate aggregation cache.  #### **Which analytics platforms prevent incorrect conclusions by enforcing data governance and preventing metric manipulation?** I looked for analytics platforms with strong data governance features.  - [Looker](https://www.g2.com/products/looker/reviews) **:** LookML governance, real-time data access, and seamless integration with modern data warehouses together create the governed analytics environment enterprises need to prevent metric drift across teams. - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) **:** Addresses governance through Row Level Security, workspace permissions, and certified datasets, which restrict what data individual users can see and prevent unauthorized metric redefinition. - [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) **:** It defines metrics once at the semantic model level and enforces those definitions for every downstream query and dashboard.  - [Tableau](https://www.g2.com/products/tableau/reviews) **:** Governance approach is built around Tableau Server and Tableau Cloud — where published data sources become the certified metric layer that individual report builders consume rather than create.