# Best Analytics Platforms ## How Many Analytics Platforms Products Does G2 Track? **Total Products under this Category:** 362 ### Category Stats (Jul 2026) - **Average Rating:** 4.49/5 The average rating of products in this category, based on all submitted ratings - **Top Trending Product:** Aible (+6.67%) - Among all products in this category, Aible recorded the largest rating increase compared to last month _Last updated: July 24, 2026_ ## How Does G2 Rank Analytics Platforms Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 28,800+ Authentic Reviews - 362+ 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. ## G2 Grid® for Analytics Platforms ![G2 Grid® for Analytics Platforms plotting products by satisfaction and market presence](https://www.g2.com/categories/analytics-platforms/grids.png?focus%5B%5D=29364&focus%5B%5D=1213&focus%5B%5D=10470&focus%5B%5D=1327283&focus%5B%5D=989&focus%5B%5D=604&focus%5B%5D=5048&focus%5B%5D=27024) Highlighted products: Microsoft Power BI, Tableau, Databricks, SAS Viya, Alteryx, Domo, Looker, and Kyvos Semantic Layer. Underlying data: [Grid® JSON](https://www.g2.com/categories/analytics-platforms/grids.json?focus%5B%5D=microsoft-microsoft-power-bi&focus%5B%5D=tableau&focus%5B%5D=databricks&focus%5B%5D=sas-sas-viya&focus%5B%5D=alteryx&focus%5B%5D=domo&focus%5B%5D=looker&focus%5B%5D=kyvos-semantic-layer) **Sponsored** ### Lightdash Lightdash is the fastest way for modern data teams to build and scale AI-powered self-serve analytics. It enables analysts to define metrics and make them accessible to non-technical users, who can explore data and build dashboards without writing SQL. Lightdash integrates with modern data stacks, supports a governed semantic layer, and offers developer-friendly features like Git-based workflows. It is designed to reduce the overhead of maintaining BI tools, streamline metric definitions, and improve access to consistent data across teams. Lightdash is used and trusted by companies of all sizes, from start ups to international publicly listed organisations. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=620&secure%5Bchosen_at%5D=2026-07-27T21%3A00%3A58Z&secure%5Bdisplayable_resource_id%5D=620&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=620&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=1215392&secure%5Bresource_id%5D=620&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fanalytics-platforms&secure%5Btoken%5D=076f2beaac0624e83b23a57015d8c3ca61d279d0a5fe5e34320c2d5507d49024&secure%5Burl%5D=https%3A%2F%2Fwww.lightdash.com%2F&secure%5Burl_type%5D=company_website) ### [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:** 3,675 #### 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: 8.4/10) - **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% Large, 36% Medium #### What Do G2 Reviewers Say About Tableau? _AI-generated summary from verified user reviews_ ##### Pros - Users find Tableau's **ease of use** essential for creating interactive dashboards without heavy coding or complex setups. - Users value Tableau's **data visualization capabilities** , enabling the creation of interactive, clear dashboards that enhance decision-making. - Users love Tableau for its **powerful visualization capabilities** , enabling clear, interactive dashboards and real-time data insights. - Users value the **interactive dashboards** of Tableau, enhancing data visualization and simplifying complex reporting tasks seamlessly. - Users love Tableau's **intuitive interface** , making complex data visualization and dashboard creation easy and accessible. ##### Cons - Users find the **learning curve steep** , making it challenging to fully integrate Tableau with Salesforce and MS products. - Users face a **steep learning curve** with Tableau, making it challenging for new users to become proficient. - Users report that the **high licensing cost** of Tableau can be a significant barrier, especially for smaller teams. - Users report **slow performance** with large datasets, impacting their experience and efficiency in Tableau. - Users find Tableau's **onboarding and complexity** challenging, making it tougher to transition from simpler tools like MS products. #### What Are Recent G2 Reviews of Tableau? **["Clear data means quick decisions."](https://www.g2.com/survey_responses/tableau-review-13124621)** **Rating:** 4.5/5.0 stars _— Libia B._ [Read full review](https://www.g2.com/survey_responses/tableau-review-13124621) **["Turning Complex Data into Actionable Insights"](https://www.g2.com/survey_responses/tableau-review-13075232)** **Rating:** 5.0/5.0 stars _— Murtuza L._ [Read full review](https://www.g2.com/survey_responses/tableau-review-13075232) ### [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:** 1,597 #### 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: 8.4/10) - **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 (13,091,739 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% Large, 37% Medium #### What Do G2 Reviewers Say About Microsoft Power BI? _AI-generated summary from verified user reviews_ ##### Pros - Users find **Microsoft Power BI extremely easy to use** , streamlining data analysis and report generation effortlessly. - Users commend the **data visualization capabilities** of Microsoft Power BI, enabling insightful analysis of large datasets. - Users appreciate the **seamless integration** with various databases, enhancing data visualization and analysis efficiently. - Users appreciate the **ease of transforming complex data into interactive insights** with Microsoft Power BI, greatly enhancing decision-making. - Users love the **powerful charting features** in Power BI, enabling easy data visualization and report creation. ##### Cons - Users find **the learning curve steep** for Power BI, particularly for beginners exploring data connections and model optimization. - Users experience **slow performance** with large datasets in Microsoft Power BI, impacting their overall efficiency and productivity. - Users experience **performance issues** with large datasets and complex reports, impacting usability and efficiency. - Users find the **complex data modeling** challenging, especially with the steep learning curve and performance issues on large datasets. - Users find **limited customization** with Microsoft Power BI, particularly regarding free custom visuals and mobile experience enhancements. #### What Are Recent G2 Reviews of Microsoft Power BI? **[""Power BI Makes Data Visualization Fast and Professional""](https://www.g2.com/survey_responses/microsoft-power-bi-review-13158851)** **Rating:** 4.0/5.0 stars _— Sameer R._ [Read full review](https://www.g2.com/survey_responses/microsoft-power-bi-review-13158851) **["No-Code Microsoft Analytics with Easy Data Connections and Drag-and-Drop Dashboards"](https://www.g2.com/survey_responses/microsoft-power-bi-review-12894515)** **Rating:** 4.5/5.0 stars _— Ashutha K._ [Read full review](https://www.g2.com/survey_responses/microsoft-power-bi-review-12894515) ### [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:** 1,325 #### 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: 8.4/10) - **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 (92,269 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% Large, 38% Medium #### What Do G2 Reviewers Say About Databricks? _AI-generated summary from verified user reviews_ ##### Pros - Users praise the **ease of use** and **comprehensive features** of Databricks for data warehousing and ML applications. - Users praise the **ease of use** of Databricks, enhancing their experience with intuitive interfaces and reliable services. - Users appreciate the **seamless integrations** of Databricks with AWS and other tools, enhancing daily operations and efficiency. - Users value the **seamless collaboration** offered by Databricks, enhancing teamwork on data projects with real-time insights. - Users praise the **integrated analytical features** of Databricks, enhancing collaborative data processing and insight visualization. ##### Cons - Users note a **steep learning curve** initially, with confusing permissions and compute modes affecting usability. - Users note that the **costs can be quite high** for utilizing Databricks effectively, especially for large data projects. - Users find a **steep learning curve** with Databricks, especially challenging for newcomers to big data tools. - Users find the **complexity** of Databricks challenging, especially for smaller teams and initial setup processes. - Users face **complex setup** challenges initially, though support helps simplify the experience over time. #### What Are Recent G2 Reviews of Databricks? **["Helpful for Managing and Analyzing Operational Data"](https://www.g2.com/survey_responses/databricks-review-13090803)** **Rating:** 4.5/5.0 stars _— Vishaka C._ [Read full review](https://www.g2.com/survey_responses/databricks-review-13090803) **["Helpful for Rider Service Operations Reporting"](https://www.g2.com/survey_responses/databricks-review-13130892)** **Rating:** 4.0/5.0 stars _— Jayesh W._ [Read full review](https://www.g2.com/survey_responses/databricks-review-13130892) ### [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:** 774 #### 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: 8.4/10) - **Reports Interface:** 8.5/10 (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, Biostatistician - **Top Industries:** Pharmaceuticals, Banking - **Company Size:** 33% Large, 33% Small #### What Do G2 Reviewers Say About SAS Viya? _AI-generated summary from verified user reviews_ ##### Pros - Users value the **ease of use** of SAS Viya, which simplifies data visualization and enhances decision-making efficiency. - Users value the **sophisticated analytical capabilities** of SAS Viya, enabling easy deployment and real-time decision-making. - Users appreciate the **advanced analytical methods** offered by SAS Viya, enhancing decision-making and logistical data analysis capabilities. - Users value the **end-to-end data lifecycle tooling** of SAS Viya, enhancing business insight and strategic decision-making. - Users love the **intuitive interface** of SAS Viya, making data analysis and model deployment effortless for all skill levels. ##### Cons - Users find SAS Viya to have a **learning difficulty** , making it challenging for non-technical individuals to navigate effectively. - Users find the **learning curve steep** , making it challenging for non-technical users to navigate SAS Viya effectively. - Users find the **visualization complexity** in SAS Viya challenging, particularly for non-technical users and beginners. - Users struggle with the **difficult learning curve** of SAS Viya, particularly for new and non-technical users. - Users find the **expensive pricing** of SAS Viya to be a significant barrier to entry for potential adoption. #### What Are Recent G2 Reviews of SAS Viya? **["Effective Data Analysis with SAS Viya"](https://www.g2.com/survey_responses/sas-viya-review-11872818)** **Rating:** 4.5/5.0 stars _— Fungai J._ [Read full review](https://www.g2.com/survey_responses/sas-viya-review-11872818) **["SAS Viya: Powerful AI & Data Analysis with Seamless Integrations"](https://www.g2.com/survey_responses/sas-viya-review-11855145)** **Rating:** 5.0/5.0 stars _— Verified User in Hospital & Health Care_ [Read full review](https://www.g2.com/survey_responses/sas-viya-review-11855145) ### [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:** 851 #### How Do G2 Users Rate Alteryx? - **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 9.1/10) - **Steps to Answer:** 8.5/10 (Category avg: 8.4/10) - **Reports Interface:** 8.0/10 (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 (26,149 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:** 63% Large, 21% Medium #### What Do G2 Reviewers Say About Alteryx? _AI-generated summary from verified user reviews_ ##### Pros - Users appreciate the **ease of use** of Alteryx, finding it user-friendly and efficient for non-technical users. - Users appreciate the **automation capabilities** of Alteryx, enhancing speed and efficiency in data preparation and analysis. - Users love the **intuitive design** of Alteryx, making data management and workflow creation effortless and efficient. - Users find Alteryx to be **very easy to learn and use** , enhancing their data workflow and automation experience. - Users appreciate the **efficiency** of Alteryx, enabling quick data processing and streamlined workflows without complex coding. ##### Cons - Users mention that Alteryx has a **high cost** which can be challenging for small teams and startups. - Users find a **steep learning curve** for advanced features, making it challenging for beginners to master Alteryx quickly. - Users point out the **missing features** in Alteryx, such as limited connectors and issues with output flexibility. - Users find **learning difficulty** in Alteryx due to confusing tools and troubleshooting errors, especially for beginners. - Users encounter **slow performance** when processing large datasets, impacting efficiency and usability in Alteryx. #### What Are Recent G2 Reviews of Alteryx? **["Powerful, Time-Saving Data Prep But Pricey, Windows-Only, and Weak on Reporting"](https://www.g2.com/survey_responses/alteryx-review-12999843)** **Rating:** 4.5/5.0 stars _— jayesh l._ [Read full review](https://www.g2.com/survey_responses/alteryx-review-12999843) **["Intuitive Drag-and-Drop Analytics That Speeds Up Data Prep and Insights"](https://www.g2.com/survey_responses/alteryx-review-12983224)** **Rating:** 4.5/5.0 stars _— Akhil S._ [Read full review](https://www.g2.com/survey_responses/alteryx-review-12983224) ### [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:** 1,030 #### 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: 8.4/10) - **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 (63,513 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/25237/ (1,299 employees on LinkedIn®) #### Who Uses This Product? - **Who Uses This:** Data Analyst, Business Analyst - **Top Industries:** Computer Software, Marketing and Advertising - **Company Size:** 49% Medium, 29% Large #### What Do G2 Reviewers Say About Domo? _AI-generated summary from verified user reviews_ ##### Pros - Users value Domo's **ease of use** , making it accessible for all, even those who aren't tech-savvy. - Users value the **flexible dashboards** in Domo, allowing seamless data visualization from various sources. - Users find Domo's **intuitive design** enhances data handling and decision-making, making it accessible for all skill levels. - Users praise Domo for its **easy integrations** , enabling seamless data connections and enhancing data management efficiency. - Users value the **seamless integration** capabilities of Domo, enhancing data management across various platforms and sources. ##### Cons - Users find the **learning curve challenging** with Domo, often requiring dedicated resources to manage updates and functionalities. - Users struggle with **missing features** in Domo, including outdated systems and limited capabilities in visualizations and connectors. - Users are frustrated by **data management issues** , including upload adjustments and poor organization of datasets in Domo. - Users find Domo to be **expensive** due to high costs associated with features and external consulting needs. - Users find Domo's **complexity** overwhelming, requiring technical expertise and extensive setup that complicate the user experience. #### What Are Recent G2 Reviews of Domo? **["Domo Turns Disconnected Reports Into a Game-Changing Sales Scorecard"](https://www.g2.com/survey_responses/domo-review-13128007)** **Rating:** 5.0/5.0 stars _— Renee G._ [Read full review](https://www.g2.com/survey_responses/domo-review-13128007) **["Fast, Reliable Dashboarding with Quick Data Connections in Domo"](https://www.g2.com/survey_responses/domo-review-13150907)** **Rating:** 4.5/5.0 stars _— Katie S._ [Read full review](https://www.g2.com/survey_responses/domo-review-13150907) ### [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:** 1,590 #### 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: 8.4/10) - **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 (31,899,995 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% Medium, 20% Large #### What Do G2 Reviewers Say About Looker? _AI-generated summary from verified user reviews_ ##### Pros - Users value the **ease of use** in Looker, enabling anyone to create reports without technical expertise. - Users value Looker's **dynamics of real-time reporting** and seamless integration, enhancing decision-making and data visualization. - Users appreciate the **easy integrations** of Looker, allowing seamless connectivity with multiple data sources for insightful analytics. - Users value the **seamless integrations** within Looker, enhancing functionality and providing quality insights across platforms. - Users appreciate Looker's **dynamic and live reporting** , which enhances real-time analytics and decision-making across multiple platforms. ##### Cons - Users find the **steep learning curve** of Looker challenging, particularly for those without technical expertise. - Users find Looker's interface challenging due to its **steep learning curve** and the complexity of LookML for beginners. - Users experience **slow loading times** with Looker, particularly when dealing with large datasets, complicating dashboard creation. - Users often experience **slow performance** with Looker, especially when dealing with complex data transformations and dashboard loading times. - Users find Looker’s **complexity** challenging, with a steep learning curve and integration issues hindering efficiency. #### What Are Recent G2 Reviews of Looker? **["Transforms Data, But Challenging for Beginners"](https://www.g2.com/survey_responses/looker-review-12784757)** **Rating:** 5.0/5.0 stars _— Rahul S._ [Read full review](https://www.g2.com/survey_responses/looker-review-12784757) **["Powerful once it's set up properly, that's once is doing a lot of work in that sentence"](https://www.g2.com/survey_responses/looker-review-12863840)** **Rating:** 4.5/5.0 stars _— Anurag S._ [Read full review](https://www.g2.com/survey_responses/looker-review-12863840) ### [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:** 266 #### 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: 8.4/10) - **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:** Senior Software Engineer, Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 57% Medium, 38% Large #### What Do G2 Reviewers Say About Kyvos Semantic Layer? _AI-generated summary from verified user reviews_ ##### Pros - Users appreciate the **ease of use** of Kyvos, enabling quick insights and a user-friendly experience for complex data. - Users love the **speed** of Kyvos for real-time insights, enabling quick queries and faster decision-making across data metrics. - Users value the **exceptional performance** of Kyvos for quickly analyzing large datasets and delivering timely insights. - Users admire the **lightning-fast analytics** of Kyvos Semantic Layer, enhancing performance and visualization of large datasets. - Users value the **fast querying capabilities** of Kyvos Semantic Layer, enabling quick analysis of large transaction datasets. ##### Cons - Users find the **learning curve steep** for advanced features and MDX queries, which can slow down usage efforts. - Users find the **difficult setup** of Kyvos Semantic Layer challenging, though support helps ease the process. - Users find the **initial setup and MDX complexity** challenging, though support helps ease the deployment process. - Users note the **feature limitations** of Kyvos, especially lacking advanced analytics and integration for seamless data exploration. - Users experience **connectivity issues** , as initial integration with existing systems can be time-consuming. #### What Are Recent G2 Reviews of Kyvos Semantic Layer? **["Fast, Consistent Data Exploration Across Dimensions with Kyvos Semantic Layer"](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-12911098)** **Rating:** 5.0/5.0 stars _— ashish r._ [Read full review](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-12911098) **["Kyvos Semantic Layer Boosts AI Accuracy with Business-Ready Data"](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-13142366)** **Rating:** 5.0/5.0 stars _— Nikhil K._ [Read full review](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-13142366) ### [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:** 402 #### 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: 8.4/10) - **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 (6,982 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:** 53% Medium, 22% Small #### What Do G2 Reviewers Say About Hex? _AI-generated summary from verified user reviews_ ##### Pros - Users find Hex **extremely user-friendly** , enjoying seamless integration and fast implementation for data projects. - Users appreciate the **seamless integration of SQL and Python** , enhancing their data analysis and visualization experience in Hex. - Users praise Hex for its **user-friendly data management** , enabling easy integration and collaboration across multiple databases. - Users appreciate the **seamless SQL querying** in Hex, enhancing data analysis with effortless integration and intuitive tools. - Users enjoy the **seamless data analysis and interactive capabilities** of Hex, enhancing collaboration and efficiency. ##### Cons - Users highlight **limited features** in Hex, noting constraints in dashboard capabilities and lack of helpful AI tools. - Users find **missing features** in Hex, such as inadequate data visualization and lack of saved result tabs. - Users note that Hex is **lacking essential features** for dashboarding and computational efficiency compared to competitors. - Users experience **slow performance** with Hex, especially in virtual machines and during routine tasks. - Users face **data management issues** with Hex, including failures in scheduled runs and challenging notebook debugging. #### What Are Recent G2 Reviews of Hex? **["All-in-One Collaborative Workspace for SQL, Python, and Interactive Dashboards"](https://www.g2.com/survey_responses/hex-review-13125920)** **Rating:** 4.5/5.0 stars _— Ravindra N._ [Read full review](https://www.g2.com/survey_responses/hex-review-13125920) **["Amazing AI and SQL Autocomplete That Speeds Up My Work"](https://www.g2.com/survey_responses/hex-review-12687305)** **Rating:** 4.0/5.0 stars _— Paco R._ [Read full review](https://www.g2.com/survey_responses/hex-review-12687305) ### [Sigma](https://www.g2.com/products/sigma-computing-sigma/reviews) Sigma is the AI runtime layer for analytics, agents, and apps built on live warehouse data. It is a cloud analytics and business intelligence platform that helps business and technical teams explore live data, build reports and applications, and deploy AI agents directly on a cloud data warehouse, using a familiar spreadsheet interface alongside SQL, Python, and AI. - Analytics: Use Sigma for classic business intelligence workloads like pixel-perfect reporting, embedded analytics, and self-service access to data in an Excel-like interface. Build with AI, spreadsheet functions, SQL, or Python. - Agents: Deploy AI agents that act on live data and run multi-step workflows. Build them with natural language and your choice of enterprise LLM or warehouse AI. - Apps: Build AI Apps for operational work like planning and forecasting, scenario modeling, and approval routing, with writeback and governance automatically configured. Every artifact in Sigma inherits the security, permissions, and row-level access already set in the cloud data warehouse. Because governance is enforced at the source, anything a team builds is IT-approved from the start. Sigma solves a persistent tradeoff in enterprise data work. Business teams typically wait on a central data team to build reports and tools, while ad hoc AI and app-building tools produce work that sits outside IT's control. Sigma removes that tradeoff by keeping analysis, reporting, applications, and agents on live warehouse data under one set of permissions, so non-technical users build what they need while IT keeps a single, auditable system of control. Founded in 2014, Sigma is a privately held company headquartered in San Francisco, with offices in New York, London, and Sydney. More than 2,000 companies trust Sigma, including DoorDash, Blackstone, JPMorgan Chase, and Figma. Sigma is the business intelligence partner of the year for both Snowflake (2023-2026) and Databricks (2025-2026). **Average Rating:** 4.4/5.0 **Total Reviews:** 544 #### 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: 8.4/10) - **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% Medium, 21% Large #### What Do G2 Reviewers Say About Sigma? _AI-generated summary from verified user reviews_ ##### Pros - Users appreciate the **ease of use** of Sigma, finding it intuitive and simple to learn and navigate. - Users love the **user-friendly interface** of Sigma, making it easy to create and manage metrics and dashboards. - Users value the **responsive customer support** from Sigma, ensuring timely resolutions and effective assistance for their needs. - Users find Sigma's **data handling capabilities** invaluable, simplifying processing and visualization for daily projects. - Users appreciate how Sigma provides **clear and interactive data visualizations** , making complex datasets easy to analyze and understand. ##### Cons - Users report that **loading times can be slow** , causing inefficiencies and frustration during use of Sigma. - Users experience **slow performance** with Sigma, especially when handling complex tasks or larger datasets. - Users find **limited customization** options in Sigma, restricting advanced visualizations and flexible formatting for their needs. - Users find the **learning curve steep** , struggling to navigate dashboard building and limited resources for support. - Users note the **missing features** in Sigma, such as limited visualizations and lack of scripting integration, affecting flexibility. #### What Are Recent G2 Reviews of Sigma? **["Sigma unlocks data value for our organization"](https://www.g2.com/survey_responses/sigma-review-10895340)** **Rating:** 5.0/5.0 stars _— Austin M._ [Read full review](https://www.g2.com/survey_responses/sigma-review-10895340) **["Easiest BI Tool: Live Snowflake Data in a Spreadsheet-Like Experience"](https://www.g2.com/survey_responses/sigma-review-12573150)** **Rating:** 4.0/5.0 stars _— Keerthan P._ [Read full review](https://www.g2.com/survey_responses/sigma-review-12573150) ### [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:** 438 #### 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: 8.4/10) - **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 (74,660 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:** 57% Large, 26% Medium #### What Do G2 Reviewers Say About IBM Cognos Analytics? _AI-generated summary from verified user reviews_ ##### Pros - Users appreciate the **ease of use** of IBM Cognos Analytics, enjoying intuitive features for reporting and analysis. - Users appreciate the **quick and easy report generation** capabilities of IBM Cognos Analytics, enhancing their data analysis experience. - Users appreciate the **in-depth adaptable dashboards** of IBM Cognos Analytics for generating insightful reports and visuals. - Users value the **effortless data visualization** of IBM Cognos Analytics, enhancing understanding through engaging and adaptable dashboards. - Users value the **intuitive user interface** of IBM Cognos Analytics, enhancing data comprehension and report creation efforts. ##### Cons - Users find the **learning curve steep** , making initial navigation and integration with other software challenging. - Users find the **learning difficulty** of IBM Cognos Analytics challenging, requiring substantial training and patience for effective use. - Users experience **slow performance** with IBM Cognos Analytics, especially with complex reports and large data sets. - Users find the **complexity** of building reports in IBM Cognos Analytics a significant barrier due to steep learning curves. - Users find the **complex usage** of IBM Cognos Analytics challenging, particularly for new users navigating advanced features. #### What Are Recent G2 Reviews of IBM Cognos Analytics? **["Powerful, Scalable Analytics with Interactive Dashboards and Strong Governance"](https://www.g2.com/survey_responses/ibm-cognos-analytics-review-12770018)** **Rating:** 4.0/5.0 stars _— Arkajit D._ [Read full review](https://www.g2.com/survey_responses/ibm-cognos-analytics-review-12770018) **["Powerful Reporting, Less Modern Interface"](https://www.g2.com/survey_responses/ibm-cognos-analytics-review-13048146)** **Rating:** 4.0/5.0 stars _— Nunna H._ [Read full review](https://www.g2.com/survey_responses/ibm-cognos-analytics-review-13048146) ### [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:** 676 #### 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: 8.4/10) - **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 (2,232,483 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (147,094 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, 37% Medium #### What Do G2 Reviewers Say About Amazon QuickSight? _AI-generated summary from verified user reviews_ ##### Pros - Users enjoy the **seamless integration with AWS services** , allowing for quick and effective dashboard creation. - Users find Amazon QuickSight to be **very user-friendly** , seamlessly integrating with AWS for a smooth experience. - Users appreciate the **easy integrations** of Amazon QuickSight, enhancing convenience with AWS and other data sources. - Users value the **interactive dashboards and data visualization tools** of Amazon QuickSight, enhancing real-time insights for their business. - Users admire the **interactive dashboard management** of Amazon QuickSight, enabling seamless data visualization and real-time insights. ##### Cons - Users find the **limited customization options** of Amazon QuickSight restricts the flexibility of their reporting needs. - Users find the **learning curve steep** , requiring additional AWS knowledge for effective use of QuickSight's features. - Users find the **visualization options limited** , making it tough to achieve desired dashboard customizations and interactivity. - Users find **missing features** in Amazon QuickSight, particularly lacking customization and advanced analytics compared to competitors. - Users find the **poor interface design** of Amazon QuickSight detracts from usability and hinders deeper data analysis. #### What Are Recent G2 Reviews of Amazon QuickSight? **["Fast, Serverless BI with Seamless AWS Integration"](https://www.g2.com/survey_responses/amazon-quicksight-review-11692248)** **Rating:** 4.5/5.0 stars _— Jawher S._ [Read full review](https://www.g2.com/survey_responses/amazon-quicksight-review-11692248) **["Streamlined Reporting with Robust Features"](https://www.g2.com/survey_responses/amazon-quicksight-review-11360363)** **Rating:** 4.0/5.0 stars _— Dilip Kumar P._ [Read full review](https://www.g2.com/survey_responses/amazon-quicksight-review-11360363) ### [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: 8.4/10) - **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 (74,660 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, 29% Medium #### What Do G2 Reviewers Say About IBM Business Analytics Enterprise? _AI-generated summary from verified user reviews_ ##### Pros - Users value the **ease of use** of IBM Business Analytics Enterprise, making day-to-day operations simple and efficient. - Users appreciate the **clear and easy-to-read analytics** of IBM Business Analytics Enterprise, enhancing decision-making and business insights. - Users value the **scalability** of IBM Business Analytics Enterprise, enabling easy integration of diverse data sources for insights. - Users value the **excellent customer support** from IBM, enhancing their experience with Business Analytics Enterprise. - Users value the **flexibility** of IBM Business Analytics, enabling seamless integration and advanced data-driven decision-making. ##### Cons - Users find the **complexity** of IBM Business Analytics Enterprise overwhelming, posing challenges for smaller companies and individual users. - Users find the **complexity of setup and integration** overwhelming, especially for those with limited data analysis experience. - Users find the **steep learning curve** of IBM Business Analytics Enterprise challenging, impacting overall user experience and efficiency. - Users report **slow customer support** responses and sometimes receive unclear answers, leading to frustration and confusion. - Users find the **aged and cumbersome UI** of IBM Business Analytics Enterprise challenging, especially for non-technical users. #### What Are Recent G2 Reviews of IBM Business Analytics Enterprise? **["Unified Analytics Powerhouse with AI-Driven Insights"](https://www.g2.com/survey_responses/ibm-business-analytics-enterprise-review-11921322)** **Rating:** 5.0/5.0 stars _— KAPIL G._ [Read full review](https://www.g2.com/survey_responses/ibm-business-analytics-enterprise-review-11921322) **["In Depth Data Analyzation"](https://www.g2.com/survey_responses/ibm-business-analytics-enterprise-review-10358960)** **Rating:** 4.5/5.0 stars _— atharv c._ [Read full review](https://www.g2.com/survey_responses/ibm-business-analytics-enterprise-review-10358960) ### [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:** 597 #### 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: 8.4/10) - **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 (1 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/202760/ (281 employees on LinkedIn®) #### Who Uses This Product? - **Who Uses This:** Data Analyst, Product Manager - **Top Industries:** Computer Software, Consumer Services - **Company Size:** 44% Medium, 40% Small #### What Do G2 Reviewers Say About GoodData.AI? _AI-generated summary from verified user reviews_ ##### Pros - Users find the **ease of use** of GoodData.AI impressive, with an intuitive interface that enhances their business operations. - Users appreciate the **easy-to-use data visualization** features of GoodData.AI, enhancing accessibility for trend analysis and reporting. - Users value the **quick integration** of GoodData.AI, enhancing workflow with seamless connections to various systems. - Users highlight the **intuitive user interface** of GoodData.AI, making it easy for newcomers to utilize its features. - Users value the **customization capabilities** of GoodData.AI, enhancing flexibility and enabling tailored business insights. ##### Cons - Users find the **learning curve steep** for GoodData.AI, especially for those unfamiliar with data modeling concepts. - Users find **learning GoodData.AI challenging** , especially those with backgrounds in non-relational databases, due to its complexity. - Users find the **missing features** of GoodData.AI frustrating, limiting customization and functionality for workflows and graphics. - Users find GoodData.AI's interface **complex** , especially when dealing with advanced features and data relationships. - Users find the **limited customization** options in GoodData.AI restrictive, hindering their ability to tailor the product effectively. #### What Are Recent G2 Reviews of GoodData.AI? **["Mosaic & Semantic Layer Unite Our Data, Plus Helpful AI Dashboard Suggestions"](https://www.g2.com/survey_responses/gooddata-ai-review-12912613)** **Rating:** 5.0/5.0 stars _— Jeff P._ [Read full review](https://www.g2.com/survey_responses/gooddata-ai-review-12912613) **["Embedded Dashboards and Consistent Metrics That Cut Support Tickets"](https://www.g2.com/survey_responses/gooddata-ai-review-13032302)** **Rating:** 5.0/5.0 stars _— Yash G._ [Read full review](https://www.g2.com/survey_responses/gooddata-ai-review-13032302) ### [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: 8.4/10) - **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 (827,997 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% Large, 27% Medium #### What Do G2 Reviewers Say About Oracle Analytics Cloud? _AI-generated summary from verified user reviews_ ##### Pros - Users value the **robust self-service analytics** tools of Oracle Analytics Cloud, enabling effortless dashboard creation and data exploration. - Users appreciate the **strong data visualization** capabilities of Oracle Analytics Cloud, making analytics accessible and attractive for all. - Users value the **intuitive and accessible interface** of Oracle Analytics Cloud, enabling effortless data exploration for all skill levels. - Users value the **scalability** of Oracle Analytics Cloud, allowing for customizable solutions tailored to their evolving needs. - Users appreciate the **customized business improvement solutions** Oracle Analytics Cloud offers, enhancing collaborative analytics for organizations. ##### Cons - Users face a **steep learning curve** with Oracle Analytics Cloud, making it challenging for new users to adapt. - Users find the **complexity** of Oracle Analytics Cloud daunting, particularly during the initial setup and learning advanced features. - Users find the **complex usage** of Oracle Analytics Cloud challenging, particularly during setup and when learning advanced features. - Users find that **customization options are limited** , making it difficult to meet highly specialized requirements on Oracle Analytics Cloud. - Users note the **infrequent software updates** of Oracle Analytics Cloud, leading to concerns about security vulnerabilities. #### What Are Recent G2 Reviews of Oracle Analytics Cloud? **["Fast Navigation, Customizable Oracle Analytics Cloud, and Strong Automation"](https://www.g2.com/survey_responses/oracle-analytics-cloud-review-5176664)** **Rating:** 4.0/5.0 stars _— Shawn A._ [Read full review](https://www.g2.com/survey_responses/oracle-analytics-cloud-review-5176664) **["Transform complex data into actionable insights!"](https://www.g2.com/survey_responses/oracle-analytics-cloud-review-12093722)** **Rating:** 4.0/5.0 stars _— Gaurav G._ [Read full review](https://www.g2.com/survey_responses/oracle-analytics-cloud-review-12093722) - ‹ Prev‹ Prev - 1 - [2](/categories/analytics-platforms?order=g2_score&page=2#product-list) - [3](/categories/analytics-platforms?order=g2_score&page=3#product-list) - [4](/categories/analytics-platforms?order=g2_score&page=4#product-list) - [5](/categories/analytics-platforms?order=g2_score&page=5#product-list) - … - [24](/categories/analytics-platforms?order=g2_score&page=24#product-list) - [25](/categories/analytics-platforms?order=g2_score&page=25#product-list) - [Next ›Next ›](/categories/analytics-platforms?order=g2_score&page=2#product-list) Spotlight Categories [CPQ Software](https://www.g2.com/categories/cpq) [Project Management Software](https://www.g2.com/categories/project-management) [Applicant Tracking Systems (ATS)](https://www.g2.com/categories/applicant-tracking-systems-ats) [Security Awareness Training Software](https://www.g2.com/categories/security-awareness-training) [Mobile Device Management (MDM) Software](https://www.g2.com/categories/mobile-device-management-mdm) Similar Categories - [Agentic Analytics](/categories/agentic-analytics) - [AI Search and Discovery Platforms](/categories/ai-search-and-discovery-platforms) - [Business Intelligence](/categories/business-intelligence) - [Data Virtualization](/categories/data-virtualization) - [Drone Analytics](/categories/drone-analytics) - [Enterprise Search Software](/categories/enterprise-search-software) - [Graph Visualization Tools](/categories/graph-visualization-tools) - [Insight Engines](/categories/insight-engines) - [Other Analytics](/categories/other-analytics) - [Predictive Analytics](/categories/predictive-analytics) - [Site Search Software](/categories/site-search-software) - [Statistical Analysis](/categories/statistical-analysis) - [Text Analysis](/categories/text-analysis) - [Time Series Intelligence](/categories/time-series-intelligence) - [Visitor Behavior Intelligence](/categories/visitor-behavior-intelligence) [Browse Analytics Platforms Themes](/categories/analytics-platforms/themes) ![Tian Lin](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Tian Lin") TL Researched and written by [Tian Lin](https://research.g2.com/insights/author/tian-lin) Updated July 2, 2025 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 Tools at a Glance | | Flexible visual dashboard exploration | User Review "Turning Complex Data into Actionable Insights" | | | Microsoft-connected interactive dashboards | User Review ""Power BI Makes Data Visualization Fast and Professional"" | | | Governed lakehouse analytics and ML workflows | User Review "Helpful for Managing and Analyzing Operational Data" | | | Cloud analytics for governed data science | User Review "Effective Data Analysis with SAS Viya" | | | No-code data preparation and automation | User Review "Intuitive Drag-and-Drop Analytics That Speeds Up Data Prep and Insights" | | | Centralized self-service business dashboards | User Review "Domo Turns Disconnected Reports Into a Game-Changing Sales Scorecard" | | | Governed shared BI metrics | User Review "Transforms Data, But Challenging for Beginners" | | | Semantic-layer acceleration for enterprise BI | User Review "Kyvos Semantic Layer Boosts AI Accuracy with Business-Ready Data" | | | SQL and Python notebook analytics apps | User Review "All-in-One Collaborative Workspace for SQL, Python, and Interactive Dashboards" | | | Warehouse-native spreadsheet-style analytics | User Review "Easiest BI Tool: Live Snowflake Data in a Spreadsheet-Like Experience" | * * * Show More * * * ## 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.