# Best Analytics Platforms *By [Tian Lin](https://research.g2.com/insights/author/tian-lin)* Analytics platforms provide a tool set for businesses to transform raw data into meaningful, actionable insights. They enable organizations to explore data, uncover trends, forecast future outcomes, and support informed decision making. Unlike tools limited to reporting on past performance, analytics platforms often include advanced capabilities such as predictive modeling, statistical analysis, and machine learning (ML). These platforms are designed to be flexible and scalable, supporting a wide range of use cases across the business. These platforms are used in nearly every business function, from marketing and sales to finance, operations, and HR, supporting both strategic planning and day-to-day performance monitoring. From data analysts and scientists to business stakeholders and executives, analytics platforms are used by a wide range of personas. While analysts focus on exploring data and generating insights, self-service tools now enable non-technical users to interact directly with data. IT teams support platform integration and security, reflecting the growing push to democratize data access and embed analytics into daily decision-making across the organization. Analytics platforms support critical functions such as data blending and modeling, enabling users to combine data from diverse sources and build robust, interconnected data models. The visual outputs — dashboards, reports, and interactive charts — help users explore trends, drill down into granular details, and communicate insights clearly. Unlike standalone data visualization tools, which are limited to presenting information, analytics platforms encompass the full analytical workflow. Many also offer advanced capabilities such as embedded analytics, natural language query, and augmented analytics, which leverage ML to automate insight discovery and make data exploration more accessible to a broader audience. Analytics platforms and [business intelligence (BI) software](https://www.g2.com/categories/business-intelligence) often work in tandem to support data-driven organizations. While BI tools focus on tracking and reporting historical performance through dashboards and key performance indicators (KPI), analytics platforms provide broader capabilities that support exploratory analysis and strategic planning. BI answers "what happened," while analytics platforms help users understand why it happened and what might happen next. Rather than replacing BI, analytics platforms complement it by enabling deeper insights and empowering a wider range of users across the organization. To qualify for inclusion in the Analytics Platforms category, a product must: - Ingest and integrate data from a wide range of structured and semi-structured sources - Prepare and transform data using built-in tools for cleaning, enrichment, and formatting - Support connections to diverse data sources, including file uploads, databases, application programming interfaces (API), and SaaS apps - Enable users to model data relationships, join datasets, and explore data interactively - Offer tools to build meaningful business reports, dashboards, and visualizations - Allow creation and sharing of internal analytics applications or embedded insights across teams ## Category Overview **Total Products under this Category:** 332 ## Trust & Credibility Stats **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 27,200+ Authentic Reviews - 332+ 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. ## Top Analytics Platforms at a Glance | # | Product | Rating | Best For | What Users Say | |---|---------|--------|----------|----------------| | 1 | [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) | 4.5/5.0 (1,560 reviews) | Microsoft-connected interactive dashboards | "[Power BI Turns Messy Data into Clear, Fast, User-Friendly Insights](https://www.g2.com/survey_responses/microsoft-power-bi-review-12743782)" | | 2 | [Tableau](https://www.g2.com/products/tableau/reviews) | 4.4/5.0 (3,531 reviews) | Flexible visual dashboard exploration | "[Instant Insights with Interactive Dashboards](https://www.g2.com/survey_responses/tableau-review-12784839)" | | 3 | [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) | 4.3/5.0 (754 reviews) | Cloud analytics for governed data science | "[Powerful & Transforming Data into Decisions—Effortlessly and Intelligently.](https://www.g2.com/survey_responses/sas-viya-review-12682824)" | | 4 | [Databricks](https://www.g2.com/products/databricks/reviews) | 4.6/5.0 (740 reviews) | Governed lakehouse analytics and ML workflows | "[Perfect for Cross-team Collaboration and Intensive Data Applications](https://www.g2.com/survey_responses/databricks-review-12745626)" | | 5 | [Looker](https://www.g2.com/products/looker/reviews) | 4.4/5.0 (1,570 reviews) | Governed shared BI metrics | "[All Our Metrics in One Place with a User-Friendly, Easy-to-Navigate Looker UI](https://www.g2.com/survey_responses/looker-review-12585179)" | | 6 | [Domo](https://www.g2.com/products/domo/reviews) | 4.3/5.0 (986 reviews) | Centralized self-service business dashboards | "[All-in-One Platform for Real-Time Analytics and Dashboards](https://www.g2.com/survey_responses/domo-review-12676104)" | | 7 | [Sigma](https://www.g2.com/products/sigma-computing-sigma/reviews) | 4.4/5.0 (543 reviews) | Warehouse-native spreadsheet-style analytics | "[Easiest BI Tool: Live Snowflake Data in a Spreadsheet-Like Experience](https://www.g2.com/survey_responses/sigma-review-12573150)" | | 8 | [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) | 4.8/5.0 (249 reviews) | Semantic-layer acceleration for enterprise BI | "[Kyvos Unified Our Business Logic with a Single Semantic Model](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-12797024)" | | 9 | [Amazon QuickSight](https://www.g2.com/products/amazon-quicksight/reviews) | 4.3/5.0 (671 reviews) | AWS-native serverless BI dashboards | "[Turns Raw Data into Interactive Dashboards for Better Trend Monitoring](https://www.g2.com/survey_responses/amazon-quicksight-review-12740199)" | | 10 | [Hex](https://www.g2.com/products/hex-tech-hex/reviews) | 4.5/5.0 (384 reviews) | SQL and Python notebook analytics apps | "[Effortless Data Analysis with Powerful AI](https://www.g2.com/survey_responses/hex-review-12262172)" | ## Best Analytics Platforms At A Glance - **Leader:** [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) - **Highest Performer:** [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) - **Easiest to Use:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Top Trending:** [Hex](https://www.g2.com/products/hex-tech-hex/reviews) - **Best Free Software:** [Tableau](https://www.g2.com/products/tableau/reviews) ## Which Type of Analytics Platforms Tools Are You Looking For? - [Analytics Platforms](https://www.g2.com/categories/analytics-platforms) *(current)* - [Data Visualization Tools](https://www.g2.com/categories/data-visualization-tools) - [Predictive Analytics Software](https://www.g2.com/categories/predictive-analytics) - [Embedded Business Intelligence Software](https://www.g2.com/categories/embedded-business-intelligence) - [Marketing Analytics Software](https://www.g2.com/categories/marketing-analytics) - [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) - [ETL Tools](https://www.g2.com/categories/etl-tools) - [Data Preparation Software](https://www.g2.com/categories/data-preparation) --- **Sponsored** ### Zoho Analytics Zoho Analytics is a GenAI-powered self-service BI and analytics platform that helps businesses collect, prepare, analyze, and present insights from their data, all within minutes. Integrate with 500+ native data sources, like files, feeds, local and cloud databases, cloud storage, and popular business apps. Clean, transform, enrich, and catalog data with our agentic AI-powered, self-service data preparation and management capabilities. Create and manage complex ETL data pipelines using our visual pipeline builder, process stream data for real-time analytics, and set up a strong metrics layer for quality analysis and management. Zoho Analytics comes with 100+ domain-specific prebuilt reports and dashboards, pre-trained NLQ models, intelligently modeled and blended data across business applications, and lots more. Use our intuitive drag-and-drop visualization builder to build insightful and interactive reports and dashboards using a wide variety of visual components, like charts, widgets, pivot tables, tabular views and more. Generate customized reports and dashboards using simple, natural language with our AI-powered conversational agent, Zia. Ask Zia goes beyond basic reporting; it enables users to perform diagnostic analytics, forecast key metrics, and receive intelligent insights and recommendations. Users can assign tasks and trigger actions just by conversing with our agentic AI, Zia, streamlining their workflows and empowering data-driven decisions. Seamlessly embed Ask Zia into your custom or business applications to deliver contextual insights and actions. Enrich your analysis with automated insights by using our NLG-powered narration engine, Zia Insights. With diagnostic analytics for smarter decision-making, Zia Insights takes automated insights to the next level by bubbling up key drivers for particular business tasks. Enumerate complex business scenarios with what-if analyses, forecast KPIs, uncover trends and patterns with advanced analytical capabilities. Leverage cognitive analysis for keyword extraction and sentiment analysis, and more. Evaluate the best ML models with no-code assistance or develop custom models and functions using Python Code Studio. Zoho Analytics comes with pre-packed ML models (AutoML) that allow you to evaluate and pick the best model for your use case. Alternatively custom models and functions using Python Code Studio Embed our full-fledged analytics platform into other software applications. Craft and present immersive data stories through slideshows or purpose-built analytics portals. Collaborate securely through contextual comment threads and real-time messaging. Zoho Analytics has a robust set of APIs that enables elaborate customization and highly extensible low-code and no-code integration with any technology stack. It also offers a high degree of deployment flexibility (private, public, multi-cloud, and on-premises) and platform extensibility (professional services, partner support, and marketplace). Zoho Analytics is modern and scalable, and it can readily match growing data volumes and usage. Its time-tested and accredited enterprise-grade security features and governance framework ensure continuous data management and stewardship. On top of everything else, the TCO for Zoho Analytics—including licensing, implementation, customization, training, and support—is the lowest in our market. [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%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=3431&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=a4cfda32bbe5236668878cd91b9e0c035b6d97bce1d1a16324bf9d2adaaa87f8&secure%5Burl%5D=https%3A%2F%2Fwww.zoho.com%2Fanalytics%2F%3Futm_source%3DG2%26utm_medium%3Dcpc%26utm_campaign%3DAnalytics_Platforms&secure%5Burl_type%5D=custom_url) --- ## Buyer Guide: Key Questions for Choosing Analytics Platforms Software ### What do Analytics Platforms do? When I explain analytics platforms, I frame them as systems that help teams turn business data into usable insight. These platforms bring dashboards, reports, data modeling, metric exploration, visualizations, and insight sharing into one workflow. Instead of relying on scattered spreadsheets, static reports, or disconnected data views, teams can access information faster, understand performance more clearly, and make decisions with more confidence. ### Why do businesses use Analytics Platforms? From the G2 reviewer patterns I evaluated, businesses use Analytics Platforms because data sits across many systems and takes too much manual effort to organize. Users mention fragmented reports, delayed insight, inconsistent metrics, and hard-to-present data. Common benefits include: - Faster dashboard and report creation. - Clearer visibility into KPIs, sales, marketing, operations, and product data. - Stronger exploration through filters, drilldowns, SQL, and visual workflows. - Easier sharing of insights with teams and stakeholders. - Better integration with databases, cloud platforms, spreadsheets, CRM tools, and warehouses. ### Who uses Analytics Platforms primarily? After I evaluated G2 reviewer roles, I found that Analytics Platforms serve technical and business users. - **Data analysts** build dashboards, reports, and recurring performance views. - **BI teams** manage metrics, models, and governed workflows. - **Data engineers** connect sources and prepare datasets. - **Data scientists** use notebooks, models, and advanced analytics features. - **Business analysts** translate data into operational decisions. - **Executives** use dashboards to understand trends. ### What types of Analytics Platforms should I consider? From the way reviewers describe the category, buyers should compare several product types: - **BI and dashboard platforms** for reports, visualization, and executive views. - **Self-service analytics tools** for fast business exploration. - **Cloud analytics platforms** for larger datasets and scalable processing. - **Data science and notebook platforms** for SQL, Python, models, and collaboration. - **Embedded analytics tools** for customer-facing dashboards. - **Semantic layer platforms** for shared metrics and reusable definitions. ### What are the core features to look for in Analytics Platforms? When I break down G2 reviews for this category, I look closely at the themes users repeatedly mention: - Dashboard and report builders with flexible layouts that help teams organize business data into views people can actually use. - Data visualization, charting, and interactive filters that help users explore trends, compare performance, and understand metrics faster. - Connectors for databases, spreadsheets, cloud tools, and business applications that help bring data from different systems into one place. - SQL support, data modeling, and calculated metrics that help analysts define, structure, and customize business logic. - AI-assisted search or automated insights that help users find patterns and answers without building every report manually. - Collaboration, permissions, scheduled delivery, and sharing controls that help teams distribute insights securely and consistently. - Performance controls for complex dashboards that help reports load reliably even when data volume or usage grows. - Documentation, training resources, and responsive support that help teams onboard faster and troubleshoot issues with less friction. ### What trends are shaping Analytics Platforms right now? From the G2 review patterns I evaluated, several trends stand out: - **AI-assisted analytics** is helping users ask questions, generate summaries, and discover insights faster. - **Self-service reporting** is becoming more important as business teams look for answers without depending on analysts for every request. - **Cloud and application integrations** are expanding as companies connect analytics platforms to more data sources and business systems. - **Governed metrics and semantic layers** are gaining value as teams work to keep definitions consistent across dashboards and reports. - **Performance optimization** remains a priority as users expect complex dashboards to load quickly and reliably. - **Usability and guided setup** are shaping adoption as buyers look for platforms that are powerful without creating a steep learning curve. ### How should I choose Analytics Platforms? For me, the strongest Analytics Platforms fit depends on data sources, governance needs, and scale. I would prioritize products reviewers praise for intuitive dashboards, flexible visualization, strong integrations, reliable performance, and clear collaboration. I would also examine concerns around customization, complex setup, pricing, learning curve, and AI accuracy before making a final choice. --- ## Top-Rated Products (Ranked by G2 Score) ### 1. [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) **Average Rating:** 4.5/5.0 **Total Reviews:** 1,560 **Why buyers love it?:** When I reviewed Microsoft Power BI’s G2 feedback, the strongest theme was its role in turning complex datasets into interactive dashboards inside a familiar Microsoft environment. Reviewers highlight Excel, Azure, SQL Server, and broader Microsoft integrations, along with visuals that make analysis easier to share. The product reads as a strong fit for teams that need dashboard creation, data modeling, and business reporting connected to existing Microsoft workflows. Users also cite a learning curve for advanced DAX, performance strain with very large datasets, customization limits, licensing complexity, and occasional refresh issues. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users appreciate the **ease of use** of Microsoft Power BI, enabling effortless data analysis for everyone. - Users value the **data visualization capabilities** of Power BI, enhancing insights and reporting with ease. - Users appreciate the **seamless integrations** in Power BI, enabling efficient data usage across various platforms and databases. - Users value the **advanced data analysis and visualization** capabilities of Microsoft Power BI for insightful reporting. - Users value the **seamless data integration** capabilities of Power BI, allowing connections to numerous data sources effortlessly. **Cons:** - Users find the **learning curve challenging** , especially for beginners trying to master data connections and DAX. - Users experience **slow performance** with Microsoft Power BI, especially when handling large datasets and complex data models. - Users experience **performance issues** with DAX queries and large data volumes, affecting overall efficiency and usability. - Users find the **complex data modeling** in Power BI challenging, leading to a steep learning curve and potential frustration. - Users find the **limited customization options** in Microsoft Power BI restrictive, hindering effective data presentation. #### Key Features - Reports Interface - Data Column Filtering - Predictive Analytics - Data Modeling - Connectors #### Recent Reviews **"[Power BI Turns Messy Data into Clear, Fast, User-Friendly Insights](https://www.g2.com/survey_responses/microsoft-power-bi-review-12743782)"** **Rating:** 4.0/5.0 stars *— Connie J.* [Read full review](https://www.g2.com/survey_responses/microsoft-power-bi-review-12743782) --- **Rating:** 5.0/5.0 stars *— Anderson F.* [Read full review](https://www.g2.com/survey_responses/microsoft-power-bi-review-12794774) --- #### Trending Discussions - [What is Microsoft Power BI Desktop used for?](https://www.g2.com/discussions/what-is-microsoft-power-bi-desktop-used-for) - 4 comments, 1 upvote - [What are some of the top features of Microsoft BI?](https://www.g2.com/discussions/what-are-some-of-the-top-features-of-microsoft-bi) - 3 comments - [Is Power BI a Microsoft tool?](https://www.g2.com/discussions/is-power-bi-a-microsoft-tool) - 5 comments, 3 upvotes ### 2. [Tableau](https://www.g2.com/products/tableau/reviews) **Average Rating:** 4.4/5.0 **Total Reviews:** 3,531 **Why buyers love it?:** A recurring pattern in Tableau’s G2 feedback when I reviewed it was that its strength in visual exploration across varied data sources. Reviewers highlight simple dashboard creation, flexible integrations, clear KPI views, and fast access to business insights for sales, operations, and leadership use cases. The product reads as a fit for teams that want polished visual dashboards and interactive data exploration across multiple systems. Users also note friction with dashboard layout control, data prep, live connection speed, pricing, and some API gaps. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users enjoy Tableau's **ease of use** , allowing them to create complex dashboards with minimal coding skills. - Users value Tableau for its **interactive dashboards** , which enhance data visibility and decision-making through seamless integration. - Users highlight the **powerful data visualization** of Tableau, enabling clear, interactive dashboards for informed decision-making. - Users value the **interactive dashboards** of Tableau, enhancing data visualization and decision-making with minimal coding required. - Users find Tableau **intuitive** , enabling easy dashboard creation and effective data visualization without extensive coding. **Cons:** - Users find the **learning curve steep** , making it challenging for new users to effectively navigate Tableau. - Users encounter a **steep learning curve** with Tableau, making it challenging for newcomers to become proficient quickly. - Users find Tableau's **high licensing cost** discouraging, especially for smaller teams, impacting accessibility and performance. - Users experience **slow performance** with large datasets in Tableau, often due to high resource demands and timeout issues. - Users find **onboarding to be complex** , noting that even simple tasks require extra steps in Tableau. #### Key Features - Steps to Answer - Calculated Fields - Predictive Analytics - Data Modeling - Data Mining #### Recent Reviews **"[Instant Insights with Interactive Dashboards](https://www.g2.com/survey_responses/tableau-review-12784839)"** **Rating:** 5.0/5.0 stars *— Rahul S.* [Read full review](https://www.g2.com/survey_responses/tableau-review-12784839) --- **"[Effortless Data Visualization, High Licensing Costs](https://www.g2.com/survey_responses/tableau-review-12793833)"** **Rating:** 4.0/5.0 stars *— Atharva P.* [Read full review](https://www.g2.com/survey_responses/tableau-review-12793833) --- #### Trending Discussions - [How are business intelligence professionals using Tableau's latest visualization tools to interpret complex data sets?](https://www.g2.com/discussions/how-are-business-intelligence-professionals-using-tableau-s-latest-visualization-tools-to-interpret-complex-data-sets) - 2 comments - [What is Salesforce CRM Analytics (formerly Tableau CRM) used for?](https://www.g2.com/discussions/what-is-salesforce-crm-analytics-formerly-tableau-crm-used-for) - [Do I need Tableau Desktop if I have Tableau Server?](https://www.g2.com/discussions/tableau-do-i-need-tableau-desktop-if-i-have-tableau-server) - 2 comments ### 3. [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) **Average Rating:** 4.3/5.0 **Total Reviews:** 754 **Why buyers love it?:** The signal I found in SAS Viya’s G2 feedback was its appeal for advanced analytics work that spans coding, model development, and cloud-scale data processing. Reviewers highlight strong performance, memory handling, multi-language support, and a modern environment that extends familiar SAS workflows. The product reads as a fit for organizations that need statistical analysis, machine learning, and governed analytics in a cloud-based platform. Users also mention setup complexity, a steep learning curve for new users, migration friction from older SAS environments, and high licensing or implementation costs. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users appreciate the **user-friendly interface** of SAS Viya, making data analysis accessible to all skill levels. - Users value the **robust analytics capabilities** of SAS Viya, enabling effective decision-making across various industries. - Users appreciate the **advanced analytics capabilities** of SAS Viya, enhancing decision-making and providing critical business insights. - Users value the **end-to-end data lifecycle tooling** of SAS Viya, enhancing business insights and predictive analysis capabilities. - Users appreciate the **user-friendly interface** of SAS Viya, which simplifies analytics for various technical skill levels. **Cons:** - Users find SAS Viya **difficult for non-technical users** , struggling with navigation and overall user-friendliness. - Users find the **learning curve steep** , making it challenging for non-technical users to navigate and utilize SAS Viya effectively. - Users find the **visualization complexity** of SAS Viya challenging, especially for non-technical users and newcomers. - Users find the **difficult learning curve** of SAS Viya challenging, especially for new and non-technical users. - Users find the **expensive pricing** of SAS Viya to be a significant drawback, affecting initial engagement decisions. #### Key Features - Steps to Answer - Calculated Fields - Data Visualization - WYSIWYG Report Design - Data Visualizations #### Recent Reviews **"[Powerful & Transforming Data into Decisions—Effortlessly and Intelligently.](https://www.g2.com/survey_responses/sas-viya-review-12682824)"** **Rating:** 4.5/5.0 stars *— Venkatesh D.* [Read full review](https://www.g2.com/survey_responses/sas-viya-review-12682824) --- **"[SAS Viya is a Powerful Analytics](https://www.g2.com/survey_responses/sas-viya-review-11702846)"** **Rating:** 5.0/5.0 stars *— Deependra G.* [Read full review](https://www.g2.com/survey_responses/sas-viya-review-11702846) --- #### Trending Discussions - [What is SAS Visual Data Mining and Machine Learning used for?](https://www.g2.com/discussions/what-is-sas-visual-data-mining-and-machine-learning-used-for) - 1 comment ### 4. [Databricks](https://www.g2.com/products/databricks/reviews) **Average Rating:** 4.6/5.0 **Total Reviews:** 740 **Why buyers love it?:** What stood out to me in Databricks’ G2 feedback was how reviewers connect analytics, data engineering, and machine learning work in one governed workspace. Reviewers highlight Unity Catalog, lineage, incremental ingestion, notebook collaboration, Power BI integration, and natural-language assistance for querying datasets. The product reads as a strong fit when teams need large data workflows that support both BI and machine learning use cases. Users also cite operational complexity, performance tuning needs, migration effort, cost control concerns, and the need for skilled data teams. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users appreciate the **advanced AI features and robust data security** of Databricks, enhancing their machine learning capabilities. - Users find **Databricks easy to use** , especially for model hosting and serving with seamless integrations and real-time data management. - Users value the **seamless integrations** of Databricks with various tools, enhancing data handling and collaboration significantly. - Users value the **excellent collaboration** features of Databricks, enabling real-time teamwork among data engineers and analysts. - Users love the **effective data management features** of Databricks, simplifying complex tasks with powerful tools and integrations. **Cons:** - Users experience a **steep learning curve** with Databricks, complicating adoption and understanding due to its complexity. - Users find Databricks to be **expensive** , particularly when handling large datasets and uncertain pricing structures. - Users face a **steep learning curve** with Databricks, making initial adoption challenging for teams. - Users struggle with the **missing features** of Databricks, limiting customization and complicating the development process. - Users find the **complexity** of Databricks challenging, particularly due to the steep learning curve and integration limitations. #### Key Features - Cloud - Data Lake - Spark Integration - Spark Integration - Workload Processing #### Recent Reviews **"[Databricks Simplifies Big Data Processing and Team Collaboration](https://www.g2.com/survey_responses/databricks-review-12654916)"** **Rating:** 4.5/5.0 stars *— Praveen M.* [Read full review](https://www.g2.com/survey_responses/databricks-review-12654916) --- **"[Perfect for Cross-team Collaboration and Intensive Data Applications](https://www.g2.com/survey_responses/databricks-review-12745626)"** **Rating:** 5.0/5.0 stars *— Artemij V.* [Read full review](https://www.g2.com/survey_responses/databricks-review-12745626) --- #### Trending Discussions - [What does Databricks software do?](https://www.g2.com/discussions/what-does-databricks-software-do) - 3 comments - [What is Databricks unified analytics platform?](https://www.g2.com/discussions/what-is-databricks-unified-analytics-platform) - 3 comments - [What is Lakehouse in Databricks?](https://www.g2.com/discussions/what-is-lakehouse-in-databricks) - 4 comments, 2 upvotes ### 5. [Looker](https://www.g2.com/products/looker/reviews) **Average Rating:** 4.4/5.0 **Total Reviews:** 1,570 **Why buyers love it?:** In Looker’s G2 feedback, the most consistent thread was governed data access paired with shareable dashboards. Reviewers highlight quick report creation, GA and business dashboard use cases, flexible customization, team sharing, and integrations that help stakeholders review common metrics. The product reads to me as a fit for organizations that need reusable BI views and consistent reporting across teams. Users also cite slow performance in large dashboards, limits around custom metrics or plots, manual blending, dated chart experiences, and a learning curve. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users enjoy the **ease of use** of Looker, enabling quick dashboard creation and data sharing for informed decisions. - Users value the **centralized dashboards and real-time insights** in Looker, enhancing data-driven decision-making across teams. - Users value the **easy integrations** of Looker, enhancing collaboration and streamlining data analysis across platforms. - Users appreciate the **easy integration** with various data sources, enabling seamless collaboration and interactive reporting. - Users highlight the **flexibility in data visualization** with Looker, enabling tailored insights for specific reporting needs. **Cons:** - Users find the **learning curve challenging** , particularly for those with limited IT literacy or beginner experience. - Users note the **steep learning curve** of Looker, finding its complexity and LookML challenging to navigate. - Users experience **slow loading times** with large datasets, hindering functionality and impacting user friendliness. - Users experience **slow performance** with Looker, facing long loading times and lagging user interface issues. - Users find the **complexity** of Looker daunting, particularly with its non-intuitive learning curve and heavy reliance on code. #### Key Features - Steps to Answer - Search - Predictive Analytics - Integration APIs - Retroactive Reporting #### Recent Reviews **"[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) --- **"[All Our Metrics in One Place with a User-Friendly, Easy-to-Navigate Looker UI](https://www.g2.com/survey_responses/looker-review-12585179)"** **Rating:** 4.5/5.0 stars *— Aayush M.* [Read full review](https://www.g2.com/survey_responses/looker-review-12585179) --- #### Trending Discussions - [What is Looker used for?](https://www.g2.com/discussions/what-is-looker-used-for) - 1 comment - [Why is looker so powerful?](https://www.g2.com/discussions/why-is-looker-so-powerful) - [Is looker an ETL tool?](https://www.g2.com/discussions/is-looker-an-etl-tool) - 3 comments ### 6. [Domo](https://www.g2.com/products/domo/reviews) **Average Rating:** 4.3/5.0 **Total Reviews:** 986 **Why buyers love it?:** When I assessed Domo’s G2 feedback, reviewers consistently framed it as a central place for data, dashboards, and business visibility. Reviewers highlight easy setup, a user-friendly interface, Magic ETL, AI features, broad insights sharing, and accessibility for less technical users. The product reads as a fit for organizations that want to connect data sources and give business teams a direct path to dashboards and analysis. Users also mention credit usage concerns, pricing complexity, occasional bugs, limited SQL depth, and feature maturity issues. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users appreciate the **ease of use** of Domo, simplifying reporting and making it accessible for everyone. - Users value the **flexible dashboards** in Domo for seamless data visualization and intuitive insights. - Users admire Domo's **intuitive design** , making data management accessible even for non-tech-savvy individuals. - Users appreciate the **easy integrations** of Domo, enhancing their data management and decision-making processes effectively. - Users value Domo's **seamless integration** capabilities, enhancing data management and facilitating real-time collaboration and insights. **Cons:** - Users find the **learning curve challenging** , often needing to adapt to frequent updates and complex functionality. - Users often find **missing features** in Domo, such as limited card displays and outdated MySQL implementation. - Users face significant **data management issues** with Domo, struggling with uploads, organization, and limitations on analytics capabilities. - Users find Domo to be **expensive** due to high costs associated with features, setup, and external support. - Users find Domo's **complexity overwhelming** , requiring technical expertise and extensive setup, affecting functionality and efficiency. #### Key Features - Steps to Answer - Data Column Filtering - Data Visualization - Data Modeling - Connectors #### Recent Reviews **"[All-in-One Platform for Real-Time Analytics and Dashboards](https://www.g2.com/survey_responses/domo-review-12676104)"** **Rating:** 5.0/5.0 stars *— Anuj T.* [Read full review](https://www.g2.com/survey_responses/domo-review-12676104) --- **"[Comprehensive Analytics Tool with a Learning Curve](https://www.g2.com/survey_responses/domo-review-9326769)"** **Rating:** 5.0/5.0 stars *— John L.* [Read full review](https://www.g2.com/survey_responses/domo-review-9326769) --- #### Trending Discussions - [What is Domo used for?](https://www.g2.com/discussions/what-is-domo-used-for) - 1 comment - [How much does Domo cost?](https://www.g2.com/discussions/how-much-does-domo-cost) - [What is Domo data?](https://www.g2.com/discussions/what-is-domo-data) ### 7. [Sigma](https://www.g2.com/products/sigma-computing-sigma/reviews) **Average Rating:** 4.4/5.0 **Total Reviews:** 543 **Why buyers love it?:** The clearest pattern I saw in Sigma’s G2 feedback was its appeal to teams that want cloud-warehouse data in a familiar analytical interface. Reviewers highlight easy access to critical data, dashboard interactivity, large dataset handling, Snowflake connectivity, and the ability to explore data directly inside workbooks. The product reads as a fit for business and data teams that need self-service analytics without removing data from the warehouse context. Users also cite workbook performance issues, advanced-feature complexity, limited visual customization, and some database compatibility gaps. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users praise the **ease of use** of Sigma, highlighting its intuitive design and simplicity for quick onboarding. - Users love Sigma for its **easy integration and intuitive interface** , enabling seamless data management and dashboard customization. - Users commend the **excellent customer support** of Sigma, noting responsiveness and helpfulness with their inquiries. - Users find Sigma's **data visualization capabilities** exceptional, simplifying complex data processing and enhancing dashboard creation. - Users appreciate the **easy data aggregation** capabilities of Sigma, streamlining processing and visualization for improved project completion. **Cons:** - Users experience **slow loading times** with Sigma, which can lead to inefficiencies and frustration in usage. - Users experience **slow performance** with Sigma, especially when connecting to large data sources, leading to frustration. - Users express frustration with the **limited customization** options for visuals and dashboard layouts in Sigma. - Users find the **learning curve steep** , requiring guidance to effectively build dashboards in Sigma initially. - Users find Sigma lacking in **essential features** , impacting functionality and limiting advanced analytics capabilities. #### Key Features - Reports Interface - Data Column Filtering - Big Data Services - Data Transformation - Connectors #### Recent Reviews **"[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) --- #### Trending Discussions - [What is Sigma used for?](https://www.g2.com/discussions/what-is-sigma-used-for) - 1 comment ### 8. [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) **Average Rating:** 4.8/5.0 **Total Reviews:** 249 **Why buyers love it?:** Kyvos Semantic Layer’s G2 feedback was unusually focused on performance at enterprise data scale. Reviewers highlight fast query responses, instant dashboard refreshes, Snowflake and BI tool connections, and the ability to analyze years of customer data without slowdowns. To me the product reads as a fit for teams that need a consistent semantic layer to speed up analytics across large data estates. Users also mention initial setup challenges, a learning curve for advanced features, and a need for clearer examples around conversational analytics scenarios. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users value the **ease of use** in Kyvos, enabling quick analysis and independent handling of analytics effortlessly. - Users love the **rapid data processing** of Kyvos, enabling instant insights and swift decision-making across channels. - Users value the **fast data processing** capability of Kyvos, enabling quick insights and efficient fraud detection. - Users value the **independent data exploration** capabilities of Kyvos, enhancing analytics and speeding up insights generation. - Users value the **fast querying capabilities** of Kyvos, allowing quick analysis of large datasets and immediate insights. **Cons:** - Users face a **steep learning curve** initially, finding features complex until they become more familiar with the system. - Users find the **difficult setup** of Kyvos Semantic Layer challenging, but responsive support helps ease the process. - Users find the **initial setup and MDX learning curve complex** , though support helps streamline the process. - Users note **feature limitations** in Kyvos, particularly with legacy features and advanced graphical options for visualization. - Users find the **learning difficulty** notable, especially for teams new to big data and MDX complexities. #### Key Features - Graphs and Charts - Calculated Fields - Data Visualization - Data Modeling - Data Querying #### Recent Reviews **"[Kyvos Unified Our Business Logic with a Single Semantic Model](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-12797024)"** **Rating:** 5.0/5.0 stars *— Sujal S.* [Read full review](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-12797024) --- **"[Kyvos Makes Asking and Following Up on Data Questions Effortless](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-12469581)"** **Rating:** 4.5/5.0 stars *— Vaishnavi M.* [Read full review](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-12469581) --- ### 9. [Amazon QuickSight](https://www.g2.com/products/amazon-quicksight/reviews) **Average Rating:** 4.3/5.0 **Total Reviews:** 671 **Why buyers love it?:** Amazon QuickSight’s G2 feedback pointed to a product valued most when analytics sits close to AWS data and infrastructure. Reviewers highlight serverless setup, AWS integration, SPICE performance, calculated metrics, interactive dashboards, live data updates, and newer AI-assisted exploration features. From my evaluation, the product reads as a fit for teams that want BI dashboards inside an AWS-centered environment without heavy infrastructure management. Users also mention limited visual customization, fewer chart options, a less intuitive interface, slower downloads, and a learning curve for non-technical users. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users appreciate the **seamless integration with AWS services** that enhances dashboard creation and data insights. - Users find Amazon QuickSight to be **easy to use** , facilitating quick integration and dashboard creation with minimal effort. - Users praise the **easy integrations** of Amazon QuickSight with AWS resources, streamlining data analysis and decision-making. - Users value the **powerful data visualization** capabilities of Amazon QuickSight, enabling quick insights from large datasets. - Users appreciate the **intuitive dashboard creation** in Amazon QuickSight, enhancing data accessibility and decision-making efficiency. **Cons:** - Users find the **limited customization** of Amazon QuickSight frustrating, especially compared to more flexible BI tools. - Users find the **learning curve challenging** , requiring basic AWS knowledge to effectively utilize QuickSight's features. - Users note the **limited visualization options** in Amazon QuickSight, impacting usability and customization possibilities. - Users note the **missing features** in QuickSight, limiting customization and advanced visualization compared to competitors. - Users find the **poor interface design** of Amazon QuickSight complicates usability, especially for advanced features and new users. #### Key Features - Reports Interface - Calculated Fields - Data Visualization - Data Transformation - Scripting #### Recent Reviews **"[Turns Raw Data into Interactive Dashboards for Better Trend Monitoring](https://www.g2.com/survey_responses/amazon-quicksight-review-12740199)"** **Rating:** 4.0/5.0 stars *— Aviral G.* [Read full review](https://www.g2.com/survey_responses/amazon-quicksight-review-12740199) --- **"[Amazon QuickSight’s New QuickSuite UI and Chat/AI Search Make Reporting Easier](https://www.g2.com/survey_responses/amazon-quicksight-review-12532508)"** **Rating:** 5.0/5.0 stars *— SAURABH B.* [Read full review](https://www.g2.com/survey_responses/amazon-quicksight-review-12532508) --- #### Trending Discussions - [What is Amazon QuickSight used for?](https://www.g2.com/discussions/what-is-amazon-quicksight-used-for) - 2 comments, 1 upvote - [What is new in QuickSight?](https://www.g2.com/discussions/what-is-new-in-quicksight) - 1 comment, 1 upvote - [Is AWS QuickSight free?](https://www.g2.com/discussions/is-aws-quicksight-free) - 2 comments, 1 upvote ### 10. [Hex](https://www.g2.com/products/hex-tech-hex/reviews) **Average Rating:** 4.5/5.0 **Total Reviews:** 384 **Why buyers love it?:** The pattern that I found in Hex’s G2 feedback was its usefulness for analysts who move between code, notebooks, and shareable data apps. Reviewers highlight an intuitive interface, SQL and Python in one workspace, AI-assisted query and chart generation, custom controls, and flexible exploration. The product reads as a fit for teams that want collaborative analysis to move from notebook work into stakeholder-facing outputs. Users also cite heavy-query slowness, app-builder layout constraints, connection stability issues, CSV handling problems, and challenges preserving ad hoc results. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users commend the **ease of use** of Hex, highlighting its seamless integrations and user-friendly interface for data analysis. - Users value the **ease of combining SQL with Python** , enhancing accessibility for data analysis and visualization. - Users admire the **user-friendly data management** of Hex, valuing seamless integration and powerful analytics capabilities. - Users appreciate the **SQL querying capabilities** of Hex, enhancing their data analysis with AI and seamless integrations. - Users appreciate the **seamless integration of SQL and Python** in Hex, enhancing efficiency in data analysis and visualization. **Cons:** - Users note the **limited features** of HEX, citing a need for more graph types and styling options for better analysis. - Users find **missing features** in Hex, particularly in data privacy controls and advanced analytics capabilities. - Users find Hex **lacking crucial features** , limiting control over data management and impacting overall performance and usability. - Users face **data management issues** such as confusing CTE usage, organization problems, and limited data access across projects. - Users express frustration over **limited visualization capabilities** in Hex, struggling with data size and filter options. #### Key Features - Steps to Answer - Collaboration / Workflow - Predictive Analytics - WYSIWYG Report Design - Drag and Drop #### Recent Reviews **"[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) --- **"[Effortless Data Analysis with Powerful AI](https://www.g2.com/survey_responses/hex-review-12262172)"** **Rating:** 5.0/5.0 stars *— Hunter P.* [Read full review](https://www.g2.com/survey_responses/hex-review-12262172) --- #### Trending Discussions - [What is Hex Technologies used for?](https://www.g2.com/discussions/what-is-hex-technologies-used-for) ### 11. [IBM Business Analytics Enterprise](https://www.g2.com/products/ibm-business-analytics-enterprise/reviews) **Average Rating:** 4.4/5.0 **Total Reviews:** 21 **Why buyers love it?:** IBM Business Analytics Enterprise reviews pointed to unified analytics as the main value theme. Reviewers highlight a combined IBM environment for Cognos, planning, AI-assisted insight, hybrid cloud data access, governance, and predictive analysis. The product reads as a fit for organizations that want BI, planning, and analytics capabilities consolidated across IBM tools. Users also mention high cost, setup and customization effort, UI concerns, and performance issues with demanding data scenarios. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users find IBM Business Analytics Enterprise to be **user-friendly** , making day-to-day analytics and data manipulation straightforward. - Users value the **real-time data integration** in IBM Business Analytics Enterprise for its timely insights and decision-making support. - Users value the **seamless integrations** of IBM Business Analytics Enterprise, enhancing data management and analytics across various platforms. - Users value the **efficient unified analytical experience** offered by IBM Business Analytics Enterprise, streamlining data analysis and reporting. - Users value the **seamless data integration** of IBM Business Analytics, enhancing decision-making through real-time analytics and insights. **Cons:** - Users highlight the **steep learning curve** of IBM Business Analytics Enterprise, making it challenging for beginners to navigate. - Users find the **complexity** of IBM Business Analytics Enterprise overwhelming, especially for smaller organizations lacking data expertise. - Users find the **difficult customization** process to be time-consuming and often in need of specialized skills. - Users find IBM Business Analytics Enterprise to be **expensive** , making it challenging for smaller businesses to adopt. - Users find the **complex usage** of IBM Business Analytics Enterprise to be overwhelming and time-consuming, particularly for smaller organizations. #### Key Features - Reports Interface - Calculated Fields - Predictive Analytics - Data Querying #### Recent Reviews **"[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) --- **"[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) --- ### 12. [Yellowfin BI](https://www.g2.com/products/yellowfin-bi/reviews) **Average Rating:** 4.4/5.0 **Total Reviews:** 414 **Why buyers love it?:** Yellowfin BI’s G2 feedback centered on making analytics more approachable through guided dashboard and report experiences. Reviewers highlight intuitive navigation, drag-and-drop report creation, visual exploration, automated insights, data storytelling, and browser or mobile access. To me the product reads as a fit for teams that want BI outputs that explain results rather than only display charts. Users also cite slower performance with complex datasets, advanced-feature learning effort, and a need for stronger tutorials and enablement resources. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users appreciate the **user-friendly interface** of Yellowfin BI, enabling effortless dashboard and report creation for all skill levels. - Users value the **quality and ease of stunning, customizable dashboards** in Yellowfin BI for impactful data insights. - Users value the **intuitive dashboard customization** of Yellowfin BI, enhancing their ability to create impactful reports easily. - Users find Yellowfin BI to be **intuitive and user-friendly** , making it essential for efficient data analysis and insights. - Users value the **automated report generation** in Yellowfin BI, enhancing efficiency and decision-making through insightful analytics. **Cons:** - Users find the **learning curve steep** , especially for advanced features and customization, requiring technical expertise initially. - Users face **slow performance** when exporting reports, especially with large datasets, causing crashes and frustrating experiences. - Users experience **performance issues** when handling large data sets, impacting report refresh and export capabilities. - Users experience **performance issues** , particularly with large datasets and problematic report exports that often crash. - Users feel that Yellowfin BI has **limited customization** , making it less suitable for advanced analytics and unique visual designs. #### Key Features - Reports Interface - Collaboration / Workflow - Data Visualization - Data Transformation - Connectors #### Recent Reviews **"[Clear, Shareable Dashboards That Turn Complex Data Into Insights](https://www.g2.com/survey_responses/yellowfin-bi-review-12469436)"** **Rating:** 4.0/5.0 stars *— Mike Gyro P.* [Read full review](https://www.g2.com/survey_responses/yellowfin-bi-review-12469436) --- **"[Good Balance of Ease-of-Use and Functionality for HR Case Reporting](https://www.g2.com/survey_responses/yellowfin-bi-review-12159531)"** **Rating:** 5.0/5.0 stars *— Jose G.* [Read full review](https://www.g2.com/survey_responses/yellowfin-bi-review-12159531) --- #### Trending Discussions - [What is Yellowfin BI used for?](https://www.g2.com/discussions/what-is-yellowfin-bi-used-for) - [What should I look for in a BI tool?](https://www.g2.com/discussions/what-should-i-look-for-in-a-bi-tool) - [What is the best BI software?](https://www.g2.com/discussions/what-is-the-best-bi-software) - 1 comment ### 13. [Oracle Analytics Cloud](https://www.g2.com/products/oracle-analytics-cloud/reviews) **Average Rating:** 4.1/5.0 **Total Reviews:** 293 **Why buyers love it?:** Oracle Analytics Cloud’s G2 feedback to me emphasized self-service analysis within a broader enterprise data environment. Reviewers highlight Oracle ecosystem integration, strong visualizations, accessible report creation, large-scale data handling, and clear presentation of information for decision support. The product reads as a fit for organizations that need cloud analytics connected to Oracle and non-Oracle sources. Users also mention complex setup, a steep learning curve for advanced capabilities, and the need for tuning when datasets become especially demanding. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users appreciate the **robust self-service analytics tools** of Oracle Analytics Cloud for effortless data exploration and visualization. - Users praise the **strong data visualization** capabilities of Oracle Analytics Cloud, enhancing insights and decision-making across teams. - Users value the **intuitive interface** of Oracle Analytics Cloud, enabling easy data exploration across all technical levels. - Users value the **scalability** of Oracle Analytics Cloud, allowing for flexible and customized solutions that adapt to their needs. - Users value the **robust customized solutions** of Oracle Analytics Cloud, enhancing business intelligence and collaborative analytics. **Cons:** - Users struggle with the **high learning curve** of Oracle Analytics Cloud, making it challenging for new users to adapt. - Users find the **complexity** of Oracle Analytics Cloud challenging, especially during initial setup for newcomers. - Users find the **complex usage** of Oracle Analytics Cloud challenging, particularly during initial setup and learning advanced features. - Users find the **limited customization** options in Oracle Analytics Cloud restrictive for their specialized needs. - Users are concerned about the **infrequent software updates** leading to potential security vulnerabilities in Oracle Analytics Cloud. #### Key Features - Reports Interface - Calculated Fields - Predictive Analytics - Data Transformation - Connectors #### Recent Reviews **"[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) --- #### Trending Discussions - [What is Oracle Analytics Cloud used for?](https://www.g2.com/discussions/what-is-oracle-analytics-cloud-used-for) - [How do I import an RPD into Oracle Analytics Cloud?](https://www.g2.com/discussions/how-do-i-import-an-rpd-into-oracle-analytics-cloud) - [What comes out of the box with Oracle Analytics for applications?](https://www.g2.com/discussions/what-comes-out-of-the-box-with-oracle-analytics-for-applications) ### 14. [Alteryx](https://www.g2.com/products/alteryx/reviews) **Average Rating:** 4.6/5.0 **Total Reviews:** 651 **Why buyers love it?:** When I reviewed Alteryx’s G2 feedback, the strongest theme was reducing manual data preparation through visual workflows. Reviewers highlight drag-and-drop data manipulation, fast blending across sources, large dataset handling, Excel-heavy workflow acceleration, automation, and improved report efficiency. The product reads as a fit for teams that need repeatable preparation and analysis workflows without line-by-line code. Users also mention slower performance in complex workflows, unclear error guidance, high memory use, and concerns around cloud processing requirements. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users appreciate the **ease of use** of Alteryx, finding it user-friendly and efficient for all skill levels. - Users praise Alteryx for its **automation capabilities** , which significantly enhance speed and efficiency in data processing. - Users praise Alteryx for its **intuitive interface** , making data management and analysis effortless and efficient. - Users find Alteryx **easy to learn and use** , benefiting from its intuitive layout and helpful community support. - Users appreciate the **efficiency** of Alteryx, enabling quick data processing and seamless integration for streamlined workflows. **Cons:** - Users find Alteryx **expensive** , making it challenging for small teams or startups to justify the cost. - Users face a **steep learning curve** when tackling advanced features, which can delay effective usage of Alteryx. - Users find it frustrating that Alteryx has **missing features** like essential connectors and support for special characters. - Users find that **learning difficulty** can arise from troubleshooting errors and confusing tool names, especially for beginners. - Users often face **slow performance** when handling large datasets, impacting their overall experience with Alteryx. #### Key Features - Data Column Filtering - Data Visualization - WYSIWYG Report Design - Data Unification - Data Visualization #### Recent Reviews **"[powerful data prep made simple with drag-and-drop](https://www.g2.com/survey_responses/alteryx-review-12714902)"** **Rating:** 4.0/5.0 stars *— Sravya A.* [Read full review](https://www.g2.com/survey_responses/alteryx-review-12714902) --- **"[Easy, Time-Saving Data Automation with Alteryx’s Drag-and-Drop Workflows](https://www.g2.com/survey_responses/alteryx-review-12594796)"** **Rating:** 4.0/5.0 stars *— Kapil S.* [Read full review](https://www.g2.com/survey_responses/alteryx-review-12594796) --- ### 15. [GoodData.AI](https://www.g2.com/products/gooddata-ai/reviews) **Average Rating:** 4.2/5.0 **Total Reviews:** 554 **Product Description:** 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. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users find GoodData's **ease of use** exceptional, with an intuitive UI and quick integration enhancing their experience. - Users appreciate the **ease of data visualization** in GoodData, enhancing their ability to analyze and present metrics effectively. - Users appreciate the **quick integration with other systems** , enhancing their ability to customize and manage dashboards effortlessly. - Users find GoodData's **intuitive interface** makes it easy for new users to navigate and utilize its features effectively. - Users praise the **easy integrations** of GoodData, highlighting seamless connections to various data sources. **Cons:** - Users find the **learning curve steep** for GoodData, especially for those unfamiliar with relational databases. - Users highlight the **missing features** in GoodData, noting limitations in customization, performance, and documentation support. - Users find the **learning difficulty** with GoodData challenging, especially for those unfamiliar with relational databases. - Users find GoodData's interface **complex to navigate** , especially when dealing with intricate data models and features. - Users find the **limited customization** options of GoodData frustrating, hindering their ability to tailor solutions effectively. #### Recent Reviews **"[Saves Time with Multi-Source Metrics and Flexible, Customizable Dashboards](https://www.g2.com/survey_responses/gooddata-ai-review-12550554)"** **Rating:** 4.0/5.0 stars *— Martin P.* [Read full review](https://www.g2.com/survey_responses/gooddata-ai-review-12550554) --- **"[GoodData Saves Time with an Intuitive, Executive-Ready Dashboard](https://www.g2.com/survey_responses/gooddata-ai-review-12623029)"** **Rating:** 5.0/5.0 stars *— Dibyendu G.* [Read full review](https://www.g2.com/survey_responses/gooddata-ai-review-12623029) --- ### 16. [IBM Cognos Analytics](https://www.g2.com/products/ibm-cognos-analytics/reviews) **Average Rating:** 4.1/5.0 **Total Reviews:** 394 **Why buyers love it?:** IBM Cognos Analytics’ G2 feedback focused on report creation, dashboard delivery, and access to historical business data. Reviewers highlight clear visual outputs, customizable reports, long-range order or transaction history, Excel export, and support for executive-facing presentations. The product reads as a fit for enterprises that need structured BI reporting with flexible views of established business data. Users also cite an outdated interface, slow report generation, a learning curve for new users, and complexity in advanced report creation. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users value the **ease of use** in IBM Cognos Analytics, praising its intuitive interface and drag-and-drop functionality. - Users commend the **easy report generation** in IBM Cognos Analytics, enhancing their data analysis capabilities efficiently. - Users value the **in-depth analytics and adaptable dashboards** in IBM Cognos Analytics for effective data insights. - Users appreciate the **effortless data visualization** of IBM Cognos Analytics, enhancing understanding through engaging dashboards and reports. - Users appreciate the **intuitive user interface** of IBM Cognos Analytics, making data understanding effortless and engaging. **Cons:** - Users face a **challenging learning curve** with IBM Cognos Analytics, requiring time and training to master its functionality. - Users find the **learning difficulty** of IBM Cognos Analytics challenging, requiring extensive training and practice to navigate effectively. - Users experience **slow performance** with complex reports, affecting efficiency and interaction during virtual meetings. - Users find the **complexity** of IBM Cognos Analytics challenging, especially with report building and feature accessibility. - Users find the **complex usage** of IBM Cognos Analytics challenging, especially for new users navigating advanced features. #### Key Features - Reports Interface - Data Column Filtering - Predictive Analytics - Data Transformation - Connectors #### Recent Reviews **"[Powerful Data Analysis with Intuitive Drag-and-Drop Visualizations](https://www.g2.com/survey_responses/ibm-cognos-analytics-review-12153976)"** **Rating:** 4.5/5.0 stars *— Konjengbam M.* [Read full review](https://www.g2.com/survey_responses/ibm-cognos-analytics-review-12153976) --- **"[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) --- #### Trending Discussions - [What is IBM Cognos Analytics with Watson used for?](https://www.g2.com/discussions/what-is-ibm-cognos-analytics-with-watson-used-for) - 1 comment ### 17. [Count](https://www.g2.com/products/count/reviews) **Average Rating:** 4.8/5.0 **Total Reviews:** 101 **Product Description:** Count is a modern data collaboration platform that helps data teams actually work together. It combines a data notebook with a real-time collaborative canvas for analysis and visualisation, so everyone can query, debug, and explore data in one place. Count replaces the mess of disconnected tools with a single workspace where analysts, engineers, and stakeholders can write SQL or Python, build visualisations, and share insights instantly. You can import and debug dbt models, see live results from connected CTEs, and export back to dbt Cloud, GitHub, or full SQL scripts. Used by over 500 data teams, including Accenture, Cleo AI, and Too Good To Go, Count helps teams move beyond static dashboards and focus on solving real business problems. It’s data collaboration that actually works. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users appreciate the **seamless team collaboration** in Count, enhancing storytelling and data transparency for effective analytics. - Users find Count's **ease of use** makes data investigations quick, insightful, and accessible even for laymen. - Users commend Count for its **responsive and helpful customer support** , enhancing the overall user experience and productivity. - Users value the **flexibility** of Count, facilitating quick data investigations and effective visualization of metrics. - Users appreciate the **user-friendly data visualizations** that facilitate easy exploration and understanding of underlying data. **Cons:** - Users find the **learning curve overwhelming** , especially with the numerous features and the new canvas approach. - Users report **slow performance** during usage, particularly with larger datasets, despite improvements over time by the Count team. - Users express concerns about **limited customization** options in Count, impacting flexibility for advanced use cases and growth. - Users note a **lack of basic visuals** and limited pivot table options, highlighting areas for improvement in Count. - Users experience **layout issues** that can lead to a cluttered canvas, impacting navigation and data exploration. #### Recent Reviews **"[All-in-One Analytics Powerhouse with Outstanding Support](https://www.g2.com/survey_responses/count-review-12119061)"** **Rating:** 5.0/5.0 stars *— Julian Martin D.* [Read full review](https://www.g2.com/survey_responses/count-review-12119061) --- **"[Easy Data Integration with Flexible SQL and Python in One Dashboard](https://www.g2.com/survey_responses/count-review-12378210)"** **Rating:** 5.0/5.0 stars *— Juan Ignacio P.* [Read full review](https://www.g2.com/survey_responses/count-review-12378210) --- ### 18. [Deepnote](https://www.g2.com/products/deepnote/reviews) **Average Rating:** 4.5/5.0 **Total Reviews:** 377 **Product Description:** Deepnote is a data workspace where agents and humans work together. It's designed to simplify data exploration, accelerate analysis, and quickly deliver actionable insights for you and your team. Unlike outdated tools such as Jupyter, Deepnote is built with the next decade in mind. Deepnote gives anyone working with data superpowers. It unifies your data workflow through an integrated semantic layer, preparing your data for advanced AI applications. You can also leverage our AI data copilot to chat with your data, create charts, write code, or turn your AI notebooks into fully-fledged data dashboards or apps. Combine data, SQL or Python code, and visualizations side-by-side on a flexible canvas - enhanced with cutting-edge AI reasoning models. 🤖 Analyze with AI • Generate code and visualizations by describing your goal. • Auto-write, run, and debug code with AI. • Move faster with context-aware AI suggestions. 🔗 Unify • Connect to 60+ data sources like BigQuery, Snowflake, and PostgreSQL. • Combine Python and SQL in one notebook. • Build reusable ETL, analytics, and metric modules. • Create a semantic layer with shared definitions and trusted metrics. ⚖️ Scale • Instantly boost compute power, more included than Colab. • Schedule jobs and get notified with fresh results. • Organize work in projects and folders for team clarity. • Manage workflows via REST API. 🚀 Launch • Turn notebooks into dashboards or data apps, natively or with Streamlit. • Let users explore data with interactive inputs. • Share secure, live apps in one click. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users love the **ease of use** of Deepnote, enabling quick collaboration and efficient task management with a simple interface. - Users appreciate the **real-time collaboration** features of Deepnote, enhancing teamwork for data analysis and project sharing. - Users appreciate the **easy integrations** of Deepnote, simplifying connections to data tools like Redshift and S3. - Users value the **collaborative capabilities** of Deepnote, enhancing teamwork and streamlining dataset generation across teams. - Users value the **easy data management** in Deepnote, enhancing collaboration through seamless integrations and visualizations. **Cons:** - Users find the **slow performance** of Deepnote frustrating, especially with large datasets and in low-connectivity situations. - Users find **limited features** in Deepnote, especially lacking essential options for seamless data manipulation and version control. - Users experience **data management issues** with Deepnote, facing challenges like context loss and import statement problems. - Users note the **missing features** in Deepnote, highlighting the need for SQL querying across dataframes and Git integration. - Users find Deepnote **lacking features** , particularly in project awareness, visualization tools, and inline editing speed. #### Recent Reviews **"[Deepnote’s Real-Time Collaboration and Cloud Notebooks Shine](https://www.g2.com/survey_responses/deepnote-review-12687317)"** **Rating:** 5.0/5.0 stars *— Jolina Mae A.* [Read full review](https://www.g2.com/survey_responses/deepnote-review-12687317) --- **"[Clarity for complex nutrition work](https://www.g2.com/survey_responses/deepnote-review-12699174)"** **Rating:** 5.0/5.0 stars *— Marine A.* [Read full review](https://www.g2.com/survey_responses/deepnote-review-12699174) --- #### Trending Discussions - [How do you use a deep note?](https://www.g2.com/discussions/how-do-you-use-a-deep-note) - [Is Deepnote open source?](https://www.g2.com/discussions/is-deepnote-open-source) - [Is Deepnote good?](https://www.g2.com/discussions/is-deepnote-good) - 1 comment ### 19. [Incorta](https://www.g2.com/products/incorta/reviews) **Average Rating:** 4.4/5.0 **Total Reviews:** 55 **Product Description:** Incorta is the first and only open data delivery platform that enables real-time analysis of live, detailed data across all systems of record—without the need for complex ETL processes. By enabling direct analysis on raw, source-identical data, Incorta provides faster, more accurate insights while removing barriers to exploration. With intuitive low-code/no-code tools, AI-powered querying through Nexus, and prebuilt business data applications, enterprise teams can quickly surface insights, break down technical roadblocks, and make smarter decisions without heavy engineering effort. For more information, please visit www.incorta.com. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users value the **ease of data integration** with various sources and types, enhancing efficiency in analytics. - Users appreciate the **easy integrations** of Incorta, making it simple to connect various data sources effortlessly. - Users value the **ease of integration** with various data sources and types, enhancing their analytical capabilities. **Cons:** - Users experience **bugs** with the local data agent not supporting the latest JRE builds, leading to functionality issues. #### Recent Reviews **"[Facilitating presentation and information access](https://www.g2.com/survey_responses/incorta-review-9467627)"** **Rating:** 5.0/5.0 stars *— Elsayed H.* [Read full review](https://www.g2.com/survey_responses/incorta-review-9467627) --- **"[Great platformand support](https://www.g2.com/survey_responses/incorta-review-10853785)"** **Rating:** 5.0/5.0 stars *— Jeff W.* [Read full review](https://www.g2.com/survey_responses/incorta-review-10853785) --- #### Trending Discussions - [Is Incorta a data warehouse?](https://www.g2.com/discussions/is-incorta-a-data-warehouse) - [What is Incorta?](https://www.g2.com/discussions/what-is-incorta) - [What do you know about Incorta?](https://www.g2.com/discussions/what-do-you-know-about-incorta) ### 20. [FICO Analytics Workbench™](https://www.g2.com/products/fico-analytics-workbencha/reviews) **Average Rating:** 4.3/5.0 **Total Reviews:** 11 **Product Description:** FICO® Analytics Workbench™ is a comprehensive predictive analytics tool designed to empower businesses in developing and deploying explainable machine learning models. It caters to both business users and data scientists, facilitating data exploration, visual data wrangling, decision strategy design, and machine learning within a unified environment. The platform is built on the high-performance FICO® Decision Management Platform, ensuring scalability and integration with real-time business operations. Key Features and Functionality: - Explainable AI Toolkit: Provides transparency in AI-derived decisions, enabling users to validate and interpret machine learning models effectively. - Integrated Development Environment: Combines decision trees, scorecards, and machine learning techniques, offering a versatile toolkit for model development. - User-Friendly Interface: Designed for users with varying skill sets, from business analysts to data scientists, promoting collaboration and productivity. - Cloud-Based Deployment: Offers a cloud-ready solution, allowing for scalable and flexible deployment options. - Regulatory Compliance Support: Automates the production of necessary documentation to meet internal review and external regulatory requirements. Primary Value and Problem Solving: FICO® Analytics Workbench™ addresses the growing need for transparent and interpretable AI models in business decision-making. By providing tools that make machine learning models explainable, it helps organizations comply with regulatory standards and build trust in AI-driven decisions. The platform's intuitive design and comprehensive features enable faster time-to-value, enhanced productivity, and improved business outcomes through analytically powered decisions. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users appreciate the **ease of use** of FICO Analytics Workbench™, enhancing their overall experience and efficiency. #### Recent Reviews **"[Fico Analytics Review](https://www.g2.com/survey_responses/fico-analytics-workbench-review-11044918)"** **Rating:** 5.0/5.0 stars *— Starr C.* [Read full review](https://www.g2.com/survey_responses/fico-analytics-workbench-review-11044918) --- **"[From Messy Data to Magic Models: FICO Workbench to the Rescue](https://www.g2.com/survey_responses/fico-analytics-workbench-review-9701975)"** **Rating:** 4.5/5.0 stars *— Verified User in Industrial Automation* [Read full review](https://www.g2.com/survey_responses/fico-analytics-workbench-review-9701975) --- ### 21. [Dataiku](https://www.g2.com/products/dataiku/reviews) **Average Rating:** 4.4/5.0 **Total Reviews:** 185 **Product Description:** Dataiku is the Platform for AI Success that unites people, orchestration, and governance to turn AI investments into measurable business outcomes. It helps organizations move from fragmented experimentation to coordinated, trusted execution at scale. Built for AI success: Dataiku brings business experts and AI specialists into the same environment, embedding business context into analytics, models, and AI agents. Business teams can self-serve and innovate, while AI experts build, deploy, and optimize quickly, closing the gap between pilots and production. Orchestration that scales: Dataiku connects data, AI services, and enterprise apps across analytics, machine learning, and AI agents. Integrated workflows deliver value across any cloud or infrastructure without vendor lock-in or fragmentation. Governance you can trust: Dataiku embeds governance across the AI lifecycle, enabling teams to track performance, cost, and risk to keep systems explainable, compliant, and auditable. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users love the **ease of use** of Dataiku, simplifying complex tasks and enhancing productivity in ML development. - Users appreciate the **user-friendly learning resources** of Dataiku, which simplify the ML development process significantly. - Users appreciate the **user-friendly interface** of Dataiku, allowing seamless collaboration and ease of use for all skill levels. - Users love the **easy integrations** in Dataiku, enabling seamless connections to various data sources and platforms. - Users value the **productivity improvement** from Dataiku's unified platform, enabling efficient data management and analysis for everyone. **Cons:** - Users find the **steep learning curve** challenging, especially when trying to utilize advanced features effectively. - Users find the **steep learning curve** challenging, particularly for beginners wanting to use advanced features. - Users experience **slow performance** in Dataiku, particularly with large datasets and complex scenario executions. - Users find the **difficult learning curve** of Dataiku challenging, especially for those new to advanced features and integration. - Users highlight the **expensive pricing structure** as a major drawback, particularly for smaller organizations or teams. #### Recent Reviews **"[Dataiku: User-Friendly Collaboration Across the Full Data Lifecycle](https://www.g2.com/survey_responses/dataiku-review-12256413)"** **Rating:** 4.5/5.0 stars *— Mahmoud H.* [Read full review](https://www.g2.com/survey_responses/dataiku-review-12256413) --- **"[Dataiku:A plug in tool for Data Science](https://www.g2.com/survey_responses/dataiku-review-8032719)"** **Rating:** 4.5/5.0 stars *— Rakshith N.* [Read full review](https://www.g2.com/survey_responses/dataiku-review-8032719) --- #### Trending Discussions - [Is Dataiku an ETL tool?](https://www.g2.com/discussions/is-dataiku-an-etl-tool) - [Is Dataiku web based?](https://www.g2.com/discussions/is-dataiku-web-based) - [What is DSS Dataiku?](https://www.g2.com/discussions/what-is-dss-dataiku) ### 22. [Coefficient](https://www.g2.com/products/coefficient/reviews) **Average Rating:** 4.7/5.0 **Total Reviews:** 183 **Product Description:** Coefficient is a new way to work with your company data better, faster, and more accurately without ever leaving your spreadsheet, integrating with the tools you already use. Install the Coefficient Excel or Google Sheets extension and use it in a new or existing sheet in seconds. Once installed, Coefficient lives as a sidebar companion so your company data is only a couple of clicks away at any time. Any data source that you work with is available directly in your Coefficient sidebar – such as Salesforce, HubSpot, Snowflake, NetSuite, QuickBooks, MySQL, and Looker – with the ability to consolidate your data from multiple systems into one spreadsheet. Use Coefficient filters to easily customize your imports to only work with the data you need, keeping your spreadsheets performant. Quickly go back anytime to add more data in the same report. Never rebuild the same analysis twice by keeping your data up to date with scheduled updates. And, use Coefficient alerts to trigger Slack or email messages anytime your spreadsheet updates. Now, you can turn your spreadsheet into the most flexible, powerful monitoring system across all of your company data. Say “goodbye” to manual data workflows and “hello” to connected spreadsheets. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users find Coefficient's **ease of use** remarkable, enabling seamless data integration and effortless dashboard creation. - Users appreciate the **seamless automation** in Coefficient, enhancing efficiency and simplifying data integration from multiple platforms. - Users appreciate the **seamless integrations** of Coefficient, enhancing simplicity and efficiency in their workflows. - Users highlight the **time-saving benefits** of Coefficient, enabling quick data imports and effortless refreshing. - Users highlight the **easy integrations** with Coefficient, simplifying connections to databases and enhancing productivity seamlessly. **Cons:** - Users find the **limited features** of Coefficient disappointing, particularly for small organizations on a budget. - Users find the **feature limitations** of Coefficient, such as lack of bulk updates, somewhat restrictive for their needs. - Users face **limitations in data processing** , including bulk update restrictions and row number constraints in Coefficient. - Users note the **missing features** , such as bulk updating and specific connectors, which limit integration capabilities. - Users note **integration issues** with Coefficient, including limited customizability with Salesforce and slow data pulling into Google Sheets. #### Recent Reviews **"[Effortless Salesforce to Sheets Integration](https://www.g2.com/survey_responses/coefficient-review-12701723)"** **Rating:** 5.0/5.0 stars *— Ellen Dericks C.* [Read full review](https://www.g2.com/survey_responses/coefficient-review-12701723) --- **"[Effortless Scheduled Refreshes. Eliminates Manual Exports and Makes Revenue Data Actionable](https://www.g2.com/survey_responses/coefficient-review-12318636)"** **Rating:** 5.0/5.0 stars *— Jacob R.* [Read full review](https://www.g2.com/survey_responses/coefficient-review-12318636) --- ### 23. [SAS Enterprise Guide](https://www.g2.com/products/sas-enterprise-guide/reviews) **Average Rating:** 4.3/5.0 **Total Reviews:** 112 **Product Description:** SAS Enterprise Guide is a Windows-based client application that provides a user-friendly, point-and-click interface to the powerful analytics capabilities of SAS software. Designed to cater to both novice and experienced users, it facilitates data access, management, analysis, and reporting without the need for extensive programming knowledge. By integrating a wide array of analytical tasks with an intuitive graphical interface, SAS Enterprise Guide empowers users to efficiently conduct complex analyses and share results across their organization. Key Features and Functionality: - Intuitive Interface and Wizards: Offers guided access to SAS capabilities, from basic reporting to advanced analyses, through flexible wizards and an intuitive process flow diagram facility. - Comprehensive Analytical Tasks: Includes over 100 prebuilt tasks for descriptive statistics, predictive modeling, regression analysis, and more, enabling users to perform complex analyses without writing code. - Data Management: Provides a powerful graphical query builder for accessing and manipulating various data types, including SAS datasets and native Windows data types, without requiring SQL expertise. - OLAP Access and Visualization: Supports dynamic slicing, drilling, and pivoting of data for exploration, with integration capabilities for SAS OLAP Server and other third-party vendors supporting OLE DB for OLAP. - Result Distribution and Sharing: Facilitates the distribution of results through multiple channels, including SAS BI report/content repository, Microsoft Office documents, and email, ensuring seamless sharing and collaboration. - High-Performance Computing and Grid Enablement: Automatically detects grid environments for efficient processing, analyzes SAS programs to optimize performance, and enables parallel execution of tasks on the same server. Primary Value and User Solutions: SAS Enterprise Guide addresses the need for a self-service analytics environment that empowers business analysts and other users to perform sophisticated data analyses without relying heavily on IT departments. By providing guided access to data integration, preparation, analytics, and reporting, it enables users to quickly access data, conduct analyses, and distribute results, thereby accelerating decision-making processes. The integration with SAS Viya further enhances its capabilities, allowing users to leverage modern, cloud-based platforms for scalable and efficient analytics. This comprehensive toolset ultimately helps organizations harness their data effectively, leading to more informed business decisions and improved operational efficiency. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users find the **ease of use** in SAS Enterprise Guide enhances their experience, especially for beginners. - Users value the **flexible user interface** of SAS Enterprise Guide, enabling coding and GUI features for ease of use. - Users value the **powerful data analysis capabilities** of SAS Enterprise Guide, enhancing efficiency and decision-making in analytics. - Users love the **easy-to-use data visualization** in SAS Enterprise Guide, which streamlines analytics and decision-making. - Users find SAS Enterprise Guide’s **ease of learning** remarkable, making it accessible for both beginners and experts. **Cons:** - Users are frustrated by the **slow performance** of SAS Enterprise Guide, as it impacts data handling and debugging. - Users struggle with the **complex usage** due to a cluttered interface and difficulty finding options. - Users find the **learning curve steep** , with a clunky interface making it hard to navigate menus and options. - Users find SAS Enterprise Guide to be **buggy and slow** , making data manipulation frustrating and challenging. - Users struggle with **integration issues** , facing challenges when trying to connect SAS Enterprise Guide to various systems. #### Recent Reviews **"[Dynamic and User-Friendly with Robust Performance](https://www.g2.com/survey_responses/sas-enterprise-guide-review-12706111)"** **Rating:** 4.5/5.0 stars *— Charles A.* [Read full review](https://www.g2.com/survey_responses/sas-enterprise-guide-review-12706111) --- **"[Versatile Tool with Room for UI Improvement](https://www.g2.com/survey_responses/sas-enterprise-guide-review-12713363)"** **Rating:** 5.0/5.0 stars *— Alec E.* [Read full review](https://www.g2.com/survey_responses/sas-enterprise-guide-review-12713363) --- #### Trending Discussions - [What is SAS Enterprise Guide used for?](https://www.g2.com/discussions/sas-enterprise-guide-what-is-sas-enterprise-guide-used-for) - [How much is SAS Enterprise Guide?](https://www.g2.com/discussions/how-much-is-sas-enterprise-guide) - [What is the latest version of SAS Enterprise Guide?](https://www.g2.com/discussions/what-is-the-latest-version-of-sas-enterprise-guide) ### 24. [Luzmo](https://www.g2.com/products/luzmo/reviews) **Average Rating:** 4.6/5.0 **Total Reviews:** 70 **Product Description:** Luzmo helps businesses embed data products hassle-free, empowering their users with fast, confident decisions in record time. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users value the **ease of use** of Luzmo, allowing quick dashboard creation and fast team onboarding. - Users value Luzmo's **embedding features** for fast market delivery, enhancing user experience and analytics integration significantly. - Users value the **customization capabilities** of Luzmo, enabling tailored BI solutions for diverse customer needs remarkably. - Users value Luzmo for its **intuitive interface and rapid deployment** , enabling fast analytics and customer success. - Users value the **easy setup** of Luzmo, facilitating quick adoption and efficient deployment of embedded analytics. **Cons:** - Users find the **complexity of setup and performance issues** with Luzmo frustrating, especially for large datasets. - Users feel Luzmo has **limited options** for complex queries and chart functionalities, which hinders data analysis. - Users face **performance issues** with larger datasets on Luzmo, affecting workflow and dashboard capabilities. - Users experience **table limitations** in Luzmo, hindering complex queries and leading to cumbersome data setups. - Users experience **performance and sync issues** with larger datasets in Luzmo, affecting overall usability and efficiency. #### Recent Reviews **"[Powerful Embedded BI with Proactive Support Addressing Performance at Scale](https://www.g2.com/survey_responses/luzmo-review-12246440)"** **Rating:** 4.5/5.0 stars *— Verified User in Logistics and Supply Chain* [Read full review](https://www.g2.com/survey_responses/luzmo-review-12246440) --- **"[Fast, Beautiful Dashboards and Effortless Embedding](https://www.g2.com/survey_responses/luzmo-review-11991075)"** **Rating:** 4.5/5.0 stars *— Shanti B.* [Read full review](https://www.g2.com/survey_responses/luzmo-review-11991075) --- #### Trending Discussions - [What is Cumul.io used for?](https://www.g2.com/discussions/what-is-cumul-io-used-for) ### 25. [Knowi](https://www.g2.com/products/knowi/reviews) **Average Rating:** 4.9/5.0 **Total Reviews:** 27 **Product Description:** Knowi is an end-to-end AI-powered analytics platform designed for modern data, enabling enterprises of all sizes to dramatically accelerate the journey from raw data to actionable insights. With native integration to virtually any data source—including SQL, NoSQL (MongoDB, Elasticsearch, InfluxDB), REST APIs, cloud databases, and documents—Knowi eliminates the need for complex data transformation processes required by alternative solutions. Data teams can integrate, blend, visualize, and analyze data from any source 10X faster, all within a single platform. They can then leverage AI to uncover insights, embed analytics into applications, share dashboards with business users, and automate reporting. ### Quick AI Summary Based on G2 Reviews *Generated from real user reviews* **Pros:** - Users commend Knowi's **exceptional customer support** , highlighting their responsiveness and tailored assistance throughout the onboarding process. - Users value Knowi for its **exceptional onboarding and proactive support** , boosting their confidence as they scale their businesses. - Users commend the **AI capabilities** of Knowi, enhancing their analytics experience with responsive support and tailored solutions. - Users value the **automation features** of Knowi, which simplify reporting and save significant time in data management. - Users praise the **intuitive dashboarding** of Knowi, allowing effortless data management and significant time savings. **Cons:** - Users find the **learning curve challenging** when setting up API connections and understanding advanced features in Knowi. - Users note a significant **lack of guidance** for API connections, making initial setup with Knowi time-consuming. - Users feel there is a significant **lack of tutorials** for API connections with popular platforms, making setup challenging. #### Recent Reviews **"[Chief Executive Officer](https://www.g2.com/survey_responses/knowi-review-12447928)"** **Rating:** 5.0/5.0 stars *— Paul N.* [Read full review](https://www.g2.com/survey_responses/knowi-review-12447928) --- **"[Intuitive Dashboarding with Exceptional Support](https://www.g2.com/survey_responses/knowi-review-12378559)"** **Rating:** 5.0/5.0 stars *— Jose D.* [Read full review](https://www.g2.com/survey_responses/knowi-review-12378559) --- ## Parent Category [Analytics Tools & Software](https://www.g2.com/categories/analytics-tools-software) ## Related Categories - [Data Visualization Tools](https://www.g2.com/categories/data-visualization-tools) - [Predictive Analytics Software](https://www.g2.com/categories/predictive-analytics) - [Embedded Business Intelligence Software](https://www.g2.com/categories/embedded-business-intelligence) - [Marketing Analytics Software](https://www.g2.com/categories/marketing-analytics) - [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) - [ETL Tools](https://www.g2.com/categories/etl-tools) - [Data Preparation Software](https://www.g2.com/categories/data-preparation) --- ## Buyer Guide ### 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.