# Best Enterprise Predictive Analytics Software *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)* Products classified in the overall Predictive Analytics category are similar in many regards and help companies of all sizes solve their business problems. However, enterprise business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Enterprise Business Predictive Analytics to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Enterprise Business Predictive Analytics category. In addition to qualifying for inclusion in the Predictive Analytics Software category, to qualify for inclusion in the Enterprise Business Predictive Analytics Software category, a product must have at least 10 reviews left by a reviewer from an enterprise business. ## How Many Predictive Analytics Software Products Does G2 Track? **Total Products under this Category:** 287 ## How Does G2 Rank Predictive Analytics Software Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 30,000+ Authentic Reviews - 287+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## Which Predictive Analytics Software Is Best for Your Use Case? - **Best for Small Businesses:** [Amazon QuickSight](https://www.g2.com/products/amazon-quicksight/reviews) - **Best for Mid-Market:** [Tableau](https://www.g2.com/products/tableau/reviews) - **Best for Enterprise:** [Tableau](https://www.g2.com/products/tableau/reviews) - **Highest User Satisfaction:** [Clari](https://www.g2.com/products/clari/reviews) - **Best Free Software:** [Altair AI Studio](https://www.g2.com/products/rapidminer-studio/reviews) --- **Sponsored** ### Alteryx Alteryx, through it's Alteryx One platform, helps enterprises transform complex, disconnected data into a clean, AI-ready state. Whether you’re creating financial forecasts, analyzing supplier performance, segmenting customer data, analyzing employee retention, or building competitive AI applications from your proprietary data, Alteryx One makes it easy to cleanse, blend, and analyze data to unlock the unique insights that drive impactful decisions. AI-Guided Analytics Alteryx automates and simplifies every stage of data preparation and analysis, from validation and enrichment to predictive analytics and automated insights. Incorporate generative AI directly into your workflows to streamline complex data tasks and generate insights faster. Unmatched flexibility, whether you prefer code-free workflows, natural language commands, or low-code options, Alteryx adapts to your needs. Trusted. Secure. Enterprise-Ready. Alteryx is trusted by over half of the Global 2000 and 19 of the top 20 global banks. With built-in automation, governance, and security, your workflows can scale and maintain compliance while delivering consistent results. And it doesn’t matter if your systems are on-premises, hybrid, or in the cloud; Alteryx fits effortlessly into your infrastructure. Easy to Use. Deeply Connected. What truly sets Alteryx apart is our focus on efficiency and ease of use for analysts and our active community of 700,000 Alteryx users to support you at every step of your journey. With seamless integration to data everywhere including platforms like Databricks, Snowflake, AWS, Google, SAP, and Salesforce, our platform helps unify siloed data and accelerate getting to insights. Visit Alteryx.com for more information, and to start your free trial. [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=499&secure%5Bdisplayable_resource_id%5D=499&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=499&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=989&secure%5Bresource_id%5D=499&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fpredictive-analytics&secure%5Btoken%5D=f3a88359c4a324ff6aec0778ff6346f979d3e20f6de48d9e44747e6f03c18c41&secure%5Burl%5D=https%3A%2F%2Fwww.alteryx.com%2Ftrial%3Futm_source%3Dg2%26utm_medium%3Dreviewsite%26utm_campaign%3DFY25_Global_AllRegions_AlwaysOn_AllPersonas_IndustryAgnostic%26utm_content%3Dg2_freetrial&secure%5Burl_type%5D=free_trial) --- ## What Are the Top-Rated Predictive Analytics Software Products in 2026? ### 1. [Tableau](https://www.g2.com/products/tableau/reviews) Tableau is the world’s leading AI-powered analytics platform. Whether you are a business user or an analyst, Tableau turns trusted data into actionable insights. With our flexible, interoperable platform, you can: Turn data into action at scale with human and agent collaboration. Tableau Next delivers agentic AI for faster data-insight-action workflows. It surfaces insights, provides proactive recommendations, and helps you take action in the flow of work. Scale data-driven insights with complete operational confidence. Tableau Cloud enables fully managed analytics at scale. It accelerates your time to value and gives you access to the latest AI-powered innovations. Deploy visual, self-service analytics with unmatched control and flexibility. Tableau Server meets your organization's governance and security needs. It provides enterprise-grade, self-service analytics on-premise or in your private cloud. **Average Rating:** 4.4/5.0 **Total Reviews:** 3,534 **How Do G2 Users Rate Tableau?** - **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 9.0/10) - **AI Text Summarization:** 8.0/10 (Category avg: 8.1/10) - **Algorithms:** 8.4/10 (Category avg: 8.5/10) - **AI Text Generation:** 8.0/10 (Category avg: 8.1/10) **Who Is the Company Behind Tableau?** - **Seller:** [Salesforce](https://www.g2.com/sellers/salesforce) - **Company Website:** https://www.salesforce.com/ - **Year Founded:** 1999 - **HQ Location:** San Francisco, CA - **Twitter:** @salesforce (581,426 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3185/ (88,363 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Analyst, Business Analyst - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 41% Enterprise, 36% Mid-Market #### What Are Tableau's Pros and Cons? **Pros:** - Ease of Use (632 reviews) - Data Visualization (561 reviews) - Visualization (423 reviews) - Features (348 reviews) - Intuitive (316 reviews) **Cons:** - Learning Curve (280 reviews) - Learning Difficulty (239 reviews) - Expensive (224 reviews) - Slow Performance (154 reviews) - Difficulty (137 reviews) ### 2. [Clari](https://www.g2.com/products/clari/reviews) Clari+Salesloft is a category-transforming AI company architecting the future of revenue. By building the world’s first Predictive Revenue System, we help organizations move beyond fragmented applications and systems of record to a model that continuously drives and adapts revenue execution. Our platform captures deal data signals, and uses tailor-built AI to create the right context and drive action across sales teams. Instead of disconnected insights and siloed workflows, sales teams operate with shared understanding, faster decisions, and execution that stays aligned to the business. Trusted by thousands of enterprises including Adobe, 3M, IBM, and Zoom, Clari+Salesloft powers the forecast, surfaces pipeline risk, and drives proactive execution—returning thousands of hours to the field and enabling predictable, scalable growth. **Average Rating:** 4.6/5.0 **Total Reviews:** 5,493 **How Do G2 Users Rate Clari?** - **Has the product been a good partner in doing business?:** 9.2/10 (Category avg: 9.0/10) - **Algorithms:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Clari?** - **Seller:** [Salesloft](https://www.g2.com/sellers/salesloft) - **Company Website:** https://salesloft.com - **Year Founded:** 2011 - **HQ Location:** Atlanta, GA - **Twitter:** @Salesloft (18,446 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/2296178/ (1,109 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Account Executive, Account Manager - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 47% Mid-Market, 41% Enterprise #### What Are Clari's Pros and Cons? **Pros:** - Ease of Use (241 reviews) - Features (180 reviews) - Helpful (171 reviews) - Forecasting (139 reviews) - Salesforce Integration (125 reviews) **Cons:** - Learning Curve (80 reviews) - Limitations (63 reviews) - Missing Features (61 reviews) - Limited Customization (59 reviews) - Not Intuitive (57 reviews) ### 3. [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) BigQuery is a fully managed, AI-ready data analytics platform that helps you maximize value from your data and is designed to be multi-engine, multi-format, and multi-cloud. Store 10 GiB of data and run up to 1 TiB of queries for free per month. **Average Rating:** 4.5/5.0 **Total Reviews:** 1,157 **How Do G2 Users Rate Google Cloud BigQuery?** - **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 9.0/10) - **AI Text Summarization:** 7.8/10 (Category avg: 8.1/10) - **Algorithms:** 8.8/10 (Category avg: 8.5/10) - **AI Text Generation:** 7.5/10 (Category avg: 8.1/10) **Who Is the Company Behind Google Cloud BigQuery?** - **Seller:** [Google](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google (31,890,350 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®) - **Ownership:** NASDAQ:GOOG **Who Uses This Product?** - **Who Uses This:** Data Engineer, Data Analyst - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 38% Enterprise, 35% Mid-Market #### What Are Google Cloud BigQuery's Pros and Cons? **Pros:** - Ease of Use (155 reviews) - Speed (142 reviews) - Fast Querying (119 reviews) - Integrations (117 reviews) - Query Efficiency (114 reviews) **Cons:** - Expensive (126 reviews) - Query Issues (78 reviews) - Cost Issues (63 reviews) - Cost Management (60 reviews) - Learning Curve (54 reviews) ### 4. [IBM Cognos Analytics](https://www.g2.com/products/ibm-cognos-analytics/reviews) IBM Cognos Analytics is a business intelligence and analytics solution that uses agentic AI to help teams transform trusted data into actionable insights, build governed analytical applications, and make better decisions. Teams can explore data, monitor KPIs, analyze performance, forecast trends, and share insights across the business. The solution is built for business leaders, analysts, report authors, IT teams, and data governance teams that need governed reporting, self-service analytics, data modeling, and flexible deployment options. Common use cases include enterprise reporting, operational reporting, financial reporting, dashboarding, performance management, forecasting, and governed self-service analytics. It supports both centralized BI teams and distributed users who need consistent access to trusted analytics. Key capabilities: 1. Create governed reports and dashboards: Build, schedule, distribute, and manage reports, dashboards, and visualizations for teams, executives, and stakeholders. Support routine reporting, business reviews, and purpose-built analytical applications with consistent information. 2. Explore data with control: Use self-service analytics, certified data models, governed metrics, access controls, and auditability to keep reporting consistent across teams and departments. 3. Analyze and forecast faster: Use natural-language assistance, automated insights, and forecasting to help users understand data faster in supported versions and deployments. 4. Put Reporting Agents to work: Use agentic AI capabilities in supported versions and deployments to find reports, summarize results, share insights, and create or refine reports using natural language. 5. Deploy where the business needs it: Run Cognos Analytics in on-premises, IBM-hosted, hybrid, or certified container environments to align with infrastructure, security, and governance requirements. Cognos Analytics helps organizations reduce repetitive reporting work, improve consistency across metrics and dashboards, and make governed data analytics easier to access across the business. **Average Rating:** 4.1/5.0 **Total Reviews:** 394 **How Do G2 Users Rate IBM Cognos Analytics?** - **Has the product been a good partner in doing business?:** 7.8/10 (Category avg: 9.0/10) - **AI Text Summarization:** 8.0/10 (Category avg: 8.1/10) - **Algorithms:** 8.6/10 (Category avg: 8.5/10) - **AI Text Generation:** 7.9/10 (Category avg: 8.1/10) **Who Is the Company Behind IBM Cognos Analytics?** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Company Website:** https://www.ibm.com/us-en - **Year Founded:** 1911 - **HQ Location:** Armonk, NY - **Twitter:** @IBM (709,298 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Analyst - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 59% Enterprise, 26% Mid-Market #### What Are IBM Cognos Analytics's Pros and Cons? **Pros:** - Ease of Use (36 reviews) - Report Generation (17 reviews) - Analytics (15 reviews) - Data Visualization (15 reviews) - User Interface (12 reviews) **Cons:** - Learning Curve (18 reviews) - Learning Difficulty (10 reviews) - Slow Performance (9 reviews) - Complexity (8 reviews) - Complex Usage (6 reviews) ### 5. [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) SAS Viya is a cloud-native data and AI platform that enables teams to build, deploy and scale explainable AI that drives trusted, confident decisions. It unites the entire data and AI life cycle and empowers teams to innovate quickly while balancing speed, automation and governance by design. Viya unifies data management, advanced analytics and decisioning in a single platform, so organizations can move from experimentation to production with confidence, delivering measurable business impact that is secure, explainable and scalable across any environment. Key capabilities required to deliver trusted decisions include: • End-to-end clarity across the data and AI life cycle, with built-in lineage, auditability and continuous monitoring to support defensible decisions. • Governance by design, enabling consistent oversight across data, models and decisions to reduce risk and accelerate adoption. • Explainable AI at scale, so insights and outcomes can be understood, validated and trusted by business and regulators alike. • Operationalized analytics, ensuring value continues beyond deployment through monitoring, retraining and life cycle management. • Flexible, cloud-native deployment, allowing organizations to start anywhere and scale everywhere while maintaining control. **Average Rating:** 4.3/5.0 **Total Reviews:** 754 **How Do G2 Users Rate SAS Viya?** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 9.0/10) - **AI Text Summarization:** 6.7/10 (Category avg: 8.1/10) - **Algorithms:** 8.6/10 (Category avg: 8.5/10) - **AI Text Generation:** 6.3/10 (Category avg: 8.1/10) **Who Is the Company Behind SAS Viya?** - **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2) - **Company Website:** https://www.sas.com/ - **Year Founded:** 1976 - **HQ Location:** Cary, NC - **Twitter:** @SASsoftware (60,974 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,519 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Student, Statistical Programmer - **Top Industries:** Pharmaceuticals, Banking - **Company Size:** 33% Enterprise, 33% Small-Business #### What Are SAS Viya's Pros and Cons? **Pros:** - Ease of Use (316 reviews) - Features (218 reviews) - Analytics (196 reviews) - Data Analysis (166 reviews) - User Interface (147 reviews) **Cons:** - Learning Difficulty (151 reviews) - Learning Curve (144 reviews) - Complexity (143 reviews) - Difficult Learning (117 reviews) - Expensive (108 reviews) ### 6. [Adobe Analytics](https://www.g2.com/products/adobe-analytics/reviews) Adobe Analytics empowers marketing, product, and business teams with insights to understand their customers and the journeys they take across digital channels, products, content, and services. From digital data collection and relational clickstream processing to in-depth analysis, and reporting, Adobe Analytics helps you understand visitor engagement across your digital properties — making it possible to optimize digital marketing strategies, improve user experience, and drive business growth. Features: - Collect and ingest behavioral data in real-time from your web and mobile channels. - Automatically convert raw data for unlimited analysis to discover customer patterns, spot anomalies, identify friction in your digital experiences, and uncover insights from digital journeys. - Equip marketers and analysts with AI capabilities that speed through analyses so they can quickly and confidently generate insights to improve the digital experience. - Integrate and share insights, segments, and outputs from your data across other business applications. **Average Rating:** 4.2/5.0 **Total Reviews:** 1,142 **How Do G2 Users Rate Adobe Analytics?** - **Has the product been a good partner in doing business?:** 8.0/10 (Category avg: 9.0/10) - **AI Text Summarization:** 9.1/10 (Category avg: 8.1/10) - **Algorithms:** 8.6/10 (Category avg: 8.5/10) - **AI Text Generation:** 8.8/10 (Category avg: 8.1/10) **Who Is the Company Behind Adobe Analytics?** - **Seller:** [Adobe](https://www.g2.com/sellers/adobe) - **Company Website:** https://adobe.com - **Year Founded:** 1982 - **HQ Location:** San Jose, CA - **Twitter:** @Adobe (959,186 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1480/ (41,539 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Analyst, Analyst - **Top Industries:** Marketing and Advertising, Information Technology and Services - **Company Size:** 42% Enterprise, 30% Mid-Market #### What Are Adobe Analytics's Pros and Cons? **Pros:** - Insights (84 reviews) - Analytics (77 reviews) - Ease of Use (74 reviews) - Features (59 reviews) - Reporting (37 reviews) **Cons:** - Learning Curve (51 reviews) - Steep Learning Curve (27 reviews) - Expensive (23 reviews) - Slow Performance (23 reviews) - Complexity (18 reviews) ### 7. [SAP HANA Cloud](https://www.g2.com/products/sap-hana-cloud-2025-10-01/reviews) SAP HANA Cloud is a modern database-as-a-service (DBaaS) powering the next generation of intelligent data applications. SAP HANA Cloud offers a competitive edge by incorporating advanced machine learning and predictive tools grounded in modern data science. Its powerful in-memory performance safeguards efficient data processing. By securely storing vast amounts of data with its integrated multitier storage and handling various types on a single copy in its native multi-model database, SAP HANA Cloud simplifies data management and connects to other data sources. The seamless integration of these capabilities in a reliable, unified foundation makes it easier for developers to build high-demand intelligent data apps. **Average Rating:** 4.3/5.0 **Total Reviews:** 519 **How Do G2 Users Rate SAP HANA Cloud?** - **Has the product been a good partner in doing business?:** 8.5/10 (Category avg: 9.0/10) - **AI Text Summarization:** 7.2/10 (Category avg: 8.1/10) - **Algorithms:** 8.8/10 (Category avg: 8.5/10) - **AI Text Generation:** 6.9/10 (Category avg: 8.1/10) **Who Is the Company Behind SAP HANA Cloud?** - **Seller:** [SAP](https://www.g2.com/sellers/sap) - **Company Website:** https://www.sap.com/ - **Year Founded:** 1972 - **HQ Location:** Walldorf - **Twitter:** @SAP (297,214 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,341 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Consultant, SAP Consultant - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 61% Enterprise, 26% Mid-Market #### What Are SAP HANA Cloud's Pros and Cons? **Pros:** - Ease of Use (55 reviews) - Easy Integrations (41 reviews) - Integrations (40 reviews) - Speed (39 reviews) - Scalability (35 reviews) **Cons:** - Complexity (33 reviews) - Expensive (32 reviews) - Learning Curve (30 reviews) - Difficult Learning (28 reviews) - Complex Setup (20 reviews) ### 8. [Amazon QuickSight](https://www.g2.com/products/amazon-quicksight/reviews) Amazon QuickSight is a cloud-based unified business intelligence (BI) service at hyperscale. With QuickSight, all users can meet varying analytic needs from the same source of truth through modern interactive dashboards, paginated reports, natural language queries and embedded analytics. With Amazon Q in QuickSight, business analysts and business users can use natural language to build, discover, and share meaningful insights in seconds, turning insights into impact faster. Over 100,000 customers use Amazon QuickSight. Learn more at https://quicksight.aws **Average Rating:** 4.3/5.0 **Total Reviews:** 671 **How Do G2 Users Rate Amazon QuickSight?** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 9.0/10) - **AI Text Summarization:** 8.1/10 (Category avg: 8.1/10) - **Algorithms:** 8.1/10 (Category avg: 8.5/10) - **AI Text Generation:** 8.2/10 (Category avg: 8.1/10) **Who Is the Company Behind Amazon QuickSight?** - **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd) - **Year Founded:** 2006 - **HQ Location:** Seattle, WA - **Twitter:** @awscloud (2,226,638 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®) - **Ownership:** NASDAQ: AMZN **Who Uses This Product?** - **Who Uses This:** Data Analyst, Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 40% Small-Business, 37% Mid-Market #### What Are Amazon QuickSight's Pros and Cons? **Pros:** - Integrations (72 reviews) - Ease of Use (71 reviews) - Easy Integrations (60 reviews) - Data Visualization (44 reviews) - Dashboard Management (42 reviews) **Cons:** - Limited Customization (69 reviews) - Learning Curve (38 reviews) - Limited Visualization (28 reviews) - Missing Features (22 reviews) - Poor Interface Design (20 reviews) ### 9. [Dataiku](https://www.g2.com/products/dataiku/reviews) 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. **Average Rating:** 4.4/5.0 **Total Reviews:** 185 **How Do G2 Users Rate Dataiku?** - **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 9.0/10) - **AI Text Summarization:** 8.3/10 (Category avg: 8.1/10) - **Algorithms:** 8.0/10 (Category avg: 8.5/10) - **AI Text Generation:** 8.6/10 (Category avg: 8.1/10) **Who Is the Company Behind Dataiku?** - **Seller:** [Dataiku](https://www.g2.com/sellers/dataiku) - **Company Website:** https://Dataiku.com - **Year Founded:** 2013 - **HQ Location:** New York, NY - **Twitter:** @dataiku (22,943 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/dataiku/ (1,609 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Scientist, Data Analyst - **Top Industries:** Financial Services, Pharmaceuticals - **Company Size:** 60% Enterprise, 22% Mid-Market #### What Are Dataiku's Pros and Cons? **Pros:** - Ease of Use (82 reviews) - Features (82 reviews) - Usability (46 reviews) - Easy Integrations (43 reviews) - Productivity Improvement (42 reviews) **Cons:** - Learning Curve (45 reviews) - Steep Learning Curve (26 reviews) - Slow Performance (24 reviews) - Difficult Learning (23 reviews) - Expensive (22 reviews) ### 10. [SAP Analytics Cloud](https://www.g2.com/products/sap-analytics-cloud/reviews) With the SAP Analytics Cloud solution, you can bring together analytics and planning with unique integration to SAP applications and smooth access to heterogenous data sources. As the analytics and planning solution within SAP Business Technology Platform, SAP Analytics Cloud supports trusted insights and integrated planning processes enterprise-wide to help you make decisions without doubt. **Average Rating:** 4.2/5.0 **Total Reviews:** 740 **How Do G2 Users Rate SAP Analytics Cloud?** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 9.0/10) - **AI Text Summarization:** 8.9/10 (Category avg: 8.1/10) - **Algorithms:** 8.0/10 (Category avg: 8.5/10) - **AI Text Generation:** 8.7/10 (Category avg: 8.1/10) **Who Is the Company Behind SAP Analytics Cloud?** - **Seller:** [SAP](https://www.g2.com/sellers/sap) - **Company Website:** https://www.sap.com/ - **Year Founded:** 1972 - **HQ Location:** Walldorf - **Twitter:** @SAP (297,214 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,341 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Senior Consultant, Consultant - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 49% Enterprise, 28% Mid-Market #### What Are SAP Analytics Cloud's Pros and Cons? **Pros:** - Ease of Use (68 reviews) - Data Analysis (52 reviews) - Data Visualization (51 reviews) - Easy Integrations (40 reviews) - Analytics (39 reviews) **Cons:** - Slow Performance (36 reviews) - Learning Curve (35 reviews) - Learning Difficulty (33 reviews) - Performance Issues (32 reviews) - Large Dataset Handling (30 reviews) ### 11. [JMP](https://www.g2.com/products/jmp/reviews) JMP, data analysis software for Mac and Windows, combines the strength of interactive visualization with powerful statistics. Importing and processing data is easy. The drag-and-drop interface, dynamically linked graphs, libraries of advanced analytic functionality, scripting language and ways of sharing findings with others, allows users to dig deeply into their data, with greater ease and speed. Originally developed in the 1980’s to capture the new value in GUI for personal computers, JMP remains dedicated to adding cutting-edge statistical methods and special analysis techniques from a variety of industries to the software’s functionality with each release. The organization's founder, John Sall, still serves as Chief Architect. To see a full list of data connectors please visit https://www.jmp.com/en/software/analytic-workflow/data-connectors **Average Rating:** 4.5/5.0 **Total Reviews:** 206 **How Do G2 Users Rate JMP?** - **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.0/10) - **AI Text Summarization:** 10.0/10 (Category avg: 8.1/10) - **Algorithms:** 8.7/10 (Category avg: 8.5/10) - **AI Text Generation:** 10.0/10 (Category avg: 8.1/10) **Who Is the Company Behind JMP?** - **Seller:** [JMP Statistical Discovery](https://www.g2.com/sellers/jmp-statistical-discovery) - **Company Website:** https://www.jmp.com - **Year Founded:** 1989 - **HQ Location:** Cary, North Carolina - **Twitter:** @JMP_software (2,764 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/jmp/ (1,002 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Student - **Top Industries:** Higher Education, Information Technology and Services - **Company Size:** 42% Enterprise, 33% Small-Business #### What Are JMP's Pros and Cons? **Pros:** - Ease of Use (10 reviews) - Data Visualization (9 reviews) - Statistical Analysis (5 reviews) - Visualization (5 reviews) - User Interface (4 reviews) **Cons:** - Expensive (6 reviews) - Learning Curve (4 reviews) - Limited Flexibility (4 reviews) - Learning Difficulty (3 reviews) - Limitations in Storage (2 reviews) ### 12. [Nixtla](https://www.g2.com/products/nixtla/reviews) TimeGPT is a cutting-edge foundation model specifically designed for time series forecasting and anomaly detection. This innovative solution empowers users to harness the full potential of their time series data, enabling more informed decision-making across various domains. With its advanced capabilities, TimeGPT stands out as a pivotal tool for organizations looking to optimize their data-driven strategies. Targeted at data scientists, analysts, and business decision-makers, TimeGPT caters to a wide range of industries, including finance, energy, and meteorology. Its ability to process and analyze vast amounts of time series data makes it an invaluable resource for those seeking to improve operational efficiency, enhance predictive accuracy, and identify unusual patterns that may indicate underlying issues. Whether it’s forecasting stock prices, predicting energy consumption, or analyzing weather trends, TimeGPT provides the necessary tools to tackle complex time series challenges. One of the key features of TimeGPT is its zero-shot inference capability, which allows users to generate forecasts and detect anomalies without the need for prior training data. This feature significantly reduces the time and resources typically required for model training, enabling users to quickly gain insights from their data. Additionally, TimeGPT has been extensively trained on over 100 billion time series data points, ensuring that it can deliver reliable and accurate predictions across various contexts. TimeGPT also offers fine-tuning options, allowing users to adapt the model to their specific datasets. This flexibility ensures that organizations can tailor the model to their unique time series characteristics, enhancing its predictive performance. Furthermore, the model supports the integration of exogenous variables, which can improve forecast accuracy by accounting for external factors that may influence the data. With robust API access, TimeGPT can be seamlessly integrated into existing applications, making it easy for organizations to leverage its capabilities. It is also compatible with Azure Studio and can be deployed on private infrastructure, providing users with the flexibility to choose the deployment method that best suits their needs. The ability to forecast multiple time series simultaneously further optimizes workflows, allowing organizations to manage resources effectively while enhancing their analytical capabilities. In addition to its forecasting prowess, TimeGPT excels in anomaly detection, automatically identifying unusual patterns in time series data. This feature is particularly beneficial for organizations that need to monitor systems in real-time and respond swiftly to potential issues. By incorporating exogenous features, users can further enhance the model's performance, ensuring that they are equipped to handle the complexities of their time series data. **Average Rating:** 4.7/5.0 **Total Reviews:** 50 **How Do G2 Users Rate Nixtla?** - **Has the product been a good partner in doing business?:** 9.4/10 (Category avg: 9.0/10) - **AI Text Summarization:** 4.3/10 (Category avg: 8.1/10) - **Algorithms:** 9.6/10 (Category avg: 8.5/10) - **AI Text Generation:** 4.6/10 (Category avg: 8.1/10) **Who Is the Company Behind Nixtla?** - **Seller:** [Nixtla](https://www.g2.com/sellers/nixtla) - **Company Website:** https://www.nixtla.io/ - **Year Founded:** 2021 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/nixtlainc (32 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Scientist - **Top Industries:** Computer Software, Retail - **Company Size:** 44% Enterprise, 38% Small-Business #### What Are Nixtla's Pros and Cons? **Pros:** - Ease of Use (31 reviews) - Easy Integrations (16 reviews) - Customer Support (15 reviews) - Machine Learning (13 reviews) - Implementation Ease (12 reviews) **Cons:** - Missing Features (7 reviews) - Expensive (6 reviews) - Lack of Guidance (5 reviews) - Limited Features (5 reviews) - Learning Curve (3 reviews) ### 13. [Board](https://www.g2.com/products/board/reviews) Board is the Enterprise Planning Platform built to accelerate business performance and enable continuous planning with greater forecast accuracy to drive confident, aligned decisions. Its fully integrated planning solution spans the entire enterprise, delivering value for financial and operational planning in one unified platform. Using internal data and expert-curated external indicators, Board provides real-time visibility into your business. Board offers the most flexible, collaborative and adaptive user experience, powering executive dashboards and analytic capabilities that allows clear explanation and interpretation delivered through generative AI. This allows organizations to respond in an agile manner: capitalize on opportunities, optimize resources and mitigate risks. **Average Rating:** 4.4/5.0 **Total Reviews:** 300 **How Do G2 Users Rate Board?** - **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 9.0/10) - **AI Text Summarization:** 6.3/10 (Category avg: 8.1/10) - **Algorithms:** 8.6/10 (Category avg: 8.5/10) - **AI Text Generation:** 6.7/10 (Category avg: 8.1/10) **Who Is the Company Behind Board?** - **Seller:** [Board International](https://www.g2.com/sellers/board-international) - **Company Website:** https://www.board.com - **Year Founded:** 1994 - **HQ Location:** Chiasso, Ticino - **Twitter:** @BoardSoftware (27,159 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/98246/ (929 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Software Engineer, Software Developer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 57% Mid-Market, 40% Enterprise #### What Are Board's Pros and Cons? **Pros:** - Ease of Use (26 reviews) - Flexibility (21 reviews) - Features (20 reviews) - Efficiency (18 reviews) - Customization (16 reviews) **Cons:** - Slow Performance (9 reviews) - Learning Curve (8 reviews) - Missing Features (8 reviews) - Poor Visualization (8 reviews) - Complexity (7 reviews) ### 14. [IBM SPSS Statistics](https://www.g2.com/products/ibm-spss-statistics/reviews) IBM SPSS Statistics is an end-to-end statistical solution that simplifies advanced statistical analysis across industries for users of any statistical expertise. It offers comprehensive resources, expert support, and proven reliability to transform complex data into impactful decisions IBM SPSS Statistics recent version 31 release comes up with powerful new features such as AI Output Assistant, UX and security enhancements and advanced algorithms. IBM SPSS Statistics • offers an easy to use drag and drop interface along with AI Output Assistant to interpret complex statistical output in easy language. • simplifies complex data analysis using advanced statistical techniques that performs data preparation and management, to analysis and reporting. • performs predictive analysis using advanced forecasting procedures to uncover patterns and predict future trends. • creates compelling visual representations to identify trends, derive accurate conclusions and deliver graphs and presentation-ready reports Explore how both individuals and organizations spanning across Industries can simply complex statistical test through an easy to use, accurate ,reliable and secure solution. Use Cases 1. Market Research - Statistical procedures highlighting how to do market research with IBM SPSS 2. Client Acquisition – Emphasizes on how can organizations can acquire more clients and understand consumer behavior 3. Forecasting – Analyze historical sales data, evaluate key trends, predict outcomes relevant to inventory planning 4. Healthcare - Enabling healthcare organizations to improve patient outcomes 5. Government - Empowering Government institutions to take smarter policy decisions 6. Supply Chain - Utilize Statistical Algorithms to make data-driven decisions across procurement, inventory, logistics, and demand planning. Visit here to see what's new in v31 - https://www.ibm.com/products/spss-statistics/whats-new **Average Rating:** 4.2/5.0 **Total Reviews:** 891 **How Do G2 Users Rate IBM SPSS Statistics?** - **Has the product been a good partner in doing business?:** 8.0/10 (Category avg: 9.0/10) - **AI Text Summarization:** 10.0/10 (Category avg: 8.1/10) - **Algorithms:** 7.7/10 (Category avg: 8.5/10) - **AI Text Generation:** 10.0/10 (Category avg: 8.1/10) **Who Is the Company Behind IBM SPSS Statistics?** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Company Website:** https://www.ibm.com/us-en - **Year Founded:** 1911 - **HQ Location:** Armonk, NY - **Twitter:** @IBM (709,298 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Research Assistant, Assistant Professor - **Top Industries:** Higher Education, Research - **Company Size:** 42% Enterprise, 30% Mid-Market #### What Are IBM SPSS Statistics's Pros and Cons? **Pros:** - Ease of Use (33 reviews) - Statistical Analysis (19 reviews) - Data Management (15 reviews) - User Interface (13 reviews) - Analysis Capabilities (12 reviews) **Cons:** - Expensive (19 reviews) - Poor Visualization (12 reviews) - Learning Curve (11 reviews) - Outdated Interface (7 reviews) - Performance Issues (7 reviews) ### 15. [APEX](https://www.g2.com/products/leandna-apex/reviews) APEX by LeanDNA is the factory-focused platform for AI-powered expert execution to establish command of supply planning and materials management. It powers optimized decisions and operations through materials visibility, predictive insights, supplier collaboration, and recommended actions. APEX synchronizes people, materials, and sites with data centralization, AI, and machine learning to analyze supply conditions, predict risks, connect across suppliers, and outline the best actions to protect and optimize production. With core capabilities spanning data visibility and AI, supply insights, procurement management, and inventory optimization, APEX transforms fragmented data into supply chain intelligence that drives confident, precise execution. Teams eliminate operational guesswork, respond faster to disruption, and improve performance with smarter prioritization and optimized supply strategies. **Average Rating:** 4.5/5.0 **Total Reviews:** 128 **How Do G2 Users Rate APEX?** - **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 9.0/10) - **AI Text Summarization:** 6.3/10 (Category avg: 8.1/10) - **Algorithms:** 7.6/10 (Category avg: 8.5/10) - **AI Text Generation:** 6.2/10 (Category avg: 8.1/10) **Who Is the Company Behind APEX?** - **Seller:** [LeanDNA](https://www.g2.com/sellers/leandna) - **Company Website:** https://www.leandna.com - **Year Founded:** 2014 - **HQ Location:** Austin, Texas, United States - **LinkedIn® Page:** https://www.linkedin.com/company/leandna/ (100 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Approvisionneur - **Top Industries:** Manufacturing, Aviation & Aerospace - **Company Size:** 51% Mid-Market, 38% Enterprise #### What Are APEX's Pros and Cons? **Pros:** - Ease of Use (31 reviews) - Customer Support (21 reviews) - Inventory Management (20 reviews) - Features (15 reviews) - Time-saving (13 reviews) **Cons:** - Complex Usability (10 reviews) - Limited Customization (6 reviews) - Missing Features (6 reviews) - Data Inaccuracy (5 reviews) - Learning Curve (5 reviews) ### 16. [IBM SPSS Modeler](https://www.g2.com/products/ibm-spss-modeler/reviews) The IBM SPSS Modeler is a leading, visual data science and machine learning solution. It helps enterprises accelerate time to value and desired outcome by speeding the operational tasks for data scientists. Leading organizations worldwide rely on IBM for data discovery, predictive analytics, model management and deployment, and machine learning to monetize data assets. The IBM SPSS Modeler empowers organizations to tap data assets and modern applications with complete, out-of-box algorithms and models, suited for hybrid, multi-cloud environments with robust governance and security posture. • Take advantage of open source based innovation including R or Python • Empower data scientists of all skills – programmatic and visual • Exploit multi-cloud approach - on-prem, public or private clouds • Start small and scale to enterprise-wide, governed approach **Average Rating:** 4.0/5.0 **Total Reviews:** 128 **How Do G2 Users Rate IBM SPSS Modeler?** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 9.0/10) - **AI Text Summarization:** 7.3/10 (Category avg: 8.1/10) - **Algorithms:** 8.0/10 (Category avg: 8.5/10) - **AI Text Generation:** 7.3/10 (Category avg: 8.1/10) **Who Is the Company Behind IBM SPSS Modeler?** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Year Founded:** 1911 - **HQ Location:** Armonk, NY - **Twitter:** @IBM (709,298 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®) - **Ownership:** SWX:IBM **Who Uses This Product?** - **Top Industries:** Higher Education, Education Management - **Company Size:** 53% Enterprise, 24% Mid-Market #### What Are IBM SPSS Modeler's Pros and Cons? **Pros:** - Analysis Capabilities (1 reviews) - Analytics (1 reviews) - Data Access (1 reviews) - Data Management (1 reviews) - Data Visualization (1 reviews) **Cons:** - Expensive (1 reviews) - Expensive Licensing (1 reviews) ### 17. [Alteryx](https://www.g2.com/products/alteryx/reviews) Alteryx, through it's Alteryx One platform, helps enterprises transform complex, disconnected data into a clean, AI-ready state. Whether you’re creating financial forecasts, analyzing supplier performance, segmenting customer data, analyzing employee retention, or building competitive AI applications from your proprietary data, Alteryx One makes it easy to cleanse, blend, and analyze data to unlock the unique insights that drive impactful decisions. AI-Guided Analytics Alteryx automates and simplifies every stage of data preparation and analysis, from validation and enrichment to predictive analytics and automated insights. Incorporate generative AI directly into your workflows to streamline complex data tasks and generate insights faster. Unmatched flexibility, whether you prefer code-free workflows, natural language commands, or low-code options, Alteryx adapts to your needs. Trusted. Secure. Enterprise-Ready. Alteryx is trusted by over half of the Global 2000 and 19 of the top 20 global banks. With built-in automation, governance, and security, your workflows can scale and maintain compliance while delivering consistent results. And it doesn’t matter if your systems are on-premises, hybrid, or in the cloud; Alteryx fits effortlessly into your infrastructure. Easy to Use. Deeply Connected. What truly sets Alteryx apart is our focus on efficiency and ease of use for analysts and our active community of 700,000 Alteryx users to support you at every step of your journey. With seamless integration to data everywhere including platforms like Databricks, Snowflake, AWS, Google, SAP, and Salesforce, our platform helps unify siloed data and accelerate getting to insights. Visit Alteryx.com for more information, and to start your free trial. **Average Rating:** 4.6/5.0 **Total Reviews:** 651 **How Do G2 Users Rate Alteryx?** - **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.0/10) - **AI Text Summarization:** 7.2/10 (Category avg: 8.1/10) - **Algorithms:** 8.3/10 (Category avg: 8.5/10) - **AI Text Generation:** 7.0/10 (Category avg: 8.1/10) **Who Is the Company Behind Alteryx?** - **Seller:** [Alteryx](https://www.g2.com/sellers/alteryx) - **Company Website:** https://www.alteryx.com - **Year Founded:** 1997 - **HQ Location:** Irvine, CA - **Twitter:** @alteryx (26,205 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/903031/ (2,268 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Analyst, Analyst - **Top Industries:** Financial Services, Accounting - **Company Size:** 62% Enterprise, 23% Mid-Market #### What Are Alteryx's Pros and Cons? **Pros:** - Ease of Use (331 reviews) - Automation (146 reviews) - Intuitive (131 reviews) - Easy Learning (102 reviews) - Efficiency (102 reviews) **Cons:** - Expensive (86 reviews) - Learning Curve (80 reviews) - Missing Features (62 reviews) - Learning Difficulty (55 reviews) - Slow Performance (41 reviews) ### 18. [Minitab Statistical Software](https://www.g2.com/products/minitab-statistical-software/reviews) Minitab Statistical Software is a comprehensive data analysis and statistical solution designed to assist users in exploring data, identifying trends, and making informed, data-driven decisions. This software caters to a diverse audience, ranging from beginners to seasoned analysts, by combining powerful statistical methods with an intuitive interface. This design approach simplifies complex analyses while ensuring depth and accuracy are not compromised. The capabilities of Minitab are extensive, encompassing a variety of statistical techniques such as descriptive statistics, hypothesis testing, regression analysis, ANOVA, time series analysis, design of experiments (DOE), reliability analysis, and predictive analytics. The software's guided workflows and Assistant feature are particularly beneficial, as they help users select appropriate tools, interpret results effectively, and communicate findings in a clear manner. This accessibility makes advanced analytics feasible for users across different skill levels, promoting a culture of data-driven decision-making within organizations. Minitab is available in both desktop and cloud-based versions, providing users with the flexibility to work from various locations while ensuring secure access to their data and analyses. The cloud version enhances collaboration among teams by allowing them to share projects and standardize analyses without the need for local installation. This feature is particularly advantageous for organizations with remote teams or those looking to streamline their analytical processes. The software also includes built-in data preparation tools that facilitate the cleaning and organization of data, which is crucial for accurate analysis. Furthermore, Minitab seamlessly integrates with other Minitab solutions and supports quality improvement methodologies such as Six Sigma. This integration helps organizations enhance their processes, reduce variation, and achieve measurable business outcomes. By providing a robust suite of tools and features, Minitab Statistical Software stands out as a valuable resource for organizations aiming to leverage data for strategic advantage. **Average Rating:** 4.6/5.0 **Total Reviews:** 219 **How Do G2 Users Rate Minitab Statistical Software?** - **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 9.0/10) - **AI Text Summarization:** 7.4/10 (Category avg: 8.1/10) - **Algorithms:** 8.3/10 (Category avg: 8.5/10) - **AI Text Generation:** 7.3/10 (Category avg: 8.1/10) **Who Is the Company Behind Minitab Statistical Software?** - **Seller:** [Minitab](https://www.g2.com/sellers/minitab-14ca02fe-fdeb-44c4-b0db-904058d0221b) - **Company Website:** https://www.minitab.com - **Year Founded:** 1972 - **HQ Location:** State College, Pennsylvania, United States - **Twitter:** @Minitab (5,022 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/39142/ (703 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Quality Manager - **Top Industries:** Automotive, Manufacturing - **Company Size:** 47% Enterprise, 32% Mid-Market #### What Are Minitab Statistical Software's Pros and Cons? **Pros:** - Ease of Use (63 reviews) - Data Analysis (54 reviews) - Statistical Analysis (39 reviews) - Analysis (32 reviews) - Analysis Capabilities (30 reviews) **Cons:** - Expensive (23 reviews) - Learning Curve (22 reviews) - Not User-Friendly (14 reviews) - Complexity (13 reviews) - Limited Features (11 reviews) ### 19. [Qlik Sense](https://www.g2.com/products/qlik-sense/reviews) Qlik Sense empowers people to make better data-driven decisions and take action. The solution provides augmented analytics for every business need from visualization and dashboards to natural language analytics, custom and embedded analytics, reporting and alerting. Our unique associative technology enhances human intuition with AI-powered insights, offering unmatched capabilities for combining data and exploring information. It indexes the associations in your data, and exposes related and unrelated values as you click, revealing hidden insights that would be missed by query-based tools. And it performs calculations as fast as you can think. Qlik Sense helps users move from passive to active analytics for real-time collaboration and action. And you get robust data integration, application automation and the convenience of SaaS with hybrid multi-cloud capabilities. See why we’ve been named a Gartner Magic Quadrant Leader for Analytics and BI platforms for 11 years in a row. Visit us at [https://www.qlik.com/us/](- https://www.qlik.com/us/products/qlik-sense?utm_medium=referral&utm_source=G2&utm_team=DIG&utm_term=QlikSense&utm_mpt_id=CKMP5D) **Average Rating:** 4.4/5.0 **Total Reviews:** 761 **How Do G2 Users Rate Qlik Sense?** - **Has the product been a good partner in doing business?:** 8.5/10 (Category avg: 9.0/10) - **AI Text Summarization:** 8.3/10 (Category avg: 8.1/10) - **Algorithms:** 8.3/10 (Category avg: 8.5/10) - **AI Text Generation:** 8.5/10 (Category avg: 8.1/10) **Who Is the Company Behind Qlik Sense?** - **Seller:** [Qlik](https://www.g2.com/sellers/qlik) - **Year Founded:** 1993 - **HQ Location:** Radnor, PA - **Twitter:** @qlik (64,247 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/10162/ (4,529 employees on LinkedIn®) - **Phone:** 1 (888) 994-9854 **Who Uses This Product?** - **Who Uses This:** Data Analyst, Consultant - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 41% Enterprise, 41% Mid-Market #### What Are Qlik Sense's Pros and Cons? **Pros:** - Ease of Use (55 reviews) - Data Visualization (31 reviews) - Analytics (28 reviews) - Insights Discovery (24 reviews) - Features (22 reviews) **Cons:** - Limited Features (17 reviews) - Missing Features (16 reviews) - Expensive (14 reviews) - Data Management (13 reviews) - Learning Curve (13 reviews) ### 20. [TrendMiner](https://www.g2.com/products/trendminer/reviews) TrendMiner offers a fast, powerful, and intuitive industrial analytics & AI software platform. With a focus on highly digitized manufacturing industries, energy companies, and organizations with operations that need to maintain controlled environments. TrendMiner combines self-service, low-code data analysis for time series and event data with sophisticated machine learning (ML) and Agentic AI tools to deliver industrial data visualization, monitoring, and predictive capabilities. TrendMiner, a Vercore company, was founded in 2008 with a global headquarters located in Belgium, and offices in the U.S., Germany, Spain, and the Netherlands. TrendMiner has strategic partnerships with Amazon, Microsoft, SAP, GE Digital, Siemens, and Aveva, and offers standard integrations with a wide range of data platforms such as AVEVA PI, Yokogawa Exaquantum, AspenTech IP.21, Honeywell PHD, GE Proficy Historian, Canary, and Aveva Historian. **Average Rating:** 4.6/5.0 **Total Reviews:** 178 **How Do G2 Users Rate TrendMiner?** - **Has the product been a good partner in doing business?:** 9.3/10 (Category avg: 9.0/10) - **AI Text Summarization:** 6.6/10 (Category avg: 8.1/10) - **Algorithms:** 7.7/10 (Category avg: 8.5/10) - **AI Text Generation:** 6.6/10 (Category avg: 8.1/10) **Who Is the Company Behind TrendMiner?** - **Seller:** [TrendMiner](https://www.g2.com/sellers/trendminer) - **Company Website:** https://www.trendminer.com/ - **Year Founded:** 2008 - **HQ Location:** Hasselt, Flemish Region - **Twitter:** @TrendMining (779 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/trendminer/ (91 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Process Engineer, Process Analytics Engineer - **Top Industries:** Chemicals, Oil & Energy - **Company Size:** 53% Enterprise, 35% Mid-Market #### What Are TrendMiner's Pros and Cons? **Pros:** - Ease of Use (25 reviews) - Speed (12 reviews) - Dashboard Trends (9 reviews) - Data Analysis (9 reviews) - Easy Learning (8 reviews) **Cons:** - Complex Usability (12 reviews) - Difficult Learning (6 reviews) - Learning Curve (5 reviews) - Limited Accessibility (4 reviews) - Complexity (3 reviews) ### 21. [Salesloft](https://www.g2.com/products/salesloft/reviews) Clari + Salesloft is a category-transforming AI company architecting the future of revenue. By building the world’s first Predictive Revenue System, we help organizations move beyond fragmented applications and systems of record to a model that continuously drives and adapts revenue execution. Our platform captures deal data signals, and uses tailor-built AI to create the right context and drive action across sales teams. Instead of disconnected insights and siloed workflows, sales teams operate with shared understanding, faster decisions, and execution that stays aligned to the business. Trusted by thousands of enterprises including Adobe, 3M, IBM, and Zoom, Clari + Salesloft powers the forecast, surfaces pipeline risk, and drives proactive execution—returning thousands of hours to the field and enabling predictable, scalable growth. **Average Rating:** 4.5/5.0 **Total Reviews:** 4,153 **How Do G2 Users Rate Salesloft?** - **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.0/10) **Who Is the Company Behind Salesloft?** - **Seller:** [Salesloft](https://www.g2.com/sellers/salesloft) - **Company Website:** https://salesloft.com - **Year Founded:** 2011 - **HQ Location:** Atlanta, GA - **Twitter:** @Salesloft (18,446 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/2296178/ (1,109 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Account Executive, Sales Development Representative - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 56% Mid-Market, 23% Small-Business #### What Are Salesloft's Pros and Cons? **Pros:** - Ease of Use (256 reviews) - Helpful (158 reviews) - Features (155 reviews) - Automation (146 reviews) - Time-saving (138 reviews) **Cons:** - Missing Features (95 reviews) - Call Issues (65 reviews) - Integration Issues (62 reviews) - Learning Curve (60 reviews) - Limitations (55 reviews) ### 22. [IBM Watson Studio](https://www.g2.com/products/ibm-watson-studio/reviews) IBM Watson Studio on IBM Cloud Pak for Data is a leading data science and machine learning solution that helps enterprises accelerate AI-powered digital transformation. It allows businesses to scale trustworthy AI and optimize decisions. Build, run, and manage AI models on any cloud through an automated end-to-end AI lifecycle--simplifying experimentation and deployment, speeding up data exploration and preparation, and improving model development and training. Govern and monitor models to mitigate drift and bias, and manage model risk. Build a ModelOps practice that synchronizes application and model pipelines to operationalize responsible, explainable AI across your enterprise. As a key offering of IBM Cloud Pak for Data, a unified data and AI platform, Watson Studio integrates seamlessly with data management services, data privacy and security capabilities, AI application tooling, open source frameworks, and a robust technology ecosystem. It unites teams and empowers businesses to build the modern information architecture that AI requires and infuse it across the organization. IBM Watson Studio is code-optional, allowing both data scientists and business analysts to work on the same platform by providing the best of open source tools along with visual, drag-and-drop capabilities. It enables organizations to tap into data assets and inject predictions into business processes and modern applications—helping them maximize their business value. It's suited for hybrid multicloud environments that demand mission-critical performance, security, and governance. Features include: • AutoAI that eliminates time-consuming, repetitive tasks by automating data preparation, model development, feature engineering and hyperparameter optimization. • Text Analytics for uncovering insights from unstructured data • Drag-and-drop visual model-building with SPSS Modeler • Broad data access – flat files, spreadsheets, major relational databases • Sophisticated graphics engine for building stunning visualizations • Support for Python 3 Notebooks Watson Studio is available via several deployment options: • IBM Cloud Pak for Data – An open, extensible data and AI platform that runs on any cloud • IBM Cloud Pak for Data System – A hybrid cloud, on-premises platform-in-a-box • IBM Cloud Pak for Data as a Service – A set of IBM Cloud Pak for Data platform services fully managed on the IBM Cloud **Average Rating:** 4.2/5.0 **Total Reviews:** 160 **How Do G2 Users Rate IBM Watson Studio?** - **Has the product been a good partner in doing business?:** 8.0/10 (Category avg: 9.0/10) - **AI Text Summarization:** 8.3/10 (Category avg: 8.1/10) - **Algorithms:** 8.1/10 (Category avg: 8.5/10) - **AI Text Generation:** 9.3/10 (Category avg: 8.1/10) **Who Is the Company Behind IBM Watson Studio?** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Year Founded:** 1911 - **HQ Location:** Armonk, NY - **Twitter:** @IBM (709,298 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®) - **Ownership:** SWX:IBM **Who Uses This Product?** - **Who Uses This:** Software Engineer, CEO - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 50% Enterprise, 30% Small-Business #### What Are IBM Watson Studio's Pros and Cons? **Pros:** - AI Capabilities (4 reviews) - AI Technology (4 reviews) - Ease of Use (4 reviews) - Machine Learning (4 reviews) - AI Integration (3 reviews) **Cons:** - Expensive (3 reviews) - Learning Curve (3 reviews) - Steep Learning Curve (3 reviews) - Complex Interface (1 reviews) - Complexity (1 reviews) ### 23. [Seeq](https://www.g2.com/products/seeq/reviews) Seeq is the only enterprise SaaS platform that is purpose-built for time series data, and is trusted by the most recognizable names in oil & gas, pharmaceuticals, specialty chemicals, utilities, renewable energy, and numerous other vertical industries. Seeq accelerates digital transformation efforts and ROI impact by providing live connectivity to hundreds of data sources, and empowering more people across the organization to leverage a broad range of AI capabilities including advanced analytics, machine learning, and generative AI (GenAI). **Average Rating:** 4.6/5.0 **Total Reviews:** 145 **How Do G2 Users Rate Seeq?** - **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 9.0/10) - **AI Text Summarization:** 6.9/10 (Category avg: 8.1/10) - **Algorithms:** 7.6/10 (Category avg: 8.5/10) - **AI Text Generation:** 7.2/10 (Category avg: 8.1/10) **Who Is the Company Behind Seeq?** - **Seller:** [Seeq Corporation](https://www.g2.com/sellers/seeq-corporation) - **Company Website:** https://www.seeq.com - **Year Founded:** 2013 - **HQ Location:** Seattle, Washington - **Twitter:** @SeeqCorporation (952 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3126445/ (293 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Process Engineer - **Top Industries:** Oil & Energy, Chemicals - **Company Size:** 58% Enterprise, 32% Mid-Market #### What Are Seeq's Pros and Cons? **Pros:** - Data Analysis (42 reviews) - Ease of Use (37 reviews) - Features (27 reviews) - Analysis Capabilities (22 reviews) - Data Visualization (16 reviews) **Cons:** - Complex Usability (26 reviews) - Difficult Learning (18 reviews) - Learning Curve (12 reviews) - Learning Difficulty (9 reviews) - Complexity (8 reviews) ### 24. [SAS Enterprise Miner](https://www.g2.com/products/sas-enterprise-miner/reviews) SAS Enterprise Miner is a comprehensive data mining and predictive analytics software designed to streamline the process of developing descriptive and predictive models. It enables users to analyze vast amounts of data efficiently, uncovering patterns and relationships that inform better decision-making. With an intuitive graphical user interface, SAS Enterprise Miner facilitates the entire data mining process, from data preparation to model assessment, making advanced analytics accessible to both technical and non-technical users. Key Features and Functionality: - User-Friendly Interface: An interactive GUI allows users to build process flow diagrams, simplifying the modeling process. - Advanced Data Preparation: Tools for handling missing values, filtering outliers, and performing data transformations enhance data quality. - Diverse Modeling Techniques: Supports a wide range of algorithms, including decision trees, neural networks, and regression models, catering to various analytical needs. - Open Source Integration: Seamless integration with R enables users to perform data transformations and model training within the platform. - High-Performance Capabilities: Incorporates high-performance data mining nodes to boost processing efficiency. - Automated Scoring: Generates score code in multiple languages (SAS, C, Java, PMML) for deployment across various environments. - Model Comparison and Management: Features for comparing multiple models using lift curves and statistical diagnostics to identify the best-performing models. Primary Value and Solutions Provided: SAS Enterprise Miner empowers organizations to harness the full potential of their data by providing a robust platform for developing accurate predictive models. It addresses challenges such as fraud detection, risk minimization, resource demand forecasting, and customer attrition reduction. By automating and simplifying complex data mining tasks, it enables users to make informed, data-driven decisions, ultimately enhancing operational efficiency and competitive advantage. **Average Rating:** 4.2/5.0 **Total Reviews:** 185 **How Do G2 Users Rate SAS Enterprise Miner?** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 9.0/10) - **AI Text Summarization:** 7.4/10 (Category avg: 8.1/10) - **Algorithms:** 8.0/10 (Category avg: 8.5/10) - **AI Text Generation:** 7.4/10 (Category avg: 8.1/10) **Who Is the Company Behind SAS Enterprise Miner?** - **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2) - **Year Founded:** 1976 - **HQ Location:** Cary, NC - **Twitter:** @SASsoftware (60,974 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,519 employees on LinkedIn®) - **Phone:** 1-800-727-0025 **Who Uses This Product?** - **Top Industries:** Higher Education, Information Technology and Services - **Company Size:** 60% Enterprise, 28% Mid-Market #### What Are SAS Enterprise Miner's Pros and Cons? **Pros:** - Ease of Installation (1 reviews) - Ease of Use (1 reviews) - Statistical Analysis (1 reviews) **Cons:** - Learning Curve (1 reviews) - Not User-Friendly (1 reviews) - Steep Learning Curve (1 reviews) ### 25. [Qlik Predict](https://www.g2.com/products/qlik-predict/reviews) Qlik AutoML (automated machine learning) brings AI-generated machine learning models and predictive analytics directly to your organization’s larger community of analytics users and teams, in a simple user experience focused on augmenting their intuition through machine intelligence. With AutoML, you can easily generate machine learning models, make predictions, and plan decisions – all within an intuitive, code-free user interface. Machine learning (ML) is a branch of artificial intelligence (AI) focused on the process of recognizing patterns in historical data to predict outcomes in the future. ML uses historically observed data as an input, applies a mathematical process against that data, and creates an output called a machine learning model based on patterns in historical data. This model can then be used to make future predictions and test scenarios. **Average Rating:** 4.4/5.0 **Total Reviews:** 78 **How Do G2 Users Rate Qlik Predict?** - **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.0/10) - **AI Text Summarization:** 6.7/10 (Category avg: 8.1/10) - **Algorithms:** 8.2/10 (Category avg: 8.5/10) - **AI Text Generation:** 6.7/10 (Category avg: 8.1/10) **Who Is the Company Behind Qlik Predict?** - **Seller:** [Qlik](https://www.g2.com/sellers/qlik) - **Year Founded:** 1993 - **HQ Location:** Radnor, PA - **Twitter:** @qlik (64,247 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/10162/ (4,529 employees on LinkedIn®) - **Phone:** 1 (888) 994-9854 **Who Uses This Product?** - **Who Uses This:** Data Analyst - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 38% Enterprise, 31% Small-Business #### What Are Qlik Predict's Pros and Cons? **Pros:** - Automation (5 reviews) - Ease of Use (5 reviews) - AI Integration (4 reviews) - Machine Learning (4 reviews) - AI Capabilities (3 reviews) **Cons:** - Limited Customization (4 reviews) - Deployment Issues (2 reviews) - Lacking Features (2 reviews) - Required Knowledge (2 reviews) - Tool Limitations (2 reviews) ## What Is Predictive Analytics Software? [Analytics Tools & Software](https://www.g2.com/categories/analytics-tools-software) ## What Software Categories Are Similar to Predictive Analytics Software? - [Analytics Platforms](https://www.g2.com/categories/analytics-platforms) - [Machine Learning Software](https://www.g2.com/categories/machine-learning) - [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) --- ## How Do You Choose the Right Predictive Analytics Software? ### What You Should Know About Predictive Analytics Software ### What are predictive analytics tools and software? Predictive analytics software is all about making business outcomes predictable. Data scientists and data analysts can do this by using data mining and predictive modeling to analyze historical data. By better understanding the past, businesses can gain insights into the future. [Predictive analytics](https://www.g2.com/articles/predictive-analytics) is a step further than general [business intelligence](https://www.g2.com/glossary/business-intelligence-definition), which companies use to pull actionable insights from their data sets. Instead, users can develop [machine learning algorithms](https://www.g2.com/articles/what-is-machine-learning) and predictive models to help forecast and achieve business-critical numbers. The reason businesses can hit those critical numbers and become more predictive is due to the boom of big data. Companies can harness their data like never before. By recording and owning more and more historical and real-time data, data scientists have larger sample sizes to work with, meaning they can be much more accurate. Additionally, companies investing in predictive analytics without ensuring that their data is accurate, clean, and accessible will ultimately be wasting their time. However, those who can wrangle their data properly will create a significant competitive edge and hold an advantage in the market. ### Benefits of using predictive analytics tools - Accurately predict and forecast revenue numbers based on a wide range of variables - Understand and account for customer churn and retention - Predict employee churn based on historical factors for turnover - Make more precise, data-driven decisions in all departments based on available data - Determine both risks and opportunities that were otherwise hidden within company data ### Why use predictive analytics solutions? There are a number of applications for predictive analytics software and reasons businesses should adopt them, but they all boil down to understanding what has happened in the past, what could happen in the future, and what should be done to ensure positive business outcomes. These are considered [descriptive analytics, predictive analytics, and prescriptive analytics](https://www.g2.com/articles/types-of-data-analytics). **Descriptive Analytics (understanding the past) —** Descriptive analytics deals with understanding what has happened in the past and how it has influenced where a business is in the present. This means undergoing data mining on a company’s historical data. This type of analysis can be obtained by using business intelligence tools, big data analytics, or time-series data. Regardless of how it is attained, providing descriptive analytics is a key foundation of predictive analytics and creating data-driven decision-making processes. It requires thorough data preparation and organizing the data for easy descriptive analysis. **Predictive Analytics (knowing what is possible) —** Predictive analytics allows users and businesses to know and anticipate potential outcomes. Building predictive models based on descriptive analysis can ensure that businesses do not make the same mistake twice. It can also provide more accurate forecasting and planning, which helps to optimize efficiency. Ultimately, this analysis makes the unknown known. **Prescriptive Analytics (so now what?) —** The final step and ultimate reason for using predictive analytics tools is to make clear actions based on the suggestions and recommendations of the predictive models. This is where machine learning and deep learning functionality come into play. Some predictive analytics solutions can provide actionable insights without human intervention. For example, it can provide a short list of sales accounts that should close quickly based on several variables. Becoming prescriptive takes analytics a step further and is the ultimate reason for adopting advanced, predictive analytics. ### Who uses predictive analytics platforms? To fully take advantage of predictive analytics platforms, businesses need to hire highly skilled data scientists with knowledge in machine learning development and predictive modeling. These skilled workers are not abundant, so they are often paid very well. Dedicating financial resources to these positions may not be an option for every company, but those who can afford data scientists have a leg up on the competition. While data scientists or data analysts are the employees tasked with using predictive analytics software, there are many industries and departments that can be impacted by using predictive analytics: **Manufacturing and Supply Chain—** One area that can be greatly enhanced by using predictive analysis is demand planning for manufacturing companies. With more accurate forecasting, businesses can avoid risks like shortages and surpluses. Additionally, companies can become predictive about quality management and production issues. By analyzing what has caused production failures in the past, companies can anticipate and avoid production breakdowns in the future. Distribution is another major aspect of the supply chain that can be further optimized with predictive modeling. By better estimating where goods will need to be delivered and the risks that may hold up distribution modes, businesses can provide better service and more efficiently deliver their products to customers. Taking into account historical data, such as weather, traffic, and accident records, shipping can become a more precise science. **Retail —** Retail is another industry that is ripe for optimization with the help of predictive analytics. Retail predictive analytics can provide businesses with insights on everything from pricing optimization to understanding how shoppers navigate brick-and-mortar stores for better in-store organization of merchandise. E-commerce businesses can track these factors in a much more efficient manner. All e-commerce interactions can be recorded into a database and influenced by predictive models. This is one of the main reasons Amazon has been so successful and disruptive to brick-and-mortar retailers. Every decision can be made predictive with the help of data. **Marketing and Sales —** Being able to predict the actions of customers and prospects is an invaluable service for any business. Marketing teams can leverage predictive analytics software to project how marketing campaigns may perform, which segment of prospects to target with ads, and the potential conversion rates of each campaign. Understanding how these efforts impact the bottom line is critical to the success of marketing teams and translates into a much more efficient and productive sales team. At the same time, sales teams can leverage predictive modeling in such areas as lead scoring, determining which accounts to target first because they have a higher chance of closing. Ensuring that sales representatives are working smarter instead of harder means more revenue. A few [CRM](https://www.g2.com/categories/crm) and [marketing automation solutions](https://www.g2.com/categories/marketing-automation) provide some level of predictive functionality, but data scientists can separately funnel that data into dedicated predictive analytics tools to find cross-departmental correlations. **Financial Services—** The banking industry has long been ripe for disruption, but financial administrations are using predictive analytics solutions to better predict risk. Historical data can power predictive analytics software to predict fraudulent transactions and determine credit risks, among other functions. ### Types of predictive analytics software Predictive modeling is a complex science that requires years of training to understand. There is a reason data scientists are in high demand: not many people have a complete grasp of how to build predictive models. There are two main types of predictive models: classification and regression models. **Classification Models—** Simply put, classification puts a piece of data into a bucket or a class and labels it as such. Classification models essentially label data based on what an algorithm has already learned. The ultimate goal of classification models is to accurately bucket new data points into the proper classes so that the data can become predictive and prescriptive. **Regression Models—** Regression models analyze the relationship between two separate data points and help forecast what happens when they are placed side by side. For example, in baseball, teams may perform a regression analysis on the relationship between the number of fastballs thrown and the number of home runs hit. **Decision Trees —** One common type of classification model is a decision tree. These models predict several possible outcomes based on a variety of inputs. For example, if a sales team builds $1 million in a pipeline, they can close $100,000 in revenue, but if they create $10 million in a pipeline, they should be able to close $1 million in revenue. **Neural Networks—** Neural networks, known in the AI world as artificial neural networks, are extremely complex predictive models. These models can predict and analyze unstructured, nonlinear relationships between data points. These solutions provide pattern recognition and can help track anomalies. Artificial neural networks were originally created and built to mimic the synapses and neural aspects of the human brain. They are one of the contributing factors to the accelerated growth in artificial intelligence and deep learning. Other types of predictive modeling include Bayesian analysis, memory-based reasoning, k-nearest neighbor, support vector machines, and time-series data mining. ### Potential issues with predictive analytics software solutions **Lack of Skilled Employees—** The main issue with adopting predictive analytics software is the need for a skilled data scientist to interact with the data and build the models. There is a distinct skill gap in terms of finding users who understand how to pull data and build models and the implications that the data has on the overall business. For this reason, data scientists are in very high demand and, thus, expensive. **Data Organization—** Many companies face the challenge of organizing data so that it can be easily accessed. Harnessing big data sets that contain historical and real-time data is not easy in today's world. Companies often need to build a data warehouse or a data lake that can combine all the disparate data sources for easy access. This, again, requires highly knowledgeable employees. ### Software and services related to predictive analytics tools Predictive analytics software relates to many other analytics and [artificial intelligence software](https://www.g2.com/categories/artificial-intelligence) categories. [**Machine Learning Software**](https://www.g2.com/categories/machine-learning) **—** Machine learning algorithms are a key component of building effective predictive models. Many machine learning algorithms are built to provide recommendations or suggestions, which is also the end goal of predictive analytics software. Developers use these tools to embed machine learning inside applications, often to provide predictive and prescriptive analysis. [**Business Intelligence Platforms**](https://www.g2.com/categories/business-intelligence) **—** These tools are the traditional analytics solutions used to understand a company’s data. Data analysts use BI platforms to visualize and understand how specific actions impact business-critical initiatives. Some of these platforms offer predictive features, but their core purpose is not predictive modeling. [**Big Data Analytics**](https://www.g2.com/categories/big-data-analytics) **—** Big data analytics software, like business intelligence platforms, often provides predictive modeling functionality. However, these solutions are used more to track real-time data than to understand historical data. Big data analytics software connects to Hadoop or proprietary Hadoop distributions to better understand structured and unstructured data. These same data sources may be important for data scientists who are tasked with building predictive models.