# Best Predictive Analytics Tools and Software

  *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)*

   Predictive analytics software mines and analyzes historical data patterns to predict future outcomes by extracting information from data sets to determine patterns and trends. Using a range of statistical analysis and algorithms, analysts use predictive analytics tools to build decision models, which business managers can use to plan for the best possible outcome. Analysts, business users, data scientists, and developers all use predictive analytics solutions to better understand customers, products, and partners and to identify potential risks and opportunities for a company.

Predictive analytics platforms enable organizations to use big data (both stored and real-time) to move from a historical view to a forward-looking perspective of the customer. These tools and techniques can be deployed both on premise (usually for enterprise users) and in the cloud. While the majority of predictive analytics software is proprietary, versions that are based on open-source technology do exist. Recent trends in software for predictive analytics show its integration with [business intelligence platforms](https://www.g2.com/categories/business-intelligence-platforms), [ERP systems](https://www.g2.com/categories/erp-systems), or other [digital analytics software](https://www.g2.com/categories/digital-analytics).

To qualify for inclusion in the Predictive Analytics category, a product must:

- Mine and analyze structured and/or unstructured data 
- Create datasets and/or data visualizations from compiled data 
- Create predictive models to forecast future probabilities 
- Adapt to change and revisions 
- Allow import and export from office suites or other data-collecting channels 





## Category Overview

**Total Products under this Category:** 285


## Trust & Credibility Stats

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 29,900+ Authentic Reviews
- 285+ 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.


## Best Predictive Analytics Software At A Glance

- **Leader:** [Tableau](https://www.g2.com/products/tableau/reviews)
- **Highest Performer:** [Nixtla](https://www.g2.com/products/nixtla/reviews)
- **Easiest to Use:** [Nixtla](https://www.g2.com/products/nixtla/reviews)
- **Top Trending:** [Tableau](https://www.g2.com/products/tableau/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.



[Try for Free](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=ba95c65b41706af49626da84365ade47aeddebb1a74a49ca75080c5cea3628c3&secure%5Burl%5D=&secure%5Burl_type%5D=custom_url)

---

## Top-Rated Products (Ranked by G2 Score)
  ### 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:** 3,494

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.5/10 (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)


**Seller Details:**

- **Seller:** [Salesforce](https://www.g2.com/sellers/salesforce)
- **Company Website:** https://www.salesforce.com/
- **Year Founded:** 1999
- **HQ Location:** San Francisco, CA
- **Twitter:** @salesforce (580,768 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3185/ (88,363 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst, Business Analyst
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 41% Enterprise, 35% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (634 reviews)
- Data Visualization (563 reviews)
- Visualization (424 reviews)
- Features (351 reviews)
- Intuitive (317 reviews)

**Cons:**

- Learning Curve (282 reviews)
- Learning Difficulty (240 reviews)
- Expensive (225 reviews)
- Slow Performance (155 reviews)
- Difficulty (139 reviews)

  ### 2. [Clari](https://www.g2.com/products/clari/reviews)
  Clari's revenue platform improves efficiency, predictability, and growth across the entire revenue process. Clari gives revenue teams total visibility into their business to drive process rigor, align buyers and sellers, spot risk and opportunity in the pipeline, increase forecast accuracy, and drive overall efficiency. Hundreds of thousands of revenue professionals at leading companies, including Okta, Adobe, Workday, Zoom, and Finastra use Clari to make their revenue process more connected, efficient, and predictable. Visit us at clari.com and follow us @clari on LinkedIn.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 5,490

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.2/10 (Category avg: 9.0/10)


**Seller Details:**

- **Seller:** [Salesloft](https://www.g2.com/sellers/salesloft)
- **Company Website:** https://salesloft.com
- **Year Founded:** 2011
- **HQ Location:** Atlanta, GA
- **Twitter:** @Salesloft (18,439 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2296178/ (1,137 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Account Executive, Account Manager
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 47% Mid-Market, 41% Enterprise


#### Pros & 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:** 1,156

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,840,340 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Data Analyst
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 37% Enterprise, 35% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (156 reviews)
- Speed (143 reviews)
- Fast Querying (120 reviews)
- Integrations (118 reviews)
- Query Efficiency (114 reviews)

**Cons:**

- Expensive (127 reviews)
- Query Issues (78 reviews)
- Cost Issues (63 reviews)
- Cost Management (60 reviews)
- Learning Curve (54 reviews)

  ### 4. [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews)
  SAS Viya is a cloud-native data and AI platform that enables teams to build, deploy and scale explainable AI that drives trusted, confident decisions. It unites the entire data and AI life cycle and empowers teams to innovate quickly while balancing speed, automation and governance by design. Viya unifies data management, advanced analytics and decisioning in a single platform, so organizations can move from experimentation to production with confidence, delivering measurable business impact that is secure, explainable and scalable across any environment. Key capabilities required to deliver trusted decisions include: • End-to-end clarity across the data and AI life cycle, with built-in lineage, auditability and continuous monitoring to support defensible decisions. • Governance by design, enabling consistent oversight across data, models and decisions to reduce risk and accelerate adoption. • Explainable AI at scale, so insights and outcomes can be understood, validated and trusted by business and regulators alike. • Operationalized analytics, ensuring value continues beyond deployment through monitoring, retraining and life cycle management. • Flexible, cloud-native deployment, allowing organizations to start anywhere and scale everywhere while maintaining control.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 704

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **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 (60,957 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,238 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Student, Statistical Programmer
  - **Top Industries:** Pharmaceuticals, Computer Software
  - **Company Size:** 33% Small-Business, 33% Enterprise


#### Pros & 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)

  ### 5. [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:** 668

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **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 (2,220,862 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst, Software Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 40% Small-Business, 37% Mid-Market


#### Pros & 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)

  ### 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:** 1,128

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Adobe](https://www.g2.com/sellers/adobe)
- **Company Website:** https://adobe.com
- **Year Founded:** 1982
- **HQ Location:** San Jose, CA
- **Twitter:** @Adobe (955,605 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1480/ (41,539 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst, Analyst
  - **Top Industries:** Marketing and Advertising, Information Technology and Services
  - **Company Size:** 43% Enterprise, 30% Mid-Market


#### Pros & 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. [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:** 889

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **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 (708,000 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Research Assistant, Assistant Professor
  - **Top Industries:** Higher Education, Research
  - **Company Size:** 43% Enterprise, 30% Mid-Market


#### Pros & 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)

  ### 8. [IBM Cognos Analytics](https://www.g2.com/products/ibm-cognos-analytics/reviews)
  IBM Cognos Analytics acts as your trusted co-pilot for business with the aim of making you smarter, faster, and more confident in your data-driven decisions. IBM Cognos Analytics gives every user — whether data scientist, business analyst or non-IT specialist — more power to perform relevant analysis in a way that ties back to organizational objectives. It shortens each user’s journey from simple to sophisticated analytics, allowing them to harness data to explore the unknown, identify new relationships, get a deeper understanding of outcomes and challenge the status quo. Create reports , dashboards , visualize, analyze, integrating reporting, modeling, dashboards, data exploration and share actionable insights about your data with anyone in your organization with IBM Cognos Analytics.


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 393

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 7.8/10 (Category avg: 9.0/10)
- **AI Text Summarization:** 8.1/10 (Category avg: 8.1/10)
- **Algorithms:** 9.0/10 (Category avg: 8.5/10)
- **AI Text Generation:** 8.0/10 (Category avg: 8.1/10)


**Seller Details:**

- **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 (708,000 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst
  - **Top Industries:** Information Technology and Services, Financial Services
  - **Company Size:** 59% Enterprise, 26% Mid-Market


#### Pros & 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)

  ### 9. [Amazon Forecast](https://www.g2.com/products/amazon-forecast/reviews)
  Amazon Forecast is a fully managed service that uses machine learning to deliver highly accurate forecasts.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 100

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.0/10)
- **AI Text Summarization:** 8.8/10 (Category avg: 8.1/10)
- **Algorithms:** 8.7/10 (Category avg: 8.5/10)
- **AI Text Generation:** 9.6/10 (Category avg: 8.1/10)


**Seller Details:**

- **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 (2,220,862 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 50% Small-Business, 36% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (14 reviews)
- Forecasting Accuracy (13 reviews)
- Accuracy (11 reviews)
- Machine Learning (10 reviews)
- Quality (7 reviews)

**Cons:**

- Expensive (11 reviews)
- Complexity (9 reviews)
- Learning Curve (6 reviews)
- Cost Issues (5 reviews)
- Large Dataset Handling (5 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:** 729

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [SAP](https://www.g2.com/sellers/sap)
- **Company Website:** https://www.sap.com/
- **Year Founded:** 1972
- **HQ Location:** Walldorf
- **Twitter:** @SAP (297,024 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,341 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Senior Consultant, Consultant
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 49% Enterprise, 28% Mid-Market


#### Pros & 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. [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:** 508

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [SAP](https://www.g2.com/sellers/sap)
- **Company Website:** https://www.sap.com/
- **Year Founded:** 1972
- **HQ Location:** Walldorf
- **Twitter:** @SAP (297,024 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,341 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Consultant, SAP Consultant
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 61% Enterprise, 26% Mid-Market


#### Pros & 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)

  ### 12. [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:** 186

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Dataiku](https://www.g2.com/sellers/dataiku)
- **Company Website:** https://Dataiku.com
- **Year Founded:** 2013
- **HQ Location:** New York, NY
- **Twitter:** @dataiku (22,923 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/dataiku/ (1,609 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Scientist, Data Analyst
  - **Top Industries:** Financial Services, Pharmaceuticals
  - **Company Size:** 59% Enterprise, 22% Mid-Market


#### Pros & 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)

  ### 13. [Pure1 AIOps](https://www.g2.com/products/pure1-aiops/reviews)
  Pure1 Meta is global intelligence built from a massive collection of storage array health and performance data. By continuously scanning call-home telemetry from Pure’s installed base, Pure1 Meta uses machine learning predictive analytics to help resolve potential issues and optimize your workloads.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 12

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.2/10 (Category avg: 9.0/10)
- **AI Text Summarization:** 10.0/10 (Category avg: 8.1/10)
- **Algorithms:** 10.0/10 (Category avg: 8.5/10)
- **AI Text Generation:** 10.0/10 (Category avg: 8.1/10)


**Seller Details:**

- **Seller:** [Pure Storage](https://www.g2.com/sellers/pure-storage)
- **Year Founded:** 2009
- **HQ Location:** Santa Clara, US
- **Twitter:** @purestorage (65,706 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/pure-storage/ (6,976 employees on LinkedIn®)
- **Ownership:** PSTG

**Reviewer Demographics:**
  - **Company Size:** 42% Mid-Market, 33% Enterprise


#### Pros & Cons

**Pros:**

- Customer Support (1 reviews)
- Ease of Use (1 reviews)
- Implementation Ease (1 reviews)
- Security (1 reviews)

**Cons:**

- Cost Issues (1 reviews)
- Expensive (1 reviews)

  ### 14. [Minitab Statistical Software](https://www.g2.com/products/minitab-statistical-software/reviews)
  Minitab® Statistical Software is a comprehensive data analysis solution designed to assist users in making informed, data-driven decisions through visualizations, statistical analysis, and predictive analytics. This software caters to a diverse audience, enabling individuals and organizations—regardless of their statistical expertise or geographical location—to harness the power of data analysis with user-friendly tools. The software is particularly beneficial for businesses and institutions seeking to identify trends, solve complex problems, and extract valuable insights from their data. With nearly 50 years of experience in the field, Minitab has established itself as a trusted partner for organizations of all sizes, including many of the top Fortune 500 companies. Its suite of tools, which includes Minitab Engage®, Minitab Workspace™, Minitab Connect®, Quality Trainer®, and Salford Predictive Modeler®, is designed to streamline the process of data analysis and process improvement across various industries. Key features of Minitab® Statistical Software include a wide range of statistical tests, graphical representations, and predictive modeling capabilities. Users can easily create visualizations that help to clarify complex data sets, making it simpler to identify patterns and trends. The software also offers robust statistical analysis tools that allow users to perform hypothesis testing, regression analysis, and control charts, among other functions. These features empower users to make faster and more accurate decisions, ultimately driving business excellence. Minitab stands out in its category due to its unparalleled ease of use, which allows users with varying levels of statistical knowledge to engage with the software effectively. The intuitive interface and comprehensive support resources ensure that users can quickly learn how to leverage the software’s capabilities to address their specific needs. By providing access to powerful analytics tools, Minitab enables organizations to foster a culture of data-driven decision-making, leading to improved operational efficiency and enhanced strategic planning. In summary, Minitab® Statistical Software is an essential tool for organizations looking to enhance their data analysis capabilities. By offering a suite of powerful features designed for users of all backgrounds, Minitab not only simplifies the process of data analysis but also empowers organizations to unlock the full potential of their data for informed decision-making.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 218

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **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/ (706 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Quality Manager
  - **Top Industries:** Automotive, Manufacturing
  - **Company Size:** 46% Enterprise, 32% Mid-Market


#### Pros & 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)

  ### 15. [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:** 4.8/5.0
  **Total Reviews:** 47

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **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 (26 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Scientist
  - **Top Industries:** Computer Software, Retail
  - **Company Size:** 49% Enterprise, 32% Small-Business


#### Pros & 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)

  ### 16. [Salesloft](https://www.g2.com/products/salesloft/reviews)
  Salesloft powers durable revenue growth for the world’s most demanding companies. The industry-leading Revenue Orchestration Platform uses purpose-built AI to help market-facing teams prioritize and take action on what matters most, from first touch to upsell and renewal. Targeting a diverse audience that includes sales professionals, marketing teams, and revenue operations leaders, Salesloft is used successfully by organizations of all sizes. More than 5,000 customers including Google, 3M, IBM, Shopify, Square, and Cisco gain a performance force multiplier with Salesloft by shifting to a durable revenue engagement model, helping them solve the complexities of modern B2B sales and unlock revenue efficiency. Salesloft provides a suite of capabilities and solutions designed to support modern sales teams, including advanced analytics, sales forecasting, sales automation, sales coaching, deal management, revenue intelligence, sales engagement, conversation intelligence, and integrated communication capabilities. With Salesloft's Revenue Orchestration Platform powering a durable revenue engagement model, businesses can unlock profitable, efficient growth. For more information visit www.salesloft.com.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 4,147

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.0/10)


**Seller Details:**

- **Seller:** [Salesloft](https://www.g2.com/sellers/salesloft)
- **Company Website:** https://salesloft.com
- **Year Founded:** 2011
- **HQ Location:** Atlanta, GA
- **Twitter:** @Salesloft (18,439 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2296178/ (1,137 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Account Executive, Sales Development Representative
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 56% Mid-Market, 23% Small-Business


#### Pros & 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)

  ### 17. [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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **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®)

**Reviewer Demographics:**
  - **Who Uses This:** Approvisionneur
  - **Top Industries:** Manufacturing, Aviation & Aerospace
  - **Company Size:** 51% Mid-Market, 38% Enterprise


#### Pros & 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)

  ### 18. [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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **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 (2,759 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/jmp/ (1,002 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Student
  - **Top Industries:** Higher Education, Information Technology and Services
  - **Company Size:** 42% Enterprise, 33% Small-Business


#### Pros & 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)

  ### 19. [Microland smartThink](https://www.g2.com/products/microland-smartthink/reviews)
  Manage IT specific issues around networks, servers, storage, applications, middleware, IoT, cloud deployments, etc., with real-time data analysis and correlation


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 10

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.0/10)
- **AI Text Summarization:** 9.0/10 (Category avg: 8.1/10)
- **Algorithms:** 8.5/10 (Category avg: 8.5/10)
- **AI Text Generation:** 9.4/10 (Category avg: 8.1/10)


**Seller Details:**

- **Seller:** [Microland](https://www.g2.com/sellers/microland-1ab552b6-0e70-41c3-8841-1de035fc3822)
- **Year Founded:** 1989
- **HQ Location:** Bangalore
- **Twitter:** @MicrolandLtd (2,254 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microland (6,416 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 40% Enterprise, 30% Mid-Market


#### Pros & Cons

**Pros:**

- Business Growth (1 reviews)
- Customization (1 reviews)
- Dashboard Management (1 reviews)
- Ease of Learning (1 reviews)
- Ease of Use (1 reviews)

**Cons:**

- Compatibility Issues (1 reviews)
- Cost Issues (1 reviews)
- Lack of Guidance (1 reviews)

  ### 20. [Pecan](https://www.g2.com/products/pecan/reviews)
  Pecan AI is a predictive analytics platform that helps business teams understand what’s likely to happen next, while there is still time to act. With Pecan’s Predictive AI Agent, teams can turn business questions into reliable predictions for use cases like customer churn, demand forecasting, and lifetime value, without relying on long, complex data science projects. The platform automatically handles data preparation, feature engineering, modeling, validation, and delivery, and provides transparent, explainable predictions that integrate into tools like Salesforce, HubSpot, Snowflake, and BI systems to drive real business outcomes.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 35

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 9.0/10)
- **Algorithms:** 8.6/10 (Category avg: 8.5/10)
- **AI Text Generation:** 8.0/10 (Category avg: 8.1/10)


**Seller Details:**

- **Seller:** [Pecan.ai](https://www.g2.com/sellers/pecan-ai)
- **Company Website:** https://www.pecan.ai
- **Year Founded:** 2018
- **HQ Location:** US, Israel
- **Twitter:** @pecan_ai (1,139 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/pecan-ai/ (83 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Retail
  - **Company Size:** 53% Mid-Market, 21% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (25 reviews)
- Customer Support (18 reviews)
- Speed (15 reviews)
- Problem Solving (13 reviews)
- Implementation Ease (11 reviews)

**Cons:**

- Learning Difficulty (9 reviews)
- Limitations (8 reviews)
- Limited Features (8 reviews)
- Learning Curve (7 reviews)
- Limited Customization (5 reviews)

  ### 21. [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:** 759

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Qlik](https://www.g2.com/sellers/qlik)
- **Year Founded:** 1993
- **HQ Location:** Radnor, PA
- **Twitter:** @qlik (64,263 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10162/ (4,529 employees on LinkedIn®)
- **Phone:** 1 (888) 994-9854

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst, Consultant
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 41% Enterprise, 41% Mid-Market


#### Pros & 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)

  ### 22. [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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Qlik](https://www.g2.com/sellers/qlik)
- **Year Founded:** 1993
- **HQ Location:** Radnor, PA
- **Twitter:** @qlik (64,263 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10162/ (4,529 employees on LinkedIn®)
- **Phone:** 1 (888) 994-9854

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 38% Enterprise, 31% Small-Business


#### Pros & 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)

  ### 23. [GoodData](https://www.g2.com/products/gooddata/reviews)
  GoodData is the full-stack, AI-native decision intelligence platform that helps businesses turn data into actionable, enterprise-grade insights. Designed for governed, scalable analytics, GoodData enables organizations to build, operationalize, and embed decisions, workflows, and AI agents directly within products and business workflows. The platform combines Analytics as Code, a governed semantic and metrics layer, APIs, SDKs, and open AI interoperability to help teams create composable analytics and AI experiences across products, workflows, and customer environments. From embedded analytics and dashboards to assistants, AI workflows, and interoperable agents, GoodData gives teams the foundation to move from insight to action with governance, performance, and deployment flexibility built in. Today, GoodData serves over 140,000 companies and 3.2 million users worldwide.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 555

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.4/10 (Category avg: 9.0/10)
- **Algorithms:** 6.7/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [GoodData](https://www.g2.com/sellers/gooddata)
- **Year Founded:** 2007
- **HQ Location:** San Francisco, CA
- **Twitter:** @gooddata (12,648 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/202760/ (283 employees on LinkedIn®)
- **Phone:**  (415) 200-0186

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst, Product Manager
  - **Top Industries:** Computer Software, Consumer Services
  - **Company Size:** 44% Mid-Market, 39% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (52 reviews)
- Data Visualization (34 reviews)
- Integrations (34 reviews)
- Intuitive (30 reviews)
- Customization (28 reviews)

**Cons:**

- Learning Curve (28 reviews)
- Learning Difficulty (19 reviews)
- Missing Features (19 reviews)
- Complexity (13 reviews)
- Limited Customization (12 reviews)

  ### 24. [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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Year Founded:** 1911
- **HQ Location:** Armonk, NY
- **Twitter:** @IBM (708,000 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®)
- **Ownership:** SWX:IBM

**Reviewer Demographics:**
  - **Top Industries:** Higher Education, Education Management
  - **Company Size:** 53% Enterprise, 24% Mid-Market


#### Pros & 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)

  ### 25. [Coveo](https://www.g2.com/products/coveo/reviews)
  Coveo is a composable AI search & generative experience platform. It’s the intelligence layer that powers individualized, trusted, and connected experiences. You can delight customers, augment employee capabilities, and drive superior business outcomes with semantic search, AI recommendations, unified personalization and GenAI answering. What it does: Brings the most relevant content to any interface for search, recommendations & agentic experiences, so people always find what helps them most. Our single SaaS platform and robust suite of AI & GenAI models are specifically built to transform the total experience: from CX to EX across websites, commerce, service, and workplace. Deepen content personalization across multiple touchpoints. Personalize interactions to improve conversions, customer satisfaction, content discovery – or all of the above. With a content personalization engine as powerful as Coveo, you can start small and plan big. Stay one step ahead with an intelligent recommender. From browsing to searching, use Coveo’s state-of-the-art recommendation engine to help people discover content, products, and services they want or are likely to need next. Generative AI is powerful, but needs fresh content to work. To succeed with GenAI, you need a solution that unifies knowledge and retrieves the most relevant content to deliver reliable answers while scaling securely. Meet Relevance Generative Answering (RGA), the next evolution of AI search: producing answers using your company’s best content and tailored to your unique context. We handle the AI and LLMs, so you can focus on innovation. Live for customers in 4–6 weeks. Start from scratch or accelerate development with pre-built integrations and tools for every stack. Our API-led architecture lets you integrate and extend the Coveo relevance platform to meet your needs. Our platform is certified ISO 27001, HIPAA compliant, SOC2 compliant, and 99.999% SLA resilient. We are a Salesforce Summit ISV Partner, an SAPⓇ Endorsed App, and an Adobe Gold Partner.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 142

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 9.0/10)
- **AI Text Summarization:** 8.3/10 (Category avg: 8.1/10)
- **Algorithms:** 8.9/10 (Category avg: 8.5/10)
- **AI Text Generation:** 8.3/10 (Category avg: 8.1/10)


**Seller Details:**

- **Seller:** [Coveo Solutions Inc](https://www.g2.com/sellers/coveo-solutions-inc)
- **Company Website:** https://www.coveo.com
- **Year Founded:** 2005
- **HQ Location:** Quebec City, Canada
- **Twitter:** @coveo (4,302 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/coveo (829 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Retail, Computer Software
  - **Company Size:** 54% Enterprise, 39% Mid-Market


#### Pros & Cons

**Pros:**

- Search Efficiency (27 reviews)
- Ease of Use (26 reviews)
- Search Functionality (19 reviews)
- Analytics (18 reviews)
- Features (18 reviews)

**Cons:**

- Learning Curve (13 reviews)
- Difficult Learning (11 reviews)
- Learning Difficulty (10 reviews)
- High Learning Curve (8 reviews)
- Poor Customer Support (8 reviews)



## Parent Category

[Analytics Tools & Software](https://www.g2.com/categories/analytics-tools-software)



## Related Categories

- [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)



---

## Buyer Guide

### 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.