# Best Analytics Platforms

  *By [Tian Lin](https://research.g2.com/insights/author/tian-lin)*

   Analytics platforms provide a tool set for businesses to transform raw data into meaningful, actionable insights. They enable organizations to explore data, uncover trends, forecast future outcomes, and support informed decision making.

Unlike tools limited to reporting on past performance, analytics platforms often include advanced capabilities such as predictive modeling, statistical analysis, and machine learning (ML). These platforms are designed to be flexible and scalable, supporting a wide range of use cases across the business.

These platforms are used in nearly every business function, from marketing and sales to finance, operations, and HR, supporting both strategic planning and day-to-day performance monitoring. From data analysts and scientists to business stakeholders and executives, analytics platforms are used by a wide range of personas. While analysts focus on exploring data and generating insights, self-service tools now enable non-technical users to interact directly with data. IT teams support platform integration and security, reflecting the growing push to democratize data access and embed analytics into daily decision-making across the organization.

Analytics platforms support critical functions such as data blending and modeling, enabling users to combine data from diverse sources and build robust, interconnected data models. The visual outputs — dashboards, reports, and interactive charts — help users explore trends, drill down into granular details, and communicate insights clearly.

Unlike standalone data visualization tools, which are limited to presenting information, analytics platforms encompass the full analytical workflow. Many also offer advanced capabilities such as embedded analytics, natural language query, and augmented analytics, which leverage ML to automate insight discovery and make data exploration more accessible to a broader audience.

Analytics platforms and [business intelligence (BI) software](https://www.g2.com/categories/business-intelligence) often work in tandem to support data-driven organizations. While BI tools focus on tracking and reporting historical performance through dashboards and key performance indicators (KPI), analytics platforms provide broader capabilities that support exploratory analysis and strategic planning. BI answers "what happened," while analytics platforms help users understand why it happened and what might happen next. Rather than replacing BI, analytics platforms complement it by enabling deeper insights and empowering a wider range of users across the organization.

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

- Ingest and integrate data from a wide range of structured and semi-structured sources
- Prepare and transform data using built-in tools for cleaning, enrichment, and formatting
- Support connections to diverse data sources, including file uploads, databases, application programming interfaces (API), and SaaS apps
- Enable users to model data relationships, join datasets, and explore data interactively
- Offer tools to build meaningful business reports, dashboards, and visualizations
- Allow creation and sharing of internal analytics applications or embedded insights across teams





## Best Analytics Platforms At A Glance

- **Leader:** [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews)
- **Highest Performer:** [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews)
- **Easiest to Use:** [Databricks](https://www.g2.com/products/databricks/reviews)
- **Top Trending:** [Hex](https://www.g2.com/products/hex-tech-hex/reviews)
- **Best Free Software:** [Tableau](https://www.g2.com/products/tableau/reviews)


---

**Sponsored**

### ThoughtSpot

ThoughtSpot is the Agentic Analytics Platform company for the enterprise. With natural language and AI, ThoughtSpot empowers everyone in an organization to ask data questions, get answers, and take action. Code-first for data teams and code-free for business users, ThoughtSpot is intuitive enough for anyone to use, yet built to handle large, complex cloud data at scale. Customers like Coca-Cola, Hilton Worldwide, and Capital One are unlocking the full potential of their data with ThoughtSpot.



[Book a Demo](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=620&secure%5Bdisplayable_resource_id%5D=620&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=620&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=6232&secure%5Bresource_id%5D=620&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fanalytics-platforms&secure%5Btoken%5D=8d12eef92a92e6fffb0de59ec152c9c2c80366e7b9320e47254ca2eddd49676d&secure%5Burl%5D=https%3A%2F%2Fwww.thoughtspot.com%2Fdemo%3Futm_source%3Dg2%26utm_medium%3Daggregatorads%26utm_term%3Dcompete%26utm_content%3Dtext_ads%26utm_campaign%3Dppc_g2compete26&secure%5Burl_type%5D=book_demo)

---

## Top-Rated Products (Ranked by G2 Score)
  ### 1. [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews)
  Power BI Desktop puts visual analytics at your fingertips. With this powerful authoring tool, you can create interactive data visualizations and reports. Connect, mash up, model, and visualize your data. Place visuals exactly where you want them, analyze and explore your data, and share content with others by publishing to the Power BI web service. Power BI Desktop is part of the Power BI product suite. To monitor key data and share dashboards and reports, use the Power BI web service. To view and interact with your data on any mobile device, get the Power BI Mobile app on the AppStore, Google Play or the Microsoft Store. To embed stunning, fully interactive reports and visuals into your applications use Power BI Embedded.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 1,520

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 9.1/10)
- **Steps to Answer:** 8.4/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.9/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.6/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft (13,090,464 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®)
- **Ownership:** MSFT

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst, Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 42% Enterprise, 37% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (148 reviews)
- Data Visualization (143 reviews)
- Integrations (72 reviews)
- Powerful BI (69 reviews)
- Data Integration (54 reviews)

**Cons:**

- Learning Curve (83 reviews)
- Slow Performance (68 reviews)
- Performance Issues (31 reviews)
- Complex Data Modeling (28 reviews)
- Limited Customization (26 reviews)

  ### 2. [Tableau](https://www.g2.com/products/tableau/reviews)
  Tableau is the world’s leading AI-powered analytics platform. Whether you are a business user or an analyst, Tableau turns trusted data into actionable insights. With our flexible, interoperable platform, you can: Turn data into action at scale with human and agent collaboration. Tableau Next delivers agentic AI for faster data-insight-action workflows. It surfaces insights, provides proactive recommendations, and helps you take action in the flow of work. Scale data-driven insights with complete operational confidence. Tableau Cloud enables fully managed analytics at scale. It accelerates your time to value and gives you access to the latest AI-powered innovations. Deploy visual, self-service analytics with unmatched control and flexibility. Tableau Server meets your organization's governance and security needs. It provides enterprise-grade, self-service analytics on-premise or in your private cloud.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 3,481

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.5/10 (Category avg: 9.1/10)
- **Steps to Answer:** 8.2/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.7/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.5/10 (Category avg: 8.4/10)


**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)

  ### 3. [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:** 700

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 9.1/10)
- **Steps to Answer:** 8.1/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.4/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.3/10 (Category avg: 8.4/10)


**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% Enterprise, 32% Small-Business


#### 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)

  ### 4. [Databricks](https://www.g2.com/products/databricks/reviews)
  Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and over 60% of the Fortune 500 — rely on Databricks to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified Data Intelligence Platform that includes Agent Bricks, Lakeflow, Lakehouse, Lakebase and Unity Catalog.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 721

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.1/10)
- **Steps to Answer:** 7.9/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.5/10 (Category avg: 8.6/10)
- **Calculated Fields:** 7.9/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Databricks Inc.](https://www.g2.com/sellers/databricks-inc)
- **Company Website:** https://databricks.com
- **Year Founded:** 2013
- **HQ Location:** San Francisco, CA
- **Twitter:** @databricks (89,234 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3477522/ (14,779 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Senior Data Engineer
  - **Top Industries:** Information Technology and Services, Financial Services
  - **Company Size:** 44% Enterprise, 40% Mid-Market


#### Pros & Cons

**Pros:**

- Features (288 reviews)
- Ease of Use (278 reviews)
- Integrations (189 reviews)
- Collaboration (150 reviews)
- Data Management (150 reviews)

**Cons:**

- Learning Curve (112 reviews)
- Expensive (97 reviews)
- Steep Learning Curve (96 reviews)
- Missing Features (69 reviews)
- Complexity (64 reviews)

  ### 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: 9.1/10)
- **Steps to Answer:** 8.0/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.2/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.0/10 (Category avg: 8.4/10)


**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. [Domo](https://www.g2.com/products/domo/reviews)
  Domo's AI and Data Products Platform empowers organizations to turn data into actionable insights and solutions. It allows users to seamlessly connect diverse data sources, prepare data for use, and generate dynamic reports and visualizations—all within a single interface. With built-in AI and automation capabilities, teams can easily build and use AI agents, streamline workflows, and create tailored solutions.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 983

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 9.1/10)
- **Steps to Answer:** 7.9/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.5/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.2/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Domo](https://www.g2.com/sellers/domo)
- **Company Website:** https://www.domo.com
- **Year Founded:** 2010
- **HQ Location:** American Fork, UT
- **Twitter:** @Domotalk (63,693 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/25237/ (1,334 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst, Business Analyst
  - **Top Industries:** Computer Software, Marketing and Advertising
  - **Company Size:** 49% Mid-Market, 29% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (248 reviews)
- Data Visualization (116 reviews)
- Intuitive (95 reviews)
- Easy Integrations (93 reviews)
- Integrations (88 reviews)

**Cons:**

- Learning Curve (66 reviews)
- Missing Features (59 reviews)
- Data Management Issues (55 reviews)
- Expensive (45 reviews)
- Complexity (43 reviews)

  ### 7. [Sigma](https://www.g2.com/products/sigma-computing-sigma/reviews)
  Sigma is the AI apps and analytics platform connected to the cloud data warehouse. Using Sigma, business and technical teams can build intelligent, production-ready AI apps that accelerate and automate operational workflows. Sigma provides a spreadsheet interface, SQL and Python editors, visual builders, and native AI to help teams turn live data into interactive applications, analysis, reports, and embedded experiences.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 543

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.1/10 (Category avg: 9.1/10)
- **Steps to Answer:** 8.4/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.6/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.7/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Sigma Computing](https://www.g2.com/sellers/sigma-computing)
- **Company Website:** https://www.sigmacomputing.com/
- **Year Founded:** 2014
- **HQ Location:** San Francisco, California
- **Twitter:** @sigmacomputing (1,546 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/7801411/ (1,437 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst, Customer Success Manager
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 58% Mid-Market, 21% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (73 reviews)
- User Interface (29 reviews)
- Customer Support (27 reviews)
- Data Visualization (27 reviews)
- Data Handling (26 reviews)

**Cons:**

- Slow Loading (23 reviews)
- Slow Performance (22 reviews)
- Limited Customization (20 reviews)
- Learning Curve (17 reviews)
- Missing Features (14 reviews)

  ### 8. [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews)
  Kyvos is a semantic layer for AI and BI. It gives organizations a single, consistent, business-friendly view of their entire data estate. By standardizing how data is defined and understood, Kyvos eliminates metric drift across BI tools and ensures that LLMs and AI agents work with governed business semantics rather than raw tables. Kyvos also delivers lightning-fast analytics at massive scale and high concurrency — including granular multidimensional analysis on the cloud — without the sluggish query times and escalating cloud costs that typically come with it. Why Organizations Use Kyvos Unified Semantic Foundation for AI and BI Kyvos semantic layer standardizes how metrics, KPIs, dimensions, hierarchies, relationships, calculations, and business rules are modelled across the enterprise — so that dashboards, analytics tools, notebooks, and AI systems all operate on the same understanding of the business. Kyvos enables: - Shared semantics — one common data language across every tool, team, and system - Governed access — data exploration within defined security, role, and permission boundaries - Platform interoperability — consistent semantic context across diverse platforms and environments - AI readiness — LLMs and agents work with governed business semantics rather than raw tables or ambiguous schema AI Grounded in Business Context Kyvos grounds AI systems in the governed semantic model, ensuring they operate on established business context rather than raw schemas — improving the accuracy, traceability, and reliability of AI-generated insights. Consistent Metrics Across BI Tools Kyvos centralizes metric and KPI definitions in the semantic layer and applies them consistently across every analytics interface — eliminating metric drift and improving trust in analytics. High-Performance Analytics at Scale Kyvos delivers high-performance analytics that scale with demand, enabling: - Sub-second query performance across massive datasets - High concurrency across thousands of users and workloads - Consistent response times regardless of data volume or concurrency - No performance degradation as adoption grows - Multidimensional Analytics on the Cloud Kyvos enables deep multidimensional analytics, supporting: - Granular analysis across billions of rows - Thousands of measures and dimensions in a single model - Fast drill-down across complex hierarchies - Full analytical depth without sacrificing query speed Cloud Cost Efficiency Kyvos serves analytics through its semantic layer rather than routing every query to the warehouse — reducing compute consumption across analytics and AI workloads. As adoption grows, organizations can scale users, workloads, and analytical complexity without a corresponding rise in warehouse compute costs.


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 249

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.6/10 (Category avg: 9.1/10)
- **Steps to Answer:** 9.3/10 (Category avg: 8.3/10)
- **Reports Interface:** 9.6/10 (Category avg: 8.6/10)
- **Calculated Fields:** 9.4/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Kyvos Insights](https://www.g2.com/sellers/kyvos-insights)
- **Company Website:** https://www.kyvosinsights.com
- **Year Founded:** 2014
- **HQ Location:** Los Gatos, CA
- **Twitter:** @KyvosInsights (690 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/kyvos-insights-inc-/ (150 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer, Senior Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 55% Mid-Market, 40% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (125 reviews)
- Speed (92 reviews)
- Performance (56 reviews)
- Analytics (54 reviews)
- Fast Querying (50 reviews)

**Cons:**

- Learning Curve (35 reviews)
- Difficult Setup (34 reviews)
- Complexity (10 reviews)
- Feature Limitations (7 reviews)
- Learning Difficulty (7 reviews)

  ### 9. [Looker](https://www.g2.com/products/looker/reviews)
  Looker, Google Cloud’s business intelligence platform, enables you to chat with your data. Organizations turn to Looker for self-service and governed BI, to build custom applications with trusted metrics, or to bring Looker modeling to their existing environment. The result is improved data engineering efficiency and true business transformation.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 1,553

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.1/10)
- **Steps to Answer:** 8.2/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.6/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.4/10 (Category avg: 8.4/10)


**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 Analyst, Data Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 60% Mid-Market, 20% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (126 reviews)
- Insights (75 reviews)
- Easy Integrations (70 reviews)
- Integrations (70 reviews)
- Data Visualization (62 reviews)

**Cons:**

- Learning Curve (58 reviews)
- Learning Difficulty (42 reviews)
- Slow Loading (36 reviews)
- Slow Performance (36 reviews)
- Complexity (32 reviews)

  ### 10. [Hex](https://www.g2.com/products/hex-tech-hex/reviews)
  Hex is the world’s best AI Analytics platform. With Hex, anyone can explore data using natural language, with or without code, all on trusted context, in one AI-powered platform. Get started now \> https://app.hex.tech/signup?source=g2 Get a demo \> https://hex.tech/request-a-demo/source=g2


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 379

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.1/10 (Category avg: 9.1/10)
- **Steps to Answer:** 7.6/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.3/10 (Category avg: 8.6/10)
- **Calculated Fields:** 7.7/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Hex Tech](https://www.g2.com/sellers/hex-tech)
- **Company Website:** https://hex.tech/
- **Year Founded:** 2019
- **HQ Location:** San Francisco, US
- **Twitter:** @_hex_tech (6,762 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/hex-technologies/ (222 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst, Data Scientist
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 54% Mid-Market, 22% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (130 reviews)
- SQL Queries (81 reviews)
- Data Management (79 reviews)
- SQL Querying (74 reviews)
- Data Analysis (62 reviews)

**Cons:**

- Limited Features (45 reviews)
- Missing Features (41 reviews)
- Lacking Features (38 reviews)
- Limited Visualization (30 reviews)
- Data Management Issues (29 reviews)

  ### 11. [Oracle Analytics Cloud](https://www.g2.com/products/oracle-analytics-cloud/reviews)
  Oracle Analytics Cloud is a comprehensive cloud analytics platform that empowers you to fundamentally change how you analyze and act on information. Empower leaders, analysts, and IT to access data from wherever they are, even using mobile devices. Oracle Analytics Cloud helps organizations discover unique insights faster with machine learning. With augmented analytics, combine data from across your organization with third-party data and automate important and time-consuming tasks such as data preparation, visualization, forecasting, and reporting.


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 292

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 7.8/10 (Category avg: 9.1/10)
- **Steps to Answer:** 7.9/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.4/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.2/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Oracle](https://www.g2.com/sellers/oracle)
- **Year Founded:** 1977
- **HQ Location:** Austin, TX
- **Twitter:** @Oracle (826,383 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1028/ (199,301 employees on LinkedIn®)
- **Ownership:** NYSE:ORCL

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Financial Services
  - **Company Size:** 61% Enterprise, 27% Mid-Market


#### Pros & Cons

**Pros:**

- Analytics (3 reviews)
- Data Visualization (3 reviews)
- Ease of Use (3 reviews)
- Scalability (3 reviews)
- Business Improvement (2 reviews)

**Cons:**

- Learning Curve (4 reviews)
- Complexity (2 reviews)
- Complex Usage (2 reviews)
- Limited Customization (2 reviews)
- Bugs (1 reviews)

  ### 12. [Yellowfin BI](https://www.g2.com/products/yellowfin-bi/reviews)
  Yellowfin is the only analytics suite that successfully combines action based dashboards with industry-leading automated analysis and data storytelling. By delivering the best analytical experience, Yellowfin provides your users with unique ways to engage with and act on their data, and addresses the needs of data analysts, business users, customers and developers who want to build, deploy or use amazing analytical experiences. Analytics for software companies Integrate and embed analytics with a difference into your app, your way \* Replace legacy or home grown reporting tools \* Embed a modern self-service analytics suite \* Deliver an exceptional customer experience Analytics for enterprise Get more value from your data in new and innovative ways \* Migrate from spreadsheets to a modern analytics platform \* Replace legacy BI applications \* Embed analytics into operational workflows Analytical Application Builders Leverage your domain expertise to create data products that delight \* Create unique data driven applications \* Close the loop on analytics \* Deliver insights as a service


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 414

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.1/10)
- **Steps to Answer:** 8.0/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.6/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.2/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Yellowfin](https://www.g2.com/sellers/yellowfin)
- **Year Founded:** 2003
- **HQ Location:** Austin, Texas
- **Twitter:** @YellowfinBI (5,793 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/358856/ (63 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** General Manager, Business Analyst
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 46% Small-Business, 35% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (98 reviews)
- Data Visualization (47 reviews)
- Dashboard Customization (35 reviews)
- Intuitive (32 reviews)
- Report Generation (32 reviews)

**Cons:**

- Learning Curve (42 reviews)
- Slow Performance (30 reviews)
- Large Data Handling (29 reviews)
- Performance Issues (22 reviews)
- Limited Customization (17 reviews)

  ### 13. [IBM Business Analytics Enterprise](https://www.g2.com/products/ibm-business-analytics-enterprise/reviews)
  IBM Business Analytics Enterprise is a comprehensive suite designed to unify and streamline business intelligence, planning, budgeting, reporting, and forecasting processes across organizations. By integrating data from multiple sources and vendors into a single, no-code content hub, it empowers users to make informed, data-driven decisions efficiently. Key Features and Functionality: - Composite Dashboards: Consolidate content assets from various business intelligence tools into a unified, integrated view accessible to all users. - Insightful Decision-Making: Leverage real metrics and insights to make confident business decisions, eliminating guesswork. - Easy Collaboration: Facilitate seamless collaboration across the organization, allowing teams to scale and adjust business objectives without overhauling existing processes. - Enhanced Customer Service: Optimize resource allocation and manufacturing decisions to provide more streamlined delivery for customers. - Data Management: Integrate multiple assets from different data sources into a single dashboard for easy access and faster decision-making. - Visualization Layer: Discover, access, personalize, and recommend content across multiple BI vendors and solutions from a centralized hub. - Personalization: Utilize AI to recommend content to users and allow for customized searches, aligning the platform with organizational branding and customer experience. - Forecast Optimization: Integrate operational, profitability, and financial planning with automated tools to optimize decision-making, using predictive analytics to identify trends and seasonal patterns. - Integrated Planning: Adjust organizational plans and forecasts in real time, adapting to changing demands swiftly with AI-infused extended planning and analysis. - Enterprise Reporting: Provide scalable reporting to enhance the data analytics culture, delivering the right data to the right people at the right time. Primary Value and Solutions Provided: IBM Business Analytics Enterprise addresses the challenge of data silos by offering a unified platform that integrates various analytics and planning tools. This consolidation enables organizations to: - Break Down Data Silos: Provide a single point of entry for users to access the data they need, enhancing collaboration and data consistency. - Enhance Decision-Making: Equip teams with comprehensive insights, allowing for informed decisions that drive business performance. - Improve Operational Efficiency: Streamline planning and forecasting processes, enabling organizations to respond swiftly to market changes and operational demands. By integrating analytics tools into a cohesive environment, IBM Business Analytics Enterprise empowers organizations to harness the full potential of their data, fostering a culture of informed decision-making and strategic agility.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 21

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.1/10)
- **Steps to Answer:** 8.6/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.9/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.8/10 (Category avg: 8.4/10)


**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:**
  - **Company Size:** 43% Small-Business, 29% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (5 reviews)
- Data Integration (4 reviews)
- Easy Integrations (4 reviews)
- Efficiency (4 reviews)
- Data Analysis (3 reviews)

**Cons:**

- Learning Curve (4 reviews)
- Complexity (3 reviews)
- Difficult Customization (3 reviews)
- Expensive (3 reviews)
- Complex Usage (2 reviews)

  ### 14. [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:** 647

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.1/10)
- **Steps to Answer:** 8.2/10 (Category avg: 8.3/10)
- **Reports Interface:** 7.6/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.9/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Alteryx](https://www.g2.com/sellers/alteryx)
- **Company Website:** https://www.alteryx.com
- **Year Founded:** 1997
- **HQ Location:** Irvine, CA
- **Twitter:** @alteryx (26,204 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/903031/ (2,268 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst, Analyst
  - **Top Industries:** Financial Services, Accounting
  - **Company Size:** 62% Enterprise, 22% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (333 reviews)
- Automation (148 reviews)
- Intuitive (132 reviews)
- Easy Learning (102 reviews)
- Efficiency (102 reviews)

**Cons:**

- Expensive (88 reviews)
- Learning Curve (80 reviews)
- Missing Features (62 reviews)
- Learning Difficulty (55 reviews)
- Slow Performance (41 reviews)

  ### 15. [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: 9.1/10)
- **Steps to Answer:** 7.6/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.1/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.1/10 (Category avg: 8.4/10)


**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)

  ### 16. [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: 9.1/10)
- **Steps to Answer:** 8.3/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.6/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.2/10 (Category avg: 8.4/10)


**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)

  ### 17. [Deepnote](https://www.g2.com/products/deepnote/reviews)
  Deepnote is a data workspace where agents and humans work together. It's designed to simplify data exploration, accelerate analysis, and quickly deliver actionable insights for you and your team. Unlike outdated tools such as Jupyter, Deepnote is built with the next decade in mind. Deepnote gives anyone working with data superpowers. It unifies your data workflow through an integrated semantic layer, preparing your data for advanced AI applications. You can also leverage our AI data copilot to chat with your data, create charts, write code, or turn your AI notebooks into fully-fledged data dashboards or apps. Combine data, SQL or Python code, and visualizations side-by-side on a flexible canvas - enhanced with cutting-edge AI reasoning models. 🤖 Analyze with AI • Generate code and visualizations by describing your goal. • Auto-write, run, and debug code with AI. • Move faster with context-aware AI suggestions. 🔗 Unify • Connect to 60+ data sources like BigQuery, Snowflake, and PostgreSQL. • Combine Python and SQL in one notebook. • Build reusable ETL, analytics, and metric modules. • Create a semantic layer with shared definitions and trusted metrics. ⚖️ Scale • Instantly boost compute power, more included than Colab. • Schedule jobs and get notified with fresh results. • Organize work in projects and folders for team clarity. • Manage workflows via REST API. 🚀 Launch • Turn notebooks into dashboards or data apps, natively or with Streamlit. • Let users explore data with interactive inputs. • Share secure, live apps in one click.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 374

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 9.1/10)
- **Steps to Answer:** 7.7/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.0/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.2/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Deepnote](https://www.g2.com/sellers/deepnote)
- **Company Website:** https://www.deepnote.com
- **Year Founded:** 2019
- **HQ Location:** San Francisco , US
- **Twitter:** @DeepnoteHQ (5,240 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/deepnote (25 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Student, Data Analyst
  - **Top Industries:** Computer Software, Higher Education
  - **Company Size:** 68% Small-Business, 24% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (170 reviews)
- Collaboration (120 reviews)
- Easy Integrations (76 reviews)
- Team Collaboration (76 reviews)
- Data Management (67 reviews)

**Cons:**

- Slow Performance (61 reviews)
- Limited Features (32 reviews)
- Data Management Issues (29 reviews)
- Missing Features (26 reviews)
- Lacking Features (25 reviews)

  ### 18. [Count](https://www.g2.com/products/count/reviews)
  Count is a modern data collaboration platform that helps data teams actually work together. It combines a data notebook with a real-time collaborative canvas for analysis and visualisation, so everyone can query, debug, and explore data in one place. Count replaces the mess of disconnected tools with a single workspace where analysts, engineers, and stakeholders can write SQL or Python, build visualisations, and share insights instantly. You can import and debug dbt models, see live results from connected CTEs, and export back to dbt Cloud, GitHub, or full SQL scripts. Used by over 500 data teams, including Accenture, Cleo AI, and Too Good To Go, Count helps teams move beyond static dashboards and focus on solving real business problems. It’s data collaboration that actually works.


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 101

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.5/10 (Category avg: 9.1/10)
- **Steps to Answer:** 8.5/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.5/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.8/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Count Technologies](https://www.g2.com/sellers/count-technologies)
- **Company Website:** https://count.co/
- **Year Founded:** 2016
- **HQ Location:** London, United Kingdom
- **Twitter:** @counthq (2,098 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/counthq/ (29 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst, Analytics Engineer
  - **Top Industries:** Financial Services, Information Technology and Services
  - **Company Size:** 80% Mid-Market, 10% Small-Business


#### Pros & Cons

**Pros:**

- Team Collaboration (34 reviews)
- Ease of Use (28 reviews)
- Customer Support (20 reviews)
- Flexibility (20 reviews)
- Data Visualization (19 reviews)

**Cons:**

- Learning Curve (18 reviews)
- Slow Performance (9 reviews)
- Limited Customization (7 reviews)
- Missing Features (7 reviews)
- Layout Issues (5 reviews)

  ### 19. [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: 9.1/10)
- **Steps to Answer:** 7.7/10 (Category avg: 8.3/10)
- **Reports Interface:** 7.8/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.2/10 (Category avg: 8.4/10)


**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:** 60% 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)

  ### 20. [Incorta](https://www.g2.com/products/incorta/reviews)
  Incorta is the first and only open data delivery platform that enables real-time analysis of live, detailed data across all systems of record—without the need for complex ETL processes. By enabling direct analysis on raw, source-identical data, Incorta provides faster, more accurate insights while removing barriers to exploration. With intuitive low-code/no-code tools, AI-powered querying through Nexus, and prebuilt business data applications, enterprise teams can quickly surface insights, break down technical roadblocks, and make smarter decisions without heavy engineering effort. For more information, please visit www.incorta.com.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 55

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.4/10 (Category avg: 9.1/10)
- **Steps to Answer:** 9.2/10 (Category avg: 8.3/10)
- **Reports Interface:** 9.3/10 (Category avg: 8.6/10)
- **Calculated Fields:** 9.0/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Incorta](https://www.g2.com/sellers/incorta)
- **Company Website:** https://www.incorta.com/
- **Year Founded:** 2013
- **HQ Location:** San Mateo, CA
- **LinkedIn® Page:** https://www.linkedin.com/company/incorta/ (325 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 56% Enterprise, 29% Mid-Market


#### Pros & Cons

**Pros:**

- Data Integration (1 reviews)
- Easy Integrations (1 reviews)
- Integrations (1 reviews)

**Cons:**

- Bugs (1 reviews)

  ### 21. [FICO Analytics Workbench™](https://www.g2.com/products/fico-analytics-workbencha/reviews)
  FICO® Analytics Workbench™ is a comprehensive predictive analytics tool designed to empower businesses in developing and deploying explainable machine learning models. It caters to both business users and data scientists, facilitating data exploration, visual data wrangling, decision strategy design, and machine learning within a unified environment. The platform is built on the high-performance FICO® Decision Management Platform, ensuring scalability and integration with real-time business operations. Key Features and Functionality: - Explainable AI Toolkit: Provides transparency in AI-derived decisions, enabling users to validate and interpret machine learning models effectively. - Integrated Development Environment: Combines decision trees, scorecards, and machine learning techniques, offering a versatile toolkit for model development. - User-Friendly Interface: Designed for users with varying skill sets, from business analysts to data scientists, promoting collaboration and productivity. - Cloud-Based Deployment: Offers a cloud-ready solution, allowing for scalable and flexible deployment options. - Regulatory Compliance Support: Automates the production of necessary documentation to meet internal review and external regulatory requirements. Primary Value and Problem Solving: FICO® Analytics Workbench™ addresses the growing need for transparent and interpretable AI models in business decision-making. By providing tools that make machine learning models explainable, it helps organizations comply with regulatory standards and build trust in AI-driven decisions. The platform's intuitive design and comprehensive features enable faster time-to-value, enhanced productivity, and improved business outcomes through analytically powered decisions.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 11

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.7/10 (Category avg: 9.1/10)
- **Steps to Answer:** 8.8/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.8/10 (Category avg: 8.6/10)
- **Calculated Fields:** 9.2/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [FICO](https://www.g2.com/sellers/fico)
- **Year Founded:** 1956
- **HQ Location:** Bozeman, Montana
- **LinkedIn® Page:** https://www.linkedin.com/company/fico/ (3,790 employees on LinkedIn®)
- **Ownership:** NYSE:FICO
- **Total Revenue (USD mm):** $1,294

**Reviewer Demographics:**
  - **Company Size:** 45% Enterprise, 27% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (1 reviews)


  ### 22. [Coefficient](https://www.g2.com/products/coefficient/reviews)
  Coefficient is a new way to work with your company data better, faster, and more accurately without ever leaving your spreadsheet, integrating with the tools you already use. Install the Coefficient Excel or Google Sheets extension and use it in a new or existing sheet in seconds. Once installed, Coefficient lives as a sidebar companion so your company data is only a couple of clicks away at any time. Any data source that you work with is available directly in your Coefficient sidebar – such as Salesforce, HubSpot, Snowflake, NetSuite, QuickBooks, MySQL, and Looker – with the ability to consolidate your data from multiple systems into one spreadsheet. Use Coefficient filters to easily customize your imports to only work with the data you need, keeping your spreadsheets performant. Quickly go back anytime to add more data in the same report. Never rebuild the same analysis twice by keeping your data up to date with scheduled updates. And, use Coefficient alerts to trigger Slack or email messages anytime your spreadsheet updates. Now, you can turn your spreadsheet into the most flexible, powerful monitoring system across all of your company data. Say “goodbye” to manual data workflows and “hello” to connected spreadsheets.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 175

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 9.1/10)


**Seller Details:**

- **Seller:** [Coefficient](https://www.g2.com/sellers/coefficient)
- **Company Website:** https://coefficient.io/
- **Year Founded:** 2020
- **HQ Location:** Palo Alto, CA
- **Twitter:** @coefficient_io (351 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/coefficientworks/ (70 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 49% Mid-Market, 35% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (72 reviews)
- Automation (42 reviews)
- Integrations (42 reviews)
- Time-saving (36 reviews)
- Easy Integrations (31 reviews)

**Cons:**

- Limited Features (18 reviews)
- Feature Limitations (17 reviews)
- Limitations (13 reviews)
- Missing Features (12 reviews)
- Integration Issues (11 reviews)

  ### 23. [Luzmo](https://www.g2.com/products/luzmo/reviews)
  Luzmo helps businesses embed data products hassle-free, empowering their users with fast, confident decisions in record time.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 70

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.4/10 (Category avg: 9.1/10)
- **Steps to Answer:** 8.2/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.8/10 (Category avg: 8.6/10)
- **Calculated Fields:** 8.1/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Luzmo NV](https://www.g2.com/sellers/luzmo-nv)
- **Year Founded:** 2015
- **HQ Location:** Brooklyn, US
- **Twitter:** @luzmo_official (895 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10198259/ (51 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** CEO
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 59% Small-Business, 32% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (7 reviews)
- Embedding Features (5 reviews)
- Customization (4 reviews)
- Insights (4 reviews)
- Setup Ease (4 reviews)

**Cons:**

- Complexity (2 reviews)
- Limited Options (2 reviews)
- Performance Issues (2 reviews)
- Table Limitations (2 reviews)
- Bugs (1 reviews)

  ### 24. [Knowi](https://www.g2.com/products/knowi/reviews)
  Knowi is an end-to-end AI-powered analytics platform designed for modern data, enabling enterprises of all sizes to dramatically accelerate the journey from raw data to actionable insights. With native integration to virtually any data source—including SQL, NoSQL (MongoDB, Elasticsearch, InfluxDB), REST APIs, cloud databases, and documents—Knowi eliminates the need for complex data transformation processes required by alternative solutions. Data teams can integrate, blend, visualize, and analyze data from any source 10X faster, all within a single platform. They can then leverage AI to uncover insights, embed analytics into applications, share dashboards with business users, and automate reporting.


  **Average Rating:** 4.9/5.0
  **Total Reviews:** 27

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.7/10 (Category avg: 9.1/10)
- **Steps to Answer:** 9.3/10 (Category avg: 8.3/10)
- **Reports Interface:** 9.7/10 (Category avg: 8.6/10)
- **Calculated Fields:** 9.0/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Knowi](https://www.g2.com/sellers/knowi)
- **Year Founded:** 2015
- **HQ Location:** Oakland, California
- **Twitter:** @knowico (2,640 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3675224/ (22 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 48% Small-Business, 37% Mid-Market


#### Pros & Cons

**Pros:**

- Customer Support (4 reviews)
- Business Growth (3 reviews)
- AI Capabilities (2 reviews)
- Automation (2 reviews)
- Ease of Use (2 reviews)

**Cons:**

- Learning Curve (2 reviews)
- Lack of Guidance (1 reviews)
- Lack of Tutorials (1 reviews)

  ### 25. [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: 9.1/10)
- **Steps to Answer:** 8.0/10 (Category avg: 8.3/10)
- **Reports Interface:** 8.3/10 (Category avg: 8.6/10)
- **Calculated Fields:** 7.9/10 (Category avg: 8.4/10)


**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)



## Parent Category

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



## Related Categories

- [Data Visualization Tools](https://www.g2.com/categories/data-visualization-tools)
- [Predictive Analytics Software](https://www.g2.com/categories/predictive-analytics)
- [Embedded Business Intelligence Software](https://www.g2.com/categories/embedded-business-intelligence)



---

## Buyer Guide

### What You Should Know About Analytics Platforms

### What are analytics software platforms?

Analytics platforms, also known as business intelligence (BI) platforms, enable companies to gain visibility into their data through data integration, cleansing, blending, enrichment, discovery, and more. These tools are robust systems that sometimes require IT and data science skills to access and decipher company data through custom queries. 

Analytics platforms offer a comprehensive look into a company’s data by pulling from structured and unstructured data sources through detailed queries. Casual business users also benefit from analytics platforms, which offer customizable dashboards and the ability to drill into particular data points and trends.

### What types of analytics tools and platforms exist?

#### **All-in-one software**

##### **Self-service analytics platforms**

Self-service analytics platforms do not require coding knowledge, so business end users can use them for data needs. Cloud-based business analytics software often provides drag-and-drop functionality for building dashboards, prebuilt templates for querying data, and, occasionally, natural language querying for data discovery. 

##### **Embedded BI software**

Embedded BI software can integrate proprietary analytics functionality within other business applications. Businesses may choose an embedded product to promote user adoption; by placing the analytics inside regularly used software, companies enable employees to take advantage of available data. These solutions provide self-service functionality so average business end users can use data for improved decision-making.

#### **Point solutions**

##### **Root cause analysis**

Companies of all sizes produce vast amounts of data from a host of different sources. It can be difficult to keep track of the ebbs and flows of data and to spot outliers and trends across tens if not hundreds (sometimes even thousands) of data sources. Some solutions provide the user with a bird' s-eye view of their data and intelligently alert them to changes in real time. Once alerted, they are able to dive in to evaluate the situation and solve it.

### What are the common features of analytics solutions?

Analytics software platforms are a great aid to any organization needing timely data visualization of high-level analytics. The following are some core features within analytics platforms that can help users make the most of them:

**Data preparation:**  Although standalone [data preparation software](https://www.g2.com/categories/data-preparation) exists that assists in discovering, blending, combining, cleansing, and enriching data—so large datasets can be easily integrated, consumed, and analyzed—analytics platforms must incorporate these functionalities into their core offering. In particular, analytics platforms must support data blending and modeling, allowing the end user to combine data across different databases and other data sources and to develop robust data models of this data. This is a critical step in making meaning out of the chaos by combining data from various sources.

**Data management:** Once the data is properly integrated, it must be managed. This includes restricting data access to certain users, for example. Although some companies opt for a standalone data management solution, such as a data warehouse, analytics platforms must, by definition, provide some level of data management.

**Data modeling and blending:** As mentioned, it is not efficient and often not effective to examine data when it is sprawled across many systems. As a business cloud, analytics platforms help businesses consolidate data and combine data points to understand the relationship between data and derive deep insights.

**Reports and dashboards:** Multilayered, real-time dashboards are a central feature of analytics platforms. Users can program their analytics software to display metrics of their choice and create multiple dashboards that show analytics related to specific teams or initiatives. From predictive website traffic analytics to customer conversion rates over a specified period, users can choose their preferred metrics to feature in dashboards and create as many dashboards as necessary. 

Administrators can adjust the permissions of different dashboards so they are accessible to the users in the company who need them the most. Users can share specific dashboards on office monitors or take screengrabs of dashboards to save and share as needed. Some analytics platform products may allow users to explore dashboards on their mobile devices.

[**Self service**](https://www.g2.com/categories/analytics-platforms/f/self-service) **:** Organizations use these tools to build interactive dashboards for discovering actionable insights. This enables business users like sales representatives, human resource managers, marketers, and other non-data team members to make decisions based on relevant business data.

**Advanced analytics:** Many analytics solutions are incorporating advanced features, sometimes called augmented analytics, to better understand a business’s data, even without IT support. These can include predictive analytics capabilities and data discovery, which includes intelligent suggestions for data visualization and machine learning-powered suggestions for deeper insights.

Other features include [Anomaly detection](https://www.g2.com/categories/analytics-platforms/f/anomaly-detection), [Query based](https://www.g2.com/categories/analytics-platforms/f/query-based), [Search](https://www.g2.com/categories/analytics-platforms/f/search), [Traditional](https://www.g2.com/categories/analytics-platforms/f/traditional)

### What are the benefits of using analytics platforms?

**Replace old or disparate software:** Businesses can replace outdated data storage solutions and reporting tools and migrate to an all-inclusive business cloud as an analytics platform. However, data migration is not essential for deploying an analytics solution, as businesses may not have the time or resources to do so. Therefore, it should be noted that these platforms can integrate with a whole host of solutions, such as [enterprise resource planning (ERP)](https://www.g2.com/categories/erp-systems) and [customer relationship management (CRM) software](https://www.g2.com/categories/crm).

**Improve productivity:** The days of sorting through tens, if not hundreds, of systems and needing immense support from IT have passed. With analytics platforms (especially those that are self-service and have features such as natural language search), anyone looking for data and data analysis, including average business users, can derive insights from their data.

**Save time (automation):** For most analytics platforms, users no longer need a strong background in query languages. Instead, data discovery and root cause analysis allow users to automatically receive alerts and insights into their data and get notified if the data has changed meaningfully.

**Reduce errors:** Although standalone data preparation tools may be the right solution for businesses with particularly complex data, analytics platforms allow users to clean and prepare their data through data mapping and deduplication methods.

**Consolidate data:** In this data-driven era, essentially every program and device a business has produces massive data. To understand this diverse data in the best way possible, combining it through methods such as data blending, which allows users to integrate data from multiple sources into a functioning dataset, is often necessary.

**Improve processes:** Without an analytics platform to be used across a business, processes can be slow and inefficient as interested parties seek data from disparate sources and request data from various people. Analytics platforms can help a business user quickly access data and data analysis and share it with internal and external stakeholders.

### **Who uses analytics tools?**

Analytics platforms can have both internal and external users. 

#### **Internal users**

**Data analysts and data scientists:** These employees are generally the power users of analytics tools, creating complex queries inside the platforms to gather a deeper understanding of business-critical data. These teams may also be tasked with building self-service dashboards to distribute to other teams.

**Sales teams:** Sales teams use self-service analytics tools and embedded analytics solutions to obtain insights into prospective accounts, sales performance, and pipeline forecasting, among many other use cases. Using analytics tools in a sales team can help businesses optimize their sales processes and influence revenue.

**Marketing teams:** Marketing teams often run different types of campaigns, including email marketing, digital advertising, or even traditional advertising campaigns. Analytics tools allow marketing teams to track the performance of those campaigns in one central location.

**Finance teams:** Finance teams leverage analytics software to gain insight into the factors impacting an organization's bottom line. By integrating financial data with sales, marketing, and other operations data, accounting and finance teams pull actionable insights that might not have been uncovered using traditional tools.

**Operations and supply chain teams:** Analytics solutions often utilize a company's ERP system as a data source. These applications track everything from accounting to supply chain and distribution; supply chain managers can optimize several processes to save time and resources by inputting supply chain data into an analytics platform. 

#### **External users**

**Consultants:** Businesses, especially larger ones, do not always understand the breadth and depth of their data, perhaps not even knowing where to begin. An external consultant wielding a powerful analytics platform can help businesses better understand their data and, as a result, make more informed business decisions. 

Users may consider contacting [BI consulting partners](https://www.g2.com/categories/business-intelligence-bi-consulting) to help determine the most relevant analytics and data to capture about their company’s overall success. Following a proper consultation, these agencies may offer assistance with setting up or choosing BI tools. A number of these agencies can assist businesses with the entire BI process, from complete data analysis to the shaping of processes or protocols related to data collection. A relationship with these consultants can prove highly beneficial for users who have never performed data analysis before or want to optimize their company’s reporting.

**Partners:** Partnerships between companies often involve data sharing and cross-company collaboration. As a result, a centralized repository of data, which would allow for data management, data querying, and data insights, can provide an essential tool for these businesses to succeed together, providing them with a birds-eye view of their data.

### **What are the alternatives to analytics platforms?**

Alternatives to analytics platforms can replace this type of software, either partially or completely:

[**Marketing analytics software**](https://www.g2.com/categories/marketing-analytics) **:** Businesses looking for tools geared toward marketing use cases and marketing data (e.g., related to targeting prospects) should look at marketing analytics solutions that are purpose-built for this.

[**Sales analytics software**](https://www.g2.com/categories/sales-analytics) **:** Although sales data such as revenue forecasts and closed deals can be imported and analyzed in general-purpose analytics platforms, sales analytics platforms can provide a more granular analysis of sales-related data and might have better integrations with sales tools such as CRMs. 

[**Log analysis software**](https://www.g2.com/categories/log-analysis) **:**  If a business wants to focus on analyzing its log data from applications and systems, it could benefit from log analysis software, which helps enable the documentation of application log files for records and analytics.

[**Predictive analytics software**](https://www.g2.com/categories/predictive-analytics) **:** Broad-purpose analytics platforms allow businesses to conduct various forms of analysis, such as prescriptive, descriptive, and predictive. Since analytics platforms allow for these different types of analyses, they might not provide the most robust features for any type. Therefore, businesses focused on looking at past and present data to predict future outcomes can use predictive analytics software for a more fine-tuned solution. 

[**Text analysis software**](https://www.g2.com/categories/text-analysis) **:** Analytics platforms are focused on structured or numerical data, allowing users to drill down and dig into numbers to inform business decisions. Text analysis solutions are the best bet if the user is looking to focus on unstructured or text data. These tools help users quickly understand and pull sentiment analysis, key phrases, themes, and other insights from unstructured text data.

[**Data visualization software**](https://www.g2.com/categories/data-visualization) **:** Data visualization tools can be an excellent place for businesses to start when looking to better understand their data. With capabilities including dashboards and reporting, data visualization software can often be quick and easy to set up and is frequently cheaper than more robust analytics platforms. 

However, it is essential to recognize their limitations. Data visualization solutions do what they say on the box: visualization. They do not give the user an end-to-end analytics solution from data preparation to data insights, nor do they provide significant data management capabilities.

### **Software and services related to analytics platforms**

Related solutions that can be used together with analytics platforms include:

[**Embedded business intelligence software**](https://www.g2.com/categories/embedded-business-intelligence) **:** Analytics platforms are standalone platforms that help companies analyze data. Businesses who want to build analytics capabilities into applications, whether that be for internal or external use, can use embedded BI software to accomplish this goal.

[**Database software**](https://www.g2.com/categories/database-software) **:** There are a plethora of solutions for storing, organizing, and sharing large amounts of data that can later be accessed and analyzed by analytics tools. Database software includes everything from [big data software](https://www.g2.com/categories/big-data) to traditional table-based [relational databases](https://www.g2.com/categories/relational-databases). Businesses should research and implement whichever database tools make the most sense for their particular data types or analytical needs. 

When considering an analytics solution, users should investigate which databases can integrate with the tool to make the most logical product choice for their situation. Analytics products would not serve much purpose without one or more company databases to pull data from when the time comes.

### Challenges with analytics platforms

**Configuration:** Analytics solutions may have a highly technical setup process, requiring IT or developmental expertise. When trying to implement one of these platforms without an in-house data scientist or IT professional, users may struggle with getting the technology off the ground, integrating it with the appropriate solutions, and creating queries for data collection. This could mean a significant loss of resources and an inability to use the tool as intended. Users can contact BI consulting providers for assistance setting up a program or, in some cases, for handling the entirety of BI reporting.

**Overreliance:** Focusing too much on data and analytics can also be problematic. Data-driven decisions are critical to a business’s success, but data-only decisions ignore the various voices from within and without the organization. Successful companies combine rigorous analytics with anecdotal storytelling and thoughtful conversations about the business's success and components.

**Integrations:** If the analytics tool does not fully integrate with existing software, getting a complete view of a business’s operational performance becomes challenging. Similarly, if an integration experiences a communication error or other issue during a data query, it causes an incorrect or incomplete reading. Users should make a point to monitor these connections and any potential performance issues throughout their software stack to ensure that correct, complete, and up-to-date information is being processed and displayed on dashboards.

**Data security:** Companies must consider security options to ensure the right users see the correct data and guarantee strict data security. Effective analytics solutions should offer security options that enable administrators to assign verified users different levels of access to the platform based on their security clearance or level of seniority.

### How to choose the best analytics tools

#### Requirements Gathering (RFI/RFP) for Analytics Platforms

If a company is just starting and looking to purchase the first analytics platform, or maybe an organization needs to update a legacy system--wherever a business is in its buying process, g2.com can help select the best analytics platform.

The particular business pain points might be related to all the manual work that must be completed. If the company has amassed a lot of data, it needs to look for a solution that can grow with the organization. Users should think about the pain points and jot them down; these should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees needing this software, as this drives the number of licenses they will likely buy.

Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist is a detailed guide with necessary and nice-to-have features, including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more.

Depending on the deployment scope, producing an RFI, a one-page list with a few bullet points describing what is needed from an analytics platform might be helpful.

#### Compare Analytics Platforms Products

**Create a long list**

From meeting the business functionality needs to implementation, vendor evaluations are essential to the software buying process. For ease of comparison, after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.

**Create a short list**

From the long list of vendors, it is helpful to narrow the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list, businesses can produce a matrix to compare the features and pricing of the various solutions.

**Conduct demos**

To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition. 

#### Selection of analytics platforms

**Choose a selection team**

Before getting started, creating a winning team that will work together throughout the process, from identifying pain points to implementation, is crucial. The software selection team should consist of organization members with the right interests, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the primary decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. The vendor selection team may be more minor in smaller companies, with fewer participants, multitasking, and taking on more responsibilities.

**Analyze the data**

As analytics platforms are all about the data, the user must ensure that the selection process is also data-driven. The selection team should compare notes and facts and figures that they noted during the process, such as time to insight, number of visualizations, and availability of advanced analytics capabilities.

**Negotiation**

Just because something is written on a company’s pricing page does not mean it is gospel (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to discount multiyear contracts or recommend the product to others.

**Final decision**

After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and received, the buyer can be confident that the selection was correct. If not, it might be time to return to the drawing board.

### How much do analytics software platforms cost?

As mentioned above, analytics platforms come as both on-premises and cloud solutions. Pricing between the two might differ, with the former often coming with more upfront costs for setting up the infrastructure. 

As with any software, analytics platforms are frequently available in different tiers, with the more entry-level solutions costing less than the enterprise-scale ones. The former will often not have as many features and may have caps on usage. Vendors may have tiered pricing, in which the price is tailored to the users’ company size, the number of users, or both. This pricing strategy may come with some support, which might be unlimited or capped at a certain number of hours per billing cycle.

Once set up, analytics platforms, especially those deployed in the cloud, do not often require significant maintenance costs.

As these platforms often come with many additional features, businesses looking to maximize the value of their software can contract third-party consultants to help them derive insights from their data and get the most out of the software.

#### Return on Investment (ROI)

Businesses deploy analytics platforms to derive a return on investment (ROI). As they are looking to recoup the losses they spent on the software, it is critical to understand its costs. As mentioned above, analytics platforms are typically billed per user, sometimes tiered, depending on the company size. More users will generally translate into more licenses, which means more money.

Users must consider how much is spent and compare that to what is gained in terms of efficiency and revenue. Therefore, businesses can compare processes between pre- and post-deployment software to understand better how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from using an analytics tool.

### Implementation of analytics software solutions

**How are analytics software Implemented?**

Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether an implementation specialist from the vendor or a third-party consultancy. With vast experience under their belts, they can help businesses understand how to connect and consolidate their data sources and use the software efficiently and effectively.

**Who is responsible for analytics platform implementation?**

Properly deploying an analytics platform may require many people or teams. This is because, as mentioned, data can cut across teams and functions. As a result, one person or even one team rarely has a complete understanding of all of a company’s data assets. With a cross-functional team, a business can begin to piece together its data and begin the analytics journey, starting with proper data preparation and management.

### Emerging trends related to analytics platforms

**Increase data accessibility**

Business data is no longer locked up in silos. With analytics platforms, more users across a business can find, access, and analyze this data. In addition, [artificial intelligence (AI) tools](https://www.g2.com/categories/artificial-intelligence) such as [natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) help make searching through and for data more accessible and powerful, providing more accurate results.

With the amount of data accessible to businesses today, it is a near necessity that they implement some type of analytics software to understand and act on that data better. Implementing analytics software has been a significant initiative for companies undergoing digital transformation, as these tools offer deeper visibility into an organization's data. Companies adopt these solutions to make sense of large datasets collected from various sources.

**Shift from on-premises to cloud**

The move from on-premises data analytics to the cloud has been underway for several years, with more and more businesses moving their data and data insights into the cloud. This is taking place for various reasons, such as time to insight. Moving away from on-premises infrastructure has helped many companies enable data work anywhere one has access to the cloud—anywhere with internet access. However, not all data users have the luxury of working in the cloud for several reasons, including data security and issues related to latency. In industries such as health care, strict regulations such as the [Health Insurance Portability and Accountability Act (HIPAA)](https://learn.g2.com/health-insurance-portability-and-accountability-act) require data to be secure. Although it is possible to ensure this security in the cloud, it can be more complicated.

**Conversational AI**

Historically, to query data within an analytics solution, users needed to master a query language like SQL. With the rise of conversational interfaces, users uncover the data and insights they seek using intuitive language. Intuitive methods of querying data enable a larger user base to access and make sense of company data.

**Machine learning**

AI is quickly becoming a promising feature of analytics solutions throughout the data journey, from ingestion to insights. From AI-powered data preparation to smart insights, in which the platform suggests visualizations to the end user, analytics platforms are quickly becoming more powerful. Machine learning is helping end users discover hidden insights, allowing them to make sense of data and understand what they are seeing.