Best Analytics Platforms - Page 25

How Many Analytics Platforms Products Does G2 Track?

Total Products under this Category: 607

Category Stats (Oct 2026)

  • Average Rating: 4.52/5 (↑0.01 vs Sep 2026) The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: inzicht (+3.71%) - Among all products in this category, inzicht recorded the largest rating increase compared to last month

Last updated: October 13, 2026

How Does G2 Rank Analytics Platforms Products?

Why You Can Trust G2's Software Rankings:

  • 30 Analysts and Data Experts
  • 29,800+ Authentic Reviews
  • 607+ Products
  • Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

G2 Grid® for Analytics Platforms

G2 Grid® for Analytics Platforms plotting products by satisfaction and market presence

Highlighted products: Microsoft Power BI, Tableau, Databricks, Alteryx, SAS Viya, Domo, Sigma, and Looker.

Underlying data: [Grid® JSON](https://www.g2.com/categories/analytics-platforms/grids.json?focus%5B%5D=microsoft-microsoft-power-bi&focus%5B%5D=tableau&focus%5B%5D=databricks&focus%5B%5D=alteryx&focus%5B%5D=sas-sas-viya&focus%5B%5D=domo&focus%5B%5D=sigma-computing-sigma&focus%5B%5D=looker)

Edilitics | Data To Decisions

Edilitics: A Deterministic Control Plane for Trustworthy AI Analytics Most companies are adopting AI faster than they are making their data trustworthy. Gartner estimates 60% of AI projects will be abandoned through 2026 without an AI-ready data foundation, and research from Anthropic finds accuracy swings from roughly 20% to over 95% between ungoverned data and a governed context layer. Edilitics is a governed data-to-decision platform built to close that gap. Edilitics is not another BI tool. It is not another AI chatbot for your data. And it is not another place to copy your data so someone else's AI can make sense of it. It is a single governed data foundation spanning data integration, transformation, visualization, and conversational AI analysis, where analytical numbers are computed deterministically by Edilitics, not by a language model, and every AI-assisted step remains traceable and reversible. The platform connects to a company's existing data wherever it lives: databases and warehouses, MongoDB and other NoSQL stores, spreadsheets and flat files, and cloud storage, across 24 connectors, without requiring data to be copied elsewhere to get started. How the foundation works: The system works in one direction. Integrate grades what you have. Transform raises that grade and writes it to one canonical table. Visualize and AskEdi are only ever allowed to read from that single governed table. One column has one governed definition that can be reused across the platform, which is what keeps every dashboard and every AI answer working from the same information. Integrate: Integrate connects to source systems and profiles every column automatically, producing two separate scores. Data Quality measures the condition of the data itself, based on completeness, uniqueness, and compliance. AI Readiness separately measures whether the meaning and context of that data has been sufficiently understood and validated by a human. Edilitics treats data quality and AI readiness as separate problems. Data can be clean and still be unready for AI if its meaning, definitions, or context have not been validated. AI can draft column descriptions, but a column only reaches a top grade once a person confirms it. Transform: Transform is a no-code, code, and hybrid workspace with 25 point-and-click operations, including filtering, joins, deduplication, type casting, text and datetime handling, pivoting, and window functions, plus a Python/Polars code editor for steps that genuinely need it. AI suggests fixes and the reasoning behind them, but nothing is applied automatically. Every change requires human approval before it runs. The data quality score recalculates after every single operation, so teams can see whether an action actually improved the data rather than just changed how it looks. The output is one governed destination table, the canonical source everything downstream reads from. Visualize: Visualize builds dashboards from that canonical table with 30+ chart types, automatic chart generation, and AI-written dashboard summaries. There is no formula box anywhere in the chart layer. A metric is defined once, in the governed data layer, and every chart reads that same definition, so the same metric means the same thing everywhere. Sharing options include internal team access, external sharing with OTP verification, and no-login embedding with domain-restricted tokens. AskEdi: AskEdi lets users ask questions in plain language and get back root cause analysis, forecasting, what-if scenarios, category comparisons, and decision-support recommendations. The AI writes the query. Edilitics validates that it is read-only, executes it, and computes the answer in code. The model never computes the number itself. Every answer ships with the exact query that produced it, plus an independently generated methodology note explaining how the answer was derived. If the underlying data cannot support a reliable answer, Edilitics refuses to guess. AI does not get your raw rows: This is not a policy. It is an architectural constraint. In every privacy mode, raw data rows never reach an AI model. Instead, the AI works only with the structural and statistical context it needs, such as table names, column descriptions, data types, and quality statistics. Private, Balanced, and Full Context modes control how much of that structure is exposed, but none of them send raw rows. In the most restrictive mode, even column names are anonymized before reaching the model, with Edilitics mapping back to the real column names before execution and keeping both versions available for audit. Governance built into the architecture: Raw rows are never stored on Edilitics infrastructure. The only data-adjacent artifact is a small encrypted preview sample used while building a Transform pipeline, which is deleted on save or exit. Credentials, schema metadata, pipeline configurations, run history, and AskEdi conversations are encrypted per workspace, with keys derived via PBKDF2-HMAC-SHA256 across three independent sources. Every connection, edit, share, and AI query is logged with who, what, and when. Joins and table relationships are always human-defined; AI is never allowed to infer them. AskEdi queries are validated as read-only before they run. This architecture reflects the kind of governance frameworks like the EU AI Act and India's Digital Personal Data Protection Act are designed to require. Who Edilitics is built for: For companies without a data team, Edilitics brings the pieces together in one place: connecting, cleaning, visualizing, and answering questions without code. For companies that already have a data team, it provides a practical layer for getting existing data ready for AI, adding governance, quality scoring, and an audit trail on top of what they already run. It is designed for use in regulated environments such as fintech and healthcare, by agencies managing per-client data isolation within a single workspace, and by SaaS and e-commerce teams that want one platform covering the full path from raw source data to governed dashboards and AI-assisted decisions. What makes it different: What makes Edilitics different is architectural, not a claim layered on top. AI can suggest, summarize, and explain throughout the platform, but it never computes or fabricates a number a user will act on. Data you have. Data you can trust. Decisions you can act on. AI you can rely on.

Who Is the Company Behind Edilitics | Data To Decisions?

ElegantJ

Who Is the Company Behind ElegantJ?

AI can help you find the answers. G2 helps you trust them.

Connect G2 to Claude or ChatGPT for answers grounded in G2's trusted reviews, comparisons, and pricing from real user insights.

How it works

Evolbi

Evolbi is an AI-powered business intelligence platform for fractional CFOs, operators, and growing businesses. Evolbi helps organizations connect financial and operational data, automate reporting, monitor KPIs, and uncover actionable insights through intuitive dashboards and AI-assisted analytics. Our mission is to help businesses make better decisions faster with reliable, accessible business intelligence.

Who Is the Company Behind Evolbi?

ExecusBI

ExecusBI is an AI-powered Sales Intelligence and Business Intelligence platform that helps companies access, analyze and activate Italian company data for growth, lead generation, risk management and strategic decision-making. The platform provides business information on more than 6 million Italian companies and over 15 million people with corporate roles, combining company profiles, official business data, financial indicators, advanced search, dashboards, reports, company scoring and AI-supported analysis. ExecusBI is designed for sales, marketing, finance, operations and executive teams that need reliable B2B data and actionable market intelligence to identify opportunities, evaluate companies and make faster, data-driven decisions. For sales and marketing teams, ExecusBI supports prospecting, lead generation, market segmentation, account targeting and CRM enrichment. Users can search and filter Italian companies, create targeted company lists, prioritize high-potential prospects and improve go-to-market activities with structured business data. For finance, procurement and management teams, the platform helps assess customers, suppliers, partners and competitors through financial analysis, solvency indicators, company scoring, credit reports and business monitoring. ExecusBI helps organizations move beyond fragmented information by turning complex company and financial data into clear, practical insights. Its AI capabilities are designed to simplify business analysis, support natural-language and filter-based search, highlight relevant patterns and help teams transform data into concrete business actions. The platform also offers dashboards, dynamic reports, monitored company lists and alerts to support ongoing market analysis and company tracking. A key differentiator of ExecusBI is the availability of comprehensive APIs across its functionalities. Companies can integrate ExecusBI’s data, search, scoring, enrichment, financial analysis and business intelligence capabilities directly into CRMs, ERPs, internal applications, data platforms and automated workflows. This makes ExecusBI especially valuable for organizations that need scalable B2B data integration, automated company enrichment and embedded business intelligence, rather than relying only on a standalone interface. By combining Italian market intelligence, company data, financial insights, AI-assisted analysis and full API capabilities, ExecusBI helps businesses improve sales effectiveness, reduce commercial and financial risk, strengthen market understanding and accelerate growth in the Italian market.

Who Is the Company Behind ExecusBI?

Exponam

Who Is the Company Behind Exponam?

  • Seller: Exponam
  • Year Founded: 2017
  • HQ Location: Portsmouth , US
  • LinkedIn® Page: www.linkedin.com
    7 employees on LinkedIn®

Fastero

Fastero is an AI analyst with somewhere to put its work. Connect a SQL database, a warehouse, a Google Sheet or a spreadsheet, ask questions in plain English, and get dashboards, reports and workbooks your colleagues can open — not a chat transcript you have to screenshot. It also hosts what your team builds. Streamlit apps and Jupyter notebooks run behind your organization's login, with secrets injected rather than pasted, up to 16 GB of memory, and an always-on option so a shared link never wakes a sleeping app. The same workspace is exposed to Claude Code and claude.ai through a first-party MCP server, so an agent can query your data and build a dashboard under your own role and permissions — and what it builds is a dashboard your team opens, not a transcript. Built for data teams, analytics engineers, and the analysts who end up owning the app.

Who Is the Company Behind Fastero?

Fennix Decision Intelligence Platform

Fennix is an AI-powered Decision Intelligence Platform built for executives who need clarity beyond dashboards. Fennix links your entire data ecosystem together and aggregates it into a single, unified, AI-based intelligence layer. Fennix supports organizations across Financial Services, Healthcare, Retail & E-commerce, Manufacturing, Logistics, SaaS,Pharmaceutical, Banking, Education, Hospitalizations and Enterprise Services. Fennix empowers organizations with sustainability, intelligence, and operational efficiency.

Who Is the Company Behind Fennix Decision Intelligence Platform?

  • Seller: Fennix
  • Year Founded: 2026
  • HQ Location: Sheridan, Wyoming, United States, US
  • LinkedIn® Page: www.linkedin.com
    4 employees on LinkedIn®

Ferflow

Ferflow is a self-hosted business intelligence platform for teams whose data has to stay on their own infrastructure. It gives analysts drag-and-drop dashboards, a visual query builder (with raw SQL when they need it), templating and collaboration, datamarts and scheduled email reports, connected to the databases you already run: PostgreSQL, MySQL, SQL Server, Oracle, Snowflake and Redshift. Ferflow ships as one Docker image for Linux or Windows hosts. Query results, credentials and dashboards never leave your network. The only outbound call is license activation, and air-gapped servers can skip it. Try the live demo without signing up at ferflow.com/demo, or run a 7-day trial on your own server. FERFLOW SOFTWARE INC

Who Is the Company Behind Ferflow?

Tian Lin
TL
Researched and written by Tian Lin
Updated July 2, 2025