# Best Analytics Platforms - Page 19 ## How Many Analytics Platforms Products Does G2 Track? **Total Products under this Category:** 366 ### Category Stats (Aug 2026) - **Average Rating:** 4.5/5 (↑0.01 vs Jul 2026) The average rating of products in this category, based on all submitted ratings - **Top Trending Product:** Aible (+4.36%) - Among all products in this category, Aible recorded the largest rating increase compared to last month _Last updated: August 01, 2026_ ## How Does G2 Rank Analytics Platforms Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 28,800+ Authentic Reviews - 366+ 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](https://www.g2.com/categories/analytics-platforms/grids.png?focus%5B%5D=29364&focus%5B%5D=1213&focus%5B%5D=10470&focus%5B%5D=1327283&focus%5B%5D=989&focus%5B%5D=604&focus%5B%5D=5048&focus%5B%5D=27024) Highlighted products: Microsoft Power BI, Tableau, Databricks, SAS Viya, Alteryx, Domo, Looker, and Kyvos Semantic Layer. 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=sas-sas-viya&focus%5B%5D=alteryx&focus%5B%5D=domo&focus%5B%5D=looker&focus%5B%5D=kyvos-semantic-layer&segment=all) **Sponsored** ### Fin Fin is a single Customer Agent that can take on different roles, depending on what the conversation needs. Fin can handle sales, service, and more - all as part of one continuous experience for the customer. Key benefits of Fin AI Agent: - Automates complex tasks such as refunds, transaction disputes, and technical troubleshooting. - Easy to configure with a no-code experience that anyone on your team can manage. - Works with any helpdesk, including Salesforce, HubSpot, and Freshdesk – no migration required. - Affordable at scale with pricing starting at just $0.99 per outcome. - Provides complete visibility and control through tools to analyze, train, test, and deploy Fin across all channels. - Engages buyers as they explore your site, providing instant, relevant answers when intent is highest. - Moves prospects closer to a decision, answering questions about pricing and features, addressing objections, and matching solutions to their needs. - Closes opportunities by guiding qualified buyers to the right next step – booking meetings, starting trials, or handing off to sales with full context. How it works: Fin combines generative AI with deterministic rules to act just like your best human agents. You can give Fin detailed, step-by-step instructions, and it will follow them with speed and reliability – reducing time to resolution and improving customer satisfaction. Under the hood, Fin is powered by Fin APEX 1.0, the highest-performing, fastest model for customer service. Every layer is optimized for accuracy, speed, and reliability, so Fin can handle high volumes and complex queries with confidence. Proof of performance: - Fin resolves 67% of customer queries on average, with rates as high as 93% for some teams. - Trusted by 6,000+ customer service teams, including the world’s leading AI companies like Anthropic. - In independent tests, Fin consistently outperformed competitors, delivering higher resolution rates than Forethought, Decagon, and others. Ranked #1 AI Agent on G2, with the highest number of reviews. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list_llm&secure%5Bcategory_id%5D=620&secure%5Bchosen_at%5D=2026-08-02T09%3A42%3A19Z&secure%5Bdisplayable_resource_id%5D=1005823&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=retargeted_product&secure%5Bplacement_resource_ids%5D%5B%5D=3270&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=3270&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%3Fattributes%255B110%255D%3Dall%26page%3D19%26segment%3Dall%26selected_view%3Dgrid&secure%5Btoken%5D=4c2f94b37f26eaf8e81b34dc26a995c0ab1c31bf7e2b53bced23492d7cab91d1&secure%5Burl%5D=https%3A%2F%2Fwww.fin.ai%2Fdrlp%2Fai-agent&secure%5Burl_type%5D=custom_url) ### [Daxlate](https://www.g2.com/products/daxlate/reviews) Daxlate is a Windows desktop application that makes Power BI reports multilingual. It translates both the semantic model and the report canvas, and runs entirely on your machine with no cloud service. Most approaches to Power BI localization cover the semantic model only: cultures, plus captions and descriptions on tables, columns, measures, and hierarchies. Daxlate does that, and also translates what readers actually look at: visual titles, slicer headers, button labels, and text boxes. It is built for how translation work actually happens: - Side-by-side editing across all cultures in one grid, not one language at a time - Filter to "missing only" and every gap surfaces before a translator opens the file - Export every culture to a single Excel workbook, so translators work where they already work - Re-import with version tracking and a coverage badge per culture - Bulk culture add and remove in one click - Read-only mode for governance reviews and audits - Custom locales for regional dialects and language variants - The original .pbix is never overwritten: saves write a .pbit companion file #### Who Is the Company Behind Daxlate? - **Seller:** [Daxlate](https://www.g2.com/sellers/daxlate) - **Year Founded:** 2026 - **HQ Location:** N/A - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=c15b4adf8f55732cc7ec826df4d2341bcdb72a683d2c30e2014e1fc450babfb1&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdaxlate%2F&secure%5Burl_type%5D=linkedin_company_website) 1 employees on LinkedIn® ### [Definite](https://www.g2.com/products/definite/reviews) Definite is an all-in-one data analytics platform that combines a managed data warehouse, ELT pipelines, semantic layer, and business intelligence in a single product. Teams connect data sources using 500+ pre-built connectors for CRMs, payment platforms, marketing tools, databases, and spreadsheets. Definite automatically ingests, stores, and models the data in a managed data warehouse powered by DuckDB. Users analyze data and build dashboards using Fi, an AI-powered analytics assistant. Fi translates natural language questions into SQL queries and returns visualizations, summaries, and insights without requiring technical expertise. Key capabilities: - Managed cloud data warehouse - 500+ pre-built data connectors and ELT pipelines - Semantic layer for reusable metrics and data modeling - AI-powered natural language querying (no SQL required) - Interactive dashboards and data visualizations - Automated reporting to Slack, email, and Google Sheets - Embeddable analytics and white-label dashboards Built for: Startups, small businesses, and lean data teams who need modern data analytics without managing a complex stack of tools like Snowflake, Fivetran, dbt, and Tableau. **Average Rating:** 4.0/5.0 **Total Reviews:** 1 #### Who Is the Company Behind Definite? - **Seller:** [Definite](https://www.g2.com/sellers/definite) - **Year Founded:** 2023 - **HQ Location:** Wilmington, US - **Twitter:** @definiteapp - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=b699d0fcd333ece758867ad52d1e8ecaa71b5101aad5c3b640e6320865666079&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdefinite-app&secure%5Burl_type%5D=linkedin_company_website) 8 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 100% Small #### What Do G2 Reviewers Say About Definite? _AI-generated summary from verified user reviews_ ##### Pros - Users find the **AI analyst** invaluable for simplifying complex data queries and enhancing productivity with Definite. - Users love the **API integration** in Definite for its ease of setup and reliable data pipelines. - Users commend the **customization options** in Definite, facilitating seamless integrations and efficient data management. - Users find Definite remarkably **easy to use** , with quick setup and reliable data integrations right from day one. - Users appreciate the **easy integrations** with CRM and analytics, enjoying a seamless setup experience on day one. #### What Are Recent G2 Reviews of Definite? **["Effortless Setup and Powerful AI Analyst"](https://www.g2.com/survey_responses/definite-review-12166664)** **Rating:** 4.0/5.0 stars _— Trevor F._ [Read full review](https://www.g2.com/survey_responses/definite-review-12166664) ### [Delta IQ](https://www.g2.com/products/delta-iq/reviews) Delta IQ tracks approvals across contract versions. Existing tools compare text or store documents, but they do not preserve the decisions tied to specific clauses as agreements evolve. Delta IQ links approvals to clauses and versions. When a new amendment is uploaded, it highlights impacted provisions and shows whether prior approvals still hold or need re-review. This helps teams avoid rereading entire documents as amendments accumulate, especially in credit and risk workflows. Website - https://www.deltaiq.tech/ #### Who Is the Company Behind Delta IQ? - **Seller:** [Delta IQ](https://www.g2.com/sellers/delta-iq) - **Year Founded:** 2026 - **HQ Location:** N/A - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=b1c41e8d62dd16e06bb38f23d123649a93b77c2e65d711e212f4c35ea0a9909a&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdelta-iq-tech%2F&secure%5Burl_type%5D=linkedin_company_website) 1 employees on LinkedIn® ### [Dyntell BI](https://www.g2.com/products/dyntell-bi/reviews) Dyntell Bi is a robust visualization, analytics and prediction tool that was born from our ERP solution. Dyntell Bi takes your raw data and makes it come alive. With crystal clear visuals, you can tell the important stories that were buried in the data glut. You can share dashboards and finalized charts in one click. And you can finally turn your information into pure inspiration. #### Who Is the Company Behind Dyntell BI? - **Seller:** [Dyntell Software, Inc.](https://www.g2.com/sellers/dyntell-software-inc) - **Year Founded:** 2000 - **HQ Location:** Debrecen, HU - **Twitter:** @dyntellbi 12 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=68fb3113528d38335ca155c6255da677421529051aad26b0194d2ee87ff806b8&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdyntellsoftware&secure%5Burl_type%5D=linkedin_company_website) 82 employees on LinkedIn® ### [EasyAIBridge - AI Data Strategist and Narrative Builder](https://www.g2.com/products/easyaibridge-ai-data-strategist-and-narrative-builder/reviews) Introducing EasyAIBridge -- the only and easiest solution for Swift Analysis, Multiple Dashboards, Concurrent Multi-Source Analysis, and Prompt-Based Insights. Features: + Multi-AI model, doesn't stumble on big data like chatbots do + Multi-source, simultaneous data analysis across files + Multiple dashboards with the option to combine them + Strategist summary and actionable insights + Missing data fixes with gap-filling intelligence + Massive data extraction power + Exportable tables + Exportable charts and graphs + Data processing report for transparency #### Who Is the Company Behind EasyAIBridge - AI Data Strategist and Narrative Builder? - **Seller:** [Sorcim Technologies](https://www.g2.com/sellers/sorcim-technologies-5acb2b9d-d0ba-4063-97f6-3aa2e8bb47ac) - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=37fc37fb21034c55cd28466bab560bf98caca392baa0bf8384d96742f080470d&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fsorcim-technologies%2F&secure%5Burl_type%5D=linkedin_company_website) ### [Evolbi](https://www.g2.com/products/evolbi/reviews) 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? - **Seller:** [Evolbi](https://www.g2.com/sellers/evolbi) - **HQ Location:** N/A - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=3a11fefbcaea1b2bea346ec96cf1526381400683adc8ecc9aba88b998841fd35&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fevolbi&secure%5Burl_type%5D=linkedin_company_website) 1 employees on LinkedIn® ### [ExecusBI](https://www.g2.com/products/execusbi/reviews) 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? - **Seller:** [Execus Spa](https://www.g2.com/sellers/execus-spa) - **HQ Location:** Milan, Italy - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=48d96c19ed3aef49457d28feb4ee11d2378a7b5e3a76bdf945f9ca7ac15e0030&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fexecus%2F&secure%5Burl_type%5D=linkedin_company_website) 1 employees on LinkedIn® ### [Fennix Decision Intelligence Platform](https://www.g2.com/products/fennix-decision-intelligence-platform/reviews) 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](https://www.g2.com/sellers/fennix) - **Year Founded:** 2026 - **HQ Location:** Sheridan, Wyoming, United States, US - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ec59fa5b842d1021ea2feaad863f3473ed1c31e0951b2fcf2fa9869ced5772f9&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Ffennix-softwaresolutions%2F&secure%5Burl_type%5D=linkedin_company_website) 4 employees on LinkedIn® ### [FineBI](https://www.g2.com/products/finebi/reviews) FineBI is a self-service business intelligence and data analytics platform developed by FanRuan, a data software company founded in 2006 and now serving over 43,000 enterprise clients worldwide. FineBI helps organizations connect and govern business data, build interactive dashboards, and let teams across departments explore insights on their own, without relying on heavy SQL or custom development. FineBI covers the full self-service analytics workflow: data modeling, dashboarding, visualization, sharing, collaboration, and role-based access management. With FineBI 7.0, users can ask questions about their data in natural language, generate dashboards automatically, and surface AI-driven insights, while the built-in Dora AI Agent supports automated briefings, alerts, and follow-up workflows. For business users, analysts, and data teams, FineBI reduces manual reporting work, keeps everyone monitoring the same key metrics, and enables decisions from a single shared source of truth. It's commonly used for sales analysis, finance reporting, operations monitoring, supply chain analytics, and management dashboards. #### Who Is the Company Behind FineBI? - **Seller:** [FanRuan](https://www.g2.com/sellers/fanruan) - **HQ Location:** N/A - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=7886df2ed926834e5eb248c77dcfa8e5c815d3ab1f0fe3132ced0dba45868834&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2FNo-Linkedin-Presence-Added-Intentionally-By-DataOps&secure%5Burl_type%5D=linkedin_company_website) 1 employees on LinkedIn® ### [FireAI](https://www.g2.com/products/fireai/reviews) FireAI is an AI-powered business intelligence platform that simplifies data analysis through natural language queries, providing real-time insights and predictive analytics to help businesses make smarter decisions. #### Who Is the Company Behind FireAI? - **Seller:** [FireAI](https://www.g2.com/sellers/fireai) - **Year Founded:** 2024 - **HQ Location:** mumbai, IN - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=46e78f11500a5c3427c1b59b6690a18661e7b9d0233a892af56fc08ce7cf0cf6&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Ffireaiglobal%2F&secure%5Burl_type%5D=linkedin_company_website) 52 employees on LinkedIn® ### [GAINSystems](https://www.g2.com/products/gainsystems/reviews) GAINS Performance Optimization Platform for supply chain design and planning delivers rapid results by unlocking working capital, reducing operational costs, and improving service. With GAINS, supply chain teams can make all the right decisions at speed and scale, right-size inventory, improve performance, and fulfill customer promises. Companies of all sizes across distribution, manufacturing, retail, and service parts trust our decision automation, backed by ML, AI, and GAINS (P3)SM methodology. To learn more about GAINS, follow us on LinkedIn or visit www.gainsystems.com.like Graybar, Honda Motors, Menards, Rockwell Automation, Stuller and Textron Aviation. GAINS® is a registered trademark and Move Forward FasterSM and Proven-Path-to-Performance (P3) SM are service marks of GAINSystems. Other products mentioned in this document are registered, trademarked or service marked by their respective owners. **Average Rating:** 3.2/5.0 **Total Reviews:** 3 #### Who Is the Company Behind GAINSystems? - **Seller:** [GAINSystems](https://www.g2.com/sellers/gainsystems) - **Year Founded:** 1971 - **HQ Location:** Atlanta, Georgia, United States - **Twitter:** @GAINSystemsInc 181 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=d1a91e42a3e56fce4dbd4b23d493cb858a6b47aefb955e1147c7dc46093a2600&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fgainsystems%2F&secure%5Burl_type%5D=linkedin_company_website) 159 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 67% Large, 33% Medium #### What Are Recent G2 Reviews of GAINSystems? **["Best Tool for Supply Chain Optimisation"](https://www.g2.com/survey_responses/gainsystems-review-5235787)** **Rating:** 4.0/5.0 stars _— Sachin ._ [Read full review](https://www.g2.com/survey_responses/gainsystems-review-5235787) **["Gains has been great partner. our relationship has grown over the past few year and their service."](https://www.g2.com/survey_responses/gainsystems-review-5184274)** **Rating:** 4.5/5.0 stars _— Moin K._ [Read full review](https://www.g2.com/survey_responses/gainsystems-review-5184274) #### What Are G2 Users Discussing About GAINSystems? - [What is the use of Gainsystems?](https://www.g2.com/discussions/what-is-the-use-of-gainsystems) - [What is GAINS software?](https://www.g2.com/discussions/what-is-gains-software) - [What does GAINSystems do?](https://www.g2.com/discussions/what-does-gainsystems-do) ### [Golden Analytics](https://www.g2.com/products/golden-analytics/reviews) Golden Analytics is an AI-native BI platform built for data teams who are tired of the tradeoffs in today's tools -- the depth of self-service analytics tools without the rigidity, the accessibility of a modern design tool, and AI that actually augments how analysts work rather than getting in the way. #### Who Is the Company Behind Golden Analytics? - **Seller:** [Golden Analytics](https://www.g2.com/sellers/golden-analytics) - **Year Founded:** 2026 - **HQ Location:** Bellevue, US - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ee2f7c462c3ca364cecf61607a8dcf61d468579b78f12cd666c627a4b410b4f6&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fgoldenanalytics&secure%5Burl_type%5D=linkedin_company_website) 11 employees on LinkedIn® ### [Golden Owl Intelligence](https://www.g2.com/products/golden-owl-intelligence/reviews) Collectors are specialized tools designed to mine, evaluate, analyze, and visualize domain-specific data. Each Collector focuses on a particular dimension of intelligence, from companies and markets to media, geopolitics, or the web. They can be used independently for targeted insights or combined within a Case, where multiple Collectors interact to provide a comprehensive and connected investigation. #### Who Is the Company Behind Golden Owl Intelligence? - **Seller:** [Datintel](https://www.g2.com/sellers/datintel) - **HQ Location:** ALACANT/ALICANTE, ES - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=36337dbdbfba3523956071655206bb291e571ba793ad50f83da57ec1160aa119&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fgoldenowlosint%2F&secure%5Burl_type%5D=linkedin_company_website) 9 employees on LinkedIn® ### [Green, the AI Analyst](https://www.g2.com/products/green-the-ai-analyst/reviews) Green is an AI Analyst that answers business questions in natural language, connects data across sources, and explains how it arrived at an insight. It understands context, reasons through information, and helps teams move from data to decisions without digging through dashboards or waiting on manual analysis. #### Who Is the Company Behind Green, the AI Analyst? - **Seller:** [DecisionX](https://www.g2.com/sellers/decisionx) - **Year Founded:** 2025 - **HQ Location:** Bangalore, IN - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=f81cd30ec5b065eb3120a4eda22fcb804d8080302f70ea239995d4961482f1a5&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdecisionx-ai%2F&secure%5Burl_type%5D=linkedin_company_website) 7 employees on LinkedIn® ### [Grid Dynamics Analytics Platform](https://www.g2.com/products/grid-dynamics-analytics-platform/reviews) Promotion Planning use case is a part of the Analytical Data Platform which implements cases when you need pricing prediction based on accumulated data such as price and discount catalogs. #### Who Is the Company Behind Grid Dynamics Analytics Platform? - **Seller:** [Grid Dynamics](https://www.g2.com/sellers/grid-dynamics) - **Year Founded:** 2006 - **HQ Location:** San Ramon, California, United States - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=054552426d97101305f1a8ec896e890334c8ec2318d2de598fd9f7870c5d6ec8&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fgrid-dynamics&secure%5Burl_type%5D=linkedin_company_website) 4,296 employees on LinkedIn® - **Ownership:** NASDAQ: GDYN - [‹ Prev‹ Prev](/categories/analytics-platforms/all?attributes%5B110%5D=all&order=g2_score&page=18&selected_view=grid#product-list) - [1](/categories/analytics-platforms/all?attributes%5B110%5D=all&order=g2_score&selected_view=grid#product-list) - [2](/categories/analytics-platforms/all?attributes%5B110%5D=all&order=g2_score&page=2&selected_view=grid#product-list) - … - [15](/categories/analytics-platforms/all?attributes%5B110%5D=all&order=g2_score&page=15&selected_view=grid#product-list) - [16](/categories/analytics-platforms/all?attributes%5B110%5D=all&order=g2_score&page=16&selected_view=grid#product-list) - [17](/categories/analytics-platforms/all?attributes%5B110%5D=all&order=g2_score&page=17&selected_view=grid#product-list) - [18](/categories/analytics-platforms/all?attributes%5B110%5D=all&order=g2_score&page=18&selected_view=grid#product-list) - 19 - [20](/categories/analytics-platforms/all?attributes%5B110%5D=all&order=g2_score&page=20&selected_view=grid#product-list) - [21](/categories/analytics-platforms/all?attributes%5B110%5D=all&order=g2_score&page=21&selected_view=grid#product-list) - [22](/categories/analytics-platforms/all?attributes%5B110%5D=all&order=g2_score&page=22&selected_view=grid#product-list) - [23](/categories/analytics-platforms/all?attributes%5B110%5D=all&order=g2_score&page=23&selected_view=grid#product-list) - [24](/categories/analytics-platforms/all?attributes%5B110%5D=all&order=g2_score&page=24&selected_view=grid#product-list) - [25](/categories/analytics-platforms/all?attributes%5B110%5D=all&order=g2_score&page=25&selected_view=grid#product-list) - [Next ›Next ›](/categories/analytics-platforms/all?attributes%5B110%5D=all&order=g2_score&page=20&selected_view=grid#product-list) Spotlight Categories [Mobile Device Management (MDM) Software](https://www.g2.com/categories/mobile-device-management-mdm) [Influencer Marketing Platforms](https://www.g2.com/categories/influencer-marketing-platforms) [Spend Management Software](https://www.g2.com/categories/spend-management) [Virtual Event Platforms](https://www.g2.com/categories/virtual-event-platforms) [Marketing Analytics Tools](https://www.g2.com/categories/marketing-analytics) Similar Categories - [Agentic Analytics](/categories/agentic-analytics) - [AI Search and Discovery Platforms](/categories/ai-search-and-discovery-platforms) - [Business Intelligence](/categories/business-intelligence) - [Data Virtualization](/categories/data-virtualization) - [Drone Analytics](/categories/drone-analytics) - [Enterprise Search Software](/categories/enterprise-search-software) - [Graph Visualization Tools](/categories/graph-visualization-tools) - [Insight Engines](/categories/insight-engines) - [Other Analytics](/categories/other-analytics) - [Predictive Analytics](/categories/predictive-analytics) - [Site Search Software](/categories/site-search-software) - [Statistical Analysis](/categories/statistical-analysis) - [Text Analysis](/categories/text-analysis) - [Time Series Intelligence](/categories/time-series-intelligence) - [Visitor Behavior Intelligence](/categories/visitor-behavior-intelligence) [Browse Analytics Platforms Themes](/categories/analytics-platforms/themes) ![Tian Lin](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Tian Lin") TL Researched and written by [Tian Lin](https://research.g2.com/insights/author/tian-lin) Updated July 2, 2025 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 Show More * * * ## How Do You Choose the Right Analytics Platforms? ### 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. ### Analytics Platforms FAQs #### **Which analytics platforms have an intuitive UI that non-technical users adopt without extensive training?** I looked for analytics platforms that make it easy to find what you need also enable easy collaboration. - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) **:** Has a drag-and-drop chart building without SQL or code that makes it user-friendly. Users can connect Excel, SharePoint, and emails without technical help.  - [Tableau](https://www.g2.com/products/tableau/reviews) **:** Users can create dashboards with limited use and no deep technical background. Onboarding is as simple as sharing the links and giving the right access. - [Sigma](https://www.g2.com/products/sigma-computing-sigma/reviews) **:** Spreadsheet-like UI provides the most distinctive non-technical adoption story. Users can pull in data and work with it in complicated ways that don't require coding. - [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) **:** The semantic layer approach means business users interact with pre-defined, plain-language metrics rather than raw tables or SQL, which is the foundational mechanism for non-technical adoption.  #### **What are the best analytics platforms for business teams accessing insights without SQL or data science skills?** SQL-free access means business users can explore, filter, and create their own views without analyst dependency.  - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) **:** Non-technical users can build powerful dashboards quickly without SQL. Power BI's Power Query Editor handles data transformations through button clicks. The DAX layer exists for advanced users but is not required for standard self-service reporting. - [Sigma](https://www.g2.com/products/sigma-computing-sigma/reviews) **:** Sigma's specific differentiator for SQL-free access is its spreadsheet-like interface on live warehouse data. For teams already on Snowflake or cloud warehouses, Sigma eliminates the SQL barrier entirely at the analysis layer. - [Domo](https://www.g2.com/products/domo/reviews) **:** The no-code platform is easy to use for non-technical teams. Users can apply filtering and date range changes without any coding. The 1000+ connector ecosystem means data arrives automatically, so business users interact with dashboards rather than queries.  - [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) **:** The semantic model defines metrics in plain language; business users query pre-built dimensions and measures without writing a single line of SQL.  #### **Which analytics platforms support collaborative dashboards, annotations, and mobile access for on-the-go insights?** I looked for tools with collaborative analytics, shared dashboards, in-platform discussion, annotations, and mobile-ready access. - [Domo](https://www.g2.com/products/domo/reviews) **:** Sales teams can access data and contacts in real-time on their phones. Has platform-native discussion, report sharing, and insight annotation as part of the daily workflow.  - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) **:** Web and mobile access are daily-use features. The platform enables collaboration so everyone can see the same report at the same time, updated in real time. - [Yellowfin BI](https://www.g2.com/products/yellowfin-bi/reviews) **:** Designed around collaborative BI with built-in story, annotation, and broadcast features for sharing insights with business audiences.  - [Looker](https://www.g2.com/products/looker/reviews) **:** Comes with scheduled report delivery, so automated weekly numbers land in inboxes without anyone manually running anything. Email reports and metric notifications can be set up as daily workflow features.  #### **Which analytics solutions provide fast query response and drill-down capability for ad-hoc exploration?** Fast ad-hoc exploration means users can drill down, pivot, and filter without waiting and without writing a new query every time. - [Tableau](https://www.g2.com/products/tableau/reviews) **:** Users can drill down on data without writing queries. Extracted datasets perform significantly better for ad-hoc work. - [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) **:** The semantic layer pre-aggregates at the warehouse layer so ad-hoc queries against massive datasets return fast without full table scans.  - [Incorta](https://www.g2.com/products/incorta/reviews) **:** Its direct data mapping approach eliminates the aggregation layer that slows most BI platforms during ad-hoc queries. For organizations where query latency on complex, multi-source datasets is the primary pain, Incorta is a good choice. - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) **:** Provides intuitive filtering across countries, teams, and time periods within existing dashboards without analyst involvement. The Direct Lake connection mode specifically reduces ad-hoc query latency. #### **Which analytics solutions integrate with Snowflake, BigQuery, and Redshift seamlessly?** Native, live connections to modern data warehouses — where queries run in the warehouse rather than in the BI tool are what seamless integration actually means for data teams. - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) **:** Integration with Active Directory, SharePoint, and the Microsoft Fabric ecosystem is described as genuinely seamless for organizations already in the Microsoft stack. - [Sigma](https://www.g2.com/products/sigma-computing-sigma/reviews) **:** Built to run directly on Snowflake (and BigQuery/Redshift) without extracting data. The architecture means live warehouse queries are the default mode, not an optional feature. - [Databricks](https://www.g2.com/products/databricks/reviews) **:** It is the warehouse-layer itself for many organizations, not a BI tool connecting to a warehouse, which means integration is inherently native. For organizations that treat Databricks as the processing layer and BI tools as the visualization layer on top, Databricks' own analytics features (via SQL Warehouses and notebooks) eliminate the need for a separate integration layer. - [Looker](https://www.g2.com/products/looker/reviews) **:** BigQuery is the most-named data warehouse in Looker's review base. LookML's push-down SQL architecture means all queries run in the warehouse (Snowflake, BigQuery, Redshift) rather than being extracted into Looker. #### **Which analytics solutions come with robust caching and performance optimization to maintain fast speeds at scale?** Caching and performance optimization matter when datasets are large, dashboards are complex, and business users can't wait for queries to resolve. - [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) **:** The semantic layer pre-aggregates at the warehouse layer specifically to make large dataset queries fast. For organizations where query latency on multi-billion row datasets is the blocking problem, Kyvos is the most purpose-built and best-validated option in the category on these dimensions. - [Databricks](https://www.g2.com/products/databricks/reviews) **:** Photon engine, Delta Lake caching, and auto-scaling compute architecture are the performance mechanisms at scale.  - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) **:** Row Level Security, Premium capacity, and aggregation tables are the scale optimization toolkit. Performance at scale in Power BI is achievable but requires deliberate architecture, not automatic. - [Incorta](https://www.g2.com/products/incorta/reviews) **:** Direct data mapping eliminates the pre-aggregation step that creates scale bottlenecks in most BI tools, allowing ad-hoc queries against large transactional datasets to run without a separate aggregation cache.  #### **Which analytics platforms prevent incorrect conclusions by enforcing data governance and preventing metric manipulation?** I looked for analytics platforms with strong data governance features.  - [Looker](https://www.g2.com/products/looker/reviews) **:** LookML governance, real-time data access, and seamless integration with modern data warehouses together create the governed analytics environment enterprises need to prevent metric drift across teams. - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) **:** Addresses governance through Row Level Security, workspace permissions, and certified datasets, which restrict what data individual users can see and prevent unauthorized metric redefinition. - [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) **:** It defines metrics once at the semantic model level and enforces those definitions for every downstream query and dashboard.  - [Tableau](https://www.g2.com/products/tableau/reviews) **:** Governance approach is built around Tableau Server and Tableau Cloud — where published data sources become the certified metric layer that individual report builders consume rather than create.