Introduction
Business intelligence (BI) platforms are software tools that collect, store, analyze and visualize data to uncover insights and drive better decision making. With data playing an increasingly critical role in organizations of all sizes, demand for robust and scalable BI platforms continues to grow.
According to MarketsandMarkets, the global BI platforms market size is projected to grow from $20.5 billion in 2020 to $33.3 billion by 2025 at a Compound Annual Growth Rate (CAGR) of 10.6% during the forecast period.
With so many options to choose from, selecting the right BI platform can be a daunting task. To help you make an informed decision, we have compiled this comprehensive guide to the top 10 BI platforms for 2023 with an in-depth comparison of their key features, pros and cons, pricing, and ideal use cases.
How to Choose a BI Platform
With so many BI platforms to evaluate, it can be challenging to determine which one best fits your needs. Here are some key criteria to guide your selection process:
Key Evaluation Criteria
| Criteria | Description |
|---|---|
| User Friendliness | How easy is the platform to use for users of all technical abilities? Look for intuitive visual interfaces. |
| Data Visualization | Does the platform offer various visualization options (charts, graphs, gauges, etc.) to spot patterns and trends? |
| Data Preparation | What data transformation and modeling capabilities does it have for preparing data for analysis? |
| Embedded Advanced Analytics | Does it offer built-in statistical, predictive, and machine learning capabilities for deeper insights? |
| Customization | How flexible is the platform for customizing dashboards, visualizations, and reports? |
| Scalability | Can the platform handle large and complex data volumes as your needs grow? Evaluate technical architecture. |
| Cloud Platform Support | Does the platform allow deploying analytics in the cloud? Evaluate integration with Azure, AWS, Google Cloud. |
| Mobile Support | Are there native mobile apps for data access and analytics on the go? |
| Collaboration | Does the platform enable collaboration through sharing reports, annotations, and comments? |
| Security | What security, authentication, and access control features does it have to protect sensitive data? |
| Pricing | What is the pricing model – subscription, perpetual license, or consumption-based? Compare cost of licenses. |
| Customer Support | Is prompt technical support and troubleshooting assistance available when needed? |
Top 10 BI Platforms for 2023
1. Tableau
Overview and Key Features
Tableau is one of the most widely used BI platforms globally, renowned for its strong visual analytics capabilities. The software makes it easy for anyone to produce rich interactive reports and dashboards with advanced analytics built-in.
Some key features include:
| Feature | Description |
|---|---|
| Drag and drop interface | Enables business users to quickly analyze data using a simple drag and drop method to create charts, graphs, and dashboards. |
| Vibrant visualizations | Offers a wide variety of visualization options, including heat maps, plots, scatter plots, Gantt charts, and more. |
| Smart analytics | Provides smart analytical features like trendlines, forecasting, regression analysis, clustering, and distributions. |
| Natural language queries | Allows users to query data using conversational natural language. |
| Collaboration tools | Supports sharing insights online and offline through subscriptions, Slack integration, bookmarks, and comments. |
| Robust dashboarding | Enables building highly interactive and web-based dashboards with advanced filtering, drilling, linking, and parameterization. |
| Augmented analytics | Leverages capabilities like natural language generation and insight suggestions to enhance analysis. |
| Enterprise scalability | Tableau Server scales to support large distributed deployments in on-premise, multi-cloud, or hybrid environments. |
Pros and Cons
| Pros | Cons |
|---|---|
| Extremely intuitive and user friendly interface | Limited data preparation and modeling capabilities |
| Powerful and beautiful data visualizations | Advanced users may find it constraining |
| Smart analytics built-in for deeper insights | Can experience slow performance with large complex data |
| Collaborative dashboarding capabilities | Steep learning curve for coding custom visuals |
| Broad product ecosystem and large community |
Pricing
Tableau offers subscription-based pricing that varies based on product edition and deployment size:
| Plan | Price | User Limit |
|---|---|---|
| Tableau Creator | $12 per user/month | Unlimited (Cloud Only) |
| Tableau Explorer | $35 per user/month | Up to 100 users |
| Tableau Viewer | Free | Read-only access |
| Tableau Server | $35 – $70 per user/month | Unlimited |
They offer a free trial to evaluate the product. Discounted pricing is available for academic institutions and non-profits.
Use Cases
With its self-service model and rich visualizations, Tableau is a great fit for:
| Use Case | Description |
|---|---|
| Business Analytics | Enables different departments (sales, marketing, operations, etc.) to analyze data and create relevant dashboards. |
| Interactive Reporting | Creates visually interactive reports, allowing business users to independently explore and analyze data. |
| Data Discovery | Empowers users to quickly explore various data sources, ask questions, and uncover valuable insights. |
| Embedded Analytics | Integrates analytics into internal and external applications using Tableau’s REST APIs. |
2. Power BI
Overview and Key Features
Power BI is Microsoft’s rapidly growing cloud-based business intelligence service integrated with the Office 365 ecosystem. It offers intuitive self-service BI capabilities for data discovery, visualization and dashboarding.
Key features of Power BI include:
| Feature | Description |
|---|---|
| Interactive Reports | Empowers business users to analyze data and create dynamic, interactive reports with integrated AI features. |
| Dashboarding | Compiles reports into flexible dashboards with filtering options, enabling drill-down and drill-through. |
| Natural Language Queries | Allows users to explore data using conversational natural language queries, enhancing data exploration. |
| AI Visualizations | Automatically generates graphs and visuals through data analysis, providing insights in a visual format. |
| Predictive Analytics | Offers machine learning capabilities for tasks like forecasting, predictions, sentiment analysis, and more. |
| Mobile Apps | Provides secure native mobile apps for iOS and Android, enabling access to analytics while on the move. |
| Data Connectivity | Supports connections to 100+ data sources, including Excel, Salesforce, Azure data services, and Facebook. |
| Enterprise Scalability | Scales to accommodate large enterprises via XMLA endpoints and dataset aggregation in Premium capacity. |
Pros and Cons
| Pros | Cons |
|---|---|
| Tight integration with Microsoft data platform | Limited advanced analytics functions compared to tools like R and Python |
| Familiar Excel-like interface for business users | Dashboards can be complex to build |
| Auto AI for quick insights and smarts | On-premise gateway required for some data sources |
| Built-in natural language queries | Limited community and partner resources |
| Cost effective licensing model |
Pricing
Power BI is priced based on a monthly or annual subscription model:
| Plan | Price |
|---|---|
| Power BI Pro | $9.99 per user/month (annual plan) |
| Power BI Premium | $4,995 per dedicated cloud compute resource/month |
| Power BI Embedded | Pay per session based consumption pricing |
Free desktop trial is available. Academic pricing offers a 60% discount.
Use Cases
Power BI is ideal for:
| Use case | Description |
|---|---|
| Self-service Analytics | Empowering individual business units to independently analyze data and uncover insights. |
| Interactive Reporting | Designing visually engaging and interactive reports to facilitate data analysis. |
| Dashboard Consolidation | Creating executive dashboards by aggregating essential reports into a unified view. |
| Embedded Analytics | Customizing Power BI for embedding into internal applications, providing end-user analytics. |
3. Qlik Sense
Overview and Key Features
Qlik Sense is an end-to-end modern BI platform built around interactive data visualization, analytics and dashboards. It differentiates itself through its associative analytics engine and cognitive capabilities.
Some key capabilities include:
| Feature | Description |
|---|---|
| Associative Analytics | Permits users to explore data freely and establish associations across multiple datasets. |
| Conversational BI | Allows users to ask questions using natural language via Qlik Insight Bot and receive automated answers. |
| Smart Visualizations | Augmented intelligence suggests relevant graphs while users analyze data. |
| Geo-analytics | Enables location-based analysis through mapping, filtering, and grouping data by region. |
| Mobility | Offers responsive analytics apps accessible on any device. |
| Big Data Integration | Provides native connectivity to various data sources across cloud, on-premise, and hybrid environments. |
| Enterprise Scalability | Qlik Sense Enterprise SaaS delivers governed self-service analytics with full enterprise capabilities. |
Pros and Cons
| Pros | Cons |
|---|---|
| Speed of analysis through associative model | Short learning curve to grasp associative model |
| Conversation and insight bots for natural interaction | Limited advanced analytics functions compared to R and Python |
| Smart visual suggestions accelerate analysis | Have to build complex visuals outside using D3, WebGL |
| Data storytelling constructs narrative flow in analysis | More suited for data exploration than dashboards |
| Platform built for broad data access and scalability |
Pricing
Qlik Sense is licensed based on two subscription models:
| Plan | Price |
|---|---|
| Qlik Sense Business | $15 per user/month billed annually |
| Qlik Sense Enterprise SaaS | Starts at $10 per user/month (500 user minimum) |
Free trials are available on the cloud. Discounted pricing for academic and non-profit use.
Use Cases
Qlik Sense is a great fit for:
| Use Case | Description |
|---|---|
| Guided Analytics Apps | Developing curated analytics apps with guided stories and narratives tailored to specific business needs. |
| Self-service Exploration | Empowering business users with interactive data exploration capabilities across various data sets. |
| Embedded Analytics | Providing the ability to white-label and embed analytics within applications for partners and customers. |
| Geospatial Analytics | Mapping, filtering, and analyzing regional trends through built-in geo-analytics features. |
4. Looker
Overview and Key Features
Looker provides a modern business intelligence platform optimized for cloud applications and designed for sophisticated analytics users.
Some key capabilities include:
| Feature | Description |
|---|---|
| Browser-based Analytics | Delivers analytics and insights via the web browser, visualizing data in reports and dashboards. |
| LookML Modeling Language | Enables analysts to integrate data sources, create semantic layers for SQL queries and visualizations. |
| Embedded Analytics | Allows partners to embed interactive analytics within their applications through Looker’s APIs and SDKs. |
| Predictive Analytics | Utilizes Looker’s analytics functions (time series, clustering, anomalies) to uncover predictive insights. |
| Flexible Deployment | Supports multi-cloud, hybrid, and on-premise deployments on AWS, GCP, Azure, and private cloud. |
| Governance and Security | Provides enterprise-grade security, access control, activity audit, and governance workflows. |
Pros and Cons
| Pros | Cons |
|---|---|
| Powerful LookML modeling language for analysts | Steep learning curve for non-technical users |
| Sophisticated analytics functions for data experts | Advanced dashboard customization requires coding |
| Built for operationalization and embedding | Doesn’t integrate easily with on-premise data sources |
| Integrates natively with modern cloud data stacks | Smaller ecosystem of partners and extensions |
| Flexible multi-cloud deployment options |
Pricing
Looker offers a subscription model priced based on the size of deployment:
| Plan | Base Price | Additional User Price |
|---|---|---|
| Looker Core | $3000/month | $120 per user/month |
| Looker Standard | $5000/month | $150 per user/month |
| Looker Enterprise | $9000/month | $180 per user/month |
Volume discounts available along with discounted pricing for non-profits and startups.
Use Cases
Looker is a great choice for:
| Use Case | Description |
|---|---|
| Embedded Analytics | Offers customizable analytics experiences embedded within web and mobile applications. |
| Sophisticated Analytics | Empowers expert data analysts to create semantic models and derive insights from complex data. |
| Fully Cloud-based Analytics | Provides analytics optimized for cloud data platforms such as Snowflake, BigQuery, Redshift, and more. |
| Data-driven Workflows | Enables the triggering of actions and workflows based on analytics insights and scheduled data monitoring. |
5. ThoughtSpot
Overview and Key Features
ThoughtSpot provides an enterprise-focused modern BI platform delivering search and AI-driven analytics across cloud, hybrid and on-premise environments.
Key features include:
| Feature | Description |
|---|---|
| Search-driven Analytics | Users can query data in natural language similar to Google-like search. |
| Augmented Analytics | ThoughtSpot automatically generates relevant charts, insights, trends, and predictions. |
| Conversational Analytics | Enables asking follow-up questions and filtering data through conversations. |
| Column-level Security | Provides enterprise-grade security for sensitive data at both row and column levels. |
| Embed Analytics | Offers embedding capabilities to integrate analytics into internal applications. |
| Cloud Scale | Works across major cloud data platforms including Snowflake, Azure Synapse, and Databricks. |
| SpotApps | Prebuilt analytics apps allow customized deployments for specific industry use cases. |
Pros and Cons
| Pros | Cons |
|---|---|
| Ask data questions conversationally in plain language | Analytics workflows require learning proprietary language |
| AI-driven analytics and natural language capabilities | Custom visuals need to be built outside platform |
| Unified analytics across cloud, on-premise and hybrid | Smaller developer ecosystem than leaders |
| Prebuilt SpotApps for accelerated deployment | Limited advanced statistical analysis functions |
| Enterprise-grade governance and security |
Pricing
ThoughtSpot pricing is based on annual subscriptions tiered by features:
| Plan | Starting Price |
|---|---|
| Search & Analyze | Starts at $25,000/year |
| Build Your Own | Starts at $75,000/year |
| Embedded Analytics | Custom pricing |
Free trial available on cloud environment. Discounted pricing offered for non-profits.
Use Cases
ThoughtSpot is a great fit for:
| Use Case | Description |
|---|---|
| Conversational Analytics | Allows business users to explore data and find insights through conversation using search. |
| Guided Analytics | Involves creating interactive dashboards with guided walkthroughs of data analysis workflows. |
| Embedded Analytics | Provides partners and customers with capabilities for embedded analytics. |
| Cloud Analytics | Suits organizations looking to standardize on cloud data platforms like Snowflake, Databricks, etc. |
6. Sisense
Overview and Key Features
Sisense provides a scalable BI solution to build and embed interactive dashboards across a hybrid, multi-cloud data landscape.
Some of its key capabilities are:
| Feature | Description |
|---|---|
| Unified Data Model | Automatically merges disparate data sources into a single data model for analysis. |
| Customizable Dashboards | Enables highly customizable and interactive dashboards through drag-and-drop development. |
| AI-assisted Analytics | Provides AI-driven insights, anomaly detection, and forecasting for enhanced analysis. |
| Broad Data Connectivity | Connectors support a wide array of data sources including cloud apps, databases, APIs, and files. |
| Embedded Analytics | Offers white-label embedding, theming, and APIs to seamlessly integrate analytics into applications. |
| Hybrid and Multi-cloud Support | Facilitates deployment across hybrid and multi-cloud environments. |
Pros and Cons
| Pros | Cons |
|---|---|
| Unified data models across disparate sources | Steep learning curve for new users |
| Highly customizable and interactive dashboards | Advanced coding needed for custom visuals |
| Broad data connectors and hybrid cloud support | Less focus on end user self-service |
| Embedding analytics capabilities | Smaller partner and extension ecosystem |
| Optimized for large and complex datasets |
Pricing
Sisense offers two main subscription packages:
| Plan | Price | Minimum Users |
|---|---|---|
| Explorer | $960 per user/year | Minimum 5 users |
| Designer | $2,500 per user/year | Minimum 3 users |
It provides a free trial to test out the platform. Discounted pricing is offered for non-profits and educational institutions.
Use Cases
Sisense is a great choice for:
| Use Case | Description |
|---|---|
| Embedded Analytics | White labeling analytics for embedding in internal apps or partner products. |
| Multi-cloud Analytics | Unifying analytics across complex hybrid, multi-cloud environments. |
| Customizable Dashboards | Building highly interactive pixel-perfect custom dashboards. |
| Large Dataset Analysis | Optimized to handle large high-cardinality datasets. |
7. Domo
Overview and Key Features
Domo provides a business optimization platform with embedded BI, data management and app development capabilities. It enables real-time analytics across the entire business.
Key capabilities include:
| Feature | Description |
|---|---|
| Interactive Dashboards | Drag-and-drop interface for easy creation and customization of dashboards. |
| Data Apps | Create interactive data apps and analyze data collaboratively. |
| Mobile Optimization | Domo is mobile-optimized with native apps for iOS and Android. |
| AI Assistant | Mr. Roboto analyzes data and provides predictions and recommendations. |
| Embedded BI | Offers robust APIs and SDKs for embedding and white-labeling analytics. |
| Transactional Connections | Integrates transactional data sources like payments, POS, ecommerce, etc. |
| Prebuilt Connectors | Hundreds of connectors available for cloud apps, databases, files, social media, and more. |
Pros and Cons
| Pros | Cons |
|---|---|
| All-in-one platform beyond just BI analytics | Limited advanced statistical analysis functions |
| Mobile-first design optimized for execs | Steep learning curve for some users |
| Hundreds of prebuilt connectors | Custom visuals require development skills |
| Strong embedded analytics capabilities | Smaller ecosystem than leaders like Tableau |
| Integrates transactional data sources |
Pricing
Domo pricing is based on an annual subscription model:
| Plan | Price | Minimum Users |
|---|---|---|
| Business | $83 per user/month | Minimum 25 users |
| Enterprise | Custom pricing |
Free trial available on cloud. Discounted pricing for non-profits and education.
Use Cases
Domo works well for:
| Use Case | Description |
|---|---|
| Mobile-first Analytics | Focusing on delivering analytics for executive mobile devices. |
| Embedded Analytics | Offering partners and developers APIs to integrate analytics. |
| IoT and Streaming Data | Ingesting and analyzing real-time IoT and streaming data feeds. |
| Transactional Data Analytics | Connecting and analyzing transactional data like POS, payments, ecommerce data. |
8. Yellowfin
Overview and Key Features
Yellowfin is a flexible, integrated BI platform renowned for collaboration, automated analysis and data storytelling capabilities.
Key features include:
| Feature | Description |
|---|---|
| Data Storytelling | Interactive slideshows guide users through data narratives and analysis. |
| Collaboration | Enables annotation, commenting, and sharing of reports and dashboards. |
| Auto Analysis | Automated insights customize analysis and suggest charts for specific data sets. |
| Data Preparation | Offers tools for data profiling, cleansing, enrichment, and transformation. |
| Embedded BI | Provides APIs, JS frameworks, and plugins to embed BI content. |
| Mobile Optimization | Native iOS and Android apps for full mobile optimization. |
Pros and Cons
| Pros | Cons |
|---|---|
| Automated data analysis and insights | Complex UI can have learning curve |
| Strong focus on data storytelling | Limited advanced coding for customization |
| Interactive collaboration capabilities | Smaller analyst and developer community |
| Packaged for fast time to insight | Light on some enterprise-grade features |
| Broad device and environment support |
Pricing
Yellowfin offers value-based pricing packages:
| Plan | Price | Users | Admins |
|---|---|---|---|
| Starter | $600/month | 5 users | 1 admin |
| Standard | $1,200/month | 10 users | 1 admin |
| Premium | $2,400/month | 25 users | 2 admins |
Free one month trials are available for cloud. Academic pricing offers a 75% discount.
Use Cases
Yellowfin works well for:
| Use Case | Description |
|---|---|
| Interactive Data Storytelling | Creating guided narratives and storyboards for engaging data analysis. |
| Collaborative Analytics | Facilitating insight sharing, commenting, and discussions among users. |
| Quick Deployment | Utilizing prebuilt connectors to swiftly deploy dashboards. |
| Embedded Analytics | Offering partners APIs and iframes for embedding content into applications. |
9. Zoho Analytics
Overview and Key Features
Zoho Analytics is an end-to-end BI and analytics platform optimized for business users. It focuses on easy visual data exploration augmented by AI.
Some key features:
| Feature | Description |
|---|---|
| Interactive Dashboards | Enables intuitive dashboard creation using drag-and-drop and guided workflows. |
| Conversational AI | Utilizes Zia, Zoho’s conversational AI, to ask natural language questions. |
| Enterprise Scalability | Handles large data volumes and user loads with an in-memory analytics engine. |
| Embedded Analytics | Provides capabilities to embed reports and dashboards into other applications. |
| Mobile Access | Offers native apps for full mobile optimization across various devices. |
| Broad Data Connectivity | Integrates data from databases, files, applications, and cloud services. |
Pros and Cons
| Pros | Cons |
|---|---|
| Conversation AI and natural language search | Standard charts and visualizations |
| Guided dashboard creation workflows | Limited advanced statistical capabilities |
| Packaged for fast time to insight | Still maturing platform with some gaps |
| Part of broader Zoho One platform | Smaller partner and extension ecosystem |
| Cost effective pricing model |
Pricing
Zoho Analytics offers a simple annual subscription model priced per user:
| Plan | Price |
|---|---|
| Standard | $30 per user/year |
| Professional | $60 per user/year |
They provide free trials for evaluation. It is also included in the Zoho One platform bundle.
Use Cases
Zoho Analytics works well for:
| Use Case | Description |
|---|---|
| Self-service Analytics | Facilitating intuitive visual data analysis by business users. |
| Guided Dashboards | Creating interactive dashboards with guided workflows. |
| Conversational Analytics | Exploring data through natural language conversations. |
| Embedded Analytics | White labeling and integrating analytics into business applications. |
10. Databricks Unified Analytics Platform
Overview and Key Features
Databricks Unified Analytics combines data engineering, business intelligence and machine learning on an open lakehouse architecture powered by Delta Lake.
Key capabilities include:
| Feature | Description |
|---|---|
| Unified SQL Analytics | Provides unified analytics on Delta Lake using ANSI SQL, optimization, and BI integrations. |
| Business Intelligence Connectors | Comes pre-integrated with BI tools like Tableau, Power BI, and Looker. |
| ML-powered Insights | Combines SQL analytics with Python ML frameworks to provide ML-powered insights. |
| Low-code Visual Authoring | Simplifies dashboard creation without coding using SQL-based workflows and visual interface. |
| Open and Extensible | Integrates data science libraries, visualization tools, and notebooks. |
| Enterprise-scale | Leverages cloud scale and open formats for petabyte-scale analytics. |
Pros and Cons
| Pros | Cons |
|---|---|
| Unified SQL, BI and ML on Delta Lake | Steep learning curve without SQL knowledge |
| ANSI SQL support avoids coding for analysis | Limited advanced coding customizations |
| Scalable to petabyte-sized datasets | Lock-in to Databricks ecosystem |
| Integrated with leading BI and ML tools | Costs can be complex to estimate |
| Common metadata across analytics and ML | Not optimized for embedded analytics |
Pricing
Databricks pricing is based on cloud compute usage:
| Plan | Price |
|---|---|
| Standard | $0.08 per DBU hour |
| Premium | $0.192 per DBU hour |
Discounted rates for annual contracts. Free Community Edition available.
Use Cases
Databricks works well for:
| Use Case | Description |
|---|---|
| Unified SQL, ML and BI | Provides an end-to-end platform for data engineering, ML, and BI. |
| Petabyte-scale Analytics | Enables running complex analytics at a massive scale. |
| ML-powered Analytics | Combines traditional BI with predictive analytics using Python and R. |
| Lakehouse Architecture | Builds analytics on open Delta Lake for reliability, performance, and governance. |
Choosing the Right BI Platform: Key Factors to Consider
With the wide variety of leading BI platforms profiled above, selecting the right option for your needs depends on several factors:
| Factor | Description |
|---|---|
| User Skill Sets | Evaluate the technical abilities of users, ranging from self-service to expert orientation. |
| Available Data Sources | Assess the need to connect to existing on-premise and cloud data environments. |
| Analytics Priorities | Determine the types of analysis that are important, including self-service, dashboards, ML, etc. |
| Budget Constraints | Compare license costs, discounts, and “value” across different price tiers. |
| Cloud Strategy | Assess the plans for migrating analytics capabilities to the cloud, if applicable. |
| Embedded Analytics Needs | Consider whether robust APIs are provided for embedding analytics into applications. |
| Timeline Constraints | Factor in the timeline for delivering analytics needs, including POCs and trials. |
Focus on platforms that align best to your key requirements with flexibility to adapt as needs evolve. Leading options have strengths across core capabilities but optimize differently.
Conclusion
In closing, selecting the right business intelligence platform involves aligning key organizational requirements around user skills, analytics needs, existing infrastructure, budgets and timelines with leading solution capabilities.
Modern BI platforms profiled here like Tableau, Power BI, Looker and ThoughtSpot excel at areas like interactive visualization, natural language analytics, search-driven insights and embedded analytics. Legacy BI leaders like MicroStrategy and IBM Cognos lead on governed enterprise analytics, custom dashboards and advanced capabilities.
With data significance growing across roles and applications, investing in a contemporary BI platform tailored to your needs can help unlock the full value of data and drive competitive advantage. Reach out for guidance finding the right fit.