Quick facts
- Best for
- Embed AI-powered analytics to monetize your data.
- Pricing
- Freemium
- Editor rating
- 4.5 / 5
- Community saves
- 0
About Sisense
Sisense is an AI-powered analytics platform built to embed intelligence into various products. It offers tools for pro-code, low-code, and no-code capabilities, making it applicable to a range of users from developers to end-users. With Sisense, users can seamlessly create analytical experiences, manage analytics through the release processes, and easily access data from multiple sources. The platform also features conversational analytics capability, which enriches user experience with analytics chatbots, narratives, and AI features. Advanced users have access to a RESTful web API, and developers can run standard SQL queries to get data from data models. One key feature of Sisense is its actionable intelligence capability enabling users to take direct actions based on data-driven insights. It provides explainability, allowing users to assess data outcomes and interpret analytics. Sisense is scalable, simplifying the developing and managing of analytics, and offers the ability to join multiple data sources and create relationships for extensive data modelling. With its single sign-on feature, Sisense further improves user experience by streamlining the authentication process. The platform caters to several industries, including healthcare, retail, manufacturing, tech, financial services, and pharma and life sciences, helping businesses achieve their goals through data-driven decisions.
Pros
- Pro-code, low-code, no-code capabilities
- Seamless analytical experiences
- Scalability for analytics management
- Multi-source data access
- Conversational analytics feature
- RESTful web API for developers
- Actionable intelligence capability
- Data interpretability (Explainability)Data modelling across multiple sources
- Single sign-on feature
- Industry-specific applicability
- Git Integration
- Standard SQL queries for data extraction
Cons
- No-code usability not specified
- SQL needed for data extraction
- Complex for basic users
- Limited explainability features
- Dependent on data quality
- No obvious real-time capabilities
- Limited predictive analytics
- Learning curve for SDKIndustry-specific features not specified
- Limited tools for data cleaning