Sisense: Embedding AI Analytics for Big Data Insights
Introduction to Sisense and the Embedded Analytics Landscape
In the era of big data, organizations are increasingly seeking ways to integrate analytics directly into their applications and workflows to drive real-time, data-driven decisions. Sisense, a leading business intelligence (BI) platform, specializes in embedded analytics, enabling businesses to seamlessly incorporate AI-powered insights into their products and processes. Founded in 2004 and headquartered in New York, Sisense has evolved from a traditional BI tool to a developer-first platform focused on embedding analytics within SaaS applications, enterprise systems, and customer-facing products. This chapter explores how Sisense leverages its no-code, low-code, and pro-code capabilities, along with generative AI, to streamline big data analytics and deliver actionable insights where users need them most.
What is Sisense?
Sisense is a cloud-based analytics platform designed to simplify the process of preparing, analyzing, and embedding data insights into applications. With a focus on embedded analytics, Sisense enables businesses to integrate interactive dashboards, reports, and AI-driven insights into their existing workflows, whether in SaaS apps, internal systems, or customer portals. The platform supports over 100 data connectors, including SQL and NoSQL databases, cloud data warehouses (e.g., Snowflake, Google BigQuery), and SaaS applications (e.g., Salesforce, Google Analytics). Sisense’s proprietary ElastiCube in-memory engine and live connection modes ensure fast query performance, even with large datasets, making it a robust solution for enterprise-grade analytics.
Sisense’s key strength lies in its flexibility to cater to users of all skill levels. Its drag-and-drop interface supports no-code users, such as business analysts, while its APIs and Compose SDK empower developers to create highly customized, embedded analytics experiences. In 2025, Sisense continues to innovate, with updates like the AI Assistant and enhanced semantic layers, positioning it as a leader in the embedded analytics market.
The Power of Embedded Analytics
Embedded analytics refers to the integration of data visualization and reporting tools directly into applications, allowing users to access insights without switching platforms. Unlike traditional BI tools, which often require users to navigate separate dashboards, Sisense embeds analytics into the applications where users already work, such as Slack, Microsoft Teams, or custom SaaS platforms. This approach improves user experience, accelerates decision-making, and enhances engagement by delivering insights in context.
Benefits of Embedded Analytics with Sisense
Seamless Integration: Embed dashboards via iFrames, APIs, or the Compose SDK for a native, branded experience.
Real-Time Insights: Live connections to data sources ensure up-to-date analytics for dynamic decision-making.
Scalability: Sisense handles large datasets and multi-tenant architectures, ideal for enterprise and OEM use cases.
User Empowerment: Non-technical users can explore data with drag-and-drop reports and natural language queries.
Core Features of Sisense for AI-Powered Analytics
Sisense offers a comprehensive suite of tools to prepare, analyze, and embed big data insights, enhanced by AI and machine learning (ML). Below are its core features that streamline analytics workflows:
1. Data Integration and Connectivity
Sisense connects to a wide range of data sources, including traditional databases (MySQL, Oracle), cloud data warehouses (Snowflake, Amazon Redshift), and NoSQL systems (MongoDB, Elasticsearch). Its ElastiCube in-memory engine imports and processes data for fast querying, while live connection mode pushes queries directly to the source, minimizing latency. For example, a SaaS provider can integrate customer data from Salesforce with transaction data from Redshift to create a unified dashboard for end-users.
2. AI-Powered Insights
Sisense Intelligence, part of the Compose SDK for Fusion, incorporates generative AI to simplify analytics creation. The AI Assistant, available in beta for managed cloud customers, uses natural language processing to help users build dashboards and query data intuitively. For instance, a user can ask, “Show me sales trends by region,” and the AI generates a visualization automatically. Features like Narrative and AI Smart Matching provide auto-generated summaries and context-aware querying, enhancing data literacy for non-technical users.
3. Embedded Analytics Capabilities
Sisense offers multiple embedding options:
iFrame Embedding: Simple integration with minimal coding, ideal for quick dashboard deployment.
Fusion Embed: Allows deeper customization with JavaScript SDKs for interactive dashboards.
Compose SDK: A developer-first toolkit for building modular, API-driven analytics experiences, enabling seamless integration into React or other frameworks.
These options ensure that analytics match the look and feel of the host application, supporting white-labeling and branding.
4. Security and Compliance
Sisense prioritizes data security with certifications like SOC 2 Type II, ISO 27001, and ISO 27701. Its row-level security (RLS) and multi-tenant architecture ensure data isolation for different users or clients. For example, a healthcare provider can use RLS to restrict access to patient data based on user roles, ensuring compliance with regulations like GDPR.
5. Scalability and Performance
Sisense’s ElastiCube engine leverages in-chip technology for high-performance querying, even with billions of records. For organizations requiring real-time analytics, live connections to cloud data warehouses ensure minimal latency. This scalability makes Sisense suitable for large enterprises and OEMs embedding analytics into customer-facing products.
Leveraging Generative AI in Sisense
Sisense’s generative AI capabilities, introduced in 2025, transform how users interact with data. The AI Assistant, part of Sisense Intelligence, allows users to create analytics tools using natural language prompts, reducing the need for technical expertise. For example, a marketing team can generate a dashboard comparing campaign performance across channels by describing their needs in plain English. Additionally, the enhanced semantic layer improves AI accuracy by allowing users to add column descriptions, enabling precise querying even with ambiguous data.
Sisense’s AI features also include:
Narrative: Automatically generates plain-text summaries of visualizations, improving data interpretation.
AI Smart Matching: Enables users to query specific values without knowing exact column names, streamlining exploration.
Analytics Chatbot: Embeds conversational AI into applications, allowing users to ask questions and receive insights in real time.
These features make analytics accessible to non-technical users while empowering developers to build sophisticated, AI-driven applications.
Real-World Applications of Sisense
Sisense’s embedded analytics have transformed operations across industries. Below are examples of its impact:
Nonprofit: Funraise
Funraise, a nonprofit fundraising platform, integrated Sisense’s embedded analytics to launch its Fundraising Intelligence feature. By embedding dynamic dashboards, Funraise enabled clients to access real-time donor insights, resulting in a 7x increase in online fundraising and a 12% boost in donor retention. The platform saved 40 hours weekly by automating report creation.
Sports: USA Swimming
USA Swimming used Sisense to deliver self-serve analytics to 400,000 users, providing real-time insights into athlete performance. This reduced reporting time by 20x, enhancing competitiveness and decision-making.
Aviation: Air Canada
Air Canada leveraged Sisense to analyze safety data, enabling staff to make real-time decisions that improved passenger and employee safety. The platform’s ability to handle complex datasets ensured actionable insights.
Healthcare: Bioforum
Bioforum, a clinical trial data strategist, used Sisense’s AI capabilities to streamline data interpretation, identifying trends and gaps in patient safety data. This improved surveillance efficiency and accelerated decision-making.
Comparing Sisense to Alternatives
Sisense competes with platforms like ThoughtSpot, Qlik Sense, and Tableau. Here’s how it stacks up:
Sisense vs. ThoughtSpot: ThoughtSpot’s search-driven interface and SpotIQ AI proactively surface insights, but its visualization customization is less flexible than Sisense’s. Sisense excels in embedded use cases with its Compose SDK.
Sisense vs. Qlik Sense: Qlik’s associative engine is powerful for data exploration, but Sisense offers stronger embedding options and a more user-friendly interface for non-technical users.
Sisense vs. Tableau: Tableau focuses on visualization, while Sisense emphasizes embedded analytics and data preparation. Sisense is better suited for developers integrating analytics into applications.
Critics note that Sisense’s setup can be complex, requiring technical expertise for data modeling and embedding. Its pricing, starting at $10,000 annually for self-hosted versions and $20,000 for cloud, may be a barrier for startups. However, its robust APIs and scalability make it a top choice for enterprises.
Challenges and Criticisms
Despite its strengths, Sisense faces challenges:
Complex Setup: Embedding dashboards and modeling data can require significant developer effort, especially for custom integrations.
Pricing Transparency: Sisense’s quote-based pricing lacks transparency, making it difficult for businesses to estimate costs upfront.
Mobile Experience: The mobile version of Sisense has been criticized for limited functionality, which may hinder on-the-go analytics.
Learning Curve: Advanced features, such as custom SQL or API integrations, require technical expertise, which may challenge smaller teams.
To address these, Sisense offers a free trial and extensive documentation, but businesses may need dedicated data teams to maximize its potential.
Best Practices for Using Sisense
To optimize Sisense for embedded analytics, consider these best practices:
Leverage the Compose SDK: Use the SDK for custom, modular analytics experiences tailored to your application’s needs.
Optimize Data Models: Design efficient ElastiCube models to reduce query times and improve performance.
Use AI Features: Incorporate the AI Assistant and Narrative tools to simplify analytics for non-technical users.
Implement Security: Configure RLS and multi-tenant settings to ensure data privacy and compliance.
Test Embedding: Use the White Labeling Playground to preview and refine branded dashboards before deployment.
Conclusion
Sisense redefines big data analytics by embedding AI-powered insights directly into applications, enabling organizations to deliver real-time, actionable intelligence to users. Its no-code, low-code, and pro-code capabilities, combined with generative AI features like the AI Assistant and Narrative, make analytics accessible to diverse users while offering developers the flexibility to create customized experiences. Despite challenges like setup complexity and pricing, Sisense’s scalability, security, and robust integration options make it a leader in embedded analytics. From nonprofits like Funraise to enterprises like Air Canada, Sisense empowers organizations to transform data into a strategic asset, driving efficiency, engagement, and growth in the big data era.
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