The Ultimate Guide to Comparing Embedded Analytics Solutions

Sisense vs Alternatives

With the increasing demand to be competitive, organizations are leveraging unique, difficult-to-replicate assets and capabilities to create new transformative business models. One such critical asset is data. McKinsey found that, “Higher-performing organizations report a greater variety of actions to monetize data including adding new services to existing offerings and developing entirely new business models leading to greater revenue impact.” [1]

By delivering analytics to customers through embedded analytics in existing products or completely new data products, organizations can build a strong differentiation, improve customer satisfaction, and drive new revenue streams. 

Once a nice-to-have capability, embedded analytics for customers is fast becoming a must-have. In today’s ambitious business environment, customers have come to expect and require an analytics component as part of the product or service offering to make better business decisions. In a recent study from the Massachusetts Institute of Technology on creating competitive products with analytics, only 15% of respondents were not creating “data wraps” (an analytics-based feature or experience) [2]. The rest were in implementation phases or already had projects in the market. However, most organizations delivering data products are still new to it, and 70% deliver only standard dashboards and analysis [2]. This leaves a tremendous opportunity to be the forerunner by going beyond the traditional dashboard to infuse AI-powered intelligence into your customer workflows. 

With the critical need to stay ahead of the rapid pace of business and innovation, it is common to embed an analytics solution with a solutions provider. Leveraging an analytics provider to embed analytics into your product allows product and development teams to focus on their core business, rather than spreading resources to build, manage, and scale products they are not experts in. 

In this technical comparison guide, we will focus on the critical comparison points to consider and the different categories of analytics tools on the market, including Sisense. Armed with the knowledge of what each embedded solution can do for your product, you will be able to make the best decision for your business.

Let’s start with some definitions of the critical comparison points you will need to consider when choosing your solution.

Comparison Points — Definitions

Deployment flexibilityThe ability to deploy on any cloud including vendor-provided cloud, on-premises, or hybrid
Deployment managementMethods to deploy and manage the software and underlying resources
Data architectureData processing, analysis, and query mechanism
Data preparationHow data is set up for queries and visualization
Data preparation responsibilityThe ability to clean, structure, and join data sources for analysis
Semantic data layerLogical layer to democratize data and enable dynamic data exploration
Data explorationThe ability to self-serve and explore data and insights without the need for technical resources 
Augmented analyticsAutomated-system-generated insights and exploration paths based on AI and machine learning
Predictive analyticsZero-code/low-code predictive analytics for citizen data scientists
CollaborationDesktop or web-based
AlertsThe ability to set threshold-driven proactive alerts
ActionsClose the BI loop by sending data to external applications and systems
EmbeddabilitySDKs, APIs, and code-free toolkits to embed analytics in a variety of ways in other applications
CustomizabilityAbility to change the look & feel and white-label the analytics applications with UI-based interfaces and/or APIs
Authentication & permissioningGranular, multi-level object and data access for users, groups, and tenants or customers with single sign-on integrations for seamless user experiences
Tenant architectureIntegrating analytics to existing and future product deployment models 
PerformanceConsistent and fast user experience as volume of data, complexity, use cases, and the number of concurrent users grows
AutomationAPI-driven automation for scaling workflows and processes, including user and security implementation 
MonitoringInsights into application and analytics performance to maintain robust analytics environment
Time to valueThe time it takes to deploy and build analytics
Maintenance costsCosts of maintaining the solution as data and users grow over time

Rudimentary ways to embed analytics

Embedding analytics with business-analyst-focused tools

Business-analyst-focused tools are designed for individual analysts who can present data in a variety of colorful graphics that are easier for business users to consume. While data visualizations themselves are valuable, these tools cannot be considered a complete embedded analytics solution since they do not provide the critical components that allow you to embed analytics, including a powerful data layer, integration and automation workflows, or customization capabilities.

Many buyers are sold on the front-end visualizations and dashboards, not realizing that the software cannot manage the complex needs of embedded analytics that come into play when the solution needs to scale and grow with more customers and use cases. In embedded use cases, it is critical for the end users to have a great user experience since they are customers. Visualization tools require increased investments in tech resources (people and machines), ETL tools, and analytical databases to deliver a fast and great user experience. The maintenance only adds to overhead and time delays. The easiest way to understand the limitations of data visualization software is to request a proof of concept using your own data, not sample data, to see how accurately and quickly the tool can get your data into a format that can be visualized.

Bottom line: Many problems will arise from having only half of a data analytics tool, including the most costly: the need to invest in extra resources to scale or customize the solution.

Embedding analytics with data-analyst-focused tools

Data-analyst-focused tools are designed to help develop and refine views and analyses from larger, disparate data in a more centralized and governed manner.

As an evolution of data visualization tools, these tools are designed for the data analyst and are more robust in design and usability, providing more control when managing data. 

However, the main challenge here is that the tools can be too engineering dependent and bring us back to the days of IT-led analytics, where every request requires an analyst to go back and build custom code to answer the question. Customers will find it difficult to self-serve even if they have interactive and AI-driven approaches. 

Writing everything in code also increases complexity, creating sprawl and unmanageability. In addition, with the focus being on data management, the critical needs of customization and the time and resources required to successfully productize are left lacking.

Typically, embedded use cases require supporting large, complex data, which can cause performance issues and make prepping data more resource-intensive. Many of the most popular data-analyst-focused solutions have their own proprietary scripting language needed for data preparation, requiring companies to recruit or outsource dedicated technical expertise. In some cases, datasets need to be continually aggregated and reduced in size to make them performant. As a result, scalability is a challenge due to special skills required to develop and support evolving business requirements.

Bottom line: When it comes to embedded analytics, it is unproductive to spend an excessive amount of time and resources on data prep and the development of the dashboard and analytics rather than focusing on the core business.

Comparing embedded analytics solutions — Sisense vs. alternatives

CriterionData discovery or data visualization toolsSisense FusionBusiness benefits w/ Sisense
Deployment flexibilityVaries by vendor. Typically has strong tie-in with specific cloud providers with on-premisesCloud-native architecture. Deploy for any private/public/hybrid cloud, on-premises, or on Sisense CloudFuture-proof and cloud-agnostic, flexibility
Deployment managementOlder tech stacks or unavailability of a fully managed optionDevOps, Kubernetes, and API-powered for complete control or fully managed through the Sisense CloudRetain as much control as needed for full integrations
Data architectureEither in-memory or in-database. Option of only one or the otherBoth in-memory and in-database. Supports analysis directly on databases with live connect, including cloud data warehouses with support for a high-performance cached layer for accelerating slower sources, minimizing query costs, and supporting rapid prototypingFlexibility, speed, future-proof, lower costs
Data preparationRequires hard-coding of business logic in the modeling layer, or heavy transformations and views/extracts in the data layer or only provides drag & drop capabilities

Some tools require additional tool sets for advanced data analysis
Unified platform for code-free, AI-driven, and code-first capabilities to support all skill levels

Drag & drop capabilities or auto-created data models without any extra steps

Code-first with SQL, R, & Python for advanced and predictive needs with the power of materialized views
Faster time to value with the flexibility for deeper data analysis 
Data preparation responsibilityData team, DBA, SQL expert, IT; sometimes requiring learning proprietary technology is requiredAnalyst with minimum SQL experience or data team with advanced skills, or both

Lower operational costs
Lower operational costs
Semantic data layerVaries by vendor. Typically, a highly coupled analytical and data layer requiring pre-aggregations, business logic, and predefined joins in the data layerDecoupled data and analytical layer minimizing the need for predefined joins, views, pre-aggregations, and hard-coded business logic in the data layer enabling data model reuse, data democratization, and dynamic data explorationCustomers and end users can explora data, ask questions on the fly, and even design dashboards on their own, leading to faster time to value and deeper self-service
Data explorationQuestions are limited to predefined business logic. Limited deep dives due to limited datasets or pre-aggregated views. Limited drill-throughs of different dimensions due to predefined joins. Need tech expertise for new questions and analysis Customers and users can self-serve analytics by asking real questions on the fly. Filter, slice and dice, advanced drill-down to anywhere, or ask questions in natural language. Enable customers to even self-service dashboard design in an embedded contextBroaden user audience who can explore data without increasing costs and lower critical tech resource burden
Augmented analyticsVaries from zero augmented capabilities to minimal featuresEnhance user experience with automated insights and data journeys powered by Sisense’s proprietary knowledge graph and AI and machine learning behind the scenes including exploration paths, explanations, and smart recommendationsImprove customer satisfaction, lower tech burden
Predictive analyticsVaries by vendorIn-built predictive analytics for forecasting and trends supporting citizen data scientists powered by AI and machine learning with the power to build custom predictive models as needed using code-first capabilitiesEnhance customer value without burdening advanced tech resources
CollaborationVaries by vendor: desktop, web-based, or a mix of both100% web-basedSeamless and modern user experience with a unified web experience
AlertsVaries by vendorEnd users can set and receive data-triggered alerts on various devices themselvesIncreased product stickiness due to each end user receiving alerts they care most about
ActionsVaries by vendor. Typically unavailableIntegrate analytics to workflows through low-code action options or build custom actions with the Actions SDKClose the BI loop and drive product stickiness and adoption
EmbeddabilityVaries by vendor, with limited number of embedding optionsMultiple options for every embedded analytics sophistication level, from iframe to SDKs and JS libraries for rapid delivery or fully custom developmentNew revenue-generating streams with product or service, increased customer value proposition
CustomizabilityLimited options to customize and extend the platformPurpose-built for extension and customization. White-label and customize the entire look & feel with both UI-based options for fast setup and APIs for automation

Customize further or extend the application with JavaScript APIs, the add-on framework, and Sisense BloX to customize the entire analytics workflow
Seamless user experience for enhanced product value and stickiness, meet every unique need
Authentication and permissioningUnwieldy and complicated with an explosion of workbooks or models.

Governance chaos sometimes requires repetitive updates to every object
Multi-layered and granular from the system down to the data level with versatile,seamless SSO integrations and API-driven automation for scaleMinimize risks, seamless user experiences
Tenant architectureManaging multi-tenancy at the data level can get complicatedSingle-tenant, multi-tenant, or hybrid deployment models with easy-to-set-up data level security, parameterized themes and data connections, and API-driven automationFaster time to market with fewer risks, minimized overhead and maintenance
PerformanceSimple volume and sources of dataWeb-scale for data, on-demand scale of resources, high-performance cached data for slower data sourcesConsistent user experience as users, use cases, and data grow
AutomationLimited APIs with older and disparate tool sets pieced togetherAPI-first platform with a full suite of REST APIs to automate the entire deployment and management, including user and security, data models, and assetsMinimal growth overhead
MonitoringMinimal monitoring capabilitiesMonitoring metrics from system performance to dashboard and query performance with Grafana, Kubernetes, Sisense Monitor, and Usage AnalyticsMaintain robust system health for superior user experience
Time to valueWeeks/monthsDays/weeksFaster ROI, instant insights
Maintenance costsRequires extra tools or heavy dependence on tech or engineering teams for every use case or change, completely reliant on expensive web data or need to invest expensive resources to make data performant and available, fixed resource investment for worst case scenarioUnified platform without the need for extra tools for every team or need, agile data layer enabling rapid innovation and change, scale resources on demand, leverage cached layer to minimize query costs when appropriateFlexibility, lower costs, lower tech burden


We’ve discussed the increasing demand for differentiation in products and solutions, and how data can be the critical component for gaining a competitive edge. Embedding analytics is going beyond the simple dashboard with data. Today, embedded analytics can open new market segments and drive new revenue streams. This makes embedded analytics an important decision that can spur the growth that your company needs to be sustainable in an ever-more-competitive market. Here are the final takeaways to consider when evaluating a platform for embedded analytics:


  • Run a proof of concept on your data to understand how quickly and well the platform will support your go-to-market strategy
  • Ensure that customer success and revenue teams are part of the evaluation process
  • Test the platform with various types of users who will touch it — from your customers to the business analyst to the data engineer to the developer 


  • Ease-of-use with analyzing data not just for the product and engineering team but also for your customers
  • How you will bring innovative analytics capabilities like predictive analytics, AI, and machine learning to your customers
  • How you will go beyond the dashboard and plain vanilla embedding by looking at customization options
  • Future-proofness and how your platform will scale over time, whether it is through API-driven automation, an extensible data strategy, or architecture-level scale

Advanced embedded analytics — Sisense Fusion Analytics Platform

The Sisense Fusion Analytics Platform supports and enables the full analytics continuum: the analyst, the developer or engineer, and the everyday user, through its highly customizable, API-first analytics cloud platform. 

With Sisense, organizations can go beyond delivering just data and traditional dashboards to customers by seamlessly infusing AI-powered intelligence into products. Organizations can get to market fast with powerful code-free and AI-driven analytics, while retaining the flexibility to customize and scale the solution with code-first and API-driven workflows.

Unified platform

Take a deeper dive into the benefits of partnering with Sisense with The Embedded Analytics Platform for Rapid Development at Scale or get a quick overview with the Embedded Analytics for Your Customers Datasheet.