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.” 
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) . 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 . 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 flexibility||The ability to deploy on any cloud including vendor-provided cloud, on-premises, or hybrid|
|Deployment management||Methods to deploy and manage the software and underlying resources|
|Data architecture||Data processing, analysis, and query mechanism|
|Data preparation||How data is set up for queries and visualization|
|Data preparation responsibility||The ability to clean, structure, and join data sources for analysis|
|Semantic data layer||Logical layer to democratize data and enable dynamic data exploration|
|Data exploration||The ability to self-serve and explore data and insights without the need for technical resources|
|Augmented analytics||Automated-system-generated insights and exploration paths based on AI and machine learning|
|Predictive analytics||Zero-code/low-code predictive analytics for citizen data scientists|
|Collaboration||Desktop or web-based|
|Alerts||The ability to set threshold-driven proactive alerts|
|Actions||Close the BI loop by sending data to external applications and systems|
|Embeddability||SDKs, APIs, and code-free toolkits to embed analytics in a variety of ways in other applications|
|Customizability||Ability to change the look & feel and white-label the analytics applications with UI-based interfaces and/or APIs|
|Authentication & permissioning||Granular, multi-level object and data access for users, groups, and tenants or customers with single sign-on integrations for seamless user experiences|
|Tenant architecture||Integrating analytics to existing and future product deployment models|
|Performance||Consistent and fast user experience as volume of data, complexity, use cases, and the number of concurrent users grows|
|Automation||API-driven automation for scaling workflows and processes, including user and security implementation|
|Monitoring||Insights into application and analytics performance to maintain robust analytics environment|
|Time to value||The time it takes to deploy and build analytics|
|Maintenance costs||Costs 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
|Criterion||Data discovery or data visualization tools||Sisense Fusion||Business benefits w/ Sisense|
|Deployment flexibility||Varies by vendor. Typically has strong tie-in with specific cloud providers with on-premises||Cloud-native architecture. Deploy for any private/public/hybrid cloud, on-premises, or on Sisense Cloud||Future-proof and cloud-agnostic, flexibility|
|Deployment management||Older tech stacks or unavailability of a fully managed option||DevOps, Kubernetes, and API-powered for complete control or fully managed through the Sisense Cloud||Retain as much control as needed for full integrations|
|Data architecture||Either in-memory or in-database. Option of only one or the other||Both 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 prototyping||Flexibility, speed, future-proof, lower costs|
|Data preparation||Requires 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 responsibility||Data team, DBA, SQL expert, IT; sometimes requiring learning proprietary technology is required||Analyst with minimum SQL experience or data team with advanced skills, or both|
Lower operational costs
|Lower operational costs|
|Semantic data layer||Varies by vendor. Typically, a highly coupled analytical and data layer requiring pre-aggregations, business logic, and predefined joins in the data layer||Decoupled 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 exploration||Customers 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 exploration||Questions 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 context||Broaden user audience who can explore data without increasing costs and lower critical tech resource burden|
|Augmented analytics||Varies from zero augmented capabilities to minimal features||Enhance 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 recommendations||Improve customer satisfaction, lower tech burden|
|Predictive analytics||Varies by vendor||In-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 capabilities||Enhance customer value without burdening advanced tech resources|
|Collaboration||Varies by vendor: desktop, web-based, or a mix of both||100% web-based||Seamless and modern user experience with a unified web experience|
|Alerts||Varies by vendor||End users can set and receive data-triggered alerts on various devices themselves||Increased product stickiness due to each end user receiving alerts they care most about|
|Actions||Varies by vendor. Typically unavailable||Integrate analytics to workflows through low-code action options or build custom actions with the Actions SDK||Close the BI loop and drive product stickiness and adoption|
|Embeddability||Varies by vendor, with limited number of embedding options||Multiple options for every embedded analytics sophistication level, from iframe to SDKs and JS libraries for rapid delivery or fully custom development||New revenue-generating streams with product or service, increased customer value proposition|
|Customizability||Limited options to customize and extend the platform||Purpose-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|
|Seamless user experience for enhanced product value and stickiness, meet every unique need|
|Authentication and permissioning||Unwieldy 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 scale||Minimize risks, seamless user experiences|
|Tenant architecture||Managing multi-tenancy at the data level can get complicated||Single-tenant, multi-tenant, or hybrid deployment models with easy-to-set-up data level security, parameterized themes and data connections, and API-driven automation||Faster time to market with fewer risks, minimized overhead and maintenance|
|Performance||Simple volume and sources of data||Web-scale for data, on-demand scale of resources, high-performance cached data for slower data sources||Consistent user experience as users, use cases, and data grow|
|Automation||Limited APIs with older and disparate tool sets pieced together||API-first platform with a full suite of REST APIs to automate the entire deployment and management, including user and security, data models, and assets||Minimal growth overhead|
|Monitoring||Minimal monitoring capabilities||Monitoring metrics from system performance to dashboard and query performance with Grafana, Kubernetes, Sisense Monitor, and Usage Analytics||Maintain robust system health for superior user experience|
|Time to value||Weeks/months||Days/weeks||Faster ROI, instant insights|
|Maintenance costs||Requires 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 scenario||Unified 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 appropriate||Flexibility, 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.
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.