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

Criterion	Description
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 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 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

Summary

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:

Do:

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

Consider:

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.

Sisense vs. Alternatives: Compare Embedded Analytics Solutions

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