Sisense Alternative: Elevate Your Analytics Game with Dynamic Solutions

There are many tools to consider when evaluating BI and analytics solutions. Each brings its pros and cons, depending on the specific use case. The Sisense Fusion Analytics platform is a strong player in the embedded market and offers promising scalability, while GoodData excels in both embedding and the broader analytics/BI market. This guide provides an overview of the two platforms to help you choose between them.

First, let’s look at the must-haves of a modern analytics tool:

1. Strong dashboard and visualization engine.
2. Tooling for all key personas (consumers, business analysts, analytics developers).
3. Ease of administration.
4. Ability to handle large data volumes.
5. Scalability and performance tuning.
6. Embedding and white labeling capabilities.
7. Developer tooling and integration features.
8. AI and ML functionality for users, as well as toolkits for developers.

The above is a very broad checklist. To ensure a potential BI tool matches your specific goals, read How to evaluate BI tools to choose the best one. And if you’re interested in other tools, besides Sisense and GoodData, check out comparing the best BI tools. 

Now, on with the comparison!

What is GoodData?

GoodData is a flexible embedded analytics solution that seamlessly integrates analytics for smooth operation. Prioritizing collaboration and data sharing, it is designed for scalability and high performance. GoodData caters to both enterprises and small businesses, including startups.

In October 2023, GoodData introduced its future vision, the Analytics Lake: an API-first, AI-powered analytics platform, which empowers engineers with APIs for data definition, optimization, and visualization.

What is Sisense?

Sisense empowers organizations to seamlessly integrate analytics across various applications. The platform, which fosters collaboration and sharing, supports self-service augmented analytics and embedded use cases in a hybrid cloud. Since late 2021, it has incorporated AI-powered analytics to provide valuable insights to users and accelerated its product innovation through advanced AI/ML capabilities.

General overview

Below is a high-level overview of the features and capabilities of GoodData and Sisense.

Use cases

GoodData outshines the competition in four key areas: multitenancy that brings ease of administration, strong embedding capabilities, a robust developer experience, and cutting-edge AI and ML tooling.

Let’s dive into each of the above in turn:

Multitenancy

Both platforms are highly scalable and make it possible to scale analytics to align with internal (departments, internal teams) and external purposes (vendors, clients, customers). It is possible to achieve this via multitenancy, which can be set up in the UI or via APIs. The blueprints of each multi-tenant Sisense use case share the same purpose as GoodData:

Option three is the most popular approach, especially among companies with a larger user base, both internal and external requirements, and who are aiming to cut costs. It allows companies to establish separate analytics environments for specific entities (such as different vendors) to maintain data privacy. Within these environments, there is the flexibility to invite an unlimited number of users and organize them into groups as needed.

Pros and cons of multitenancy

The following table introduces some issues Sisense users experience according to reviews (G2, TrustRadius, and more), and how GoodData resolves these within its platform.

Embedding

Both Sisense and GoodData are highly competitive when it comes to embedding. (If embedding is important for your use case, you might like to read Comparing the best embedded analytics tools).

The two solutions offer various embedding methods with different levels of flexibility:

• Less flexible

  Sisense and GoodData offer the simplest way to embed dashboards and the analytical designer (UI) via IFrame.

• Flexible

  Sisense offers an embedding solution (especially for separate visuals) via Embed SDK, a Javascript library, which uses the IFrame in a more customized way. GoodData’s next level of flexibility uses web components: the Web components library makes embedding simple, and single visualizations can be embedded.

• Very flexible

  Sisense offers SisenseJS, a JavaScript library designed to embed individual dashboards, widgets, and filters into web apps without relying on IFrames. Additionally, there’s Compose SDK — a set of client-side libraries for composition, data visualization, and more. This allows the creation of new elements, including customized visualizations. Sisense's Compose SDK is similar to GoodData's React SDK.

  GoodData uses a React-based JavaScript library for flexible embedding, enabling the construction of responsive analytical applications by embedding pre-built dashboards and visualizations. React SDK can also be used for programmatic embedding, allowing the creation of custom, interactive visualizations, directly embedded into web apps.

Pros and cons of embedding methods

The table below shows the advantages and disadvantages of the embedded analytics the two solutions offer.

Developer experience

Sisense is a robust analytics tool that caters more to analytical engineers. It supports code-based development and analytics management to empower users with a technical background. Meanwhile, GoodData caters for varying levels of expertise, from no code/low code users to analytical engineers and developers. GoodData’s "analytics as code" approach has strengthened its position in the analytics and business intelligence market.

Sisense for developers

• Leverages an API-first approach, using Sisense REST APIs to automate processes and workflows (user and security management, dashboard and widget control, branding, and administration settings).
• Effortlessly embed insights using the Embed SDK for quick setup, or use more enhanced programmatic embedding with SisenseJS to customize the embedded solution.
• Sisense BloX (plugin available on the marketplace) to build interactive pre-built or new widgets using React.
• Git integration, available in the Sisense application, can control all software elements, including code, libraries, APIs, and analytics without the need to switch to the Git environment.

GoodData for developers

• Applies an API-first approach, using GoodData APIs to access raw data, customize the user interface, integrate new features into the analytics, or explore data with any other applications.
• Uses Headless BI to provide consistent, real-time metrics through a unified semantic layer for applications that end users already use (AI/ML tools like Jupyter Notebook, BI tools like Tableau or Power BI).
• Build integrations with CI/CD pipelines thanks to GoodData Python SDK and the API-first approach. Write your own workflow in YAML files and push it through Git actions.
• Use pre-built Web Components for embedding, or build your own highly customized visuals using React SDK for a seamless user experience.
• Leverage “as code” to manage the entire analytics environment. Use only code in developer-friendly environments through VS Code extension (specifically developed by GoodData).

Pros and cons of developer experience

These are the pros and cons of Sisense and GoodData when it comes to the developer experience.

AI-enhanced features

For GoodData, the core principle is "analytics as code," where everything is written in code with a defined structure. This is crucial for integrating AI-fueled features as it enables the use of Large Language Models (LLMs).

LLMs are used to understand data, summarize information, and make predictions. Despite analytics relying on code, LLMs can be challenging for non-technical users. GoodData's semantic layer addresses this issue, translating technical details into user-friendly terms for easy interaction and natural language requests.

Sisense also employs LLMs and a semantic layer. The company introduced Compose SDK to add generative AI-powered analytics to their solution, facilitating seamless integration into web applications, and simplifying the embedding of analytics into products, user experiences, and workflows.

Sisense and GoodData share some similar AI features (albeit with different names):

*Sisense also offers some AI-enhanced features on its marketplace, for example, Sisense Quest, which contains out-of-the-box statical models, but also allows deployment of custom ML models through the upload of Python scripts.

What other AI features does GoodData have?

• The platform utilizes ML and user behavior analytics within the analytics homepage to tailor offerings to individual user needs. This is achieved by asking simple questions in natural language, giving recommendations based on behaviors and trends, and streamlining analytics access for easy navigation of the most important content.
• Enhances the data model by incorporating model validation to rectify anomalies identified through the data model validation.
• Data profiling to identify anomalies in the data, including checking table dimensions, detecting missing values, and identifying suspicious values.

Why do companies choose GoodData over Sisense?

Next steps with GoodData

Still hesitating between GoodData and Sisense? Try our free trial to get some first-hand experience. Have questions or want to see real-time use cases? Request a demo for a platform walkthrough.

Find Out How We Stack Up Against Our Other Competitors

Check out some of our other resources to learn more about how we compare to other GoodData alternatives:

AWS QuickSight Alternative: Choosing the Right BI Tool for Your Needs

Tableau Alternative: Addressing Challenges and Solutions for Modern Analytics

Qlik alternative: Which tool is better for your analytics?

*Note: The above evaluation of features is based on our best understanding of publicly available information available at the time of publishing (Dec ‘23). To understand more specific details and feature differences readers are encouraged to perform their own research. All of the product names, logos, and brands used are for identification purposes only and remain the property of their respective owners. Use of them does not imply any affiliation with or endorsement by them.*