One popular embedded analytics tool is Sigma which has gained popularity as an internal business intelligence tool. While it offers embedded analytics capabilities, it may not always be the ideal solution, in this article we’ll explore Sigma and its alternatives.

Is Sigma the right tool for Embedded Analytics?

The success of an organization’s analytics is only as good as the tools it uses and how those tools fit together in the larger tech stack. With the rise of embedded analytics, organizations are now able to integrate data-driven insights directly into their applications, providing users with real-time information. There are many embedded analytics solutions on the market; how do you know which one to choose? The key is finding the one that fits best for your organization’s needs and technologies. 

Sigma For Embedded Analytics

The first thing to consider is the feature set needed by the organization when it comes to embedded analytics. These will vary depending on your use case, but there are a few features that are important to consider. When evaluating alternatives to Sigma, businesses should prioritize these key features to ensure the chosen solution aligns with their requirements:

1. Flexibility and Customization: An embedded analytics tool should offer flexibility in design and customization, allowing businesses to tailor the user experience to their specific needs and branding guidelines. 
2. Integration Capabilities: Seamless integration with existing data sources, applications, and frameworks is essential for embedding analytics into various workflows without disruption.
3. Scalability: The chosen solution should scale with the growing demands of the business, both in terms of data volume and user base.
4. Usability: This last point is also often overlooked but embedded analytics often has very different users than internal analytics. Is your BI that is leveraged by your data team the same tool that your users would be excited to use?

Evaluating tools for embedded analytics is different from evaluating BI tools as a whole. There are many great data visualization tools available, but many of them are often lacking in the area of embedded analytics. While Sigma excels as an internal BI tool, several factors may drive businesses to explore alternatives:

1. Cost: The pricing model of Sigma may not be conducive to embedding analytics, as it often doubles when used for external-facing applications. This pricing structure doesn’t scale well, especially for businesses serving a large user base. 
2. Design Flexibility: Sigma’s interface is tailored for internal users and lacks the flexibility required for seamless embedding. The inability to customize the design can result in a subpar user experience and may not align with the branding requirements of businesses. 
3. Dependency on Snowflake: Sigma is tightly integrated with Snowflake, limiting its compatibility with other data warehouses. Businesses relying on alternative data platforms may face constraints in accessing features.
4. Embedded-specific Features: Sigma lacks certain features essential for embedded analytics, such as mobile-specific formatting and extensive customization options for components and formatting.

Explo for Embedded Analytics

Now that you’ve got an idea what Sigma has to offer, let’s explore some alternatives; the first being Explo Analytics. Explo is a versatile platform that empowers businesses to embed dashboards directly into their applications. It provides a user-friendly interface that simplifies the process of data analysis, making it easy to use for both technical and non-technical users. Some of Explo’s key features and benefits are:

1. Intuitive Interface: As we’ve mentioned before, Explo offers an interface designed for a user base ranging from data analysts to business stakeholders. Its user-friendly design streamlines the process of data exploration and analysis, minimizing the learning curve for users.
2. Customization Options: Unlike traditional analytics tools, Explo offers extensive customization options for design, allowing businesses to tailor the user experience to their specific branding. This flexibility ensures a cohesive integration of analytics into existing applications.
3. Embedded First Features: Explo Analytics prioritizes features essential for embedded analytics, including mobile-friendly interfaces, internationalization support, and timezone management. These features ensure that embedded analytics are accessible and optimized for users across different devices and regions.
4. Scalability: One of the standout features of Explo is its scalability, catering to businesses of all sizes—from small startups to enterprise-level organizations. 
5. Custom Reports and Dashboards: Explo Analytics offers capabilities for creating custom reports and dashboards, empowering users to build personalized analytics experiences. Unlike Sigma, Explo provides an editable dashboard and report builder, allowing users to tailor their analytics to their specific needs.

While both Explo Analytics and Sigma offer embedded analytics solutions, there are several key differences worth noting:

1. Design Customization: Explo Analytics provides extensive customization options for design, enabling businesses to create a cohesive, branded analytics experience. In contrast, Sigma lacks this same type of flexibility.
2. User Experience: Explo Analytics is designed with a focus on user experience, catering to non-technical users as well as data analysts. Its intuitive interface and streamlined workflows enhance usability, making data exploration accessible to a wider audience.
3. Pricing Model: Explo Analytics offers a pricing model that scales well for businesses embedding analytics, ensuring cost-effectiveness and scalability as user bases grow. In comparison, Sigma's pricing structure may not be as conducive to embedding analytics for external-facing applications.

Looker for Embedded Analytics

Looker is a data analytics platform owned by Google making it a very well known and popular option. It provides a centralized hub for data management, specifically designed to join multiple data sources together. A few of Looker’s key features are:

1. LookML: One of Looker's strongest advantages is its LookML modeling language, which allows users to define and manage their data models in a flexible and efficient manner. LookML simplifies the process of data modeling, making it easier for organizations to maintain consistency and accuracy in their analytics.
2. Powerful Visualization Library: Looker offers a large library of visualization tools, giving users the flexibility to display data in many different ways. From simple charts and graphs to advanced visualizations, Looker provides the tools necessary to create the most effective visuals for your data

Let’s take a look specifically at  Looker and Sigma’s embedded analytics solutions, since there are some differences between the two:

1. Data Management and Exploration: Looker excels in data management, providing users with tools to manipulate and analyze data for many different sources. Its interface and querying capabilities make it a preferred choice for data analysts and business users alike.
2. LookML Advantage: The LookML modeling language gives Looker a significant edge over other analytics platforms, allowing for greater flexibility and control in data modeling. This feature sets Looker apart in terms of customization and scalability, however it does have a steep learning curve.
3. User Interface: Despite its strengths, Looker has seen minimal improvements to its user interface since its acquisition by Google. While the platform remains powerful, some users may find the interface less intuitive compared to other solutions.
4. Pricing: Looker's pricing is much higher than competitors like Sigma, making it less accessible to smaller businesses or organizations with budget constraints. However, Looker's robust feature set and flexibility may justify the higher cost for businesses with complex analytics needs.
5. Support: Since its acquisition by Google, Looker has relied heavily on third-party consultants for support, leading to limited direct support from Google itself. This reliance on external consultants can be costly for organizations seeking additional support or guidance.

Sisense for Embedded Analytics

Sisense is another leading business intelligence platform that offers a comprehensive suite of tools for data integration, visualization, and analysis. It is designed to democratize data access and enable all users of an organization to leverage data-driven insights. Some of it’s key features and benefits are:

1. Customization and Flexibility: One of Sisense's strengths lies in its customization and flexibility. The platform provides tools for tailoring dashboards, reports, and visualizations to meet the specific needs of organizations. This flexibility enables businesses to create personalized analytics experiences that align with their unique requirements and objectives. This does require heavy lift to get set up and often dedicated support as well.
2. Data Connectivity: Sisense offers support for a wide variety of data sources, allowing users to connect to disparate data sets and combine them into a single, unified view. From databases and cloud applications to APIs and flat files, Sisense enables seamless integration of data from multiple sources, providing a holistic view of business performance.
3. Embedded Analytics Options: Sisense provides both out-of-the-box embedded analytics options and a custom SDK that allows for greater flexibility in embedding analytics into applications. While the custom SDK offers enhanced customization capabilities, the out-of-the-box option provides a quick and convenient solution for embedding analytics without the need for extensive development resources.

When comparing Sisense with Sigma Embedded Analytics, several factors come into play:

1. Data Management: Sisense offers a large variety of data sources and provides robust tools for data integration and manipulation. However, one drawback is that Sisense pulls data into its own systems, which can result in longer implementation times and may require a specific data model.
2. Support: Sisense's support offerings may be limited compared to some competitors, which can be a concern for organizations requiring extensive assistance with implementation, troubleshooting, or customization.
3. Pricing Model: Sisense's pricing model can vary depending on factors such as data volume, user count, and feature requirements. While Sisense may offer competitive pricing for some businesses, others may find it more costly compared to alternatives like Sigma.

Making the Right Choice

Now that you’re familiar with the feature sets that some of the top BI tools offer, you can better consider which option will be right for your organization. Depending on your business size and its industry, you’ll have to consider which tool is going to be able to best support your current data structure, and scale well as your business grows. Also consider who will be using the tool and what their skill sets include. You’ll need to consider how much training will be needed, what team will support the ongoing maintenance of the tool and how much time each of those will take. 

Identify your organization's specific analytics needs and goals. Whether you require advanced data visualization capabilities, real-time analytics, or predictive modeling, choose a tool that offers features aligned with your objectives. Finally, weigh the cost and benefits of each tool. Choose a tool that offers value for money and fits within your budget constraints.

As you begin to explore your options for embedded analytics tools, here are some quick tips and helpful reminders to guide you along in the process:

1. Trial Periods and Demos: Take advantage of trial periods and demos offered by embedded analytics vendors. This allows you to explore the features and functionality of the tool firsthand and assess its suitability for your organization's needs.
2. Customer Reviews and Testimonials: Research customer reviews and testimonials to gain insights into the experiences of other users with the embedded analytics tool. Pay attention to feedback regarding ease of use, performance, support, and overall satisfaction.
3. Comparison of Key Features and Pricing: Compare the key features and pricing of different embedded analytics tools to identify the best fit for your organization. Consider factors such as data connectivity, customization options, scalability, and pricing.

Choosing the right embedded analytics tool requires careful consideration of all of these factors, but by taking the time to evaluate the needs of your organization, set up demos, and completing a cost/benefit analysis, you can select a tool that not only meets your immediate needs but also supports your long-term analytics objectives. 

Ultimately, investing in the right embedded analytics solution will drive innovation, enhance your market share, and unlock the full potential of data within your organization. For more information on how customers view the various embedded analytics tools, check out our article 2024 Embedded BI Reviews or go ahead and request a demo of Explo today.