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Evaluate 8 of the top embedded analytics tools
Selecting the right embedded analytics tool for your organization can be difficult. Use general criteria to evaluate eight of the top options and decide if one is the right fit.
BI tools are a core component of every enterprise technology portfolio. Embedded analytics tools address workflow disruption issues that traditional BI tools can cause, but organizations must select the right tool for the job.
Many organizations have specialists who use BI tools extensively for analytics and visualization. Many more have business users who need better data insights as part of their regular work. But traditional BI often disrupts workflows because users must toggle between multiple applications to access essential data analytics.
Embedded analytics addresses the gap by integrating real-time data visualizations, dashboards and predictive insights directly into software applications employees use daily. It makes data insights more accessible and actionable because users can make informed decisions within their familiar working environment.
For business professionals, embedded BI tools eliminate the need for specialized data analytics training. IT teams benefit because integrating analytics into already secure systems streamlines governance processes. Embedded analytics democratize data analysis more than traditional BI applications can.
Not all embedded analytics platforms are created equal
When choosing an embedded analytics tool, organizations must consider their specific needs and budget; the tools can be embedded into their existing system, which is unique. Consider a few general factors to help narrow down embedded analytics tool options:
- Ease of integration. How smoothly can the tool embed into existing applications? Include security integration in the analysis.
- User interface. Will the user experience be compatible with existing ways of working?
- Data handling. How strong are the data ingestion and processing capabilities? Will the system handle complex data at the volume you need? Can it connect to data sources -- especially legacy systems -- already in use?
- Customizability. To what extent can data teams tailor the available analytics tools for specific needs? Are there limits caused by a simple set of visualizations or analyses?
- Pricing. Is a pricing model suitable for the organization's budget and expected usage?
8 significant embedded analytics platforms
Using the criteria discussed previously, sources including Gartner, G2 and Capterra, and extensive research into the embedded analytics tools market, eight top options emerged. The list is in alphabetical order.
Entrinsik Informer
Entrinsik Informer is a curated data hub and reporting platform for integrating data and analytics into internal systems or customer-facing applications. Users share their work through dashboards, reports and interactive grids. Informer can update and share work with a scheduling engine for collaborative and data-driven decision-making. Teams can also use Informer to build AI copilots, enhancing analytical capabilities with machine learning models. Informer's strong reputation comes from higher education for cost-effective reporting on student data, financial aid and institutional metrics.
Although users don't need advanced technical skills to run Informer, Entrinsik offers integration assistance from an in-house team of Informer experts to ensure an efficient implementation process. The platform can process data from a wide range of sources, including databases, cloud APIs and documents, and it can run on premises or on the Kubernetes-based Informer Cloud architecture.
Pricing is not publicly available.
GoodData
A well-established provider with years of experience, GoodData was one of the first companies to specialize in embedded analytics. The platform features an open API ideal for embedding, and its Analytics Lake approach enables teams to store analytics artifacts and data together in a single managed system. GoodData also offers workflow automation, natural language processing (NLP) query capabilities and AI-powered insights to streamline data analysis and discovery. GoodData's scalability makes it suitable for larger organizations, while its data permission management features enable data teams to easily control individual users' access to specific data sets.
GoodData starts with transparent pricing, reflecting its strong presence in the SaaS market: $30 per user, per month for 10 to 30 users. Enterprise pricing -- and advanced enterprise features such as compliance -- are customizable as part of the enterprise procurement process.
Google Looker
Google Cloud acquired Looker in 2020, and it remains a reliable BI platform. Looker is optimized for embedding analytics and is particularly popular with application developers. Its data modeling language, LookML, enables users to create reusable data models to drive customized analytics applications. Key strengths include flexibility, scalability and ease of integration into any tech stack, especially with Google Cloud, which offers optimized data processing.
Google rebranded Google Data studio to Looker Studio in 2022. The no-code tool for non-specialist developers features a drag-and-drop interface for customizing visualization templates including charts, graphs and reports. The templates help communicate data insights with stakeholders effectively. E-commerce and technology companies extensively use Looker for real-time data access and developer-friendly modeling.
Looker pricing can be complex, with variables such as query-based or administrative API calls and user types such as developers, standard users and viewers.
Logi Analytics
Logi Analytics, now part of the Insight Software portfolio, was founded in 2000 and focuses on embeddable reports and dashboards. In 2023, the platform updated workflow automation, data governance tools and augmented analytics, which enhance its overall functionality. Logi Analytics emphasizes its strong security features, making it a preferred option for businesses prioritizing data protection. It uses multi-tenant security with stringent authorization rules for user privileges, permissions and security filters. The security features ensure sensitive data is safeguarded while still accessible to authorized users in a secure and efficient analytics environment.
Pricing information is available by request.
Luzmo
Formerly known as Cumul.io, Luzmo emphasizes intuitive data visualization capabilities and a rapid design and development process. It targets non-specialist users, but integration SDKs are available for web and mobile developers. It can connect to more than 20 databases and data warehouses, and organizations can connect their own API to the platform. In 2024, updates focus on mature features such as governance, user permissions, SSO and data access controls.
Luzmo offers transparent pricing in three tiers -- Basic, Pro and Elite -- ranging from $995 per month for one designer and 100 viewers to $3,100 per month for 30 designers and 1,000 viewers. Luzmo is particularly strong in SaaS businesses such as finance, retail and marketing.
Power BI
Microsoft's end-to-end BI offering, PowerBI, enables a range of embeddable options based on tight integration with Office 365, Azure Cloud and Microsoft Copilot -- the AI assistant can ask questions about data in an organization's model and get natural language explanations. Copilot can also help with PowerBI's sometimes-tricky DAX query language. New enhancements in May 2024 include viewing reports in OneDrive and SharePoint with live connected semantic models. A comprehensive range of visualizations is available, including customizable matrix layouts that enable hierarchical display and drilling down into data.
PowerBI starts with a free tier, followed by paid tiers such as Pro and Premium. However, most enterprise deployments of PowerBI are components of more significant Azure deals, so breaking out the cost or value of PowerBI on its own can be challenging.
Qlik Sense
The second generation of Qlik's analytics platform, Qlik Sense, focuses on embedding. It features data integration and visualization capabilities, including visualizations, reports and interactive dashboards. Open APIs enable complete customization so organizations can tailor the tool to their needs. Qlik Sense offers augmented features, such as AI assistants, that enhance data creation, preparation, conversational interaction and NLP. Automated capabilities include insight generation, machine learning and predictive analytics. The platform can also automatically trigger actions based on events in a data pipeline, with real-time monitoring and alerts.
Qlik Sense is a powerful platform designed for total analytics users, as opposed to developers looking for tools to embed.
Qlik has public pricing information, but because it's for analytics use cases, most shops that choose this tool will develop custom deals with the vendor.
The analytics platform is simple to deploy, but embedding developers often find the initial setup of customizations quite tricky for production, if not for prototype development.
Sisense
Sisense is known for easily embedding analytics with plugins for popular platforms. It offers data connectors for complex data sources, including Embed SDK and Sisense.JS, iFrame and Sisense Infusion Apps. In March 2024, Sisense released Compose SDK, targeted specifically at embedded analytics developers, enabling them to quickly build customized data products.
Sisense includes AI-driven insights such as predictive and prescriptive analytics, anomaly detection and an enhanced cloud platform. It offers innovative data mash-up capabilities, enabling more complex data analytics and insights. The scalability of Sisense has made it popular in healthcare and pharma use cases. The platform provides a range of embedding options from simple iFrame embedding to fully customized, white-labeled services, which cater to diverse integration needs across various industries and use cases.
Sisense describes its approach as "no-surprise" pricing, but does not have publicly available pricing. Instead, it provides a custom price for each customer.
The following table is a summary of the evaluation criteria for each vendor. The rankings are based on the author's market knowledge from research calls and user reviews from sites including TrustRadius, Capterra, G2 and Gartner Peer Insights.
| Vendor | Ease of integration | User experience | Data handling | Customization | Notes |
| 1= Difficult 5 = Simple |
1 = Dated, clumsy 5 = Modern, clean |
1 = Poor perf or scale, few sources 5 = High perf and scale, many sources |
1 = No customization 5 = Highly customizable |
||
| Entrinsik Informer | 3 | 4 | 4 | 4 | Known for cost-effective reporting and ease of integration with assistance from in-house experts. Good user experience and strong data handling capabilities. |
| GoodData | 5 | 4 | 5 | 4 | Strong API support, data handling, AI-powered insights and workflow automation. Suitable for larger organizations, but with transparent pricing for smaller teams. |
| Google Looker | 4 | 4 | 5 | 5 | Highly flexible and scalable, integrates well with Google Cloud, has a developer-friendly modeling language and offers high customizability. Pricing is complex. |
| Logi Analytics | 4 | 4 | 5 | 4 | Focused on secure, embeddable analytics with strong data governance and workflow automation. Prioritizes data protection with strong security features. |
| Luzmo | 4 | 3 | 4 | 3 | Emphasizes intuitive visualizations and ease of use for non-specialists, though somewhat less customizable. Transparent and relatively affordable pricing. |
| Power BI | 4 | 4 | 5 | 4 | Tight integration with Microsoft ecosystem including Copilot, comprehensive visualizations, good data handling. Pricing often as part of a larger enterprise deal. |
| Qlik Sense | 3 | 4 | 4 | 5 | Built for embedding with open APIs and a strong focus on AI-enhanced features, though initial setup for production can be tricky. Publicly priced but often negotiated. |
| Sisense | 4 | 4 | 5 | 4 | Known for ease of embedding with various SDKs, strong data mash-up capabilities and predictive analytics. Pricing is customized for each customer. |
AI and embedded analytics
Recent developments in generative AI (GenAI) affect embedded analytics. Natural language capabilities are changing the user experience and the expectations of business insights. Vendors across the market are prioritizing more conversational, intuitive experiences between users and data. Every vendor mentioned offers some level of integration with GenAI, and their capabilities are rapidly developing.
Through innovations, including voice commands and text-based querying, users can now obtain insights by asking questions. Platforms can generate automated narratives to explain key trends and patterns in plain language. Rather than clicking through dashboards, users can have a dialogue with their data, uncovering insights through interactions that feel familiar and natural.
GenAI enables systems to interpret questions, analyze data and derive meaningful responses. The user experience focuses on simple exploration and automation because GenAI reduces the need for technical expertise or specialized analytics training. As natural language analytics becomes mainstream, embedded platforms will use GenAI to make business insights more accessible.
Regardless of how useful GenAI proves to be, it does not change the criteria for evaluating embedded analytics tools. Organizations will always need to integrate with applications, handle data, customize experiences and work within their budget. GenAI affects the user experience to some extent, but visualizations are better than natural language text to explain many business insights.
However useful GenAI proves to be, it does not change the criteria for evaluating embedded analytics tools. The value of embedded analytics is that users have access to new insights within the applications they use every day. With the right tool, analytics shifts from isolated expertise to an organizational competency woven into everyday work. No one approach is correct to achieve analytics goals. Think hard about the criteria that matter most to the situation and work closely with vendors when evaluating tools.
Donald Farmer is the principal of TreeHive Strategy, who advises software vendors, enterprises and investors on data and advanced analytics strategy. He has worked on some of the leading data technologies in the market and in award-winning startups. He previously led design and innovation teams at Microsoft and Qlik.
