These days, customer-facing analytics are everywhere. 

We are, after all, in The Data Age—hence why you’re reading this blog and why we’re writing it. And because of the ubiquity of data, as consumers, we’ve become extraordinarily curious; we want to know everything about our businesses, investments, and lap times. 

Consumers are pining for insights—presented in fast-loading, beautifully customized dashboards. And so, many companies are turning to embedded analytics with [Tableau](https://www.tableau.com/). 

But as more consumers demand analytics, raising the standards of UI customization, interactivity, and speed, what if there were a more tailored, more performant way to give users the insights they need?

Well, there is: embedded analytics with Tableau and [Cube](https://cube.dev/). Let’s explain. 

## What is Embedded Analytics?

Simply put, [embedded analytics](https://cube.dev/blog/what-is-embedded-analytics) is the practice of integrating data analytics and visualization capabilities with a user’s natural workflow, such as their web portals, internal tools, or applications. 

For example: with embedded analytics, rather than needing to export sales data from their e-commerce platform and then conduct analysis separately in Excel, a seller could derive insights from dashboards and reports available to them directly within their commerce portal. 

Of course, embedded analytics __can be used in internal- and external-facing applications to provide real-time static and interactive reports, dashboards, and data visualizations__. 

There are many benefits of using embedded analytics. First, it can __save the user time and effort__ and add a lot of value. In turn, this saved time and gleaned insights __contribute to a user-centric UX that drives adoption__—even among those who aren’t as analytics-experienced. And higher adoption means a __better ROI on internal tooling__ and an __increase in revenue for external tooling__. 

## Embedded Analytics with Tableau

![](https://ucarecdn.com/1b6df403-1884-4e4d-83b1-b26788833ef4/)

So, why do so many companies turn to [embedded analytics with Tableau](https://www.tableau.com/products/embedded-analytics)?

Tableau is one of the leading platforms for data visualization and dashboarding. It's based on its own VizQL, which expresses data visually by translating drag-and-drop actions into data queries. 

This drag-and-drop capability makes data analytics and business intelligence accessible to a much wider pool of people—which is a great thing. Clearly, given its smashing success, Tableau met a hot demand for data democratization and did it well. 

*However*, things get a little more hinky when it comes to the [embedded analytics stack](https://cube.dev/blog/modern-embedded-analytics). This is because users not only use Tableau for analytics within Tableau itself but also use it to power embedded analytics within other applications. 

The problem is that __having your embedded analytics with Tableau sit directly on top of your data warehouse comes with a few shortcomings__, such as:

1. Difficulty with customizability and designing user-centric UIs

2. Duplicated effort and opportunities for inconsistency when creating multiple sets of data definitions and security contexts

3. Weaker analytics performance

### 1) Difficulty with customizability and designing user-centric UIs
Tableau has many visualization layer options, but it just doesn’t have one for every audience or use case. You can undoubtedly brand the Tableau visualizations embedded in your app; however, there are restrictions on what you can build with the visualization layers Tableau offers (e.g., extremely use-case-specific drill-downs).

And so, __Tableau’s visualization templates may work just fine for 80% of your applications, but those that truly need a highly tailored, custom UI will require you to build a custom presentation layer from scratch__. 

The problem is that using multiple different visualization layers together with Tableau—meaning, both custom-built and from Tableau—leads to…

### 2) Duplicated effort and opportunities for inconsistency

![](https://ucarecdn.com/78d63d2b-23b9-48f4-bb96-8bab43cc7ded/)

So you want to use Tableau and a separate charting library to build a custom presentation layer? That makes perfect sense when you want to combine the advantages of Tableau’s presentation layers with the flexibility of a custom UI. 

However, the challenge lies in consistently orchestrating data modeling, security, and caching across all of your presentation layers. 

__By connecting multiple presentation layers to your data warehouse, you’ll need to individually orchestrate your data models and access controls so they’re uniformly defined__. That’s a lot of duplicated effort—which opens the door to gaps and errors. 

### 3) Weaker analytics performance

Lastly, powering embedded analytics with Tableau without a BI layer between Tableau and the data warehouse can cause latency in your user-facing visualizations and dashboards.

Latency is a true user adoption killer: a Google study showed that [bounce likelihood increased by 32%](https://www.thinkwithgoogle.com/marketing-strategies/app-and-mobile/mobile-page-speed-new-industry-benchmarks/) when a page’s load time increased from one second to three, and by *90%* when page load time increased from one to five seconds. So, performance is essential.

__In a stack in which Tableau’s presentation layer sits directly on top of your data source, your users request data from the data source for every query__. At best, __this is redundant and costly__, as multiple users may be requesting the same information. 

At worst, however, building a stack like this __can result in latency and lower performance__. Request volume across users is unpredictable; you can’t control how many people simultaneously request data or consume visualizations. And so, if you have multiple presentation layers, each with duplicate data modeling, security, and caching layers, each making separate and redundant requests directly of the data source—that opens the door to latency.

Latency can be a problem even if you only use Tableau (don’t worry, we happen to have [a fix](https://cube.dev/cloud)). But, with multiple presentation layers, the chance of uneven performance across them is much higher. 

## The best of both worlds: embedded analytics with Tableau and Cube

Alright—let’s get to the solution. 

Tableau is a fantastic tool (in fact, here’s [how to use Tableau with Cube](https://cube.dev/blog/tableau-tutorial).) Combining it with custom presentation layers is a great way to get both efficient, out-of-the-box visualizations and user-centric UI for your embedded analytics. 

So how can you fix the issues above? With upstream, centralized data modeling, security, and caching orchestration. Hi, we’re [Cube](https://cube.dev/cloud). 

### Cube streamlines building embedded analytics with Tableau and custom presentation layers

![](https://ucarecdn.com/b52d016a-8ca8-4cdb-85aa-765669ff781c/)

Cube is a [headless BI](https://cube.dev/blog/headless-bi) platform that enables your data modeling, data access control, and caching layers to live upstream of every presentation layer and data application in your stack. 

So, rather than individually orchestrating r[ole-based access controls](https://cube.dev/docs/recipes/role-based-access#enforcing-role-based-access) or metrics definitions for each presentation layer, you’re centralizing and defining them once—removing duplicated effort and its inevitable inconsistencies.

### Cube ensures uniformly performant embedded analytics powered by Tableau and custom presentation layers

Cube’s robust caching layer manages the throughput of data requests and acts as a buffer between your data source and your data application. Its advanced [pre-aggregation capabilities](https://cube.dev/docs/caching#pre-aggregations), query queue, and [refresh keys](https://cube.dev/docs/caching#in-memory-cache-refresh-keys) protect your data warehouse from redundant, costly, and latency-causing queries. 

Designed to protect the data source from the possibility of even an unlimited number of concurrent requests, Cube can significantly speed up your embedded Tableau dashboards and visualizations. 

Cube also makes embedded analytics more performant even when built with a combination of Tableau and custom presentation layers. By allowing Cube to do the heavy lifting of data modeling, security, and caching orchestration, the presentation layers become very “thin”—essentially, not needing to process much. This enables them to become uniformly performant and allows the application’s embedded analytics to become more performant as a whole. 

## Learn More

Interest piqued? We thought it might be. 

Give your users a performant, data-rich embedded analytics experience with Tableau and Cube—especially since [Cube is free to use](https://cube.dev/cloud) and we already have a [tutorial for using Tableau with Cube](https://cube.dev/blog/tableau-tutorial).

Get in touch with us and the rest of the Cube community on [Slack](https://slack.cube.dev/) or [Github](https://github.com/cube-js/cube.js).

In the mood for some hands-on help with building a modern embedded analytics data stack? [Schedule a time to chat with us 1:1](https://cube.dev/contact).


Tamar Barlev

Tableau

A “How-To” about how to make your embedded analytics flexible and performant. 

Embedded Analytics with Tableau and Cube

Bartosz Mikulski

Firebolt

Lightning-fast analytics experiences for modern data engineering and dev teams.

Building terabyte-scale data apps with Firebolt and Cube

We’ve previously discussed the [modern data stack for embedded analytics](https://cube.dev/blog/modern-embedded-analytics)—but what is embedded analytics? Let’s take a step back and review this popular category of data application.

## Embedded Analytics Definition
Embedded analytics is the integration of data analytics and visualization capabilities within a user’s natural workflow—within internal tooling, web portals, and products like apps.

Such an integration enables users to conduct analytics directly inside the applications they use rather than having to switch between various applications to get insights.

Let’s illustrate with an example: a seller on an e-commerce platform, “TrapezoidSpace,” would like insights about their revenue.

Without embedded analytics on the e-commerce platform, the seller would have to manually request a sales report from the e-commerce platform, import it into secondary analytics software, then work with the data to get the necessary insights. 

However, with embedded analytics, they could get the insights they need with a real-time dashboard visualizing revenue presented to them directly within the e-commerce platform.

## Embedded Analytics Use Cases
There are many use cases for embedded analytics. We can divide them by the end users and the types of information they present.

### External vs. Internal Embedded Analytics
Embedded analytics can be internal-facing—meaning the end user is within an organization—or external-facing, meaning the end user is an organization’s customer.

Take our previous example of TrapezoidSpace: here, the seller is an external end-user of the platform’s embedded analytics features.

In contrast, let’s take the example of TrapezoidSpace’s customer success manager. They track the time-to-resolution of tickets (TTR) using a bespoke ticket management system that visualizes trends in TTR. That would be an example of internal-facing embedded analytics.

### Types of Embedded Analytics
People use embedded analytics in many ways, but real-time reports and dashboards are the most common.

__Real-time static and interactive reports__ are tabular views of information with the ability to refine data by setting parameters and scheduling. In contrast, __dashboards and data visualizations__ are charts and graphs that present and visualize metrics to make finding insights and trends easier.

## Embedded Analytics Benefits

### Generally, embedded analytics contributes to a user-centric UX that adds value and drives adoption.

Whether users are external or internal, the point of an investment in any tooling is to bring value and thereby drive the tool’s adoption. And, providing a user-centric design and seamless UX tailored to the target user is one of the only ways to get people to (willingly) use products.

So, conveniently locating BI and analytics functionality within the user’s workflow significantly enhances a product’s UX. Improved UX promotes the more effective use of the tool, creates greater value, and increases the tool’s adoption.

### Externally, embedded analytics increases revenue and competitive advantage.

In a survey of project managers, developers, and executives, [96% reported that embedded analytics contributes to revenue growth](https://c6abb8db-514c-4f5b-b5a1-fc710f1e464e.filesusr.com/ugd/e5361a_76709448ddc6490981f0cbea42d51508.pdf). They credited this to better UX and their ability to add revenue streams based on highly sought-after functionalities.

Adding high-value capabilities to products allowed businesses to introduce tiered or subscription-based analytics services—opening the door to acquiring new customers and upselling to recurring ones.

### Internally, embedded analytics increases productivity and the accessibility of data-driven decision-making.

The efficiency with which users can conduct analysis with embedded analytics significantly boosts time effectiveness (and, therefore, cost-effectiveness). With auto-generated dashboards and filtering capabilities, users can draw insights faster and avoid the cumbersome processes of manually pulling data and conducting analysis in separate applications.

Easy access to analytics also allows those with less experience in data analysis to make data-driven decisions. In a 2022 survey, [92% of companies cited their biggest obstacle in becoming ‘data-driven’](https://www.techtarget.com/searchdatamanagement/feature/Improve-data-value-by-relying-on-economic-principles) as people, processes, and change management. However, a custom-built internal embedded analytics platform—one that is tailored to the team’s workflow—doesn’t require complex training in generic BI tools. 

Rather than having to facilitate learning of generic BI tools which aren’t set in its work’s context, an organization can create tools that are already infused with the working context. And so, instrumenting intuitively tailored tools powered by embedded analytics removes training bottlenecks and dependencies—increasing productivity and value-add. 

## Embedded Analytics vs. Business Intelligence
The difference between embedded analytics and traditional business intelligence is simple. While traditional BI means you conduct your analytics in a separate set of tools, embedded analytics allows users to access insights in-app. Their data visualizations, dashboards, and reports live in the application in which they conduct operations, allowing for real-time refreshes and faster learning.

This contrast may seem somewhat minute, but it actually saves employees a lot of time—up to [5 hours](https://netstorage.ringcentral.com/documents/connected_workplace.pdf) a week. These savings increase productivity, allow for constantly updated information, and streamline the entire analytics workflow.

![embedded analytics MDS](https://ucarecdn.com/d37e69cf-2bec-4469-997b-8782fa6c5d17/)
## How Cube can help you power your Embedded Analytics

Cube is a headless business intelligence tool that accesses data from [raw data sources](https://cube.dev/docs/config/databases), [models the data](https://cube.dev/docs/schema/getting-started), [caches](https://cube.dev/docs/caching) query results, and makes the data available to downstream data applications via [REST](https://cube.dev/docs/rest-api), [GraphQL](https://cube.dev/docs/backend/graphql), and[ SQL APIs](https://cube.dev/docs/backend/sql).

“Headlessness” refers to tooling that separates its functionalities from the end user’s interface—making it the perfect basis for building branded and customizable embedded analytics for any application.

Cube is unique in that it provides a centralized, upstream semantic layer to define metrics, making all of the analytics it powers consistent and accurate. This semantic layer and its caching capabilities make sure that an application is performant and cost-effectively provides correct, up-to-date information to all of the users who access it.

Similarly, Cube’s centralized data access control supports [multi-tenancy](https://cube.dev/docs/multitenancy-setup#multitenancy) and allows [role-based](https://cube.dev/docs/recipes/role-based-access#enforcing-role-based-access) and [column-based governance](https://cube.dev/docs/recipes/column-based-access#enforcing-column-based-access), which ensures users are only accessing data appropriate to them. This type of security is especially crucial in external-facing embedded analytics, where many separate entities—for example, each seller on TrapezoidSpace—need to access their data and their data only. 

Cube enables developers to build consistent, secure, and performant embedded analytics efficiently; dashboards in hours, not weeks.

It can be the foundation for building embedded analytics applications with its [compatibility with many popular front-end tools](https://cube.dev/docs/config/downstream) and business applications. So many, in fact, that we built [a wiki about our favorite tools](https://awesome.cube.dev/) in the modern data stack—including tools for building embedded analytics (aptly named “awesome.cube.dev”). 

## Learn more
Want to learn more about how to build embedded analytics applications with Cube or headless BI? 

Get in touch with us and the rest of the Cube community on [Slack](https://slack.cube.dev/) or [Github](https://github.com/cube-js/cube.js).

In the mood for some hands-on help with building a modern embedded analytics data stack? [Schedule a time to chat with us 1:1.](https://cube.dev/contact)


Embedded Analytics

A guide to the powerful ways embedded analytics can boost your external and internal apps.

What is Embedded Analytics?

We’ve spent the past few weeks discussing [the composition of headless BI][1] and [Cube’s approach to open source data modeling][2]. Let’s briefly talk about where this technology meets the real world: embedded analytics.

## What is embedded analytics?

Embedded analytics means much more than using iframes to place charts in a dashboard. The real promise of embedded analytics is _bringing rich data experiences to users where they already are_. As [Gartner][3] puts it, “data analysis occurs within a user’s natural workflow, without the need to toggle to another application.”

Financial analysis may be built right into a personal finance application. Personal health metrics can be displayed alongside one’s medical history. Every member of a team can see the business’s KPIs and trends without logging into a BI tool. 

For businesses, embedded analytics affords full control over what data is available and how it’s presented. For data consumers, embedded analytics means finding useful insights within the sites or apps they already use—not in the same old dashboards.

At Cube, we’ve been on the embedded analytics front lines for years. Here are some recent technological and behavioral shifts we’ve observed.

## The rise of the cloud data warehouse

In the past, collecting and processing massive data was so technically complex and expensive that only a few companies did so. Now, cloud data warehouses make it possible for organizations in every industry and of every size to easily and affordably collect and process more data. This has led to cloud data warehouses’ [huge adoption][4].

But it is not enough to just collect lots of data. Businesses need to _use_ the data, too. This has increased the need for customizable, powerful data experiences. **As a result, companies’ adoption of cloud data warehouses has led to greater interest and investment in embedded analytics, too.**

## Data consumers are more diverse

At the same time as more businesses have taken interest in embedded analytics, **there are also more, and more types of, data consumers**. Business intelligence was once the purview of specialized engineers and analysts, but now, every member of an organization is expected to use and understand data to perform their job.

Who is the modern data consumer? For starters, they may be less technical. The modern data consumer [may not be familiar with data engineering concepts like OLAP][5]—and they shouldn’t have to be. The modern data consumer may not even be familiar with legacy BI tools—and, again, they shouldn’t need to be.

To make data accessible and actionable for these data consumers, companies need to build customized user interfaces that move beyond mere iframed graphs. Embedded analytics applications have grown more polished and interactive. 

## Front-end development is easier

A major change in the world of embedded analytics is **the rise of modern front-end development tools**. In the past decade, there have been incredible advances in powerful, simple front-end technologies—particularly [React][7]. It’s now easier than ever to design customized, polished user experiences.

This change hasn’t just made it easier to develop custom data experiences. It also has altered users’ expectations to make such experiences essentially mandatory. Both internal data consumers and end users have new, high standards for usability, richness, and responsiveness.

Since these expectations can’t be met with generic dashboards or out-of-the-box BI platforms, more companies are building customizable and performant embedded analytics features using modern front-end tools.

## There are new tools

It’s become easier for front-end development teams to build highly custom native UIs, but these aren’t necessary for every project. Sometimes, standard visualizations are enough and speed is key. 

**A new generation of collaborative data tools**, including [Hex][6], [Observable][8], and [Streamlit][9], make it possible for data analysts to quickly select the appropriate chart for a dataset and share reports both throughout a company and to end customers.

Organizations need the freedom to choose the right tool for each job—whether it’s a custom application built by front-end teams or shareable dashboards built by data teams. 

## Rethinking the data stack

![modern-embedded-analytics-data-stack][30]

Thinking about these developments, what are the technical requirements of a modern embedded analytics data stack?

## Data warehouse-centric architecture

As [others][10] [have][11] [noticed][12], the cloud data warehouseis a place not only to store and process data, but also to make it interactive. To actually be a backend for data analytics applications, including embedded analytics, the data warehouse needs to be at the core of your data stack.

Having data in a single place significantly reduces operational overhead. On one end, ELT is replacing ETL, and on the other, we see a move away from moving data into “serving” databases. Instead, the trend is towards powering applications with data directly from warehouses. This architecture is simpler  and less error-prone because it removes additional pipelines and storage layers. 

Warehouses still predominantly work with batch data, but there are a lot of recent exciting developments in supporting real-time data. [Firebolt][13], [Clickhouse][14] and [Materialize][15] are actively innovating in the space. 

Finally, this architecture also opens an opportunity for [bring-your-own-data-warehouse applications][16]. This may be beneficial for both vendors and consumers because it simplifies the security architecture and streamlines new software onboarding.

## Headless BI in the middle

[We’ve previously described the four essential components of headless BI][17]. To specifically support embedded analytics, a headless BI solution must have these attributes:

### First-class support for cloud data warehouses

A major piece of this is **access control** integrated with the data warehouse’s security controls, because embedded analytics always require multitenancy. A second piece is **advanced caching**. This is because the data warehouse is a great candidate for a backend, but itself does not support high-concurrency small queries with the low latency that modern data consumers expect.

### Data modeling

The diversity of new data consumers all should consume the same data: “net sales” in your dashboard should mean the same thing as “net sales” in my mobile CRM. To achieve this consistency, data modeling and metrics definition should be handled once, and this must be up-stack from every application or dashboard.

### Diverse APIs

Different data consumers have different expectations and requirements, so it’s inevitable that a company will end up supporting a whole class of data apps. For all of these applications to share the same metrics, a headless BI layer needs to be accessible via multiple APIs, e.g., SQL, GraphQL, and REST.

## A hybrid presentation layer

To support different data consumers, use cases, and teams, an embedded analytics presentation layer should be diverse. 

When high customization is required and front-end teams are looped in, different charting libraries can be used, ranging from [D3][18] to [Chart.js][19] and [Highcharts][20]. These most likely will be natively integrated with frontend application frameworks like React or [Angular][21].

When requirements are less custom, tools like Observable or [Dash][23] come into the picture. It’s faster and cheaper to build with these tools, and they still can be natively integrated into an app and combined with more custom interfaces.

Data analysts and engineers can quickly build appealing data interfaces with modern BI tools like [Superset][24] and [Metabase][25]. These are less customizable, but they don't require front-end knowledge. This usually enables even faster building and iteration. They too can be combined with other presentation layers within a single application. 

Finally, there is the emerging category of no-code/low-code tools for internal tooling. This includes [Appsmith][26] and [Retool][27]. They too can be used to build analytics interfaces.

## Conclusion

We’ve moved past the historical era when embedded analytics was considered a part of a single integrated BI solution. The latest generation of BI vendors already have innovated to run “live” queries directly on top of data warehouses, which allows for cloud data warehouse-centric architectures.

The next evolutionary step is to decapitate BIs and give organizations freedom to switch heads (frontends) or use many of them at the same time. 

This architecture will be enabled by several products, including Cube. As the category matures, and more enterprises adopt this architecture, we’ll see more exciting developments. Buckle up.

_Cube is an API‑first business intelligence platform for data engineers and application developers to make data accessible and consistent across every application. [Get started with Cube for free today][28], or [get in touch][29] to discuss your next embedded analytics project!_

[1]: https://cube.dev/blog/headless-bi
[2]: https://cube.dev/blog/open-source-data-modeling
[3]: https://www.gartner.com/en/information-technology/glossary/embedded-analytics
[4]: https://www.alliedmarketresearch.com/data-warehousing-market
[5]: https://benn.substack.com/p/ghosts-in-the-data-stack?s=r
[6]: https://hex.tech
[7]: https://www.youtube.com/watch?v=7LMsR30p1xM
[8]: http://observablehq.com
[9]: http://streamlit.io
[10]: https://medium.com/snowflake/how-to-build-a-data-app-on-snowflake-db80ac459767
[11]: https://pchase.substack.com/p/thenewbackend?s=r
[12]: https://www.youtube.com/watch?v=PEFP5fLx1dY
[13]: https://www.firebolt.io
[14]: https://clickhouse.com
[15]: https://materialize.com
[16]: https://pchase.substack.com/p/thenewbackend?s=r
[17]: https://cube.dev/blog/headless-bi
[18]: https://d3js.org
[19]: https://www.chartjs.org
[20]: https://www.highcharts.com
[21]: https://angularjs.org
[22]: https://observablehq.com
[23]: https://plotly.com/dash/
[24]: https://superset.apache.org
[25]: https://www.metabase.com
[26]: https://www.appsmith.com
[27]: https://retool.com
[28]: https://cube.dev/cloud
[29]: https://cube.dev/contact
[30]: https://ucarecdn.com/d37e69cf-2bec-4469-997b-8782fa6c5d17/

Artyom Keydunov

Deep Dives

Here’s how the embedded analytics data stack has changed.

The Modern Data Stack for Embedded Analytics

Embedded Analytics

Embedded Analytics with Tableau and Cube

Building terabyte-scale data apps with Firebolt and Cube

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What is Embedded Analytics?

The Modern Data Stack for Embedded Analytics

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