The Cube and [Explo](https://www.explo.co/) [integration](https://cube.dev/blog/cube-and-explo-easy-customer-facing-analytics-with-the-universal-semantic) is exciting for data teams and application developers working to ensure fast and rich dashboards are in the hands of customers. Data teams can build beautiful, feature-rich customer-facing dashboards on data from any of the [large list of data sources that Cube supports](https://www.explo.co/integrations/cube). 

On the one hand, Cube’s [pre-aggregations and caching](https://cube.dev/product/caching-and-data-performance) ensure that performance will always be snappy. On the other, Explo provides incredible customization options that guarantee you’ll be able to make your dashboards fit right into the rest of your application. 

And [with both Cube and Explo](https://www.explo.co/partners/explo-cube), users will feel right at home. 

## The story that has Cube and Explo working together:

At Cube, we’re building a universal semantic layer to power every data application. 

We work to help users model all of their data with our sophisticated and flexible data modeling capabilities and manage access to that data with our powerful access control tools. Our unique [Cube Store](https://cube.dev/docs/product/deployment#cube-store) technology provides [fast in-memory caching and optional pre-aggregations](https://cube.dev/product/caching-and-data-performance) to provide the fastest response times for data applications. Finally, we provide [a comprehensive suite of API endpoints](https://cube.dev/product/data-apis) that enable developers to connect to Cube however they see fit.

One of the most frequent ways our customers take advantage of these functionalities is in customer facing scenarios. Customer facing analytics takes everything that’s hard about traditional BI and ups the stakes: you’re still connecting to data, dealing with data modeling, and building dashboards—but now you’re doing it with expectations for usability and performance that are more aligned with website expectations. Even if your internal stakeholders are ok with their dashboards taking ten or more seconds to load, your customers will very much *not* be ok with that.

Picture this: you’re hard at work developing your application and building features for your users. When you’re so deep into building the best UX possible, you probably don’t want to have to think about complicated data access patterns, performance and scaling concerns, and security when adding customer facing dashboards. That’s where Cube comes in. Let us deal with the hard work on the data side.

But what if you also don’t want to build your own customer facing dashboards with a front-end framework? Maybe you don’t want to deal with the maintenance, development costs, and time-to-value?  

## Cube and Explo: The Best of Both Worlds

Well—Cube has recently partnered with [Explo](https://www.explo.co/) to allow users to build beautiful, customer facing dashboards with their easy-to-use dashboard designer and then embed them into their applications easily. 

The Cube and Explo integration ([docs](https://docs.explo.co/data-sources/connecting-to-data-sources/data-source-types/cube)) empowers you to get the best of both worlds—for truly native, performant customer facing analytics with incredible time-to-value development. 

The Explo team has written [a fantastic guide](https://www.explo.co/blog/empowering-nosql-datasource-connectivity-unveiling-the-explo-and-cube-collaboration) detailing how to get started with Explo and Cube, featuring our ability to connect to NoSQL databases like MongoDB. Check it out to learn more about how our products work together and how we can help you delight your customers with powerful, fast dashboards.

Brian Bickell

Embedded Analytics

The Cube x Explo Integration

Cube and Explo: Easy Customer Facing Analytics with the Universal Semantic Layer

Business Intelligence (BI) dashboards are powerful tools that enable organizations to make informed decisions by presenting data in a visually appealing and easily understandable manner. 

However, building BI dashboards for internal stakeholders that drive adoption and return on investment—ergo, those that they’ll actually _want_ to use—requires careful consideration of usability and user experience (UX). 

So today, we’ll explore the best practices that can help create effective business intelligence dashboards that maximize user engagement and lead to tangible business outcomes.

## Why it’s important to build business intelligence dashboards (your team will actually want to use)

Building BI dashboards that your team will actually want to use is important for _many_ reasons; evidently, your company would be investing resources in doing so for no reason. But let’s take a look at a few, shall we?

### 1. Empowering Informed Decision-Making

An engaging BI dashboard puts valuable data at stakeholders' fingertips, empowering them to make well-informed decisions swiftly. By presenting insights in a clear and visually appealing manner, stakeholders can quickly identify trends, spot opportunities, and respond to challenges effectively.

### 2. Enhancing Productivity and Efficiency

BI dashboards that are easy to use and provide real-time data reduce the time spent on data analysis and reporting. Internal stakeholders can focus on strategic initiatives, driving productivity, and efficiency across departments.

### 3. Fostering Data-Driven Culture

When stakeholders actively use BI dashboards, they embrace a data-driven culture within the organization. Data-backed decisions become the norm, promoting a collaborative environment where everyone leverages insights to drive growth and innovation.

### 4. Identifying Untapped Opportunities

An engaging BI dashboard helps uncover hidden opportunities and potential areas for improvement. Stakeholders can pinpoint underperforming aspects of the business, leading to proactive interventions and optimizing resources.

### 5. Gaining a Competitive Edge

Organizations that prioritize UX in their BI dashboards gain a competitive edge in the market. The ability to quickly access and interpret data gives them an agility advantage, enabling them to respond swiftly to market changes and customer demands.

### 6. Ensuring Return on Investment (ROI)

BI dashboards are significant investments for organizations. To ensure a meaningful ROI, it is crucial that stakeholders fully utilize the dashboard's capabilities. High adoption rates translate into greater returns as insights lead to better decision-making, cost savings, and revenue growth.

### 7. Promoting Collaborative Decision-Making

A user-friendly BI dashboard encourages collaboration among different teams and departments. With the ability to share insights and discuss data-driven strategies, stakeholders can align their efforts toward common goals. 

## How to build better business intelligence dashboards

### 1. Know Your Audience

The foundation of a successful BI dashboard lies in understanding the needs, preferences, and pain points of your internal stakeholders. Conduct thorough user research and engage in conversations with key stakeholders to identify their specific requirements. By knowing your audience, you can tailor the dashboard's design and content to meet their expectations, ultimately leading to higher adoption rates.

### 2. Define Clear Objectives

Before diving into dashboard development, establish clear objectives and key performance indicators (KPIs) that the BI dashboard is expected to address. Whether it's monitoring sales performance, tracking project progress, or analyzing customer behavior, defining measurable goals will ensure the dashboard's relevance and impact on the organization's overall success.

### 3. Prioritize Data Relevance and Accuracy 

Relevant and accurate data is the lifeblood of any BI dashboard. Ensure that the data sources are trustworthy and regularly updated. Additionally, present the most critical information prominently, prioritizing real-time or near-real-time data, if applicable, to ensure that the dashboard provides timely insights for decision-making.

### 4. Design for Intuitive Navigation

A key aspect of usability is ensuring the dashboard's navigation is intuitive and user-friendly. Arrange data elements logically, provide clear labels and descriptions, and avoid clutter. Implement a clear and consistent hierarchy to guide users seamlessly through the dashboard, allowing them to drill down to the details they need effortlessly.

### 5. Embrace Data Visualization Best Practices

Visual representations of data are powerful tools for comprehension and insight. Utilize data visualization best practices to convey complex information clearly and concisely. Choose appropriate chart types, color schemes, and label placements to facilitate easy interpretation of the data. Avoid using overly complex visuals that may confuse users or obscure key insights.

### 6. Provide Interactivity and Customization Options

Empower internal stakeholders by offering interactive features and customization options within the dashboard. Users should be able to filter, sort, and personalize the dashboard to suit their unique requirements. By allowing users to interact with the data, you enhance engagement and enable them to derive deeper insights.

### 7. Optimize for Responsiveness

In today's fast-paced business environment, stakeholders access business intelligence dashboards across various devices, including desktops, laptops, tablets, and smartphones. Ensure the dashboard's design is responsive and adaptive, so it seamlessly adjusts to different screen sizes and orientations. A mobile-friendly dashboard enables stakeholders to stay informed, even while on the go.

### 8. Monitor and Analyze User Behavior

Keep a close eye on how stakeholders interact with the dashboard. Analyze user behavior to understand which sections are most and least popular, where users drop off, and what actions lead to desired outcomes. Utilize this data to continuously improve the dashboard's usability and performance.

### 9. Foster Collaborative Workflows

business intelligence dashboards often serve as a shared resource for internal stakeholders across different departments. Facilitate collaboration by integrating features that enable users to share insights, export data, or generate custom reports. By fostering collaborative workflows, you encourage cross-functional engagement and promote a data-driven culture within the organization.

### 10. Regularly Update and Improve

A static BI dashboard loses its appeal over time. To sustain high adoption rates and drive ROI, invest in regular updates and improvements. Address user feedback, incorporate new data sources, and stay up-to-date with evolving best practices in BI dashboard design. A living dashboard that evolves with the organization's needs will keep stakeholders engaged and invested in its insights.

## Strategies for building better business intelligence dashboards

### User Persona Profiling:

Conduct thorough user persona profiling to understand the specific needs and pain points of different user groups within the organization. For instance, a sales manager might require real-time updates on key performance metrics, while a marketing analyst might need historical data for trend analysis. Tailor the dashboard's layout and content to cater to the unique requirements of each persona, ensuring high user satisfaction and adoption.

### Goal-Oriented Dashboard Design:

Create goal-oriented dashboards by aligning the key performance indicators (KPIs) with the objectives of individual departments or teams. For example, if the HR team aims to reduce employee turnover, the dashboard should prominently display metrics related to employee satisfaction, turnover rates, and retention strategies.

### Data Storytelling:

Incorporate data storytelling techniques into the dashboard design to make the insights more compelling and understandable for stakeholders. Use narrative elements such as headings, annotations, and callouts to guide users through the data and help them draw meaningful conclusions. An example could be using a storytelling approach to showcase the impact of marketing campaigns on sales figures.

### Drill-Down Functionality:

Implement drill-down functionality that allows users to explore data at different levels of granularity. For instance, a financial analyst should be able to drill down from a high-level overview of company expenses to specific cost categories and individual transactions. This empowers stakeholders to investigate anomalies and gain deeper insights.

### Actionable Alerts and Notifications:

Integrate actionable alerts and notifications into the dashboard to keep stakeholders informed about critical changes or events in real-time. For instance, if inventory levels drop below a certain threshold, the inventory management team should receive an automated alert to take necessary actions promptly.

### Collaborative Annotations:

Enable users to add comments, annotations, or share their insights within the dashboard. This fosters collaboration among teams, allowing them to exchange ideas and collectively arrive at data-driven decisions.

### Personalization Options:

Provide users with the ability to personalize their dashboard by selecting preferred data visualization styles, default filters, or customized layouts. For example, allowing sales representatives to choose their preferred sales metrics and target regions.

### Performance Optimization:

Optimize the dashboard's performance to ensure fast loading times, especially when dealing with large datasets. Implement caching mechanisms and data pre-processing techniques to deliver a smooth user experience.

### A/B Testing:

Employ A/B testing to experiment with different dashboard layouts, visualizations, or interaction designs. Measure user engagement and satisfaction to identify the most effective dashboard design that drives higher adoption and ROI.

### User Training and Support:

Provide comprehensive user training and ongoing support to ensure that stakeholders understand the dashboard's capabilities and use it to its full potential. Conduct workshops, create video tutorials, or offer documentation to aid users in harnessing the dashboard's power for data analysis and decision-making. 

---

## To sum things up.

Building business intelligence dashboards that internal stakeholders genuinely want to use is a critical success factor for any organization. By driving adoption through usability and UX best practices, organizations empower stakeholders with the right data, at the right time, in the right way. This fosters a data-driven culture, enhances productivity, and enables the organization to stay ahead in a competitive landscape. With a strong focus on building engaging and valuable business intelligence dashboards, organizations can unlock the full potential of their data, driving growth, and achieving sustained success.

Want to build better BI dashboards, faster? 

Check out Cube—the semantic layer for every data application. With upstream [data modeling](https://cube.dev/product/data-modeling), [caching](https://cube.dev/product/caching-and-data-performance), and [data access control](https://cube.dev/product/data-access-control), you can ensure your data applications are consistent, fast, and secure. And with its [data API layer](https://cube.dev/product/data-apis), you can curate your stack and tailor your app precisely to your users, without the resource demands of building an application from scratch. 

Tamar Barlev

Best Practices

Building Business Intelligence Dashboards (your team will actually want to use)

In the past, making data-driven decisions was challenging because of limited access to specialized data analytics skills and technology. Processing and analyzing large amounts of data was tough, so only professionals in data analytics could do it. Others were left out.

But today, things have changed. In our data-driven world, access to data is crucial for all employees in a business. It helps us make informed decisions and take action. Without it, staying competitive can be tough. So, businesses should prioritize making data accessible to everyone.

Luckily, advancements in the data landscape are making this possible. AI and LLM-based applications, especially, are expediting our move toward data democratization, empowering organizations to make data-driven decisions, regardless of individual data analytics skills.

And by embracing these advancements, businesses can create a culture that embraces data, empowering every employee to contribute to the organization's success through data-driven decision-making. This holistic approach ensures that no valuable insights are left behind and that the organization thrives in the ever-evolving data-driven era.

## What is Self Service Analytics?

Self service analytics is the ability to independently analyze and extract insights from data without extensive technical assistance from data scientists or specialized analysts. The concept allows the end-user to access data and analysis tools without the need for specialized technical skills—a game-changer because power is truly in the hands of the user.  

Often, self service analytics tools come equipped with intuitive interfaces that make data analysis accessible to everyone—no matter their level of expertise—in which users can quickly and easily access data, generate insights, and make data-driven decisions in real-time. 

For example, a marketing manager—hello!—can use self-service analytics to track campaign performance, identify trends, and optimize their strategy for better results. Alternatively, an accountant can use self-service analytics to monitor cash flow, conduct financial analysis, and forecast revenue streams. 

Really—these tools are simply everywhere. So, embracing self-service analytics is no longer an option in today's data-driven world; it's a necessity for companies who want to stay ahead of the pack.

## Self Service Analytics: The Past, The Present, The Future

### The Past

Let’s take it back ten years—back when the data landscape was younger, smaller, and far more limited. One glaring missing piece was the lack of user-friendly tools for data analysis. You needed specialized skills and a dedicated team just to handle the complex data processing tasks. This meant that if you didn't have a technical background, using data effectively was really hard.

And it wasn't just the tools. Data storage and analysis capabilities were also pretty limited. The amount of data being generated was way too much for the existing infrastructure to handle. So, organizations struggled to manage and extract insights from big datasets. This created isolated pockets of information and made it difficult for different teams to collaborate and get a complete picture of the data. Decisions were being made without the full story.

All these limitations ushered in the improvements to come to data processing, storage, and accessibility. We needed intuitive tools that anyone could use to analyze data. And we needed to scale up our infrastructure to handle massive amounts of data. 

### The Present

Fortunately, in recent years, advancements in technology have transformed the data landscape, revolutionizing self-service analytics and empowering data-driven decision-making. Cutting-edge technologies now enable organizations to effortlessly identify trends, patterns, and correlations within their data. These include:

**Advanced Analytics Tools:** Interactive dashboards and intuitive data visualization platforms like Tableau and Power BI have revolutionized self-service analytics. With user-friendly interfaces and drag-and-drop functionalities, business users can explore and analyze data without specialized skills or IT assistance.

**Cloud Computing:** Cloud platforms like AWS, Azure, and GCP offer scalable infrastructure and on-demand computing resources, revolutionizing data storage and processing capabilities. Cloud-based analytics services like Amazon Redshift and Google BigQuery enable complex data analysis without extensive on-premises infrastructure.

**Big Data Technologies:** Apache Hadoop and Apache Spark provide distributed processing frameworks for handling and deriving insights from large and diverse datasets. These technologies facilitate efficient data processing, storage, and analysis, even with terabytes or petabytes of data.

**Real-time Analytics:** With technology advancements, organizations can now analyze data in real time. This enables businesses to gain immediate insights from streaming data sources, like sensors, social media feeds, or IoT devices. For instance, a transportation company can use real-time analytics to monitor and optimize routes, responding quickly to changing traffic conditions. Achieving this involves integrating technologies such as complex event processing (CEP), stream processing frameworks (e.g., Apache Flink), and scalable data ingestion systems (e.g., Apache Kafka).

### The Future

Looking to the future, the data landscape holds great potential for even more exciting innovations that will significantly unlock new levels of insight and efficiency, such as:

**AI Data Applications**: which automate data analytics, leveraging advanced algorithms to extract actionable insights. For example, AI detects anomalies in real-time data, enabling immediate action. Additionally, AI recommendation systems enhance decision-making and customer experiences by providing personalized suggestions based on user behavior and preferences.

**Large Language Models (LLMs)**, which demonstrate impressive abilities in understanding and generating human-like text. They can revolutionize data interaction by enabling the development of natural language interfaces. These interfaces allow users to query and analyze data using everyday language, simplifying the process and making it accessible to a wider audience. This enhances the usability of data analysis tools, making data-driven insights more accessible to all.

**Machine Learning Predictions,** which enable organizations can make accurate predictions using historical data. For example, businesses can forecast customer demand, optimize inventory management, and identify fraud patterns. Regression, classification, and time series analysis uncover data patterns, empowering confident, data-driven decisions.

**Natural Language Processing (NLP)**, which facilitates intuitive interaction with data. Organizations can utilize NLP for tasks like sentiment analysis, entity recognition, and document summarization. Enhanced NLP empowers users to explore and analyze data using conversational interfaces, voice commands, or chatbots. A chatbot, for instance, can instantly answer questions about sales data, obviating the need for manual querying and analysis.

These advancements in AI and LLM-powered data applications, along with real-time analytics, machine learning predictions, and NLP, will shape the future of self-service analytics. They will empower companies to extract valuable insights from their data more efficiently and make data-driven decisions with greater accuracy and speed.

## An ever-expanding self service analytics stack: the problems

So—we’ve clearly laid out the massive benefits (current and potential) of self service analytics; there are many: faster insights, increased productivity, and more informed decisions. 

However, the challenges that come with an expanding modern data stack can make building self-service analytics somewhat a daunting task. They (can) include:

1. **Cross-stack incompatibility**, which leaves data siloed and difficult to access.
2. **Inconsistent data (and, therefore, insights)** in downstream tools due to manual orchestration of data modeling and metrics definitions per application.
3. **Data access control gaps**, which happen if there’s a process of repeated, _manual_ orchestration of security context in each application.
4. **Latency and slow application performance**, due to a large number of concurrent and redundant queries hitting the data source directly. 
5. **Slow-to-market entry**, because building data applications completely from scratch requires significant resources.
6. **High costs**, due to inefficient data warehouse resource consumption, as well as app development.
7. **And with AI-powered applications**: ‘AI hallucinations’ and misleading insights when data is not contextualized with semantics.

Overwhelmed yet? Read on.

## An ever-expanding self service analytics stack: the solution

So, sure—the seemingly ever-expanding modern data universe can cause some real headaches. But there _is_ a solution that addresses all these problems and enables self service analytics: a complete, universal semantic layer.

It tackles cross-stack incompatibility by connecting seamlessly to any tool and source through an API layer and integrations. It not only centralizes all data modeling and metrics definitions to address data inconsistency but also resolves data access control gaps by centralizing security upstream of every data application. 

Moreover, the semantic layer’s cache pre-aggregates and stores data, effectively addressing the challenge of slow application performance—and high data warehousing costs. It also streamlines development by separating data modeling, caching, and data access from front-end development, resulting in faster deployments—and lesser development costs. 

And, finally, the semantic layer enriches the consumed data with semantics, p[reventing 'hallucinating' AI and LLM-powered applications](https://cube.dev/blog/semantic-layer-the-backbone-of-ai-powered-data-experiences). 

In short, a complete and universal semantic layer is the key to modern data stack expansion and enabling self-service analytics.

## To sum it up.

With self service analytics, it's clear that the future (which is and has already been here for a while) will be all about democratizing data and empowering broad employee access to insights.

And that's exactly why companies need to pay close attention to their modern data stack and make sure they have the right solutions in place to fully leverage the power of self service analytics. 

Want to learn how you can serve better data to your team, better and faster? [Say hi](https://cube.dev/contact)—we’d love to talk :) 

Onwards and upwards, 

Tamar


The Future of Self Service Analytics: Data-Driven Decisions, Meet Accessibility

## What is Embedded Analytics?

Embedded analytics is the practice of integrating data analytics and visualization capabilities within a user’s natural workflow. Think about your internal SEO tooling, your fitness tracker, or your e-commerce web portals (how’s the knit scarf business going, btw?)

Embedded analytics is an essential component of a smooth user experience now that we’re all in an exclusive relationship with Our Data. It’s almost a bit painful to imagine a process in which we’d need to run reports manually, download them menially, and only _then_ analyze data in a third-party tool. And we do: the Harvard Business Review found that [employees spend 9% of their time](https://hbr.org/2022/08/how-much-time-and-energy-do-we-waste-toggling-between-applications) toggling between applications to look for information.

Therefore, many companies and organizations are turning to embedded analytics to give their internal and external users great data experiences. And that begs the question: what _are_ the components of a great embedded analytics tools stack?

## What are the components of a _great_ embedded analytics tools stack?

In the olden days—approximately 2.38 years ago—an embedded analytics tools stack began with the data source and ended with the presentation layer (with nothing else in there).  

No longer. 

The components of a _good_ modern embedded analytics stack are:

1. **Data Source:** where your data is aggregated and stored for downstream consumption. 
2. **Semantic Layer**: where your metrics definitions, data access controls, and caching live. 
3. **Presentation Layer**: where you build and how you deliver beautiful embedded analytics experiences to your users. 

So, what’s up with #2? Why shouldn’t you simply connect your data source to your presentation layer?

Actually—for many reasons. For one, if you’re using multiple presentation layers to give a customized UX, you _really_ shouldn’t be manually redefining metrics definitions and security context for each—doing so is redundant at best and hazardous (both for accuracy and for security) at worst. 

For a second reason—your users shouldn’t be querying directly from the data source; that’s _also_ redundant at best and hazardous (for application performance and cost-effectiveness) at worst. 

And so, these days, **it’s best practice to have something sit between the data source and the application—a place to consistently define how metrics are calculated, how information is protected, and how data is cached. And at Cube, we built an API-first platform that operates as all of those things: the Semantic Layer.**

## What makes good embedded analytics tools…good?

__1. They’re fast to market:__

    Embedded analytics is a wildly important feature in the user experience. It can also, however, be a wildly dragged-out and expensive thing to build if done incorrectly. This is why it’s crucial to use embedded analytics tools in your stacks _with_ a semantic layer that centralizes metrics.   

![](https://ucarecdn.com/65cf8909-b528-4747-bba7-43763ae7b1b5/)

__2. They’re granularly secure:__

When many users across different accounts access the same endpoints, ubiquitous row- and column-based data access controls are beyond critical.  Consistency is key here—which puts another nail in the coffin for manually defining security context in each presentation layer.

__3. They’re supportive of a data-rich user experience__

Whether you’re going for a more OOB, less native experience, or one with multiple custom presentation layers, your stack needs to be able to give users granular and interactive access to their data. This means the stack enables users to drill down, filter, or generate reports—allowing them to access the data they need in the most convenient ways.

__4. They’re scalable and easily integrable:__

    Meaning that your tool stack should seamlessly integrate with the other components in it and other embedded analytics tools you may want to use in the future. 

## So, what’s an example of an embedded analytics tools stack?

**We’ll start with a disclaimer: the particular stack of data source and presentation layers you might consider will differ depending on your use case, technical requirements, and business considerations**. 

But, if you’re looking to deliver a native embedded analytics experience quickly, here’s an example of a stack with all of the _essential_ components:

1. Data source
2. Semantic Layer
3. Presentation Layer(s)

### 1. Embedded Analytics Tools: Data Sources

#### General-purpose Cloud Data Warehouses

Let’s start with **[Snowflake](https://www.snowflake.com/en/)**. Snowflake is a cloud-based data platform that provides a unified and scalable solution for storing, managing, and analyzing large volumes of structured and semi-structured data. It’s designed to address the challenges associated with traditional data warehousing and analytics systems by leveraging the power of cloud computing and modern data processing techniques. 

Some major pros of Snowflake include virtually unlimited scalability and automatic resource scaling based on demand; seamless integration with many data sources, an elastic compute model enabling a pay-for-what-you-use model; and an architecture that supports high concurrency and parallel processing. 

That said, organizations should consider factors like cost management, data migration, and internet connectivity when evaluating Snowflake’s suitability for their specific use cases.

**[Databricks](https://www.databricks.com/)** is another great option—a unified analytics platform that provides a collaborative environment for big data processing and machine learning. It offers numerous practical advantages, such as streamlined data processing, easy scalability, and advanced analytics capabilities. Additionally, Databricks simplifies the integration of various data sources and supports real-time data processing.

In this case, organizations should consider factors like the learning curve associated with its complex features and some limitations when controlling infrastructure configuration. But regardless, Databricks is a powerful tool for organizations seeking efficient data analysis and machine learning solutions.

#### Fast Analytics Databases

Fast analytics databases are high-performance data storage systems optimized for quick processing of large volumes of data. They leverage advanced indexing techniques, parallel processing, and in-memory computing to deliver rapid data retrieval and analysis. These databases enable organizations to gain valuable insights and make real-time data-driven decisions.

Take, for instance, **[ClickHouse](https://clickhouse.com/)**: an open-source columnar DB management system designed for high-performance analytics. It excels at executing ad-hoc queries on large data volumes with blazing-fast speeds. ClickHouse is scalable, handles petabytes of data, and supports real-time processing for time series, log, and clickstream analysis. And so, while it does have a steeper learning curve and is optimized for read-heavy workloads, it's a powerful tool for complex analytics and data-driven insights.

Another option in the fast analytics database world is **[Druid](https://druid.apache.org/)**, an open-source columnar store designed for real-time, high-performance analytics. It's optimized for sub-second queries and handling large data volumes, making it ideal for interactive analysis, flexible data modeling, and scalability. Druid offers fast data ingestion and query response times, enabling real-time analysis of streaming and historical data. Being a specialized and complex tool, it's more suited for advanced users and specific scenarios because, as a distributed system, careful configuration and architectural considerations are needed for optimal performance.

### 2. Embedded Analytics Tools: Semantic Layer

A semantic layer is a middleware that sits between your data source and your presentation layers. It helps you manage all of the information you feed into every downstream data app in your stack. And it’s critical for building embedded analytics.  

Incorporating a semantic layer into your embedded analytics stack means you’re able to efficiently build native applications and ensure that the data that’s in them is consistent, secure, and fast. 

We happen to know a lot about semantic layers because we’re [Cube: the universal semantic layer](https://cube.dev/use-cases/semantic-layer). Cube enables you to access data from modern data stores, organize it into [consistent metrics definitions](https://cube.dev/product/data-modeling), and then deliver them to every downstream application—while also enforcing [uniform data governance](https://cube.dev/product/data-access-control) and ensuring [consistent performance and data freshness ](https://cube.dev/product/caching-and-data-performance)across every app and every team. 

And, Cube exposes REST, GraphQL, and SQL [APIs for universal compatibility](https://cube.dev/product/data-apis) and [streamlined data provisioning](https://cube.dev/docs/semantic-layer-sync/#semantic-layer-sync) with any downstream tool. Meaning, whether you’re building internal BI applications based on out-of-the-box iframes, developing custom, beautifully intricate data experiences for customers—or both—you can do it all based on Cube while only orchestrating data modeling, caching, and access controls in one place.

### 3. Embedded Analytics Tools: Presentation Layers

#### For internal-facing embedded analytics:

*Traditional BI*

Let’s start with what we’ll call “Traditional BI” software. These platforms have been around for a while and, with their long tenure, have become the dominant players in the space. Again, we’ll preface this rundown with a disclaimer that this is _not_ a complete list. 

Traditional BIs are often popular with later-stage companies that have already defined their data needs. Ergo, rather than needing tools for *data exploration* (more on those later), these data consumers know what questions they need to ask every day; they just need set dashboards with which to continuously monitor the answers (or metrics, KPIs, etc.)  

So, in one corner, we have **[Tableau](https://www.tableau.com/)**: an established, heavy-weight player in the game. I say ‘heavy-weight’ because it enables analytics with a very large feature set—and, therefore, plenty of analytical flexibility. Tableau’s substantial feature set allows for comprehensive analytics, which makes it a great tool for technically skilled analysts. 

In another corner, we have **Microsoft Power BI**, which is similar to Tableau in its popularity for building internal dashboards—but is possibly even more ubiquitous due to its frequent packaging with other Microsoft Office products. This bundling can make Power BI more cost-accessible than Tableau, although it does have tiered features and capabilities that gate advanced analytics features.

Lastly (although *absolutely* not least), we have **[Thoughtspot](https://www.thoughtspot.com/)**. Thoughtspot is a BI platform that, like Power BI and Tableau, offers data visualization. However, it differs in its *search-based* analytics. Meaning you can query it in natural language (rather than SQL) and receive a visualization or answer. This capability helps it make analytics accessible to a much wider pool of data consumers—a company’s data practitioners _and_ general business users. 

*Interactive Notebooks*

And then there’s another category of potential embedded analytics tools for internal use cases: ‘Interactive Notebooks.’

Interactive notebooks are powerful analytics tools that allow greater flexibility than OOB, self-serve, ‘traditional’ BI. These platforms enable technical data practitioners with more advanced analytics skills to:

* Connect and query multiple data sources
* Analyze information in a flexible and collaborative environment
* Build data apps and documents that are useful to both technical and non-technical data consumers
* Efficiently distribute the artifacts to those who need them.

This class of tools is popular among early-stage companies, for which the questions stakeholders are asking aren’t yet set in historical stone. Therefore, analysts are more inclined to do *data exploration* of operational analytics to find the metrics that are important to continuously track.

Let’s talk examples.

**[Hex](https://hex.tech/)** is one of the most popular data notebooks out there. It offers streamlined connectivity to data sources and SQL- and Python-based analytics workspaces, making it a fantastic tool for data scientists or analysts to build easily shareable, interactive data apps ([here’s our rundown about it](https://cube.dev/blog/hex-integration-collaborative-data-notebooks).)

Another great notebook is **[Deepnote](https://deepnote.com/home)**, which enables and streamlines real-time collaboration of the data engineers, data scientists, and analysts on your team; consider it the “Google Docs” of data. It’s both [Jupyter](https://jupyter.org/)-compatible and runs on the cloud, making it easy to run and integrate [(here’s our take on it](https://cube.dev/blog/deepnote-tutorial-jupyter-data-notebook).)

And as a final example, we have **[Count](https://count.co/)**. Count allows data consumers to collaborate on building everything from reports and dashboards to process flows and strategic plans. And, it makes itself accessible to non-technical users by also featuring templates, drag-and-drop functionality, and low-code/no-code interfaces.

#### For customer-facing embedded analytics:

And lastly, it’s time to tackle customer-facing embedded analytics—in which flexibility to build tailor-made, native UIs reign supreme. 

There are many, many options of frameworks and libraries to choose from, but we’ll highlight a few.  

First, let’s talk about **[React](https://react.dev/)**. React is a widely-used front-end framework that offers a rich ecosystem of charting libraries like Recharts and Victory. React's component-based architecture simplifies building reusable charts and seamlessly integrates with other React-based components. React's virtual DOM also enables efficient rendering, resulting in smooth and performant data visualizations. So with all of this, it’s commonly used for building interactive UIs and single-page applications ([example](https://react-dashboard-demo.cube.dev/#/)).

There’s also **[D3.js](https://d3js.org/)**. A charting library known for its flexibility and data-driven approach, D3.js is a popular choice for building customized and advanced visualizations. It provides a low-level API that gives developers complete control over the visualization process. While it requires a steeper learning curve, D3.js empowers engineers to create highly expressive and interactive charts ([example](https://d3-dashboard-demo.cube.dev/#/)).

And finally, **[Highcharts](https://www.highcharts.com/)**—is a powerful charting library with a wide range of chart types and customization options. Highcharts provides interactive and visually appealing charts that can be seamlessly integrated into applications and offers extensive documentation and a supportive community. Because it allows for a wide range of pre-built chart types, Highcharts is ideal for quickly creating interactive charts and graphs ([example](https://highcharts-demo.cube.dev/#/)).

## Best of both worlds: flexibility and time-to-value.

The important thing to note here is that today's embedded analytics tools stack is not what it was; it’s *much* better. 

Gone are the days of having to choose between inflexible, ‘force-fit’ traditional BI or the resource drains of building custom data experiences from scratch. With a semantic layer, you can simply have both. 

You can build native embedded analytics with minimal engineering resources for development and maintenance. And, you can ensure that your internal BI dashboards—[in all 76 BI apps your company uses](https://www.forrester.com/report/Use-BI-Fabric-To-Optimize-Your-Multivendor-Business-Intelligence-Environment/RES138551)—work off the same metrics definitions, caching, and data access controls. 

Curious about how others have done it? Check out our [case study with RamSoft](https://cube.dev/case-studies/how-ramsoft-gave-its-users-native-embedded-analytics-in-two-weeks), **which delivered native embedded analytics to users in two weeks with Cube.**

And if you want to learn more about how to do that, too, [reach out to us](https://cube.dev/contact). Let’s talk.  

Onward and upwards, 

Tamar


Or, simply, how to build better embedded analytics, faster.

The Critical Components: an Example Embedded Analytics Tools Stack

We’ve spent a good bit of time discussing [the composition of a semantic layer][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 to _bring 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.”

With embedded analytics financial analysis can 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—without toggling between unfriendly, third-party dashboards [a Harvard Business Review study found that switching between applications costs employees time equivalent of up to five weeks a year](https://hbr.org/2022/08/how-much-time-and-energy-do-we-waste-toggling-between-applications).

At Cube, we’ve been on the front lines ofembedded analytics 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 sets 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—which is often the most uniquely competitive asset a company has. This newfound accessibility has led to the [explosion in adoption](https://www.alliedmarketresearch.com/data-warehousing-market) of cloud data warehouses.

But it's not enough to just collect lots of data. Businesses need to _use_ the data, too—which 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

As more and more businesses invest in embedded analytics, **there is also a quickly growing variety 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.

So, 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. Moreover, the modern data consumer may not even be familiar with legacy BI tools—and, again, they shouldn’t need to be.

In order to make data accessible and actionable for these data consumers, companies need to build customized user interfaces that move beyond mere iframed graphs. And so, to match the pace of increasing diversity in data consumers, 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 tailored, streamlined user experiences.

This innovation 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, higher 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 development 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]

So, as we think 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 store and process data, but also 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, on the other end, we see a trend away from moving data into “serving” databases and towards powering applications with data directly from warehouses. This architecture is easier to orchestrate 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.

## Semantic Layer in the middle

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

### First-class support for cloud data warehouses

A major piece of a semantic layer is **[access control](https://cube.dev/product/data-access-control)** integrated with the data warehouse’s security controls, because embedded analytics always require multitenancy. A second piece is **[advanced caching](https://cube.dev/product/caching-and-data-performance)** because the data warehouse is a great candidate for a backend, it doesn't does not support high-concurrency small queries with the low latency that modern data consumers expect.

### Data modeling

Across the wide variety new data consumers every one 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 defining metrics](https://cube.dev/product/data-modeling) 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 [semantic layer must be accessible via multiple APIs](https://cube.dev/product/data-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 universally open and compatible with every frontend architecture; organizations should be able to granularly curate their stacks 

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. These tools can be natively integrated into an app and combined with more custom interface, while making it It’s faster and cheaper to build tailored applications..

And on the least customizable—but lowest barrier to entry end—data analysts and engineers can quickly build appealing data interfaces with modern BI tools like [Superset][24] and [Metabase][25]. Theses tools don't require front-end knowledge, which 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 segment includes [Appsmith][26] and [Retool][27] which can also be used to build analytics interfaces.

## Conclusion

We’ve moved far past the 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—one that’s already rapidly taking place—is decoupling  BIs from the frontend. By doing so, organizations are empowered to faciliate total consistency of the same data across every and all data applications, as well as AI agents and chatbots. 

This architecture will be enabled by several products, including Cube. And, as the category matures, and more enterprises adopt this architecture, we’ll see exciting, exponentially compounding developments. So, buckle up.

[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/43cba8de-9051-4064-a408-7278eab16218/

Artyom Keydunov

Deep Dives

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

The Modern Data Stack for Embedded Analytics

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).


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/product/configuration/advanced/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)
