paint-brush
Understanding Embedded Analytics: Definition, Benefits, and Use Casesby@goqrvey
23,699 reads
23,699 reads

Understanding Embedded Analytics: Definition, Benefits, and Use Cases

by QrveyMarch 6th, 2024
Read on Terminal Reader
Read this story w/o Javascript

Too Long; Didn't Read

Embedded analytics integrates analytics features directly into applications, offering benefits like enhanced user experience, real-time insights, and increased revenue streams. Key features include self-service dashboards, data security, and white-labeling. Choosing the right solution involves considering developer friendliness, cost structure, architecture, and data readiness. The integration process requires installation, configuration, application building, and embedding within the host software.
featured image - Understanding Embedded Analytics: Definition, Benefits, and Use Cases
Qrvey HackerNoon profile picture


What is Embedded Analytics?

Embedded analytics is the technological capability to include analytics features and functions as an inherent part of another application.


According to the Dresner Wisdom of Crowds® 2023 Embedded Business Intelligence Market Study, the current use of embedded BI is at 49 percent, and adoption plans remain strong. Additionally, eighty-six percent of industry respondents say embedded BI is critical or very important.

Embedded Analytics Meaning

An embedded analytics for SaaS solution enables users of a SaaS application to harness the power of business intelligence to analyze the data they create inside their apps. This eliminates the need to export data only to import it into a separate business intelligence tool.


12 Crucial Embedded Analytics Features

1. Self-Service, Easy-to-Build Charts & Dashboards

Users should have the capability to effortlessly create visually appealing data visualizations with just a point and click. An intuitive, self-service chart builder should be user-friendly and incorporate dashboard builder elements for crafting personalized dashboards and reports.


Learn how Qrvey empowered Impexium to swiftly enter the market and provide analytics directly to their customers. Facing the necessity to replace their outdated analytics platform, Impexium sought a modern solution equipped with self-service functionalities, responsive design, and automated data processes.

2. Any Type of Data

More than 70% of all business data is never used for analysis because most traditional analytics tools only work with structured data. To gain vital insights, you must be able to integrate all of your data, including semi and unstructured data sources like forms and images.

3. Workflow & Automation

It’s great if users can discover new insights with your analytics platform – but better yet if the platform does the discovering for users! Automation can alert users when conditions are met, and workflows can be triggered if thresholds are exceeded.


With self-service workflow builders, even non-technical users can easily automate common tasks and make things happen the moment conditions change. Automation can be triggered automatically as new data is received or when user-defined metrics and thresholds are met, allowing all new types of data-driven applications to be created.


Add powerful business logic to your workflows and applications with conditional rules and ML models. Only with automation can your analytics platform work for you 24 hours a day.

4. Shareable Insights

Once users have obtained valuable insights, they should be able to easily share and disseminate them. Look for features such as the ability to create multi-page and multi-tab reports that include full interactivity and data security built right in. With content deployment features, you can roll out templates and dashboards to specific tenants at your own pace.

5. Interactivity such as Drill Down & Drill Through

Users should be able to interact with reports to easily access additional info as needed. Drill down takes users from a high level to a more granular one, allowing users to go deeper into the data, for example from country to state. Drill through takes the user to a report relevant to the data being analyzed, passing to another report while still analyzing the same data set. Finally, advanced filtering options enable users to refine the data that is displayed in reports.

6. Data Security & Managed Access Controls

Record- and column-level security allow administrators to restrict data access at granular levels in a dataset, so each user gets only the information they are authorized to see. Grant access to data, insights, and applications according to each user’s role.


Security tools and features must support multi-tenant SaaS applications and ideally will inherit your security model, including all of your rules and policies. Seamlessly integrate analytics into your SaaS application with single sign-on.

7. Deployable to Your SaaS Platform Environment

Being able to deploy the embedded analytics software to private environments for maximum data security is a crucial feature in maintaining control over data. Additionally, this method will inherit your existing security policies rather than forcing you to rely on a third party to manage your data.


By deploying into YOUR cloud, your data never leaves your account, enabling you to keep your data in your environment under your control.


To fit into software development lifecycles (SDLC), you should also be able to deploy to code repositories and multiple development environments.

8. White Labeling & UI Customization

There are many benefits to embedding a third-party product instead of building everything in-house. But your customers don’t need to know. Embedded analytics should be fully customizable, including updating the look with CSS and themes to ensure seamless blending into your SaaS application. The user experience should be consistent and white-label embedded analytics is the way to go.


The Dresner Wisdom of Crowds® Business Intelligence Market Study recognizes the importance of customization abilities. The Study rates vendors using a 33-criteria evaluation model, including “customization and extensibility” within the category of “quality and usefulness of product.”

9. Native Multi-Tenancy

Out-of-the-box multi-tenancy is essential for SaaS use cases. This also directly affects the time to market, as many solutions require extensive custom development to force multi-tenancy. Learn more on multi-tenant architecture for embedded analytics.

10. Unlimited User Licensing

Predicting usage within a SaaS application is nearly impossible, so an ideal solution will provide unlimited user licensing. Most traditional business intelligence solutions can only offer user licensing and that tends to be a blocker to adoption. User-based licensing is a significant cost driver that can prevent you from achieving a positive ROI.

11. Everything is Embeddable

A proper embedded analytics solution must provide multiple components that are fully embeddable using Javascript and avoiding iframes for a seamless user experience. You should be able to embed dashboard and chart widgets, dashboard and chart builders, data management, automation rule management, and more.

12. Easy Data Integration

Pre-built database connectors and easy-to-use APIs are essential to rapid integration and fast time to market. Additionally, native support for both structured (SQL) and semi-structured (NoSQL) data means more flexibility, reducing the need for useless transformations and wasted processing.


Benefits of Embedded Analytics

Embedded analytics for SaaS applications can provide significant advantages for both the software vendor and end users. By embedding analytics capabilities like dashboards, reporting, and predictive insights into a SaaS application, vendors can enhance their offerings and provide additional value for customers.

Increase revenue

Monetizing analytics in several ways, through premium user tiers that unlock more advanced capabilities, add-on products that extend functionality, and professional services to help customers analyze their data alongside professionals. This presents new revenue streams beyond standard software subscriptions.


Paddle, a provider of payments infrastructure for SaaS companies, conducted a study of 512 SaaS companies showing monetization was four times more efficient than acquisition in improving growth and twice as efficient than efforts to improve retention.

Enhance customer satisfaction & engagement

Provide customers with a seamless and intuitive user experience. Allow them to access and interact with data and insights within their workflow without having to switch to a separate analytics tool or platform.

Increase customer satisfaction & retention rates

Provide customers with valuable insights to help them solve problems and achieve their goals. Higher NPI scores result from empowering users to get answers to their questions quickly without the need for technical skills or leaving the software. Additionally, the more users can do with a SaaS application, the more they rely on it. As customers are satisfied with your application and rely on it as an integral tool in their business, they’re likely to remain loyal customers.

Differentiate from competitors

Offer a unique and innovative value proposition that can help your users enhance decision-making and improve performance.

Access real-time data.

Embedding analytics also allows for access to real-time data within the application environment users are already working in. Rather than exporting data to analyze in another tool, insights are available immediately within the workflow. This leads to stickier applications that users rely on more heavily as a single source of truth.

Avoid building in-house & maintain focus on your roadmap.

Every hour you spend adding analytics to your software is an hour not spent focusing on your core competitive differentiation (that’s assuming that you’re not an analytics provider like Qrvey!) Building analytics will also put a drag on your entire product roadmap as valuable resources are slowly siphoned away. By embedding analytics functionality from a third-party vendor, you avoid building in-house and accelerate your time to market. Purchasing a third-party product also lowers development costs.

What Are the Common Challenges and Pitfalls of Embedded Analytics?

Server Fees & User-Based Licensing

Some traditional BI solutions that began with a dependence on server installations may still require licensing for each server their software is installed on. Trying to integrate with a software development process or scale a cluster becomes cost-prohibitive over time.


Additionally, user-based licensing is a significant cost driver and often an underappreciated cost over time. Companies that try to “start small” rarely realize the ROI of their investment.

Data Access & Synchronization

Odds are that your app uses more than one type of data… and if it doesn’t now, it certainly might in the future. Therefore, your analytics solution must be able to work with any type of data and to handle the complexity of combining multiple sources.


When embedding analytics, you don’t want to be locked into one architecture or have to go through the hassle of filing down a square peg to fit into a round hole.


Read about how Qrvey helped Global K9 overcome the struggle of analyzing all the data gathered via video capture. With Qrvey, Global K9 was able to definitively prove to the airlines and government agencies that their canine teams can safely process more tonnage of cargo than traditional x-ray technologies.

Forcing Data Transfer to a Third-Party Cloud

An ideal solution keeps your data right where it is…in your environment under your control. You’ll need to do a comprehensive security audit if you send your customer’s data to a third-party cloud.

No Support for Development Environments

As a SaaS company, you have a development lifecycle that is different from an internal IT department in a large company. When you cannot install your embedded analytics software in several development environments, you’re taking chances with your production experience and ultimately your user experience.

Lack of Built-in Scalability & Performance

You want your SaaS application to grow and expand, but embedded analytics solutions that don’t scale easily or natively often create a bottleneck that becomes expensive to fix. Ideally, you should be able to scale without a costly, time-consuming rebuild. As your app scales, the increase in costs should be commensurate with the growth. To achieve the next 15% of growth shouldn’t increase costs 80%.


Additionally, while scaling to accommodate growth, latency shouldn’t increase.

Not AI Ready

Many solutions offer some functionality that integrates AI, but the acronym AI is often used quite loosely. Be sure it’s something that adds value over the long run as AI technology is advancing fast.

iFrame Embeds

While many BI tools can embed dashboards and some can embed individual widgets (charts), the functionality fails to meet the needs of SaaS providers. For example, many traditional BI tools rely on iFrames for their embeds. Most infosec teams struggle to approve iFrame-based solutions due to security concerns. iFrame-based dashboards are also rarely mobile responsive.


Others that do support JavaScript widgets may lack customization options. Some vendors will offer a combination of JavaScript and iFrame-based widgets, further complicating integration into a SaaS application. Javascript-based widgets are the preferred method.

Use Cases of Embedded Analytics

SaaS applications exist in all industries today, therefore embedded analytics serve a great need across any industry. Nearly all SaaS applications are expected to have a strong analytics offering, so if you only offer static, generic dashboards, your customers are likely left wanting more. Following are some popular industry use cases.

Embedded Analytics for SaaS Applications

Reporting features for SaaS analytics within SaaS applications may seem like table stakes, but it’s often an area where SaaS companies can separate themselves from their competition. Qrvey allows SaaS companies to create richer products and bring them to market faster while lowering development costs.


Building embedded analytics in-house is a time-consuming and roadmap-intensive feature that SaaS companies don’t need to undertake.

Healthcare Analytics

With a focus on security, Qrvey’s healthcare analytics solutions enable teams to analyze data within your cloud environment.


Healthcare solutions often include various types of data – SQL, NoSQL, and unstructured data sources like forms and images. It’s vital to connect to any data source, including FHIR-Compliant patient health records. For comprehensive insights, you need to analyze multiple healthcare data sources on a single dashboard. Your analytics solution must be fully compliant with the HL7 FHIR patient medical record standards to integrate within healthcare analytics tools.


By analyzing a breadth of data sources, you can gain performance insights across an entire practice. Uncover patient trends by ingesting and analyzing FHIR analytics data and conducting granular analysis. Analyze patient data to find patterns, predict health risks, and create treatment plans. Analytics can help doctors diagnose diseases more accurately and quickly by using algorithms and machine learning to analyze symptoms, test results, and medical images. Analytics can also help doctors provide personalized and proactive care to their patients, such as identifying patients who are at risk of developing certain diseases and providing preventive measures.


For clinical trials, you can examine large data volumes to spot trends early with comprehensive automation and analyze trial spending in real-time. Improve the quality of care by collecting real-time patient feedback and analyzing outcomes as the data comes in. Boost evidence-based decision-making by empowering researchers and policymakers to analyze vast amounts of clinical data to identify trends, evaluate treatment effectiveness, and develop guidelines for best practices.


Healthcare organizations can also use analytics to improve operational efficiency. Insights from analyzing patient flow, staff productivity, and equipment usage can identify bottlenecks, delays, or waste, and lead to enhanced efficiency and reduced costs.


Detect and prevent fraud and abuse by analyzing claims data to identify suspicious activities, such as billing irregularities, duplicate claims, or false diagnoses. This approach can save money for healthcare organizations as well as protect patients from unnecessary procedures or treatments.


Analytics can also enable healthcare providers to predict patient outcomes and anticipate healthcare needs. Analyzing information, such as medical records, prescriptions, or lifestyle data, can help doctors find patients at high risk who may need extra care or check-ups. This proactive approach can allow for timely interventions, reducing hospital readmissions and improving patient satisfaction. Predictive analytics can also help healthcare providers forecast demand and supply, resulting in improved planning and resource allocation.

Financial Analytics

Transform your financial data into actionable insights with financial analytics software. Visualizing financial data allows for easier comprehension and interpretation of complex data sets. Instead of deciphering numbers and tables, visual representations provide a more intuitive understanding of financial trends and performance. With interactive visualizations, users can manipulate and explore financial data, uncovering hidden insights and patterns that may not be apparent in traditional tabular formats. By embedding interactive analytics directly into financial platforms, you can provide users with immediate access to analytics within familiar systems and accelerate time-to-value.


Financial organizations report paying more than $4 fighting fraud for every $1 of fraud loss, leaving a huge opportunity for smarter analytics to uncover potential fraud. AI in particular has great potential to identify patterns and reduce false positives. Qrvey connects directly to the AWS AI suite to power real-time machine learning augmentation for financial analysis software.


With granular analysis of large data sets, you can uncover trends and find anomalies. By connecting to any data type – SQL, NoSQL, and unstructured data sources like forms and images – you can analyze multiple financial data sources on a single dashboard. Combine data sources to unify financial software and achieve performance insights across an entire organization.


With maximum data security, teams can safely analyze sensitive data, from individual records to entire financial practice performance all within your SaaS platform.


With an API layer built for rapid development, data can be pushed directly from the source for real-time analysis within your financial analyst software solution. Automation and alerting help keep you up-to-date and keep your processes in line.

Logistics Analytics Solutions

Organizations generate large amounts of data around the procurement, processing, distribution, and transportation of goods. In particular, IoT sensors used to monitor manufacturing and logistics equipment generate large volumes of data. Supply chain analytics involves collecting and analyzing data across the supply chain to gain visibility, identify insights, and optimize planning and execution. When embedded into supply chain apps, logistics analytics solutions empower you to gain insight and extract real value from that vast amount of information. Improve operations with better process planning and forecasting.


AI is changing the face of supply chain analytics platforms. AI and machine learning can automate the analysis of large volumes of historical data and provide real-time insights as well as forward-looking decision-making. RFID data can also be analyzed for shelf-space optimization, dynamic pricing, and out-of-stock prevention. Make the most efficient use of warehouse space.


Transportation analytics and GPS technologies can enable you to minimize travel distances, reducing fuel consumption and improving driving efficiency. Logistics analytics software can quickly surface patterns and trends and offer embedded decision logic to improve efficiency, increase productivity, and dramatically lower costs in everything you do.

IT and Cybersecurity Analytics Solutions

IT software vendors are the glue that help companies plan, execute, and complete successful digital transformations. According to BetterCloud’s 2023 State of SaaSOps reportorganizations now use an average of 130 apps. This represents an increase of 18% from the previous year, despite 40% of IT professionals saying they consolidated redundant SaaS apps.


As the number of cloud services has exploded, the complexity of integration options has also grown. With substantial complexity in the age of digital transformation, the need for powerful, flexible, and scalable IT analytics solutions continues to increase.


Cybersecurity platforms must uncover misconfigurations and detect indicators of compromise to mitigate risks, but unfortunately, they’re often overrun with false positives. By enabling embedded analytics of real-time data, cybersecurity platforms can improve accuracy.


With the explosion of SaaS apps, IT costs are also rising. Analytics can equip organizations with the insights needed to reduce unnecessary costs and ensure that spending is optimized. Additionally, quantify the business value of IT to demonstrate ROI.


Analytics can also provide vital KPIs such as system response time, availability, and user satisfaction. Optimize IT processes like incident handling and predict future IT resource needs based on demand forecasts.

Embedded Analytics Requirements

To support a strong analytics feature set within a SaaS application, the data layer must first be ready to handle multi-tenant reporting.

Multi-Tenant Data Layer

Having a standard, out-of-the-box database or data warehouse is not enough to achieve a multi-tenant embedded analytics function. You will need a multi-tenant data lake that handles the security, mapping of roles and permissions, and an easy-to-use to use API suite so that integration is fast.


Being able to host this solution yourself is also a key to achieving the security that most SaaS companies require. While there are no shortage of third party, cloud host data management systems out there, as soon as your data leaves your environment it poses a security risk. Are you ready to be responsible for a third party platform?


And since data comes from many sources these days, how flexible the data solution is becomes an important question.


  • Does it force every tenant to use the same data model or can it be customized?
  • Does it only work with structured/relational data or can it handle semi/unstructured data?
  • Does it only work with prebuilt data connectors or is there an API to push on custom intervals?

Front End Visualizations

Having embedded dashboards is not enough. For true embedded analytics within a multi-tenant SaaS application, you’ll need:


  • Embedded data visualizations: full dashboards AND individual charts
  • Embedded dashboard builders
  • Embedded chart builders
  • Javascript components, not iFrames
  • White label support: full CSS controls, not just changing logos
  • Automation and alerting that users can build themselves

What are Embedded Data Widgets?

Widgets are simple, intuitive applications independent of the body of a website or device but easily embedded into it. Widget types include information, collection, control and hybrid. Data widgets display one object, or a list of objects using live data that may be programmed to respond to website identity. Types of data widgets include data view, data grid, template grid, and list view.

Traditional Business Intelligence (BI) vs Embedded Analytics

Most BI companies were founded between 2000 and 2010 and targeted enterprises needing to analyze data internally. SaaS wasn’t yet the dominant force it is today, so these systems were designed to be installed in servers owned by each customer and managed by a database administrator from the IT department.


In, “How to Select an Embedded Analytics Product,” author Wayne Eckerson writes, “Most BI tools were not designed for embedding; converting a stand-alone, commercial product into one that can be easily embedded in both single- and multi-tenant environments with full fidelity is challenging.”


As the number of SaaS products each company uses has exploded, analytics providers have struggled to pivot from a server-focused software product to a cloud-focused product. The following are four primary ways embedded analytics vs embedded BI compare:



Traditional BI software

Embedded Analytics

Developer Friendliness

Traditional BI software includes self-service tools and embedded dashboards only. It never provided the developer audience with the necessary tools (widgets, APIs, security options, etc). Developers have no chance to create multi-tenant analytics that power end-user customizations.

Built from the ground up for developers, with an API-first approach with no-code widgets that deliver real value in terms of time and cost savings.

Costs

Traditional BI systems sell server licenses and user licenses. It’s much tougher for SaaS providers to predict usage across a platform that has 500+ customer tenants on it.

Embedded analytics aligns with SaaS providers’ by charging based on value. To that end, unlimited users is the only way to scale an embedded analytics feature.

Architecture

Traditional BI software is particularly difficult to embed within a multi-tenant SaaS app. BI apps are server-based systems that were never meant to scale with cloud platforms like AWS without costly server clustering.

Qrvey’s embedded BI deploys into your AWS environment with a full suite of security tools and features that support multi-tenant SaaS applications. Your data never leaves your account.

Data Readiness

Data ingestion requirements are rigid. Most traditional BI can’t analyze semi-structured and unstructured data.Some tools require the installation and maintenance of external data sources, which adds extra layers of complexity and costs to scalability decisions.

Analyze various types of data – SQL, NoSQL, and unstructured data sources like forms and images.
Qrvey is also a multi-tenant-ready data warehouse built exclusively for SaaS applications.


Choosing the Right Solution

The right embedded analytics solution depends on several factors, but in our experience, the successful solution will


  • include a multi-tenant data lake purpose-built for multi-tenant analytics.
  • an intuitive user experience
  • be deployed and self-hosted to maximize data security
  • have a robust API suite
  • have full white label capabilities
  • most importantly, enable SaaS users to analyze data according to their own business processes

Embedded Analytics Applications

Qrvey is the only complete embedded analytics for SaaS solution to rapidly add a modern analytics layer with rich capabilities that are easily configurable for all types of users. By using Qrvey’s platform to embed analytics within their products, SaaS firms can deliver greater value, unlock new revenue streams, and ensure greater customer loyalty.


Unlike traditional BI solutions, which typically require integrating numerous, separate functions, Qrvey delivers a complete, no-code, end-to-end platform that deploys entirely within our customers’ cloud environments, lowering the time and cost of development, deployment, and maintenance.


It is the best embedded analytics platform built specifically for cloud-native environments, leveraging the best of cloud technology to offer rapid deployment of self-service analytics across any type of data. Qrvey’s platform creates the most cost-effective embedded analytics solution on the market, driven by a team with decades of experience in the analytics industry. Qrvey has been recognized as a leader by Dresner Advisory Services and voted a high performer on G2.

The Process of Embedding Analytics

The following describes the initial onboarding process for new customers of the Qrvey platform running AWS as the infrastructure platform.

Install Qrvey Software

  1. Configure AWS environments
  2. Install and configure the Qrvey platform on AWS
  3. Build your first Qrvey application in Qrvey Composer, a web-based application used by data analysts to create and manage datasets, visualizations, and dashboards to share with external users.

Create a new application

The Qrvey platform offers a wide range of features that can be used in a Qrvey application, including web forms, data connections, analytics, and automation.


  • Create a connection to a data source

  • Create a data set

  • Build a dashboard with charts

  • Publish the application

  • Share the application with the organization

  • Embed the Qrvey application into your host application


Qrvey is deployed to your AWS environment, enabling you to keep the Qrvey system within your desired AWS region and VPC.


Read more about why SaaS companies choose Qrvey for embedded analytics.


If you are interested in learning more about our embedded analytics solution or want to see how it can work for your product, please contact us for a free demo.

Also published here.