Do you want to embed real-time data dashboards into your product or application? Or are you building features in your app based on real-time data analytics? It is called “user-facing analytics,” a real-time data pattern that has become popular amongst both software developers and data engineers.
Use Case: User-facing Analytics
Problem Statement
Many applications require an on-demand analytics view for external users, where data freshness is measured in seconds or less, query latency is in milliseconds, and request concurrency grows to thousands or millions of requests per second. The traditional BI data processing approach cannot handle such a scale.
Realization Approach
Such analytics capabilities are addressed through user-facing analytics wherein analytics views are natively integrated within the software application and are driven by real-time data APIs called from application code to fetch analytics results.
Solution Space
User-facing analytics takes the form of real-time dashboards within software or SaaS applications, which are served by an optimized data infrastructure at the backend so that the analytics views achieve low latency performance on complex queries for many concurrent users.
Featured Data Infrastructure Platform
Tinybird is a developer centric data infrastructure platform to help software teams build internal data pipeline that can abstract data ingestion, storage, compute, and API development into a single workflow, and ship real-time data analytics features within days.
Understanding User-facing Analytics
User-facing analytics (also called “customer-facing analytics”) is the pattern of embedding real-time data visualizations or data-driven features into software applications. You’ll implement user-facing analytics if you offer a software service and want to provide real-time data to your end users.
Generally speaking, user-facing analytics involves capturing data about user interactions within an application, sending that data to an analytics platform, and building metrics that are then served back to the user as dashboards or features.
For example, a short link platform for marketers might provide user-facing analytics dashboards that show short link creators how many times their links have been clicked or where those clicks originated (referrers/devices/locations).
Or a content creation platform might include user-facing analytics features that show content creators how often their content is viewed, which content receives the most engagement, or how content drives their revenue.
Critically, these user-facing analytics must provide real-time data to end users without undue latency or lag in the software application.
How is User-facing Analytics Different?
User-facing analytics is but one subset of data analytics, and it differs from other analytics approaches in its end goals, dependencies, and challenges.
Here are some ways user-facing analytics differs from common data analytics approaches.
User-facing analytics vs. Business intelligence
User-facing analytics is quite different from Business Intelligence (BI). Business Intelligence utilizes batch processes to extract data from source systems, transform it to create data models, and load it into a data storage platform connected to BI tools used by a few internal stakeholders.
With BI, you have complex queries with relatively high response latency serving only a handful of concurrent users.
User-facing analytics, on the other hand, is meant to provide on-demand analytics views for external users. Data analytics are embedded into user-facing apps and refreshed as users interact with the data visualization components or data-driven features.
With user-facing analytics, you still have complex queries, but data freshness is measured in seconds or less, query latency must shrink to milliseconds, and request concurrency can grow to thousands or millions of requests per second.
User-facing analytics vs. Real-time analytics
User-facing analytics is a class of real-time analytics in which data visualizations or data-driven features are provided to end-users of software or systems. Contrast this with other forms of real-time analytics which might be used for internal monitoring (for example, operational intelligence use cases) or for real-time business processes that require complex analytics (for example, real-time fraud detection use cases).
User-facing analytics vs. Embedded analytics
User-facing analytics is a relatively new term and concept, but its end goal is similar to the more established discipline of “embedded analytics.”
Traditionally, embedded analytics involves using Business Intelligence software, originally designed for internal reporting and dashboard, and embedding data visualizations within applications often use iframes or other web embedding approaches.
While embedded analytics and user-facing analytics are nearly synonymous in terms of their goals, the term “user-facing analytics” suggests an added importance upon performance and user experience.
Embedded analytics does indeed provide data visualizations for end users, but these dashboards can be notoriously non-performant. They often load slowly due to bad data models, slow underlying compute engines, and improper storage formats. Visually, they may not match the defined styles of the software application in which they are embedded due to the use of iframes.
Unlike embedded analytics, user-facing analytics should be a fully integrated analytics experience within the software application. Instead of embedding a dashboard built in an external tool, user-facing analytics will utilize real-time data APIs called from application code to fetch analytics results and display that data natively, using the same components and styles found within the application codebase.
User-facing Analytics Examples and Use Cases
User-facing analytics often takes the form of real-time dashboards within software or SaaS applications, but the concept can extend beyond just real-time dashboards. Any system that utilizes real-time analytics to provide a dynamic and data-driven user experience can be classified as “user-facing analytics.”
Examples of user-facing analytics systems that aren’t just real-time dashboards include real-time fraud detection, real-time personalization, real-time recommendation engines, and more. These systems use analytics to influence and change user experiences dynamically based on real-time data.
For a detailed look at the various case studies of user-facing analytics implemented by Tinybird, read the original article.
