Businesses increasingly need to provide analytical data directly to their end users or customers as part of a digital product experience.
For instance, in products such as self service analytics tools, billing platforms, social networks, online gaming and real time delivery apps, it is useful to share fresh data and analytics in ways which improve the product and customer experience.
This is referred to as User Facing Analytics, and it is a growing part of the data and analytics ecosystem.
Challenges In User Facing Analytics
Exposing analytics in this way is however technically demanding to achieve, placing new requirements on the technology and approaches that we use to build data and analytics systems:
Scale and Concurrency
User facing analytics implies more data and a much larger user community, potentially in the hundreds of thousands or millions. This shift requires scalable architectures and systems to accommodate a significant increase in demand.
Demand For Fresh Data
Whilst line of business analytics might have been updated in hourly or nightly batches, digital products are expected to provide more up-to-date numbers. Consumers of digital products have higher expectations for the freshness of data, necessitating more frequent updates and real-time data processing capabilities.
Latency
User facing analytics needs data to be ingested, processed and exopsed to the user promptly. Old approaches to delayed batch processesing are simply not scalable to this demand.
Reliability
Unlike business environments where some downtime can be tolerated, end customers typically have lower tolerance for disruptions. Ensuring high reliability and uptime is crucial, requiring the development of more robust and reliable systems to meet consumer expectations.
Technical Foundations
Unfortunately, traditional tools and approaches to data and analytics do not scale to deliver solutions like this.
There are too many delays in the process, and the systems often used are not performant enough to process high volumes of data with low latency and high degrees of concurrent access.
In addition, traditional business intelligence tools are not rich and flexible enough to meet the demands of user facing analytics.
This technology stack needs to be re-invented for the cloud, with tools and architectural patterns that are built for real-time advanced use cases and predictive analytics:
Introducing Ensemble
We are Ensemble, and we help enterprise organisations build and run sophisticated data, analytics and AI systems that drive growth, increase efficiency, enhance their customer experience and reduce risks.
We have a particular focus on ClickHouse, the fastest open-source database in the market, which we believe is the fastest best data platform for systems like this.
Want to learn more? Visit our home page or download our free report that describes the process for implementing advanced analytics in your business.
