Analytics Subsystem for a Sales Engagement Platform
We helped our partner to improve and power up their analytics subsystems. This system leverages the latest technologies and can work with large datasets to provide actionable insights that can accelerate sales growth.
Industry:
Digital Transformation
Services:
Back End Development, Big Data & Analytics, Cloud / DevOps, Front End Development, QA as a Service, UI/UX Design
Location:
California, US
Challenge
Complex Data Management
Managing and processing large volumes of data presented a major challenge in developing the new analytics system. The sales engagement platform already generated terabytes of data on a daily basis from millions of customer interactions. This raw data held invaluable insights, but turning it into actionable intelligence required robust data infrastructure.
Integration With Existing Systems
When bringing in a new system like advanced analytics, planning the integration to minimize disruption is critical. Users should be able to keep working in a familiar environment. Careful integration enables the new capabilities to be added behind the scenes without interfering with day-to-day workflows.
Solution
Identifying System Flaws
At the outset of our partnership with a prominent Sales Engagement Platform (SEP) provider, a comprehensive technical audit revealed a series of critical issues plaguing the existing analytics system. These issues ranged from the failure to log objects correctly to a noticeable performance slowdown. Coupled with outdated user experience (UX) design and suboptimal backend logic, it was evident that the system required substantial upgrades to fulfill its potential.
Two-Way Synchronization and Scalability
We orchestrated the initial connection for bidirectional synchronization between systems and meticulously configured the adjustments needed for seamless integration with AWS infrastructure.
Logging Object Errors
One of the most pressing concerns was the incorrect logging of objects within the system and within third-party CRM systems like MS Dynamics and SalesForce. Our team diligently traced and rectified the root causes of this issue, ensuring that data accuracy was restored. This was crucial for providing reliable analytics and enabling data-driven decision-making for our client.
Performance Optimization
The observed performance slowdown was addressed through a combination of software optimizations and hardware enhancements. Our experts fine-tuned the system to eliminate bottlenecks, resulting in a significant boost in performance. This not only improved the user experience, but also allowed the system to handle larger workloads efficiently.
Back-End Logic Enhancement
Outdated back-end logic was a hindrance to the system's efficiency. Our team undertook a comprehensive overhaul of the system's backend, rewriting and optimizing code to ensure that it aligned with modern best practices. This not only improved system reliability but also facilitated easier maintenance and future scalability.
Revamped User Experience
Recognizing the importance of a user-friendly interface, we embarked on a UX redesign journey. The updated design not only enhanced the aesthetics but also improved user navigation and overall satisfaction. This contributed to a more positive user experience and increased engagement with the platform.
Migration to Amazon Redshift
The company faced challenges with its existing SQL-based data storage and management approach. The solution was to evolve to a big data management approach using Amazon Redshift. Redshift offered benefits over traditional SQL databases for managing huge data volumes. It leverages a massively parallel processing (MPP) architecture, columnar storage, and advanced compression to deliver fast query performance on large datasets.
Updated Microservice Logic
In addition to addressing frontend and backend issues, our team recognized the need for a more efficient microservice logic. Specifically, we focused on optimizing the logic governing data storage and retrieval. By implementing advanced techniques and streamlining the process, we significantly reduced the time required to display a specific type of dataset. This update not only improved the system's overall performance, but also enhanced the user experience by delivering data more quickly and efficiently.
Technologies Used
The analytics subsystem leverages cutting-edge technologies and software development best practices to provide a high-performance, scalable, and reliable system. The technology stack includes Java 7, Java 8, and Java 17, as well as C# for backend development.
The workflow
Kick-Off Consultation
The first step was an in-depth consultation with stakeholders to align on goals, requirements, and success metrics.This ensured all parties were on the same page from the start.
Run an Audit
A comprehensive audit analyzed the existing sales analytics stack. This revealed opportunities for performance improvements and identified legacy components to replace.
Identify Weak Points
By benchmarking the audited system against best practices, the team identified performance bottlenecks and suboptimal data flows. Addressing these weaknesses became our top priority.
Fix the Bugs
Numerous bugs in the legacy analytics pipelines hampered data accuracy. The team methodically squashed these bugs to improve data quality.
Re-design
With audit findings in hand, the team re-architected the analytics system for scalability, flexibility, and speed. New data storage, processing, and visualization layers were designed.
Release
The improved analytics system was rolled out incrementally. Rapid iteration continued based on user feedback until all goals were met.
About the team
The team size varied depending on the stage of the product development:
Team composition
PM/ PO
1
Scrum Master
1
Back-End Developers
4
Front-End Engineer
1
QA Engineers
5
Impact
The implementation of the new analytics system had a significant positive impact for the partner company. First and foremost, the system provided a bug-free performance. By thoroughly auditing and redesigning the architecture, the developers were able to eliminate bugs that had previously caused issues. This led to higher system stability and reliability.
Additionally, the solution works significantly faster than before. The partner company benefited from a much higher level of performance by transitioning to Amazon Redshift and implementing an asynchronous analytics processing system.
In our pursuit of efficient analytics processing, we adopted the Command Query Responsibility Segregation (CQRS) pattern as the foundation of our approach. Leveraging this pattern, we designed and implemented an asynchronous analytics processing system.
These achievements are not just milestones; they are transformational elements that have redefined the capabilities of our partner’s Sales Engagement Platform. By demonstrating our ability to manage data and process analytics efficiently and seamlessly, we have positioned our partners for success, enabling them to offer enhanced services, attract more users, and stay ahead of the competition. These accomplishments stand as powerful selling points, showcasing the tangible value we bring to our clients through our development services.
