Unified Customer Insights in Real Time
Turning fragmented customer data into a near-real-time enterprise capability.
ROLE
Technical Business Analyst
DOMAIN
Enterprise Data
SCALE
~$20M Initiative
Project Overview
An enterprise data initiative for a leading telecommunications company, designed to create a single, accurate and near real-time view of the customer. By making fresher customer data accessible across the organisation, the platform enables faster decision-making, richer personalisation and more responsive customer experiences.
The Challenge
Customer information existed across multiple systems and arrived at different speeds, making it difficult for teams to build a consistent and up-to-date understanding of the customer. The challenge was to create an enterprise capability that could bring this data together and make it available to downstream consumers at speed and scale.
Key Objectives
01
Unify Customer Data
Consolidate fragmented customer data into a single, accurate and trusted customer view.
04
Improve Personalisation
Enable teams to respond to changing customer behaviours and circumstances with more relevant experiences.
02
Increase Data Freshness
Make customer changes available near real time instead of relying on delayed batch data.
05
Unlock New Use Cases
Provide a scalable data foundation for analytics, machine learning, marketing and operational services.
03
Enable Enterprise Access
Provide a reusable source of trusted customer data for teams and downstream systems across the organisation.
My Contribution
Bridging business requirements, data and architecture
As a Technical Business Analyst within the data team, I worked across business and technical stakeholders to translate business needs and downstream consumer requirements into clearly defined data requirements and scalable platform capabilities.
01
Requirements & Data Contracts
Elicited business and technical requirements across multiple use cases, translating business needs into defined data requirements and collaborating with downstream consumers to establish data contracts, JSON structures and Pub/Sub integration requirements.
03
Architecture & Engineering
Collaborated with Solution Architects and Data Engineers to explore architectural patterns and implementation approaches, ensuring requirements aligned with the wider platform design.
02
Data Flows & Analysis
Developed a detailed understanding of end-to-end data flows across the platform, documenting how data was ingested, transformed and streamed to downstream consumers, while using SQL to investigate and validate the underlying data.
04
Logical Data Modelling
Contributed to the management and evolution of the platform's logical data model, ensuring data structures supported consumer and enterprise requirements.
From Design to Go-Live
Supported the initiative throughout the delivery lifecycle, adapting to evolving architecture, implementation approaches and stakeholder requirements as the platform progressed from design through to go-live.
Business Impact
11 Days → 24 Hours
Faster ML Model Rescoring
Enabled Data Science teams to access fresher customer data, reducing the SLA for model rescoring from 11 days to 24 hours.
26 Hours → 1.5 Hours
Near Real-Time Customer Engagement
Reduced the SLA for reaching at-risk broadband customers from 26 hours to 1.5 hours, enabling Marketing teams to deliver more timely, personalised retention communications and offers.
Near Real-Time
Richer Call Analytics & Compliance
Enabled Quality, Management & Analytics teams to enrich call interaction data with the latest customer portfolio information, providing a more complete view of the customer and supporting timely analytics and compliance evaluation.