The Engagement
We led the insights capture. This informed the Single Customer Identity initiative’s development. Additionally, we delivered foundational infrastructure for a customer data platform.
The Delivery – Snapshot
An 8-week discovery process was crucial. It generated necessary insights, validating the use cases. We assessed feasibility during this process. This was based on data acquisition challenges and existing technological complexity. These factors were critical for interfacing with the platform.
Our engineering team achieved a significant milestone. They laid the foundations for democratised data. This involved discovering and provisioning customer data access. Key tasks included data ingestion and quality establishment. The team set up data schemas for communication, validation, and evolution. They also ensured platform observability through monitoring, auditing, logging, and alerting. Insight discovery was another critical area, shaping the strategy for a unified customer identity across diverse group businesses.
Stakeholders
Project Partners:
- Manager – Group Risk and Compliance
- Head of Technology – Data and Analytics
- Head of Personalisation
- Group CIO
Project Collaborators:
- Vendors
- Software Engineers
- Data Scientists
- Data Analysts
- Software Architects
- DevOps Engineers
The Details
Identifying the Challenges
Identifying customers across various brands is challenging. Airlines use multiple channels for selling flights and other products. Different scenarios create unique data representations. These range from booking flights through codeshare carriers to online shopping across devices. The sales systems often generate data with accuracy issues.
For instance, during fulfilment, a particular challenge arises. Domestic regulations don’t mandate identity revelation for boarding passes. Thus, personalizing service becomes complex. Airlines cannot safely assume the purchaser is the actual traveler.
Risk Analysis
Customer data platforms have specific capabilities. They ingest, normalize, cleanse, and match customer identifiers. This process creates a persistent, unified customer view. Identity resolution depends on normalisation and quality checks during ingestion. This approach enhances the chances of high match scores. However, matching remains probabilistic, not absolute. Solutions assign confidence levels to matching outputs.
Ignoring confidence levels in matched records is risky. It could lead to incorrect assessments, violating GDPR and other privacy laws.
The Process
Myndful spearheaded the insights stream within a larger discovery process. Our role was to offer insight into the value and feasibility of potential use cases for the customer data platform.
We were also part of the engineering squad for the customer streaming data platform initiative. Our contribution was foundational, supporting customer data ingestion from various sources. We focused on security posture and governance controls.
Why Myndful
Myndful’s deep understanding of customer data sets us apart. Our integration technology ecosystem expertise is extensive. Our technical prowess in data platform and integration delivery is proven. Thus, we led customer insights capture across teams. This effort informed business case development. We validated the feasibility of platform use cases. Furthermore, we were chosen to establish real-time customer data platform infrastructure. This task demanded compliance with strict PII data privacy and GDP requirements.
Increased Opportunity
The Customer Data Platform’s implementation had several benefits. It enabled differentiated services by valuing customers accurately. Compliance risk was diminished. Customers enjoyed personalized experiences across various channels and touchpoints. The platform supported business strategy development, promoting customer insights internally. Frontline staff benefited from accurate, contextual customer information.
The Results
The 8-week discovery was enlightening. It validated use cases and assessed their implementation feasibility. This assessment considered data acquisition challenges and technological complexity.
The engineering team’s achievements were foundational. They enhanced data access, ingestion quality, and established communication protocols. Platform observability was not overlooked, including vital monitoring and auditing functions.
Being Myndful of Learnings
Group-wide initiatives require robust executive sponsorship. It’s essential for rallying stakeholder engagement. Feasibility assessments and insights may necessitate security and compliance approvals. Accessing and transferring customer data into exploratory environments demands this.
A strategic approach is advisable: go wide, start small. Use case prioritization should consider business and customer value, along with feasibility. This strategy might necessitate shifts in responsibility within shared platforms.
Implemented Cloud Technologies
We utilised various cloud technologies. These included AWS Managed Kafka (MSK), Open Tracing, Datadog and Splunk
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