The Church Pension Group (CPG) is a financial services organization established in 1917. It serves the clergy of The Episcopal Church, lay employees, and institutions within the church by offering retirement plans, health benefits, property and casualty insurance, and financial education.
Problem statement
The financial company's client database suffers from significant data redundancy, specifically with duplicate entries for legal clients. This redundancy arises from inconsistencies in data entry, variations in legal entity naming conventions, and a lack of robust data deduplication processes. This results in inaccurate client reporting, increased operational costs due to manual data reconciliation, and potential compliance risks due to fragmented client information. Furthermore, this data discrepancy negatively impacts the efficiency of client relationship management and impedes the company's ability to gain a holistic view of its client base.
Business goals
Addressing the problem of duplicate legal client entries will contribute to the following key business goals:
1. Improve Data Quality and Accuracy
2. Enhance Operational Efficiency
3. Strengthen Regulatory Compliance
4. Optimize Client Relationship Management (CRM)
5. Enable Data-Driven Decision Making
6. Reduce Costs
Specific designer’s challenges
• This short-term project involved collaborating with a new client and technical stakeholders. It required rapid self-education on AI-powered systems and related domains to ensure informed design decisions.
• Competitor research was not available for the short term of the project.
Technical research and creating personas
Potential use cases for Informatica Fuzzy String Logic Algorithm
1. Comparison by Number of Matching Words
2. Matching Words Which are Similar Phonetically or by Definition
3. Concatenated Word Matches
Data Entry Issue Reference for User Story Development
Developed personas
1. External Admin (“"Compliance Auditor Carol")
2. Internal Admin ("Data Steward David")
3. Super Admin ("Systems Architect Sarah")
Persona's example
Super Admin
• Name: Sarah Ramirez
• Name: Sarah Ramirez
Age: 42
Role: Systems Architect
Background: Sarah has a master's degree in computer science and over 15 years of experience designing and managing enterprise-level database systems.
Goals:
• Ensure the stability, performance, and security of the database infrastructure.
• Design and implement database upgrades and enhancements.
• Manage user access and security permissions at the highest level.
• Develop and maintain disaster recovery and business continuity plans.
• Ensure the database scales to meet the company's needs.
Technical Skills:
• Expert in database architecture, design, and administration.
• Proficient in database platforms.
• Strong understanding of network security and data encryption.
• Experience with system automation and scripting.
Pain Points:
• Manage complex database infrastructure and ensure high availability.
• Balancing performance optimization with security requirements.
• Staying up-to-date with emerging database technologies.
• Dealing with the pressure of maintaining a critical system.
Access Needs:
• Full administrative access to all database systems and servers.
• Ability to configure and manage security settings and user permissions.
• Tools for performance monitoring, capacity planning, and disaster recovery.
• Access to all logs and system-level information.
Personality: Strategic, technically astute, and decisive. She is focused on ensuring the reliability and security of the database infrastructure.
Conversion of the user stories and business requirements into actionable deliverables
Business Requirements
Scenarios Based on Business Requirements
1. All Admins: Search > Search Result with exact match > Click on Profile view.
2. All Admins: Search > Search Result with no exact match > Creation of new Record > No warning from the Database > Submit new Record > Click on Profile view
3. External Admin: Search > Search Result with no exact match > Creation of new Record > High confidence matches > Merge Records > Submit the result > Click on Profile view
4. Internal Admin: Search > Search Result with no exact match > Creation of new Record > High and/or Lower confidence matches > Merge records if there are High confidence matches or Merge/Bypass if there are only Lower confidence matches > Submit the result > Click on Profile view
5. Super Admin: Search > Result with no exact match > Creation of new record > High and/or Lower confidence matches > Bypass or Merge records > Submit the result > Click on Profile view.
Functional Block Diagram
An example of the UX Flow Diagram: Super Admin (Scenario 5)
The set of wireframes for Super Admin (Scenario 5)
The product team delayed certain features until the next release, so they were excluded from these wireframes.
The set of wireframes for Super Admin (Scenario 5)
Outcomes & Lessons Learned
Preliminary testing confirmed that the UX solutions matched user needs. Informatica Fuzzy String Logic integration improved data validation and integrity, resulting in more intuitive UI elements for resolving inconsistencies. Collaboration with technical teams ensured the UX matched the backend logic, making complex data-matching processes more user-friendly.
Due to the project's brief duration, I was unable to gather post-launch metrics. However, stakeholders expressed strong satisfaction with my UX design work.
Balancing data complexity with UX simplicity is crucial for financial and compliance platforms. Enterprise UX requires a deep understanding of user roles, workflows, and regulatory constraints. Iterative testing is essential for refining data-heavy interfaces, as fuzzy string matching introduces nuances that impact usability.
Due to the project's brief duration, I was unable to gather post-launch metrics. However, stakeholders expressed strong satisfaction with my UX design work.
Balancing data complexity with UX simplicity is crucial for financial and compliance platforms. Enterprise UX requires a deep understanding of user roles, workflows, and regulatory constraints. Iterative testing is essential for refining data-heavy interfaces, as fuzzy string matching introduces nuances that impact usability.
P.S.
Beyond the defined scope, I also suggested navigation improvements. While I don't have visual mockups for these, I can share examples from my other work that illustrate my approach to presenting product updates to stakeholders.
