Summary
In my charge of leading end-to-end product design initiatives, I discovered that many user inquiries were related to issues with data migration; users often struggled when attempting to transfer large volumes of data. This led to frequent errors and inaccurate calculations. I took ownership of this work, and ensuring a data migration process that is accurate, efficient, and easy to use.
The work consisted of research, ideating, testing, learning, and then we'd repeat that process continuously. I leveraged a combination of analytics data and qualitative insights as the foundation for the design hypotheses and test builds. I introduced and advocated for qualitative research that provided valuable learnings.
The outcome was a redesigned data migration architecture and workflow, improved migration foundations, enhanced error prevention and handling mechanisms, and a new standardized migration process. After the launch, support ticket requests related to data migration were reduced, and data accuracy has improved.
Case Study
Data migration is a critical process for transferring large volumes of data from legacy systems to CATAPA while preserving data integrity, quality, and security. The goal is to ensure the transition runs efficiently and with minimal disruption to business operations.
As part of the process, users must prepare a set of data that can be mapped, defining how data from the source system corresponds to CATAPA's structure. However, challenges often arise due to data misalignment, making it difficult for users to complete the migration smoothly. These difficulties can lead to errors and delays, ultimately causing bottlenecks in downstream processes such as payroll. Many users struggle to format their data to match CATAPA’s requirements, preventing them from progressing further, causing a high volume of migration-related support tickets and a lot of requests for assistance in CATAPA implementation.
Key Objectives
Users can independently process their data migration accurately.
Users can recover from errors if any failure occur during the migration process.
A decrease in complaints and requests for migration assistance during implementation.
Identifying areas for improvement to focus on
We collected feedback from customer logs, conducted usability tests, and gathered internal evaluations to identify the underlying problems and root causes behind one of the most frequently reported issues: data migration. There are 5 key pain points in the data migration process.
1
Problem Hypothesis
The migration menus are scattered and not centralized.
Expectation
Data Migration distributed based on it’s modules and menu context
2
Problem Hypothesis
Guide/explanation/information is less explainable
No guideline template standardization
Expectation
Create a complete standardization template guideline, that can accommodate user convenience
3
Problem Hypothesis
Ambiguous error messages make it difficult to distinguish between success and failure statuses
Toast placement is not clearly visible
Expectation
Visible and clear system status
4
Problem Hypothesis
Complexity: The migration process and the numerous interdependent steps add to the challenge.
Expectation
Simplify the migration process and streamline the flow.
5
Problem Hypothesis
Errors are hard to locate due to insufficient guidance and lack of detailed error information from the system.
Expectation
The system provides a detailed, explainable error description and recommends a solution to the user.
Competitor Benchmarking
The UX team conducted competitor benchmarking on several key competitors that offered similar data migration features. We performed a detailed comparison and compiled a list of strengths, weaknesses, and common patterns across their solutions.
Identifying The Most Appropriate Solutions
Before jumping into solutions in CATAPA’s migration system, we need to really consider what users actually needed. I conducted user testing using 2 different approaches to evaluate which method made it easier to recover from data migration errors.
Based on user testing, the results showed that users preferred using Excel for managing large datasets more efficiently. In contrast, CATAPA’s current limitations, such as pagination (maximum 100 rows per page), limited scrolling, performance issues with large data, and the lack of inline editing, slowed down bulk editing tasks. Excel made editing easier by allowing copy-pasting, supporting formulas, and handling larger datasets better, whereas CATAPA’s interface was more suitable for smaller-scale, individual edits.
Restructure the Flow
A standard UX workflow is applied to migration components and pages, with a consistent flow, visible status indicators, and intuitive navigation.
The workflow for migrating data
Redesigning Data Migration for Simplicity and Scale
The redesign focused on optimizing the data migration workflow with several key improvements.
1. Reorganize Data Migration Information Architect
Organize the sitemap in a logical structure that makes sense to users, based on prior knowledge of grouping functions by menu relevance, categorized according to their respective module menus.
Menus for data migration are mapped to their respective modules
2. Simplify Data Migration Flow
Simplify the migration process by combining templates that share the same context and processes. This helps users avoid switching back and forth between too many separate templates, making the process more efficient.
Foundational data needed for basic payroll operations
3. Redesign Data Migration New system Interface
Simplify the interface by avoiding using unnecessary components; focus on the core functions.
Add a progress indicator with clear system status to better indicate data progress and real-time updates to provide a more granular and predictable view.
Add a visible part where users can easily get help or guidance when they need it.
A new simplified interface with a more intuitive screen
4. Create a standardized, comprehensive template guide
Standardize behavior, terminology, file naming, file content, and other consistency rules in the user guide to help users become familiar with and aware of components of the migration process.
Standardize terms to familiarize users
5. Improve error messages in the system
Provide informative error descriptions that explain the reason for the failure and offer solutions to users, ideally with links to the relevant help pages.
Clear information explains the reason for failure
Validation
What insight that we get from the usability testing:
Participants clearly understood what state occurred on the migration page after uploading their data, and agreed that the interface design significantly aided in quickly diagnosing issues during the migration process.
Participants found the error messages helpful in identifying the causes of failure, both in the UI and the Excel template.
Some participants had partial difficulty due to unclear formatting instructions—specifically around required cell formats (e.g., text/number/date format).
Seeing Result
We collect feedback and inquiries from user from the data migration revamp:
Ticket requests have decreased, and users who frequently complained have successfully completed the process and mentioned that the improvements helped them better understand the data migration process at each touchpoint.
Users are less confused about how to import large datasets, as the steps are now clearer, more simplified workflow, and the menu placement is organized more contextually.
Data accuracy has improved due to clearer information, helpful guidance, and well-defined cell formatting.
When users make mistakes, they can now recognize and recover from errors more quickly because of clearer, more complete, and straightforward error messages.
Project Learnings
- From user feedback, dig deep to find the root cause and core problem, because what the user complains about might only be the surface issue. It’s important to fix it from the root, as a small scope can expand due to various dependencies and interconnected factors.
- Thoughtfully planned Information Architecture from the start leads to a more efficient and well-structured sitemap or menu grouping. This requires considering various use cases and conducting deeper research.
- Failure in data migration is hard to avoid, which is why good design should allow users to make mistakes but still recover easily, regardless of the cause of the error.