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B2B Apps
Custom B2B apps
COMPANY
One Data
ROLE
UX Designer
EXPERTISE
Enterprise UX,
Modular Apps,
Data Visualization
YEAR
2019 - 2020
2019 - 2020
2019 - 2020
Projects description
At One Data, I designed custom B2B applications on top of the company’s core platform. Each app was tailored to specific workflows, replacing inefficient tools like Excel with scalable digital solutions.
Due to NDA restrictions, I cannot disclose client names or full implementation details here. The following case studies highlight the design process, challenges, and outcomes without revealing sensitive information.
My role
Translated PM requirements into solutions using the One Data platform
Created wireframes, user flows, and prototypes with the shared design library
Iterated designs with internal testing and client feedback
Supported developers during implementation
Maintained and expanded the design system
Team setup
User Experience Designers, Product Managers, Data Scientists, and Developers
Projects description
At One Data, I designed custom B2B applications on top of the company’s core platform. Each app was tailored to specific workflows, replacing inefficient tools like Excel with scalable digital solutions.
Due to NDA restrictions, I cannot disclose client names or full implementation details here. The following case studies highlight the design process, challenges, and outcomes without revealing sensitive information.
My role
Translated PM requirements into solutions using the One Data platform
Created wireframes, user flows, and prototypes with the shared design library
Iterated designs with internal testing and client feedback
Supported developers during implementation
Maintained and expanded the design system
Team setup
User Experience Designers, Product Managers, Data Scientists, and Developers
App 1: Logistics Enhancement / Supply Chain & Planning
App 1: Logistics Enhancement / Supply Chain & Planning
Analyze shipments, bundle routes, and optimize cross-docking with a logistics planning tool.
Analyze shipments, bundle routes, and optimize cross-docking with a logistics planning tool.
Problem
Logistics managers lacked visibility and optimization tools for shipments.
Solutions & Impact
App 1:
Logistics Enhancement / Supply Chain & Planning
1. Descriptive Analysis
Visualize shipment flows with maps and heatmaps. Helps users quickly spot connections, shipment volumes, and bottlenecks so they can plan smarter.
2. Active Bundling
Groups smaller shipments into fewer, larger ones. This reduces the number of trucks needed, lowers costs, and makes deliveries more efficient.
3. Cross Dock
Combines shipments from different senders at a single hub before sending them out again. This cuts down duplicate routes and saves transport capacity.
Analyze shipments, bundle routes, and optimize cross-docking with a logistics planning tool.
Process
Designed dashboards with maps, heatmaps, and charts for shipment insights
Integrated optimization features like Active Bundling and Cross Docking
Iterated designs with feedback from PMs and data scientists
Supported developers through build and rollout
Problem
Logistics managers lacked visibility and optimization tools for shipments.
Solutions & Impact
1. Descriptive Analysis
Visualize shipment flows with maps and heatmaps. Helps users quickly spot connections, shipment volumes, and bottlenecks so they can plan smarter.
2. Active Bundling
Groups smaller shipments into fewer, larger ones. This reduces the number of trucks needed, lowers costs, and makes deliveries more efficient.
3. Cross Dock
Combines shipments from different senders at a single hub before sending them out again. This cuts down duplicate routes and saves transport capacity.
Together, these features help logistics managers reduce costs, use fewer trucks, and get clearer visibility of their operations (it gave managers a clearer overview, cut wasted routes, and reduced truck usage by ~16%”).
Sample feature in action: Cross Dock Optimization
This example screen shows how shipments are consolidated and optimized based on a dataset, resulting in significant route and cost improvements.
App 2: Sample Data Management / Life Sciences
Store and access biological sample information in a structured, reliable way.
Process
To structure the complexity of lab operations, we mapped out six core user stories (covering patient, sample, and run management) along with supporting scenarios that detailed filtering, browsing, validation, and error handling. Together, these became the foundation for designing prototypes that mirrored real lab workflows.
I translated these workflows into clear user stories and scenarios, which guided the design of prototypes and ensured scientific accuracy.
Problem
Manual lab workflows → errors, inefficiencies.
Solutions & Impact
The resulting sample management platform mirrors the lab process in a structured digital flow. It allows operators to add, edit, and track samples reliably while enforcing data validation and providing a clear audit trail. This translation of manual lab work into a digital system resulted in fewer errors, stronger traceability, and smoother collaboration across lab teams.
Sample feature in action: Error handling
The application ensures data quality from the very start of creating a patient record. It validates entries at the point of entry, preventing duplicate IDs, missing fields, invalid references, and non-existing links. By catching errors early, it eliminates the need for manual checks later and keeps records consistent, accurate, and traceable.
App 3: Streamlined Data Processing / Retail & Analytics
Replace Excel workflows with a faster, simplified app for large-scale data operations.
Process
Mapped analyst workflows to identify bottlenecks in Excel-based processes.
Designed a processing library where functions could be registered, searched, and reused.
Built prototypes and tested them with analysts to ensure faster execution and fewer mistakes.
Iterated designs with PM and data scientists, then supported development for release.
Problem
Analysts depended on Excel for complex data tasks. This approach was slow, error-prone, and hard to maintain — manual copy-paste, broken formulas, and version control issues often disrupted workflows.
Solutions & Impact
The new processing library app centered around three core capabilities: Build, Share, and Reuse.Faster task execution (minutes instead of hours)
Build: Analysts could create custom processors tailored to their workflows.
Share: These processors could be shared across teams, ensuring consistency and collaboration.
Reuse: Existing processors could be applied to new datasets, reducing repetitive work and saving time.
Together, these features replaced error-prone steps with a structured system that enabled faster execution, fewer errors, and less manual effort.
From Spreadsheets to Streamlined Workflows
Analysts previously relied on manual Excel sheets to process and validate data — a method prone to errors, version conflicts, and repetitive rework. The new processing library app replaces these scattered workflows with a centralized, reusable system. Functions can now be registered, searched, and reused seamlessly, enabling faster execution, fewer errors, and smoother collaboration across teams.
App 2: Sample Data Management
/ Life Sciences
/ Life Sciences
Store and access biological sample information in a structured, reliable way.
Store and access biological sample information in a structured, reliable way.
Problem
Manual lab workflows → errors, inefficiencies.
Process
To structure the complexity of lab operations, we mapped out six core user stories (covering patient, sample, and run management) along with supporting scenarios that detailed filtering, browsing, validation, and error handling. Together, these became the foundation for designing prototypes that mirrored real lab workflows.
I translated these workflows into clear user stories and scenarios, which guided the design of prototypes and ensured scientific accuracy.
Solution & Impact
The resulting sample management platform mirrors the lab process in a structured digital flow. It allows operators to add, edit, and track samples reliably while enforcing data validation and providing a clear audit trail. This translation of manual lab work into a digital system resulted in fewer errors, stronger traceability, and smoother collaboration across lab teams.
Sample feature in action: Error handling
The application ensures data quality from the very start of creating a patient record. It validates entries at the point of entry, preventing duplicate IDs, missing fields, invalid references, and non-existing links. By catching errors early, it eliminates the need for manual checks later and keeps records consistent, accurate, and traceable.
App 3: Streamlined Data Processing
App 3: Streamlined Data Processing / Retail & Analytics
/ Retail & Analytics
Replace Excel workflows with a faster, simplified app for large-scale data operations.
Replace Excel workflows with a faster, simplified app for large-scale data operations.
Problem
Analysts depended on Excel for complex data tasks. This approach was slow, error-prone, and hard to maintain — manual copy-paste, broken formulas, and version control issues often disrupted workflows.
Process
Mapped analyst workflows to identify bottlenecks in Excel-based processes.
Designed a processing library where functions could be registered, searched, and reused.
Built prototypes and tested them with analysts to ensure faster execution and fewer mistakes.
Iterated designs with PM and data scientists, then supported development for release.
Solution & Impact
The new processing library app centered around three core capabilities: Build, Share, and Reuse.Faster task execution (minutes instead of hours)
Build: Analysts could create custom processors tailored to their workflows.
Share: These processors could be shared across teams, ensuring consistency and collaboration.
Reuse: Existing processors could be applied to new datasets, reducing repetitive work and saving time.
Together, these features replaced error-prone steps with a structured system that enabled faster execution, fewer errors, and less manual effort.
From Spreadsheets to Streamlined Workflows
Analysts previously relied on manual Excel sheets to process and validate data — a method prone to errors, version conflicts, and repetitive rework. The new processing library app replaces these scattered workflows with a centralized, reusable system. Functions can now be registered, searched, and reused seamlessly, enabling faster execution, fewer errors, and smoother collaboration across teams.