WORKS / CHAPTER II /
WORKS / CHAPTER II /
WORKS / CHAPTER II /
CONFIDENTIALITY NOTICE:
CONFIDENTIALITY NOTICE:
DUE TO NDA RESTRICTIONS, SPECIFIC DETAILS, FUNCTIONALITIES AND NAMES HAVE BEEN ALTERED OR OMITTED.
MOCKUPS AND PROTOTYPES ARE ILLUSTRATIVE AND DO NOT REPRESENT THE ORIGINAL WORK.
FOR MORE INFORMATION, please contact me here.
DUE TO NDA RESTRICTIONS, SPECIFIC DETAILS, FUNCTIONALITIES AND NAMES HAVE BEEN ALTERED OR OMITTED.
MOCKUPS AND PROTOTYPES ARE ILLUSTRATIVE AND DO NOT REPRESENT THE ORIGINAL WORK.
FOR MORE INFORMATION, please contact me here.
Sample Nexus: Effortless Insight Through Advanced Visualisations
Sample Nexus is a cutting-edge solution for centralized biosample management, ensuring seamless metadata integration, accurate tracking, and insightful visualizations to enhance research and operational efficiency.
Sample Nexus is a cutting-edge solution for centralized biosample management, ensuring seamless metadata integration, accurate tracking, and insightful visualizations to enhance research and operational efficiency.
client
client
client
Global pharmaceutical leader
Global pharmaceutical leader
Global pharmaceutical leader
time
time
time
3,5 years
3,5 years
3,5 years
team members
team members
team members
SM / Release Manager
SM / Release Manager
SM / Release Manager
Product Owner
Product Owner
Product Owner
Solution Architect
Solution Architect
Solution Architect
Validation Lead
Validation Lead
Validation Lead
Validation Specialist
Validation Specialist
Validation Specialist
IT Lead
IT Lead
IT Lead
2 Tableau Developers
2 Tableau Developers
2 Tableau Developers
Python Developer
Python Developer
Python Developer
DevOps Engineer
DevOps Engineer
DevOps Engineer
UX/UI Designer (me)
UX/UI Designer (me)
UX/UI Designer (me)
Business Analyst
Business Analyst
Business Analyst
Test Lead
Test Lead
Test Lead
2 Testers
2 Testers
2 Testers
my roles
my roles
my roles
UX/UI Designer
UX/UI Designer
UX/UI Designer
UX Writer
UX Writer
UX Writer
UX Researcher
UX Researcher
UX Researcher
Audience
Audience
Audience
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Scientists
Scientists
Scientists
Experts developing biomarker strategies to identify responsive patient groups. Their tasks include analyzing complex biomarker data, tracking study progress, managing sample data scattered across systems
and utilizing standard dashboards for analysis.
Experts developing biomarker strategies to identify responsive patient groups. Their tasks include analyzing complex biomarker data, tracking study progress, managing sample data scattered across systems
and utilizing standard dashboards for analysis.
They oversee how data is organized and shared, ensuring quality standards
and coordinating with various teams. They have enough technical depth to translate
business goals into data requirements.
They oversee how data is organized and shared, ensuring quality standards and coordinating with various teams. They have enough technical depth to translate business goals into data requirements.
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Biosample Operations Representatives (BORs)
Biosample Operations Representatives (BORs)
Biosample Operations Representatives (BORs)
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Coordinators managing biosample collection, delivery and analysis. They handle large volumes of data, prepare documentation, monitor sample integrity and verify sample receipt in central labs, often relying on manual processes.
Coordinators managing biosample collection, delivery and analysis. They handle large volumes of data, prepare documentation, monitor sample integrity and verify sample receipt in central labs, often relying on manual processes.
They decide which data standards to adopt, need a holistic view of existing models,
and drive domain strategy.
They decide which data standards to adopt, need a holistic view
of existing models, and drive domain strategy.
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Clinical Data Managers (CDMs)
Clinical Data Managers (CDMs)
Clinical Data Managers (CDMs)
Professionals ensuring data integrity and regulatory compliance. Their work includes managing data queries, analyzing trends, overseeing clinical trial master files and developing tools for better data accuracy.
Professionals ensuring data integrity and regulatory compliance. Their work includes managing data queries, analyzing trends, overseeing clinical trial master files and developing tools for better data accuracy.
They want a clear overview of the data models in use across the organization, focusing
on strategic advantages, usage trends, and potential cost impacts.
They want a clear overview of the data models in use across
the organization, focusing on strategic advantages, usage trends,
and potential cost impacts.
Challenge
Challenge
Challenge
To create a unified system — a "one-stop-shop" — providing real-time, holistic visibility into biosample data. The solution should streamline workflows, ensure accurate tracking of sample lifecycles and reduce inefficiencies caused by scattered information and manual processes.
To create a unified system — a "one-stop-shop" — providing real-time, holistic visibility into biosample data. The solution should streamline workflows, ensure accurate tracking of sample lifecycles and reduce inefficiencies caused by scattered information and manual processes.
To create a unified system — a "one-stop-shop" — providing real-time, holistic visibility into biosample data. The solution should streamline workflows, ensure accurate tracking of sample lifecycles and reduce inefficiencies caused by scattered information and manual processes.
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context
context
context
This project addresses the critical need for efficient biosample management in clinical research. By consolidating fragmented data into a unified system, stakeholders can improve study efficiency, ensure sample integrity, and make data-driven decisions. Such improvements are essential for timely drug development and reducing operational costs.
This project addresses the critical need for efficient biosample management in clinical research. By consolidating fragmented data into a unified system, stakeholders can improve study efficiency, ensure sample integrity, and make data-driven decisions. Such improvements are essential for timely drug development and reducing operational costs.
This system was crucial for streamlining how teams find and use shared data standards, ultimately reducing duplication of effort and speeding up projects.
I was engaged to redesign the interface and user flows so that any role, from new hires to seasoned data experts, could quickly get what they needed. Redesigning was a key step toward making data management simpler and more transparent across the entire organization.
This system was crucial for streamlining how teams find and use shared data standards, ultimately reducing duplication of effort
and speeding up projects. I was engaged to redesign the interface and user flows so that any role, from new hires to seasoned data experts, could quickly get what they needed. Redesigning was a key step toward making data management simpler and more transparent across the entire organization.
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user problems
user problems
user problems
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Scattered information:
Scattered information:
Scattered information:
Users waste time gathering and verifying information scattered across multiple systems.
Users waste time gathering and verifying information scattered across multiple systems.
Users waste time gathering and verifying information scattered across multiple systems.
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Inconsistent data:
Inconsistent data:
Inconsistent data:
Similar data in different formats or systems causes confusion and delays decisions.
Similar data in different formats or systems causes confusion and delays decisions.
Users without deep technical knowledge felt lost, slowing adoption
and hindering collaboration.
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Manual data entry:
Manual data entry:
Manual data entry:
Biosample Operations Representatives (BORs) heavily rely on Excel for tracking samples, which is time-consuming, error-prone and counterproductive.
Biosample Operations Representatives (BORs) heavily rely on Excel for tracking samples, which is time-consuming, error-prone and counterproductive.
Biosample Operations Representatives (BORs) heavily rely on Excel for tracking samples, which is time-consuming, error-prone and counterproductive.
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Sample tracking issues:
Sample tracking issues:
Sample tracking issues:
Scientists struggle to locate samples due to inconsistent vendor data and missing custody details.
Scientists struggle to locate samples due to inconsistent vendor data and missing custody details.
Scientists struggle to locate samples due to inconsistent vendor data and missing custody details.
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Sample loss:
Sample loss:
Sample loss:
The root causes of missing samples, such as consent issues or site errors, are often unclear.
The root causes of missing samples, such as consent issues or site errors, are often unclear.
The root causes of missing samples, such as consent issues or site errors, are often unclear.
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Relabelling inefficiencies:
Relabelling inefficiencies:
Relabelling inefficiencies:
Different vendor IDs require costly relabelling processes, wasting money and resources.
Different vendor IDs require costly relabelling processes, wasting money and resources.
Different vendor IDs require costly relabelling processes, wasting money and resources.
business problems
business problems
business problems
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Operational inefficiency:
Operational inefficiency:
Operational inefficiency:
Scattered data and manual processes slow workflows, raising resource use and affecting timelines.
Scattered data and manual processes slow workflows, raising resource use and affecting timelines.
Scattered data and manual processes slow workflows, raising resource use and affecting timelines.
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Increased costs:
Increased costs:
Increased costs:
Inefficient sample tracking and relabelling cause unnecessary expenses like vendor charges and labor costs.
Inefficient sample tracking and relabelling cause unnecessary expenses like vendor charges and labor costs.
Inefficient sample tracking and relabelling cause unnecessary expenses like vendor charges and labor costs.
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Delayed decision-making:
Delayed decision-making:
Delayed decision-making:
Incomplete data delays insights, slowing study progress and impacting trial outcomes.
Incomplete data delays insights, slowing study progress and impacting trial outcomes.
Without quick access to validated data models, product updates
and launches were delayed.
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Compliance risks:
Compliance risks:
Compliance risks:
Poor sample tracking raises compliance risks and jeopardizes study validity.
Poor sample tracking raises compliance risks and jeopardizes study validity.
Poor sample tracking raises compliance risks and jeopardizes study validity.
solution
solution
solution
We began with in-depth interviews conducted by business analysts, engaging 10 participants to uncover core challenges, define user groups, and understand system requirements. These discussions revealed key pain points and helped shape the product direction.
We began with in-depth interviews conducted by business analysts, engaging 10 participants to uncover core challenges, define user groups, and understand system requirements. These discussions revealed key pain points and helped shape the product direction.
We began with in-depth interviews conducted by business analysts, engaging 10 participants to uncover core challenges, define user groups, and understand system requirements. These discussions revealed key pain points and helped shape the product direction.
With initial prototypes in hand, I led usability sessions involving 15 users from a range of roles. Their feedback directly informed iterative improvements. Each round of refinement was supported by collaborative reviews, ensuring that the evolving design addressed real-world workflows.
With initial prototypes in hand, I led usability sessions involving 15 users from a range of roles. Their feedback directly informed iterative improvements. Each round of refinement was supported by collaborative reviews, ensuring that the evolving design addressed real-world workflows.
With initial prototypes in hand, I led usability sessions involving 15 users from a range of roles. Their feedback directly informed iterative improvements. Each round of refinement was supported by collaborative reviews, ensuring that the evolving design addressed real-world workflows.
High-fidelity mockups were introduced early to clarify intent, align stakeholder expectations, and avoid miscommunication. This approach provided a clear visual reference throughout the development process.
High-fidelity mockups were introduced early to clarify intent, align stakeholder expectations, and avoid miscommunication. This approach provided a clear visual reference throughout the development process.
High-fidelity mockups were introduced early to clarify intent, align stakeholder expectations, and avoid miscommunication. This approach provided a clear visual reference throughout the development process.
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Leverage automated sample tracking to enhance decision-making
Explore models effortlessly
with powerful search
Leverage automated sample tracking to enhance decision-making
Leverage automated sample tracking to enhance decision-making
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Easily navigate all sample data from one place
Easily navigate all sample data from one place
Easily navigate all sample data from one place
Easily navigate all sample data from one place
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Monitor sample integrity in real time and stay in control
Monitor sample integrity in real time and stay in control
Monitor sample integrity in real time and stay in control
Monitor sample integrity in real time and stay in control
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Spot trends and confidently supervise lab performance
Spot trends
and confidently supervise lab performance
Spot trends and confidently supervise lab performance
Spot trends and confidently supervise lab performance
success / impact
success / impact
success / impact
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Improved operational efficiency:
Improved operational efficiency:
Improved operational efficiency:
A centralized platform helped reduce time spent gathering data, enabling faster workflows and decisions.
A centralized platform helped reduce time spent gathering data, enabling faster workflows and decisions.
A centralized platform helped reduce time spent gathering data, enabling faster workflows and decisions.
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Cost savings:
Cost savings:
Cost savings:
Consolidated vendor IDs eliminated relabeling charges, saving approximately 20% on lab costs annually.
Consolidated vendor IDs eliminated relabeling charges, saving approximately 20% on lab costs annually.
Consolidated vendor IDs eliminated relabeling charges, saving approximately 20% on lab costs annually.
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Enhanced data accuracy:
Enhanced data accuracy:
Enhanced data accuracy:
Automating tracking minimized manual input errors and supported more consistent data quality.
Automating tracking minimized manual input errors and supported more consistent data quality.
Automating tracking minimized manual input errors and supported more consistent data quality.
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Better sample visibility:
Better sample visibility:
Better sample visibility:
Real-time custody tracking ensured transparency, reducing sample loss by 30% and improving reliability.
Real-time custody tracking ensured transparency, reducing sample loss by 30% and improving reliability.
Real-time custody tracking ensured transparency, reducing sample loss by 30% and improving reliability.
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Regulatory compliance:
Regulatory compliance:
Regulatory compliance:
Unified and traceable data management supported adherence to study protocols and regulatory expectations.
Unified and traceable data management supported adherence to study protocols and regulatory expectations.
Unified and traceable data management supported adherence to study protocols and regulatory expectations.
what would i do differently?
what would i do differently?
what would i do differently?
Start designs after confirming data availability:
Start designs after confirming data availability:
Start designs after confirming data availability:
I should have confirmed the availability of critical data before starting the designs. The lack of access led to avoidable redesigns.
I should have confirmed the availability of critical data before starting the designs. The lack of access led to avoidable redesigns.
I should have confirmed the availability of critical data before starting the designs. The lack of access led to avoidable redesigns.
Dig deeper into user motivations:
Dig deeper into user motivations:
Dig deeper into user motivations:
I should have explored the "why" behind user requests for certain features more thoroughly. Understanding their motivations better could have helped refine or challenge ideas that weren’t ideal.
I should have explored the "why" behind user requests for certain features more thoroughly. Understanding their motivations better could have helped refine or challenge ideas that weren’t ideal.
I should have explored the "why" behind user requests for certain features more thoroughly. Understanding their motivations better could have helped refine or challenge ideas that weren’t ideal.
Prioritize ongoing communication:
Prioritize ongoing communication:
Prioritize ongoing communication:
More frequent early-stage check-ins with stakeholders could have reduced misalignment and clarified requirements.
More frequent early-stage check-ins with stakeholders could have reduced misalignment and clarified requirements.
More frequent early-stage check-ins with stakeholders could have reduced misalignment and clarified requirements.