Client Overview
The Liverpool School of Tropical Medicine (LSTM) is a globally renowned institution dedicated to improving health outcomes in some of the world’s most disadvantaged communities. Established in 1898 as the world’s first institution devoted exclusively to tropical medicine, LSTM continues to lead the way in tackling global health challenges.
Through pioneering research, education, and clinical initiatives, LSTM focuses on combating infectious, neglected, and tropical diseases. Its work spans critical areas such as malaria, tuberculosis, HIV/AIDS, and maternal and child health.
Collaborating with partners in over 70 countries, LSTM delivers innovative solutions that address some of the most pressing health issues of our time.
Business Objective
LSTM aimed to centralize and enhance the accuracy of research personnel data (staff and students), enforce data quality standards, and provide a consistent source of truth by integrating it into the Research Information Management System Elsevier Pure. They were looking for a solution to eliminate manual intervention, ensure data quality, and consolidate duplicate records.
Challenges Faced by LSTM
Data resided in disparate systems, and the educational institution relied on manual efforts to integrate data. Hence, they faced the following challenges due to high manual dependency and a lack of data management.
- Manual dependency is used to extract personal records via the relational database management system (SQL) and then upload them as XML files into their research information management system.
- Due to manual intervention, duplicate and multiple records were often created for the same person.
- Fragmented data sources, such as staff data, were stored in SQL Server while student data was in Salesforce, creating structure and source complexity.
- Pure (RIMS) treats all individuals as “Persons” irrespective of their role (student or staff), but the source systems did not align with this model.
How NeosAlpha Helped?
As a Boomi Partner, we leveraged Boomi’s integration and data management capabilities to design and implement a robust, automated interaction framework. Here is how our integration solution helped the education and research institute eliminate manual dependency and enhance data quality.
1. Centralized Master Data Management
LSTM lacked a single source of truth for person data, as student and staff information resided in separate systems (Salesforce and SQL Server), resulting in data inconsistency and duplication in Elsevier Pure (RIMS).
To address this, we configured Boomi Master Data Hub (MDH) as the central repository to consolidate and manage all personal data. Staff data from the relational database management system and student data from Salesforce were ingested into MDH, offering a unified view of data to feed into Pure.
2. Data Quality and Matching Rules
Lack of data quality management resulted in multiple records for the same individuals, especially for those with dual roles (staff and student) or rehires.
To identify duplicates and validate record accuracy, our Boomi experts implemented stringent matching rules and data quality controls in the Boomi Master Data Hub. After this, only accurate Golden Records were allowed into Pure for reliable tracking and reporting of individuals.
3. Automated Data Flow
Our client manually uploaded personal data into their Research Information Management System (Pure), relying on stored procedures to generate XML files, which were then uploaded manually. All the manual processes are prone to errors.
We build automated integration processes in Boomi to fetch data from the source systems, validate and transform it, and then push it directly into Pure. This eliminates manual effort, reduces errors, and improves efficiency.
4. Single Unified Entity Model
Their Research Information Management System (Pure) worked with a single “Person” entity model with affiliated roles (student and staff). However, the student and staff data had different structures in their respective source systems. Variation in data structure caused duplication and confusion in Pure.
We designed Boomi MDH to store all individuals as a single “Person,” regardless of their source system structure or role. Any person who was linked to more than one affiliation record earlier now aligns with Pure’s data model, eliminating any duplication or structural inconsistencies.
5. Custom Business Logic
LSTM requires specific business rules for conditions like – staff re-joining the institution, students also being staff, and automatic creation of missing affiliations. These solutions were not initially supported in the Boomi setup.
We implemented custom business logic within the Boomi integration layer and MDH. Re-joining staff with the same original ID were merged and historical affiliations retained, dual role individuals linked to one person record, and for missing student affiliations were created dynamically during the export to Pure.
6. Tailored Handling of Complex Identity Scenarios
Some records could not be confidently matched or automatically resolved due to ambiguous data (e.g., the same name or email across different entities). If not managed well, this resulted in potential data corruption.
Records that couldn’t be matched automatically were sent to quarantine in MDH to be reviewed by the Data Stewards. We set up rules, like checking for similar names or emails to avoid incorrect merges. Once the review is done, the correct affiliations are automatically linked to the person’s record.
Technologies Used
- Boomi Integration
- Boomi Master Data Hub (MDH)
- Salesforce
- SQL Server (via iTrent HR)
- Elsevier Pure
- Microsoft SharePoint
Results
Our Boomi integration expertise helped us to leverage the complete integration and data management capabilities, which led to significant improvements in data governance and operational efficiency:
- 90-93% automated integration flows replaced the manual process of preparing and uploading XML files.
- Reduced data duplication with matching rules and MDH validations that only record per person exists in RIMS (Pure).
- Enhanced 100% data quality by building a quarantine in MDH to check that records met organizational standards before integration.
- Consolidated person records in Pure helped LSTM to track research activities and affiliations accurately.
- Update of person data processing time across systems reduced by 60%
- Designed a scalable and future-ready integration with Boomi iPaaS for easier maintenance as data sources evolve.
