Ikenna Udeani

• ● Developed and deployed machine learning models to predict maternal and neonatal mortality leveraging CBHMIS health data sources, generating predictive insights for targeted health interventions.

• ● Collaborated with the Federal Ministry of Health to activate the Community HMIS module on the DHIS2 platform to enable reliable health data reporting at the community level.

• ● Designed and maintained analytics-ready indicator datasets integrating multi-state facility and program data for maternal and newborn health surveillance.

• ● Developed and managed the CBHMIS database and dashboard to ensure accurate and timely reporting and usage of the CBHMIS data.

• ● Performed data quality assurance leveraging machine learning techniques like anomaly detection, complemented by automated validation, reconciliation, and QA checks to ensure data accuracy, completeness, consistency, and integrity across multiple data sources.

• ● Developed Power BI semantic models and dashboards used by senior leadership and donors to monitor program KPIs.

• ● Prepared and presented monthly analytical reports and dashboards to communicate program performance and key insights to internal and external stakeholders.

• ● Administered the National Laboratory Information Management System (National LIMS) for improved user experience, reliability and reduced downtime for remediation of technical issues.

• ● Spearheaded data management for high-impact projects by building and automating reporting pipelines, consolidating and harmonizing data across multiple national health information systems (TB, HIV, HPV), and standardizing KPI definitions for consistent, trusted analytics outputs.

• ● Led the enhancement of the HPV module on the National LIMS, coordinating with software teams and stakeholders to translate technical requirements into platform upgrades and System Requirements Specifications (SRS).

• ● Conducted data cleanup, assessment, and ETL jobs to standardize heterogeneous datasets from multiple sources for analysis, supporting the PATHS, TB DNO, and EID projects.

• ● Documented architectural and platform requirements for the National LIMS, as well as quarterly newsletters for government dissemination to stakeholders, ensuring transparency and audit-readiness.

• ● Facilitated the training of healthcare workers, lab scientists, and government officials on data analysis, visualization, and data-driven decision-making.

• ● Designed ETL processes using Spark and SQL-based transformations to improve the availability and reliability of analytics datasets for the TB Diagnostic Network Optimization (DNO) project.

• ● Automated data extraction and cleaning using Python scripts, improving data quality and reducing manual reconciliation effort.

• ● Improved end-user experience by offering prompt user administration and technical support for the National LIMS, guaranteeing safe, dependable, and continuous system operations.

• ● Supported the preparation of annual and end-of-project reports to donors, communicating project progress, outcomes, and key achievements.