Andrew Sudoh
MONITORING & EVALUATION SPECIALIST · DATA ANALYST · PROJECT MANAGER
Eight years turning field data into decisions that hold up under audit. I build the systems that catch bad data before it reaches a donor report,routine data quality assessments, MIS databases, household surveys, and Excel dashboards across public health and development programs in Nigeria.
About Me
I am Andrew Sudoh, a Monitoring & Evaluation Specialist, Data Analyst, and Project Manager based in Ikot Ekpene, Akwa Ibom State, Nigeria, with over eight years of experience turning field data into decisions that hold up under audit. I didn't set out to become a data person. I started as a social volunteer doing the kind of fieldwork where you see up close what happens when a report is wrong, a stockout that shows up three months late, quantification and follow up assessments, confirming a beneficiary who falls through the cracks because two registers don't match. That's what pulled me into M&E: the realization that a program is only as good as the data it's making decisions and impact on.
Over eight years at Brokline Foundation, I worked my way up through four roles, from Assistant M&E Officer to the position of the Strategic Information Officer under the THRIVE Project, funded by United States Government (formerly USAID). Each one adding a layer of responsibility, from maintaining beneficiary databases, to running Quality Improvement Teams and community system strengthening, to now coordinating data quality and reporting for the THRIVE Project across different Local Government Areas of Akwa Ibom State. This has spanned through PEPFAR and Global Fund reporting cycles, developing, deploying and utilizing automated beneficiary tracking systems, routine data quality assessments (RDQA/DQA), and building the capacity of field staff and partners to collect and report data correctly the first time.
Alongside the program side, I've built out a technical toolkit that goes beyond the standard M&E stack. Excel dashboards built on formula logic rather than fragile manual reports, infographics, and HTML site builds like this portfolio. I also run an Excel data analysis and blogging class, and write at BlastedGist partly because teaching the fundamentals sharpens how well I understand them myself. What ties all of it together is the same instinct that pulled me into this field in the first place: find where the data can't be trusted, fix the system that let it happen, and build something field staff can actually use without a manual. I'm currently open to remote and hybrid roles in M&E, data quality, program data management, virtual assistance, and comfortable working across time zones.
Impact by numbers
Case studies
The situation
Program sites were submitting monthly data with recurring discrepancies — mismatched beneficiary counts between paper registers and the MIS, missing follow-up dates, and inconsistent indicator definitions across field teams. Donor reports were being delayed while errors were traced and corrected after the fact.
What I did
Introduced a Routine Data Quality Assessment (RDQA) cycle — spot-checking source documents against system records, standardizing indicator definitions across sites, and running supportive supervision visits that trained data clerks on the spot rather than flagging errors after submission.
The result
Reporting errors fell by over 97% across project sites. Reports started going out on time, and program teams could act on the numbers instead of second-guessing them.
The situation
A growing OVC (Orphans and Vulnerable Children) caseload across multiple communities needed a single, reliable system — beneficiary information was scattered across paper files and inconsistent spreadsheets, making it hard to track service delivery or avoid duplicate entries.
What I did
Maintained and structured an MIS database supporting 5,000+ beneficiaries, with standardized intake fields, regular data cleaning cycles, and a clear process for field staff to submit updates without corrupting existing records.
The result
Program teams had accurate, near-real-time visibility into caseloads, which fed directly into service planning and donor reporting.
Field record
- Coordinated data collection, cleaning, validation, analysis, and reporting across multiple project sites
- Ran Routine Data Quality Assessments and supportive supervision, cutting reporting errors by over 97%
- Supported development of M&E tools and frameworks for project impact measurement
- Managed the beneficiary database, safeguarding data integrity at scale
- Trained and supervised field staff and data clerks on MIS tools and reporting standards
- Supervised Quality Improvement Teams addressing GBV, stigma, and access to care
- Strengthened community-to-facility referral systems for continuum of care
- Sustained 100% follow-up in case management for GBV survivors
- Facilitated partnerships that reached 75% birth registration coverage for OVC beneficiaries
- Led data collection, validation, analysis, and reporting for the program
- Delivered 100% of targeted RDQA outcomes through routine assessment and supervision
- Produced timely analytical reports for program teams, stakeholders, and partners
- Maintained MIS databases supporting 5,000+ OVC beneficiaries
- Mentored and supervised community volunteers, improving compliance
- Supplied program teams with analytical insight for decision-making
- Developed MIS tools and a project tracking database from scratch
- Conducted needs assessments for social intervention programs
- Provided first aid and coordinated referrals to healthcare services
- Led emergency response activities for camp corps members
Toolkit
Data Collection
Data Quality
Analysis & Visualization
Program & People
RDQA Checklist Template
A standardized field checklist used to cross-verify source documents against MIS entries during supportive supervision visits.
MIS Intake & Tracking Structure
The field layout used for beneficiary intake, designed to prevent duplicate records and flag missing follow-ups automatically.
Field Staff Training Deck (excerpt)
Sample slides from a capacity-building session on data quality standards for data clerks.
Core competencies
Certifications & training
- ✓Monitoring & Evaluation Trainings — Centre for Clinical Care and Clinical Research Nigeria
- ✓Prevention of Sexual Exploitation and Abuse — United Nations
- ✓Data Science and Analytics — HP Foundation
- ✓Global Citizenship & Sustainable Development — GCED
- ✓Savings Group Information Exchange (SAVIX) Training — CCCRN
- ✓Gender M&E — Global Health eLearning Center
- ✓Data Processing and Information Technology — Gestric Infotech
Education
Degree — Computer Science
End Year — October 2021