NGO Data Analytics for Monitoring and Evaluation Training Course

Course overview

NGO programmes generate large volumes of monitoring data, but teams often struggle to turn registration records, survey responses, activity logs and partner reports into evidence that can guide delivery. Spreadsheets may contain duplicate beneficiaries, inconsistent locations, missing disaggregation fields or indicators that cannot be compared with targets. Programme managers need reliable answers to practical questions: who has been reached, which groups are being missed, whether outputs are on track, and what the data suggests should change. This course equips participants to produce those answers with defensible analytical methods.

Participants learn a structured workflow for monitoring and evaluation data: defining indicator data requirements, cleaning datasets, validating quality, calculating performance measures, analysing disaggregated results and communicating findings. Using Microsoft Excel, Power BI and KoboToolbox-style datasets, they build pivot-table analyses, calculate indicator values, identify trends and outliers, create charts and develop an interactive programme dashboard. The course also applies OECD DAC evaluation criteria to frame analysis that goes beyond activity counts and supports questions about relevance, effectiveness, efficiency and sustainability.

Training is delivered through instructor demonstrations, guided data labs and an NGO programme case study spanning beneficiary registration, baseline, routine monitoring and endline data. Participants work with realistic datasets containing common field-data problems, document their cleaning decisions and test findings against an indicator reference sheet. Each participant leaves with a documented monitoring dataset, an indicator analysis workbook, a Power BI dashboard and a practical data-improvement action plan that can be adapted for their own programme.

The course is designed for monitoring, evaluation, accountability and learning staff, programme personnel and managers who need to use data confidently in donor reporting, management reviews and programme adaptation.

Course objectives

By the end of this course, participants will be able to:

• Design an indicator data map linking results-framework indicators to sources, disaggregation fields, frequencies and responsible roles
• Clean NGO monitoring datasets by standardising categories, removing duplicates, treating missing values and documenting transformations
• Calculate output, outcome, coverage and target-achievement indicators using Excel formulas and pivot tables
• Assess data quality using completeness, timeliness, consistency, validity and integrity checks
• Analyse beneficiary results by sex, age, disability status, location and other relevant equity disaggregations
• Build a Power BI dashboard with KPI cards, trend charts, slicers and target-versus-actual visuals
• Interpret findings using OECD DAC evaluation criteria and distinguish evidence, assumptions and unsupported claims
• Produce a monitoring insight brief containing validated findings, visual evidence, limitations and recommended programme actions

Benefits of attending

Target competencies

Who should attend

• Monitoring and Evaluation Officers — who prepare indicator reports and need to validate and analyse routine programme data
• MEAL Coordinators — who establish data-quality processes and turn evidence into management decisions
• Programme Managers — who must track delivery performance, respond to variance and explain results to donors
• Data and Information Management Officers — who maintain beneficiary, activity and partner reporting datasets
• Project Coordinators — who need to use monitoring evidence to adjust implementation plans and resource allocation
• Donor Reporting Officers — who compile credible narrative and quantitative results reports against grant commitments

Requirements and prerequisites

This is a foundation-to-intermediate course. Participants should be comfortable using a computer, managing files and entering or reviewing data in spreadsheets. Familiarity with basic NGO results frameworks, indicators, targets and routine monitoring forms is helpful, because examples use these concepts throughout. Participants should ideally have used Microsoft Excel for sorting, filtering or simple formulas, but advanced Excel, statistics, Power BI, coding, database administration and prior evaluation experience are not required. Complete beginners should expect a practical introduction to data structures, formula logic, charts and dashboard concepts before progressing to applied analysis.

Training methodology

The five-day programme combines focused instructor-led sessions with daily hands-on analysis in Microsoft Excel and Power BI. Participants work through a single NGO case involving beneficiary registration, partner submissions, routine monitoring and endline findings, so each technique is applied in context. Guided labs cover cleaning, validation, disaggregation, indicator calculation and dashboard design; small-group reviews test how findings should be interpreted and reported. Each day closes with a practical output, and the final session includes an application-planning workshop using participants’ own reporting and data-quality priorities.

Course outline

Day 1: M&E data foundations and indicator architecture

• Results chains, logframes and theories of change
• Indicator reference sheets and operational definitions
• Output, outcome, coverage and target indicators
• Data sources, collection frequencies and reporting calendars
• Beneficiary-level versus aggregate monitoring datasets
• Disaggregation design for sex, age, disability and location
• Data governance roles, consent and responsible data handling

Workshop: Participants create an indicator data map for a livelihoods programme, specifying fields, sources, calculations, disaggregations and reporting responsibilities.

Day 2: Data cleaning and quality assurance

• Tidy data principles for monitoring datasets
• Excel tables, structured references and data types
• Duplicate detection using identifiers and conditional formatting
• Missing-value profiling and treatment decisions
• Category standardisation with lookup tables
• Data validation rules and range checks
• Completeness, timeliness, consistency, validity and integrity assessments

Workshop: Participants clean a flawed partner-monitoring dataset and complete a documented data-quality assessment with prioritised corrective actions.

Day 3: Indicator analysis and equity insights

• Excel formulas for numerators, denominators and achievement rates
• Pivot tables for location and partner performance analysis
• Target-versus-actual and cumulative progress calculations
• Trend analysis across reporting periods
• Cross-tabulation by sex, age, disability status and geography
• Outlier identification and plausibility checks
• Confidence, limitations and interpretation of monitoring findings

Workshop: Participants calculate and interpret a set of protection programme indicators, producing a disaggregated performance analysis workbook.

Day 4: Dashboard design for programme decisions

• Power BI data import and relationship modelling
• Power Query transformations for repeatable refreshes
• Measures and calculated columns for programme KPIs
• KPI cards, bar charts, line charts and map visuals
• Slicers for partner, location, reporting period and beneficiary group
• Target-versus-actual dashboard layouts
• Dashboard usability, accessibility and narrative annotation

Workshop: Participants build an interactive Power BI dashboard showing reach, target achievement, trends and equity gaps for the case programme.

Day 5: Evaluation-informed reporting and action planning

• OECD DAC criteria for framing evaluative questions
• Triangulation of monitoring, survey and qualitative evidence
• Distinguishing correlation, contribution and causation
• Data visualisation choices for donor and management reports
• Evidence briefs, findings statements and recommendation logic
• Communicating uncertainty, caveats and data limitations
• Monitoring data improvement plans and reporting workflows

Workshop: Participants present a monitoring insight brief and dashboard, then produce a 90-day action plan for improving a selected programme data process.

Tools & standards covered

Microsoft Excel, Microsoft Power BI, KoboToolbox, OECD DAC evaluation criteria

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions