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Data management and analytics

Clean data, transparent analysis and decision-ready evidence products.

Gesem provides data management and analysis for research and evaluation teams that need to turn raw survey, qualitative or monitoring records into defensible findings. The emphasis is on traceability: what changed in the data, how indicators were computed, which analytical choices were made, and how quantitative and qualitative evidence support the final interpretation.

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Data Management and Analytics service by Gesem
Service overview

From raw records to auditable, decision-ready evidence.

Data quality is not created at the analysis stage. It begins with the design of indicators, questionnaires, coding systems, digital forms, field protocols and validation rules. Gesem helps clients build these elements early so that datasets are easier to interpret and defend.

Our analytics work can include descriptive statistics, indicator reconstruction, disaggregated analysis, comparative analysis, data visualisation, qualitative coding, thematic synthesis and dashboard development. We communicate results in plain language so that technical findings can be used by managers, programme teams and stakeholders.

We also document analytical decisions through codebooks, syntax, notes and quality logs where appropriate, making the process more transparent and easier to review.

What this helps clients do

  • Detect and document data-quality problems before they distort indicators, comparisons or conclusions.
  • Reproduce cleaning, recoding and indicator calculations through documented syntax, codebooks and decision logs.
  • Turn quantitative and qualitative datasets into clear tables, visuals and explanations that non-technical decision-makers can use.
Our approach

An auditable path from data receipt to final analytical outputs.

We separate data audit, cleaning, analysis, interpretation and handover so each transformation can be checked, reproduced and explained.

01

Data audit and planning

We review the data source, structure, indicator definitions, missing values, coding practices and expected outputs.

02

Cleaning and validation

We check completeness, skip patterns, duplicates, outliers, inconsistent values, variable labels and documentation.

03

Analysis aligned with questions

We analyse the dataset according to evaluation questions, indicators, disaggregation needs and comparison requirements.

04

Visualisation and interpretation

We prepare clear tables, charts and narrative interpretation so that findings are understandable to non-technical users.

05

Documentation and handover

We provide clean files, codebooks, analysis outputs and explanatory notes that support transparency and future use.

Methods and tools

Methods may include

Data audit Data cleaning Codebook development Descriptive statistics Indicator computation Cross-tabulation Disaggregated analysis Qualitative coding Thematic analysis Dashboard and visualisation
Typical deliverables

Outputs clients can use

Cleaned dataset Data cleaning log Codebook Indicator tables Charts and dashboard outputs Qualitative code matrix Analytical memo Report-ready findings Presentation graphics
Applied analytics

From raw data to evidence that can be reviewed, explained and used.

Gesem treats data management as part of the evidence chain rather than a back-office task. The work connects indicator definitions, data cleaning, analysis choices, qualitative interpretation and reporting so that clients can trace how findings were produced.

Questions we help clients answer

Focus the assignment on decisions that matter

  • Are the variables, labels, skip patterns and indicator definitions internally consistent?
  • Which disaggregations and comparisons are needed to answer the assignment questions?
  • How should quantitative and qualitative evidence be integrated without overstating either source?
  • What documentation is needed so the cleaned data and analysis remain usable after handover?
Related insights: How to Maintain Data Quality in Large-Scale Surveys in Ethiopia Combining Quantitative and Qualitative Data in Evaluation Reports Using KoboToolbox for High-Quality Field Data Collection
Practical quality tool

A practical quality gate from raw data to final handover.

Use these checkpoints before analysis is signed off. They are designed to make cleaning decisions traceable, indicator production reproducible and the final data package usable after the assignment closes.

Data intake and validation

  • Confirm the authoritative source files and preserve an untouched raw-data copy.
  • Validate respondent identifiers, merge keys, variable labels and coding conventions.
  • Review missingness, duplicates, skip patterns, ranges and internal consistency.
  • Reconcile final records with sample, submission or fieldwork logs where available.
  • Agree correction, exclusion and flagging rules before applying them.

Cleaning, analysis and handover

  • Use reproducible syntax or scripts and maintain a cleaning and decision log.
  • Verify derived indicators, denominators, disaggregations and analysis assumptions.
  • Document unresolved limitations rather than hiding them in the final dataset.
  • Package the cleaned dataset with a codebook, syntax, indicator tables and quality notes.
  • Check privacy, access permissions, version labels and client acceptance before transfer.
Downloadable resource: Data Quality, Cleaning and Handover Checklist
Evidence in practice

Recent assignments show the scale and complexity of evidence Gesem manages.

The examples below are not generic analytics claims. They show the kinds of mixed-method datasets, indicator structures and reporting requirements that Gesem has handled in recent evaluation work.

Multi-region resilience evaluation

1,000 household records across 4 regions and 23 kebeles

The final evaluation also integrated 28 KIIs, 12 FGDs, 13 Most Significant Change stories and field observations into the analytical narrative and recommendations.

Child-development endline

931 valid child records in the principal outcome analysis

The assignment combined child outcome data with 30 KIIs and 15 FGDs, requiring careful data validation, disaggregation and qualitative triangulation.

SHIFT III endline

293 valid household records plus multiple qualitative streams

The evidence base included 14 KIIs, 7 FGDs, 9 MSC stories and 4 field observations, analysed against programme indicators and evaluation questions.

Questions clients ask

Frequently asked questions.

Can Gesem clean and analyse existing datasets?

Yes. We can work with existing survey or monitoring datasets, assess their quality and prepare analysis aligned with the client’s questions.

Can Gesem support qualitative data analysis?

Yes. We can code transcripts, develop themes, extract evidence and integrate qualitative interpretation with quantitative findings.

Can Gesem prepare charts and dashboards?

Yes. We can produce practical visualisations and dashboards for reports, presentations and management use.

Request support

Need data management and analytics support?

Share your terms of reference, concept note or a short description of the assignment. Gesem will help you clarify the scope, methodology, work plan and deliverables.

Request a Proposal