Data that arrives in time
to change the decision.
MSS analyses routine health data, survey data and programme records to produce evidence that reaches a decision forum while the decision can still be made — with data quality treated as part of the analysis, not an assumption behind it.
Most health systems are not short of data. They are short of analysis that is timely, quality-checked and framed around a specific decision.
Start from the decision the programme needs to make, assess the data that would inform it, and build the smallest analysis that answers the question — dashboard included only when it earns its place.
Nine analytics service areas.
Analytical work is scoped to a decision, a planning cycle or an investment question — never to fill a dashboard.
Routine Health Data Analysis
HMIS, immunisation and programme datasets analysed for coverage, timeliness and trend — with the reporting chain understood, not just the file.
Survey Data Analysis
Household and facility survey datasets analysed with appropriate weighting, confidence and clear statements about what the sample can and cannot support.
Data Quality Assessment
Completeness, timeliness, internal consistency and validity reviewed facility by facility, with findings phrased as actions rather than verdicts.
Healthcare Performance Dashboards
Dashboards built around a recurring decision — coverage by geography, reporting timeliness, stock against consumption — designed to be opened, not admired.
Programme Monitoring
Monitoring frameworks, indicator definitions and review cadences that let a programme see its own trajectory between formal evaluations.
Coverage Gap Analysis
Triangulation of service data with settlement geography to identify where coverage is thin and how large the gap is likely to be.
Evidence-Based Decision Support
Analysis packaged for a specific forum — state review meeting, partner review, investment committee — with options and trade-offs made explicit.
Health Planning & Investment Intelligence
Analysis supporting resource allocation: where additional teams, outreach points or commodities would change outcomes most.
Research Data Infrastructure
Data management for research and evaluation — instruments, capture, cleaning, versioning and reproducible analysis pipelines.
What a decision-oriented view looks like.
A monitoring view is useful when it states what happened, what it implies and what should be done next. The example below is synthetic and labelled — it demonstrates structure, not programme performance.
- —Coverage is not evenly distributed across the mapped area
- —Reporting quality limits how far the coverage figure can be trusted
- —Supervision is indicated before the next reporting period closes
A monitoring view earns its place when it converts into a scheduled action. Here the sequence would be: verify the reporting gap with the affected facilities, assign supportive supervision to the flagged areas, then re-read coverage after the next cycle — rather than re-reading the same dashboard.
How we verify implementationHow this lands in a facility.
The practical difference between a capability described and a capability delivered.
Decision first
Every analysis opens with the question it answers and the forum that will use it.
Quality stated
Limitations travel with the finding, so a number is never read as more certain than it is.
Facility-facing views
Where a facility's own reporting quality is the issue, the facility sees its own picture — not only the state summary.
No invented benchmarks
Comparative figures are used only where the underlying data supports them.
Discuss this capability with MSS.
Share your geography, programme and timeline — we will recommend an entry point and what to expect.