Data interoperability means your data can move cleanly between the systems, teams, and partners that need it without manual re-entry, reformatting, or loss of meaning in transit. In practice, most organisations we work with have data sitting in disconnected silos: programme data in one spreadsheet, financial data in another, M&E data in a third, and none of it speaking to the rest. That fragmentation costs time, introduces errors, and makes evidence-based reporting nearly impossible. We build the pipelines, standards, and infrastructure that connect those silos so data flows where it needs to, in a form that is actually usable.

With an honest audit. Before writing a single line of code or recommending a single tool, we spend time understanding what data you collect, how it is stored, who uses it, and what decisions it is supposed to inform. In our experience, most data problems are not technical at their root, they are structural. Data is collected without a clear use case, stored inconsistently, or cleaned differently by different people. We identify those root causes first, then design a workflow that addresses them systematically rather than patching symptoms. The output of that audit is a plain-language data strategy document your leadership team can actually read and act on.

Data modelling is the process of structuring your data so it accurately represents the real-world relationships in your work, which programmes relate to which beneficiaries, which activities drive which outcomes, which costs attach to which grants. You do not need to be a research institution to benefit from it. Any organisation that reports to donors, makes resource allocation decisions, or tracks programme performance across multiple sites is already doing informal data modelling, usually badly, in spreadsheets, by individuals who each have their own logic. We formalise that into a consistent structure that makes your reporting faster, your analysis more reliable, and your data credible enough to share with external partners and funders.

We start from where you are. Replacing core systems is expensive, disruptive, and usually unnecessary. In most cases we build interoperability layers and cleaning pipelines on top of your existing tools, whether that is KoBoToolbox, CommCare, DHIS2, Excel, Google Sheets, Salesforce, or a custom database. We only recommend replacing a system when it is genuinely the constraint, and when we can show clearly that the cost of replacement is lower than the cost of working around it. You will not leave a scoping conversation with us carrying a list of expensive software to buy.

With explicit protocols agreed before we touch anything. We document data handling procedures in writing at the start of every engagement, covering what we access, how it is stored during the project, who on our team sees it, and how it is disposed of or returned at close. For health data and personally identifiable information, we apply sector-appropriate standards including pseudonymisation, access controls, and audit trails. We are experienced working within the data governance requirements of major institutional donors and health agencies, and we can work within your organisation's existing data protection policies rather than asking you to adapt to ours.