Who we help
Data and technology support for research teams
Acadalytics is a specialist engineering partner for teams that need technical capacity beyond routine analysis, without building an entire engineering department.
Four kinds of teams we work with are described below. Naming a type of team here does not imply that any named organization is a client.
Research consultancies
Technical capacity for defined client work packages.
Common pain points
- A client work package needs data engineering or software beyond the team's current capacity.
- Deadlines are fixed and data preparation is the bottleneck.
- The technical work must be explainable to your client.
Matching deliverables
- Data pipelines and validated datasets for a work package
- Analysis code with documentation
- Internal or client-facing tools
Why collaborate
- Clearly scoped ownership of the technical work package.
- Documentation that lets your team explain and defend the method.
- Confidentiality and subcontracting terms agreed in writing before work starts.
University research centres and labs
Dataset preparation, reusable code, and software tools for research groups.
Common pain points
- Datasets and scripts depend on one student or researcher who is about to move on.
- Data preparation consumes time that should go into analysis.
- A tool prototype needs to become something other people can use.
Matching deliverables
- Dataset preparation with a data dictionary
- Reusable, documented code and replication materials
- Research software tools and data platforms
Why collaborate
- Documented, version-controlled work that outlasts staff turnover.
- Methods stay with your researchers, and we implement and document them.
- Institution-specific permissions and procurement are considered at scoping.
Research-active NGOs and MEL teams
Data quality, indicator pipelines, and reporting systems for monitoring, evaluation, and learning.
Common pain points
- Indicators are assembled by hand each cycle from several exports.
- Survey and partner data have quality problems that surface late.
- Donor reports take days to produce and are hard to trace.
Matching deliverables
- Data QA checks and exception reports
- Indicator pipelines with documented definitions
- Dashboards and automated reporting
Why collaborate
- Indicators stay traceable from report back to source records.
- Less manual rework each reporting cycle.
- Work designed to be maintainable by a small team.
Individual funded researchers
Well-scoped technical tasks, reproducible analysis, and custom tools.
Common pain points
- A grant-funded project needs a technical task done that the team cannot do in-house.
- Analysis exists only as manual steps and cannot be reproduced.
- One-off data preparation is slowing the project down.
Matching deliverables
- Data cleaning and preparation for a defined dataset
- Reproducible analysis scripts, tables, and figures
- A small custom tool for your project
Why collaborate
- Scope is set against your project plan before work begins.
- Plain explanations suitable for a researcher who is not a software specialist.
- An honest assessment of fit if the task is not right for us.
Not sure which group describes you?
Describe your project in the inquiry form and we will tell you honestly whether we are a good fit.