Your data is not a strategy by itself
A reporting deadline arrives. Someone exports three spreadsheets, cleans the same fields again and builds a chart. Everybody hopes they can repeat the exercise next quarter. Meanwhile the information that could help people make decisions remains scattered across forms, emails and documents.
Often the right starting point is a clear question and a shared vocabulary, not a larger platform. What are you trying to understand? Who needs to act on it? Where did a figure come from? What does the data fail to tell us?
I work across the human and technical parts of this problem: purpose, data design, standards, workflows, analysis, visualisation and the decisions made with the results.
From messy systems to usable knowledge
Data strategy
Agree the questions worth answering, the decisions they support and the smallest set of information that can help.
Architecture and flows
Map where information begins, where it moves, who maintains it and how systems should connect.
Shared definitions
Design proportionate data models, naming conventions and reusable standards so information means the same thing in different places.
Visualisation and mapping
Make patterns and uncertainty understandable through reports, maps and clear explanations — including what the data cannot support.
Open and interoperable tools
Look for portable formats and practical ways to exchange data without creating another locked-in system.
Responsible data practice
Consider permissions, minimisation, safeguarding, governance and the difference between useful sharing and unnecessary exposure.
Start small enough to learn
Frame the questions
Speak with the people who collect, interpret and rely on information. Identify real decisions, constraints and existing workarounds.
Make one flow useful
Test a manageable slice — a clearer form, a common definition, a joined dataset or a report someone can actually use.
Leave a maintainable system
Document the model, responsibilities and assumptions. Build the team's ability to challenge and adapt it when things change.
Some things built around these problems
These are public projects illustrating the approach; they are not substitutes for a tailored plan or evidence of a particular client's results.
What does useful data work look like?
Do we need to replace our existing systems?
Not necessarily. Before buying anything, map what you already have and see whether a better process, export, shared definition or small integration would make it useful.
How do we make funder reporting less painful?
Start with which information needs to be collected once, how it is defined and whether the same evidence can support several genuine learning needs. Less duplication is often a better goal than more automation.
Does open data mean making all our information public?
No. Open formats, open standards and accessible public information can coexist with strong privacy, consent and restrictions on sensitive records. The right boundary depends on the information and the people affected.
Related work: AI and technology choices and building a tool around your process.
Start with the problem, not a pitch
Have something you are working through, or just a question forming? We can talk through what is happening, what you have tried and whether working together would be useful.
Book an introductory callPrefer email? [email protected]. Based in North East England, working with organisations elsewhere too.