Which decision needs evidence?
For example, understanding a change, comparing periods or investigating factors related to an outcome.
Data analysis · Modelling
What your data says, with the evidence in view.
We analyse and model data to answer a concrete question. We deliver findings, assumptions and limits your team can review.
01Clearer questions
02Relationships examined
03Better-informed decisions
What we have learned in the field
Half of any analysis is agreeing what each number means.
Sales can mean invoiced, collected or shipped. If that is not clear, any model built on top inherits the error.
01
Exploratory analysis
Before concluding, we check the boring parts: duplicate records, dates in another format, missing months. That is where the surprises usually are.
02
Relationships between variables
Two things rising together does not mean one causes the other. We separate what the data shows from what is still a hypothesis.
03
Statistical models
A simple model the team understands is often more useful than a complex one nobody can explain. We choose based on the question.
How we work with data
From scattered sources to findings in context.
We review sources, explore patterns and model relationships to explain what the data shows and what to investigate next.
Sources
Quality, structure and available data
Exploration
Distributions, relationships and outliers
Modelling
Hypotheses, assumptions and limits
Findings
Evidence to inform a decision
Conceptual schematic with simulated data. Associations are interpreted in context and do not demonstrate causation.
How we move forward
From a question to evidence for the next step.
We say clearly what the results show and what they do not.
- 01
Frame
The question and the decision it could change.
- 02
Explore
Sources, quality and relationships in the data.
- 03
Model and explain
Findings, assumptions and next steps.
A practical question
Do your reports mean the same thing by an indicator?
Two figures can differ because of their periods, sources or definitions. Before modelling, we connect each metric to its meaning so comparisons make sense.
What your team can review
- 01
An understanding of the sources
Relevant data, definitions, quality issues and limitations that may affect the answer.
- 02
Analysis and models for review
Variables, transformations, assumptions and results documented to the agreed scope, so the team can follow the reasoning.
- 03
Findings that guide the next step
An explanation of patterns, uncertainties and further questions connected to the decision behind the work.
When it makes sense
When data is available but has more to teach.
Before automating, scaling or investing in another solution.
- 01
There is plenty of data, but it is hard to turn it into a useful answer
- 02
Different indicators seem to tell different stories
- 03
The team wants to identify factors associated with an outcome
- 04
A hypothesis needs to be examined before making a decision
Before we talk
A useful starting point.
We can start with a question and a map of available information. It does not all need to be organised beforehand.
Where is the information?
List systems, spreadsheets or reports and who can explain their fields. No need to send them in the first conversation.
Which comparisons raise questions?
Bring indicator definitions and examples of differences across teams, periods or sources.
Let's talk
Let's explore what your data can answer.
Tell us the question and where the data lives. No need to send it yet.
Let’s talk