Descriptive
What happened?Summarises amounts, changes and differences to understand observed behaviour.
Data analysis · A visual guide
How are data analysed and modelled?
A clear path from a specific question to patterns, models and findings that can help inform a decision.
From data to evidence
Data does not speak for itself. Start with the right question.
Data analysis means examining information with a question in mind: what happened, which factors are associated with an outcome, or what to investigate next. Modelling organises variables and relationships into a representation that can describe, compare or estimate scenarios.
At T2G: we connect business context with analysis, modelling and the engineering needed to explain what the data shows and what step makes sense next.
A process you can explain
From a question to a finding that stands up to review.
A clear sequence helps avoid rushed conclusions. Scope and methods depend on the question and the available data.
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01Frame the question 01Frame the question
What do we need to understand?
We agree on the subject, timeframe, measures and decision the analysis could inform.
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02Review and prepare 02Review and prepare
What is in this data?
Where needed, we connect sources and review definitions, coverage, missing values and consistency before comparing.
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03Explore patterns 03Explore patterns
What signals appear?
We look at distributions, changes over time, differences between groups and outliers that warrant another question.
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04Model and examine 04Model and examine
Which relationship best describes the evidence?
We represent variables and relationships, examine hypotheses and evaluate a model against agreed criteria; fitting the data alone is not enough.
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05Explain and decide 05Explain and decide
What can we conclude, and what is next?
We communicate findings with assumptions, limitations and next steps so a team can decide how to act.
Choose a method for the question
Modelling does not always mean machine learning.
A model is a useful representation for a task. It may be a measure, a comparison, a statistical relationship or a predictive algorithm.
Relational
Which variables move together?Studies associations and helps examine hypotheses without mistaking correlation for causation.
Predictive
What might happen?Estimates future outcomes and should be evaluated against data not used to fit it.
Questions across different contexts
Context changes the question and its interpretation.
These schematic examples use illustrative data; they do not represent T2G results or specific projects.
What explains changes between periods?
Compare categories, amounts and timing, reviewing records that differ from the pattern.
When and where does demand change?
Combine dates, products and channels to understand trends and plan scenarios.
Which signals vary between groups?
Examine measurements or image features with reproducible criteria and expert review.
Interpret with care
A chart shows evidence; it does not explain the cause by itself.
A useful conclusion should be traceable to the data and reviewed with its scope in mind.
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Correlation describes an observed association; it does not prove what caused it.
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Incomplete, biased or differently defined data can change a comparison.
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A model simplifies reality: its estimates have uncertainty and limits of use.
Analysis with purpose
How we turn analysis into a useful next step.
We start with each team’s question and context. We review available data, make assumptions explicit and communicate findings that help decide what to test, model or build next.
From uncertainty to an answerable question
What would you like to understand better with your data?
Tell us about the question, available sources and evidence that would help inform a decision.
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