Basically modelling consists of doing the analysis - and recording the results in a modelling tool of some kind, whereas analysis consists of doing the analysis and recording the results on paper or narrative document/presentation of some kind. If the analysis is done soundly it represents 90%+ of the effort or modelling, and modelling ensures that the analysis is done corrected, is recorded, and can be examined and updated.
Why then do people have the impression that doing analysis on paper is far faster than modelling? It is because they do half baked job of analysis and the method of presenting the analysis (narrative document/presentation) allows this. Often in fact it encourages partial analysis because the "analyst" has formed conclusions (based on prejudice e.g. what worked last time, what would suit them) and good analysis could undermine these conclusions.
There are a number of ways of doing and presenting analysis (e.g. business analysis, technology issues analysis etc.). Often analysis is done in the head and results presented using unstructured data e.g. Office documents (Word, Powerpoint etc.). When this is done there are a number of problems - the exact relationship between data and the conclusions is often not recorded explicitly and in detail; and this almost inevitably leads to short being taken (which exposes the a major weakness of analysis done in this way). This is find for the analysts (as the data/relationship - to the extent they are known - exist in their heads), it is not OK for the person for whom the analysis is done.
- determining the metamodel and semantics (based on the what is being analysed)
- gathering the items data
- relating the items data
- recording the data/relationships
- reporting on the conclusions (based on an analysis of the data and how it is related)
- presenting the conclusions and usually the basis of them (data/relationships)
The data/relationships includes (facts, beliefs etc. and goals, preferences) and the conclusions are usually in the form of recommendations.
Modelling basically consists of exactly the same steps - but the principle difference is a modelling tool enforces the semantics (which goes a long way to validating the data/relationships) and makes it obvious when data/relationships are incomplete and or inconsistent.
This means that in practice often gathering and relating the data with rigour (i.e. so that it is accurate, explicit, weighted etc.) is more difficult than is anticipated. This is not an issue with modelling - it is an issue with doing analysis properly.
- Why paper based approaches don't work
- Why EA can't be done on paper