1. www.monash.edu.au IMS1805 Systems Analysis Topic 4: How do you do it? Guidelines for doing analysis

2. www.monash.edu.au 2 Agenda Aim: To introduce different approaches to analysis model building technique To teach a ‘bottom-up’ approach to process and data modelling

3. www.monash.edu.au 3 1. General There are no hard-and-fast rules; what works for you is good! Compare methods for doing maths with methods for writing essays These ‘recommended’ approaches aim to help you to get started Apply them and discard them as required

4. www.monash.edu.au 4 Bottom-up, top-down and middle-out Bottom-up: start by identifying small detailed specific things; group and organise these to create broader, general ‘high-level’ things Top-down: start by identifying broad general high-level things; use these as basis for identifying small detailed specific things needed to support them Middle-out: start half-way and build up to the top and down to the bottom See tute examples discussed in class

5. www.monash.edu.au 5 2. A bottom-up approach to doing modelling On the basis of what I have seen so far, this is the approach which will suit your thinking best Try to develop the capability for top-down thinking as well; but it may take a bit of practice before you become good at it Follow the tute exercise examples discussed in the lecture to illustrate these steps

6. www.monash.edu.au 6 2(a) A bottom-up approach to Process modelling Start with action descriptions and convert them to information transformations Start with physical form and convert to logical form Start with individual process fragments and build up from them like a jigsaw puzzle

7. www.monash.edu.au 7 Step 1: Identifying actions Use physical descriptions of what happens List everything that happens Make sure you understand each physical action before you write it down

8. www.monash.edu.au 8 Step 2: Transpose combined actions to single and passive to active Every action should relate to a single specific event/act Broad general action descriptions can be eliminated (or at least set aside) at this stage Every action should be expressed in simple active language: “Do X to Y”

9. www.monash.edu.au 9 Step 3: Identify and select only the actions which TRANSFORM information To transform = to change, add to, create out of, etc Transformation of information means that the information output from the transformation is in some way different to the information input For logical processes, transforming refers to the information, not to its physical characteristics Transformation does not necessarily involve physical change - eg validating an ID, authorising a loan, etc

10. www.monash.edu.au 10 Step 4: Convert selected actions into logical process, input and output Express in logical form and remove physical details Process name = active verb + noun naming the information on which the action is done All input(s) should be used by the process to create the output(s) The output(s) is/are created entirely from the input(s)

11. www.monash.edu.au 11 Step 5: Draw data flow fragments to represent each process Should flow simply from step 4 Identify sources and destinations for each data flow involved in the process List external agents and files

12. www.monash.edu.au 12 Step 6: Bring together data flow fragments to create a coherent combined picture Jigsaw puzzle time! Re-arrange, re-draw pieces to make a readable picture; avoid crossed lines, etc May need several drafts to get it right Check for logical consistency and ‘readability’ - it should flow like a piece of writing Check against the original action list to make sure nothing is missing

13. www.monash.edu.au 13 Step 7: Partition model as required Break up into groups of pieces if needed to improve readability (use judgement and remember 5-7 pieces rule) Use these to generate higher-order levels of model Check against any higher-order functions listed in the original action list

14. www.monash.edu.au 14 2(b) A bottom-up approach to doing data modelling Start with attributes - specific data elements needed in the system Group related attributes and derive entities from these groupings Build relationships between entities from business rules/needs

15. www.monash.edu.au 15 Step 1: Identifying all likely possible entities and attributes Use statements (and inferences) of specific data items Use business documents to identify data elements List all data elements Watch for unstated attributes or composite attributes which need to be broken up Select likely entities (but treat these as provisional, to be reviewed in Step 2)

16. www.monash.edu.au 16 Step 2: Group closely-related data elements to identify entities Note that entities may be physical tangible objects (product, student) or conceptual objects (sale, unit) Make sure every data element from Step 1 is allocated to a relevant entity Make sure every entity makes sense and is complete (are more attributes needed?)

17. www.monash.edu.au 17 Step 3: Establish connections between entities to build E-R diagram Business needs define the need for inter- relationships between entities Database structure flows from E-R diagram: entities define tables, attributes define fields, etc

18. www.monash.edu.au 18 Step 4: Define nature of relationships between entities Degree of relationship: 1-1, 1-many, many- many Name of relationship Cardinality of relationship

19. www.monash.edu.au 19 Step 5: Reality check Prepare sample database tables with sample data to check for reasonableness of model Normalise as required

20. www.monash.edu.au 20 3. Points to note Note how: The choice of analytical approach drives the diagram (look at all the things from Step 1 which we have excluded from the final model) The requirements of the model drive the analysis which is needed (look at the things which the model made left us uncertain about) Analysis is the process of developing understanding; model-building is an (important) aspect of analysis Implications for your first assignment

21. www.monash.edu.au 21 Summary Bottom-up approaches relate better to your way of seeing the world and to your knowledge of how organisations work As you get more experienced other approaches may become easier Note that the approach drives the nature of the analytical tasks - data gathering, etc. Look at further in a later lecture