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Many business people consider data modelling to be a black art practiced by the enterprise IT department that brings no tangible business benefits and is solely designed to make mere mortals feel confused and inferior.
Sadly, many IT departments actually foster this view and, for those that do, the data modelling that they carry out fails to deliver any real benefits – in spite of the mumbo jumbo they chant about it. It does not have to be this way.
When done properly, data modelling can deliver enormous business benefits to any enterprise, that include:
- Higher quality information for all business activities
- Easier access to that information
- Robust information systems
- Better identification of products, profit and cost centres.
- Elimination of redundant and unneeded information
- Reduced costs and increased revenues
How do you do data analysis and modelling “properly”? Where do you start?
The Following two simple (though fundamental) rules will guide you on your way.
Rule 1: Use exactly the same sources from which you extracted your Business Functions from to extract all information for data modelling.
Rule 2: Only model data that is needed to directly support the Business Functions of the enterprise.
Starting with Rule 1 will ensure that you conform to Rule 2.
The Integrated Modelling Method provides a foolproof technique for extracting candidate entities, attributes and relationships from the sources from which the Business Functions were extracted. This technique can be used by novice and experienced analysts alike.
These sources include:
- Transcripts of taped analysis interviews with senior business managers.
- Typed up notes of supplementary information from these interviews.
- Function titles and descriptions developed during function modelling.
- Information flow diagrams produced in analysis workshops.
Technique
The basic technique:
Step 1 -Work through your data sources (best to have these in electronic format) looking for and underlining all “noun structures”, as these are “candidate” entities.
Step 2 – Extract all of these candidate entities, and the associations between them, into a separate document.
Step 3 – Convert these candidate entities and associations to actual entities, attributes and relationships.
Step 4 – Build an Entity Relationship Diagram (ERD).
Step 5 – Design any required relational databases from the ERD.
Example
The first steps in the technique are best demonstrated by way of an example.
The following is part of a transcript of an interview with a business manager, with all of the nouns underlined.
“We receive orders for our products from our customers the day before they require delivery. We check the quantity of the raw materials required to bake the products and, if necessary, we order more from our suppliers. We bake our products fresh each morning. We make deliveries to our customers several times each day. At the end of each week we invoice each customer for the deliveries made to them during the week. We accept payment or remittance from customers by cash and cheque only”.
All of the noun structures have been underlined.
The first sentence is:
“We receive orders for our products from our customers the day* before they require delivery.
Working through the sentence one underlined noun at a time we get the following list of candidate entities and associations:
order [means of ordering] product
product [ordered by means of] order
order [received from] customer
customer [the source of] order
product [delivered by means of] delivery
delivery [means of delivering] product
customer [recipient of] delivery
delivery [made to] customer
Note: day* is an attribute of order, probably “date”.
Because every association is two-way, when we document one association we immediately create its reverse.
Working through the whole of the above transcript gives us the flowing complete list (sorted alphabetically):
baking [to produce] product
customer [billed by means of] invoice
customer [recipient of] delivery
customer [source of] payment
customer [the source of] order
delivery [made to] customer
delivery [means of delivering] product
delivery [of products billed on] invoice tbv
invoice [a billing for] product
invoice [a means of billing] customer
invoice [billing for goods delivered by] delivery tbv
invoicing period
order [means of ordering] product
order [means of replenishing] raw material
order [placed with] supplier
order [received from] customer
payment [accepted from] customer
payment [made by] payment method
payment method [valid means of making] payment
product [billed for on] invoice
product [delivered by means of] delivery
product [ordered by means of] order
product [produced by] baking
product [requirement for] raw material
raw material [quantified by means of] stock check
raw material [replenished by means of] order
raw material [required to bake] product
raw material [sourced from] supplier
stock check [to establish quantity of] raw material
supplier [recipient of] order
supplier [the source of] raw material
This short extract has given us eleven unique candidate entities and thirty (15 x 2) candidate associations.
Rationalising Entities
The list of candidate entities needs a little more work to remove false or spurious entities. A typical example of a candidate item that is not a proper entity is “invoice”. An invoice is probably the most common business item to be incorrectly modelled as an entity. The invoice itself is a piece of paper that represents a business entity or a collection of entities such as a sale (of one or more products) or a billing (for one or more sales). These are the actual data entities that should be modelled – not the pieces of paper representing them.
Converting Associations to Relationships
The associations that we identified must now also be rationalised and converted to “Relationships”. Associations simply tell us that two entities are associated and give us a suggested name for that association. A relationship tells us all of the essential information that we need to know about the association. This includes
- the precise name of the relationship
- whether it is mandatory or optional
- its “degree”, that is, if the relationship is one-to-one, one-to-many or many-to-many
Example
Relationships must be capable of being read in the following way:
Each Order must be received from one and only one Customer
Each Customer may be the the source of one or more Orders
Relationships are always two way so there must always be two entries to specify what they are in both directions.
The items in Bold above are the entity names.
The underlined items show the optionality. Mandatory relationships are written as must be, optional ones as may be.
The items in italics are the relationship names. These must be named so that they can be preceded by “Must be” or “May be”.
The terms “one and only one” and “one or more” define the degree of the relationship.
The Entity Relationship Diagram
All of the preceding information is essential to know but is almost impossible to visualise and of limited use without constructing an Entity Relationship Diagram (ERD). This is the single most powerful model for use in the understanding of the data structure of any enterprise and is an essential element in the design of quality databases.
Effective Layout
On an ERD in the Integrated Modelling Method the “many” ends of a relationship are indicated by a symbol, which resembles, and is called a “crows foot”. If this symbol is turned upside down we get a “dead crow”. This gives rise to one of the most powerful, yet simple, rules for achieving a really effective layout for any ERD, which is “Dead Crows Fly East”.
The net result of this layout is that all high volume and volatile entities will appear to the top and to the left of the ERD and all low volume and more constant entities will appear to the bottom and to the right.
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Source by John J Owens
