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Subject areas can represent generic business concepts customer, product, employee and finance , as well as industry specific. The subject areas for an airline are shown in Figure 2. Subject areas can be grouped by three high-level business categories: Revenue, Operation, and Support. These groupings are significant because each represent a distinctively different business focus. Revenue types focus on revenue activities including, revenue planning, accounting, and reporting.
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Operation types represent the main business functions involved in daily operations. Support types aid the business activity, rather than represent the main business. All organizations share these high-level business groupings. Taxonomy is the science of naming, categorizing and classifying things in a hierarchical manner, based on a set of criteria.
Data Taxonomy includes several hierarchical levels of classification. At the highest level, all data can be placed into one of three classes: Foundational, Transactional, or Informational, as shown in figure 3. These classes are distinguished by patterns of data production and conception, as well as their data life cycles.
It includes reference type data, metadata, and the data required to perform business transactions. Transactional Data is the data produced or updated as the result of business transactions. It is dynamic in nature and current within operational systems.
Informational Data is historic, summarized, or derived; normally created from operational data. It is found primarily within decision support systems and occasionally used within operational systems for operational decision support. Subject areas can be categorized according to their predominant data classification.
At the detail level, subject areas contain all three data classes. The classification is based on the size, usage and implementation of that class within the subject area. An ESAM is developed working closely with the business subject matter experts, under the guidance of any existing enterprise knowledge. Organizational structure and business functions need to be identified and understood.
Subject areas common to most organizations Customer, Employee, Location, and Finance are identified first. These are then validated with the business experts. The process of defining and naming each subject area is important because it provides an opportunity to gain consensus across business boundaries on topics vital to an organization. These topics include such things as: what is a customer. If agreement can be gained at a high level, the more detail concepts will be much easier to define.
During this process, priorities are established for the more detail analysis needed in the subsequent development of the EDM. Regarding the airline subject area example; Booking is a Transactional subject area and Inventory is an Informational. This is where Data Taxonomy is valuable for understanding. Subject area names should be very clear, concise, and comprehensive; ideally one word.
When ever possible, industry standard business names Customer, Employee, and Finance are used. Definitions are formulated from a horizontal view, as all relevant information is considered. The definitions help determine the scope of a subject area. Definitions are important because they are viewed by the entire organization, so they need to be as simple, and as understandable as possible. Theoretical, academic or proprietary language should never be used.
The relationships between subject areas represent significant business interactions and dependencies.
A simple line is used to represent the major business relationship between subjects. There is no optionality relationship being required or not or cardinality numeric relationship, 0, 1, infinite at this level. All of the possible relationships are not represented because of the practicality. Each subject area and its subsequent concepts, as well as its data objects, have a distinct color. One color is used for all data concepts, entities and tables belonging to a specific subject area.
As the ESAM becomes institutionalized, the subject areas may even be referenced by their color. Creation of the ESAM follows enterprise data standards, a naming methodology and a review process. The ESAM is validated by the business in an iterative manner.
After gaining consensus across the business, the subject areas are assigned a high-level data taxonomy class Foundational, Transactional, or Informational and added to the Metadata repository. Subject areas are core to an enterprise Metadata repository strategy, because all data objects will be tied to a subject area. Subject areas are assigned one or more business area owners.
At first glance, an ESAM may appear as if it would only take a few hours to create, because it looks like a very simple diagram. However, a true ESAM will take much longer, due to the participation required across the entire organization. Coordination and consensus of this magnitude takes time.
With an average size organization and experienced design professionals, the process may take up to two or three months.
The Enterprise Data Model
To facilitate this process, meetings with business experts can be informal. It is essential to have enterprise wide participation and interaction, since the value of the ESAM is in its depth of business understanding and agreement. A method of organization is a way of grouping things into an orderly structure. The process to create the ESAM is also important. The ECM is a high-level data model with an average of concepts per subject area. The concepts convey a much greater business detail than the subject areas.
An ECM is comprised of concepts, their definition and their relationships. Concepts describe the information produced and consumed by an organization, independent of implementation issues and details. Concepts are grouped by subject areas within the ECM.
The Enterprise Data Model : A Framework for Enterprise Data Architecture, 2nd Edition
A concept can represent a relationship between subject areas. Even in this case, concepts always belong to only one subject area. The concepts help to further define the subject areas, including their scope. They are the details of the subject area definitions. Concepts may be found at different levels of granularity depending on their business relevance. Each concept may cover a very large or small area or volume of data. The point is that the concepts represent the important business ideas, not an amount of data.
The relationships between concepts define the interdependency of the data, void of optionality relationship being required or not or cardinality the numeric relationship; 0, 1, infinite. A simple line is used to represent the major business relationships between concepts. All possible relationships are not represented.
The Enterprise Data Model by Andy Graham (, Paperback) for sale online | eBay
There can be very gray boundaries between concepts, even concepts connecting subject areas. Supportive areas may contain business functions similar to the main business. For example, IT has customers, but these customers are not the airline customers. Including the IT customers into the airline customer concept causes confusion, unnecessary complexity, and does not represent data integration.
Care must be taken to have the main business drive the concept definitions. The ECM also needs to fit within the bigger picture of an industry view. Always remember the dog wags the tail, the tail does not wag the dog. Concentrating one subject area at a time, the ECM is developed from a top down approach using an enterprise view, not drawn from just one business area or specific application.