Business Data Modeling Using Joint Application Development (JAD) Facilitated Sessions with In-Session Documentation

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Need to Model Your Business Data. . .

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	Business Data Modeling JAD Sessions

People Involved
Technical Components
Typical Activities
Potential Deliverables

Our Promise:

Project scoping based on affinities
Business function based CRUD matrixes
Technology independent information usage analysis
Project independent business data modeling

Benefits:


	A data model depicts the structure of information that the organization uses independent of the underlying technology. Whether the organization uses mainframe, client/server or Intranet/Internet open architecture, the business data remains consistent. The nature of the relationships between different types of information shows which business decisions are supported.

Encourage subject matter expert interaction with the business data model
Identify business data entities and attributes before design
Resolve data issues with the appropriate level of authority
Illustrate the constraints that data place on business decision-makers
Ensure fit with corporate data strategy
Facilitate evolution to data marts and warehouses
Reduce the potential for expensive changes later in the project



		Client Representatives, System Analysts, Business Analysts, Business Managers, End Users, System Designers
		Data Analysts, Data Administrators, Database Administrators
		Business Process Owners, Operation Managers, Information Technology Managers, Project Leaders, Auditors, Security, Developers, Standards, Vendors, Quality Assurance
		We offer experienced JAD facilitation teams (session leader and session analyst) with proven track records who work virtually or at your site to deliver the best possible result.


		- Corporate data models - Existing reports and forms - Existing system documentation - Process models - Requirements documents
		System Usage Matrixes, Prioritization, Breakout Sessions, Data Modeling, Affinity Analysis, CRUD Matrixes, Interviewing Techniques, Data Normalization, Scoping Techniques, Requirements Decomposition, Requirements Validation
		Diagramming tools, Spreadsheets, Word processors, Upper CASE, Data dictionary

All of the following business system analysis activities and deliverables can also be supported via e-Coaching


	Each session is unique. We create the actual session agenda together with you based on your business needs. The selection of the deliverables is a pre-session activity.

	List all internal and external interfaces
	Assign post-session tasks and schedule post-session meeting
	Create high-level data model of major business entities
	Define data entity ownership and responsibilities
	Develop CRUD (Create, Retrieve, Update, Delete) matrixes
	Evaluate potential project scopes based on affinities
	Identify first level data entities
	Create intuitive data model
	Define key attributes for entities
	Expand key attribute metadata
	Select base data elements in existing outputs
	Resolve derivable data elements
	Create expanded attribute definitions
	Normalize data from existing outputs
	Expand on attribute metadata
	Evaluate data attribute orphan list and assign to entities
	Initiate data attribute ownership determination
	Combine intuitive and normalized data models
	Resolve model discrepancies
	Collect additional attribute definitions and metadata
	Finalize data attribute ownership assignments
	Finalize combined data model
	Decompose and prioritize new system requirements
	Create function statements for identified processes
	Finalize new system business data model
	Collect metadata for new attributes
	Evaluate organizational standards and guidelines




	Critical success factors identify information needs and systems that are critical for business continuity. List of assumptions that define the baseline against which each deliverable was established. If the baseline is changed, the validity of the other deliverables is at best questionable. Post-session task assignments define the actions that individuals have to do to clarify open questions or resolve open issues. High-level data models present major entities, potential key attributes and the business relationships between entities. Association matrixes relate data entities to business function, business function to IS system, business function to organizational chart, etc. Detailed Entity-Relationship Diagrams depict the objects (entities) that describe the business world, the relationships between objects and their primary key attribute(s). Attributes are the data elements that describe the objects and/or allow for unique identification of an object. Attribute metadata is data about data. A data dictionary is a repository for metadata, such as: Primary name is the official designator for the attribute. Attribute synonyms describe what other names different organizational units call this attribute. Attribute definitions are single, simple English (nobody's techno-babble) sentences expressing what information the attribute contains. Data rules constrain the structure of information stored in the attribute, e.g., numeric/alpha, number of characters, storage type, etc. Data validation rules constrict the values stored in the attribute. Business rules document organizational decisions and directives. Default values are assigned to attributes in the absence of an explicit value. A synopsis contains a short overview of the results of the entire session for the management review Open issues are unanswered questions and issues that must be resolved before continuing with the project.