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Chapter 10: Analyzing Systems Using Data Dictionaries Instructor: Paul K Chen.

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Presentation on theme: "Chapter 10: Analyzing Systems Using Data Dictionaries Instructor: Paul K Chen."— Presentation transcript:

1 Chapter 10: Analyzing Systems Using Data Dictionaries Instructor: Paul K Chen

2 Topics  Data Dictionaries-What it it? Usage  Data Repositories- What it it?  Data Resource Management vs. Data Dictionary & Data Repository  XML

3 Data Dictionary Support u Just as a conventional dictionary defines the words in a language, a data dictionary defines entities/components associated with systems development: Entity: Customer attributes: name; address; purchase order definition: -----

4 Sample Data Dictionary Entry for Data Flow u Data flow name: customer-stock-transaction-order u Aliases: none u Description: the formal document by which a customer places an order. u Source: Seattle branch office u Destination: Service-customer-orders u Notes: 24-hour response time to a purchase order.

5 What’s Data Dictionaries? The data dictionary is a reference of work of data about data (metadata) compiled by system analysts to guide them through analysis and design. It collects, coordinates, and confirms what a specific data term means to different people in the organization. The key!!!! u Capture and document these data terms through CASE tools and documentation tools such as Word. u Store them in a centralized data repository.

6 Data Dictionaries-Usage u Create reports, screens and forms u Generate computer program source code u Analyze the system design for completion and to detect design flaws u Provide the baseline for Reusability/reengineering/reverse engineering

7 What’s Data Repository? A data repository is a large collection of project information which includes: u Information about system data u Procedure logic u Screen and report design u Relationships between entities u Project requirements and deliverables u Project management information

8 Data Resource Management u Data dictionary and data repository are just part of Data Resource Management (DRM).

9 Data Resource Management Who’s doing it: a team of people consisting of u Process owners who create, update and delete data values. they execute the rules, standards and processes provided by the data owners. u Data owners who have responsibility and authority over data definitions. They provide the rules, standards and processes to process owners to manage the data values.

10 Data Resource Management u Data resource manager who is responsible for the technical architecture which provides the enterprise standards and processes to the data owners to manage the data definition.

11 DRM principles The following principles serve as guidelines for managing data as an enterprise data: u Strategically and technically driven The existence of data items must be justified by a business process required of either short-term or long- term goals. u Data life cycle assessment Data life cycle from acquisition or creation to production or deletion must be periodically assessed based on business needs and climates.

12 DRM principles u Data defined Data must be uniquely defined and assigned precise meaning per organization vocabulary. u Integrity Data integrity rules must be maintained to assure consistency and to control redundancy.

13 DRM Approaches u Security/confidentiality Data must be protected from unauthorized and inadvertent access, modification, destruction and disclosure u Accessibility Data must be made available when and where needed for sharing and reuse.

14 DRM Approaches u Data Stewardship Data subject areas will be managed by a team of people known as data owners and custodians. The group is responsible for assuring that data structure reflects business policies and rules. u Cost/Benefit Optimization Data must be utilized to maximize benefits at a minimum cost.

15 New Applications for Metadata u XML is a simple, flexible meta-language for creating markup languages describing particular documents or domains. u Using XML for meta data definition and management. u Designing a Meta data portal around your corporate meta data repository, and integrating structured meta data with unstructured meta data. u Strategies for incorporating operational meta data into DSS data model.

16 New Challenges for Repository u Building the XML Meta data Repository u Architecting and implementing a web-based corporate meta data repository. u How to give users web access to the meta model. u How can you build, manage and leverage a repository for managing enterprise architecture components including models, objects, data and relationships ?

17 Building the XML Meta Data Repository u Meta data repositories are useful to keep track the meta data describing the systems, to facilitate “where-used” analysis. u When implementing XML, the data type description documents (DTDs) and their relationships to the elements and attributes that make them up is valuable Meta data. u XML allows a developer to model and design an application using UML and to automatically generate XML DTDs as well as XML documents.

18 More Buzzwords u MOF– The distributed metadata management foundation from OMG (Object management Group). u CMR (Corporate Meta Data Repository) u CWR (The common Warehouse Meta Model u XMI (XM Meta Data Exchange) u EIP (Enterprise Information Portal)

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