• Authored Course

About this Course

One of the most important tasks to perform in the development of enterprise software applications is the analysis and design of the information and data structures that will be needed to support the achievement of business goals. These structures must be validated against the relevant business and software processes, so that the resulting information system is seen as fit for purpose, and contributes effectively to the realisation of corporate strategy.

Information, and the data that supports it, are among the key strategic assets available to the enterprise as it pursues success in the execution of its mission. Information and Data Modelling therefore forms an important part of a wider enterprise function, Information and Data Management. Ideally modelling would be conducted from an enterprise-wide perspective, thus avoiding the creation of isolated project or functional databases, which must then be integrated at great expense to complete the corporate view needed by senior decision makers.

This course situates Information and Data Modelling within the Data Management function, and exposes the main techniques useful to the modeller in deriving the required Information and Data structures, in accordance with recognised best practice. The techniques discussed are valuable for both the OLTP and OLAP data environments. The course makes use of industry standard notations wherever applicable, focussing on UML.

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Detailed Information

  • Introduction to Information and Data Modelling for the Enterprise
  • Basic Data Modelling concepts
  • Developing a conceptual model of information
  • Moving from conceptual information model to logical data model
  • Documenting attributes
  • Normalisation technique to 3NF
  • Rationalising and reconciling the Top-down and Bottom-Up views of data
  • Considerations for corporate Information and Data Modelling
  • Modelling for OLAP and BI/DW, including the Star Schema
  • Final Topics
    • Further normalization practices
    • Object-Relational impedance mismatch
    • Big Data, XML and NoSql
    • Aspects of physical data modelling
    • Some alternative notations for modelling data
    • Information and Data modelling quality checks
    • Data Componentisation for software applications
    • Data and Process

  • Justify the need for modelling within the Information and Data Management functions
  • Develop a Conceptual Data Model
  • Develop a Logical Data Model
  • Apply a range of techniques to validate data models against business requirements
  • Apply the normalisation technique as a way of analysing and designing attributes, and as a way of validating data models
  • Rationalise and integrate the bottom-up view of data with the top-down view of information
  • Approach the design of schemas for physical databases
  • Approach the design of schemas for BI applications based on OLAP databases
  • Articulate the need for a corporate view of information and data, and be aware of the issues involved
  • Understand some approaches to performing quality checks on the models produced

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