Conceptual and Logical Data Modeling

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Data is the fuel of the digital economy, and drives insight-driven business transactions across the entire organization. Indeed, making decisions backed up by data instead of intuition is key to an organization’s growth and success.

Conceptual and logical data models ensure all objects are accurately represented, enabling the enterprise to align IT programs and information assets with business strategy. This in turn guides integration, quality enhancement and successful data delivery.

 

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Common Challenges

Often the only means by which complex environments can be understood is through conceptual and logical data models. However, the creation process is often hindered by: 

  1. Badly Stored Data

    Data driven decision making is only successful if the organization understands everything about the data lifecycle which begins with how all of this data is being stored.

  2. Rapidly Changing Environments

    Corporate data environments are constantly evolving and are increasing in complexity. Mergers and acquisitions are a good example of this as the two companies have invariably been using different platforms, technologies and applications.

  3. Legacy Technology

    Organizations tend to have several obsolete systems, which may store valuable yet inaccessible data. Indeed, these systems may be entirely unable to integrate with the modern landscape.

  4. Weak Communication and Understanding

    Sometimes data is seen as something exclusive to IT/ technical department for some companies; within these organizations, data is never a relevant talking point whilst forming the business strategy. Consequently, common understanding of data amongst the organization won’t exist in these organizations.

  5. Data Not Used to Support the Business

    Data that is being collected should support a business service, but not knowing the understanding each level within a data model creates issues.

  6. Poor Database Design

    A poor database design results in an organization unable to gain insights from data due to poor data quality, governance, consistency etc.

Conceptual and Logical Data Modeling

Knowing how data flows will change and evolve is vital to produce useful models: The Big Data field may have big implications.

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Conceptual and Logical Data Modeling

Conceptual and Logical Data Modeling in Four Weeks

Inputs

Conceptual and logical data modeling requires several inputs:

  • A data dictionary or a data definition matrix provides detailed information on data elements, their meanings, and allowable values. While a conceptual or logical entity relationship diagram will focus on the high-level business concepts, a data dictionary provides more detail about each attribute. 
  • A data entity/component catalog identifies and maintains a list of all the data use across the enterprise, including data entities and also the components where data entities are stored. An agreed data entity/data component catalog supports the definition and application of information management and data governance policies, as well as encouraging effective data sharing and reuse.
  • Entity Relationship Diagram template examples include: 
    • UML Class diagrams 
    • Crows foot notation 
    • IDEF1X 

Week One

Establish Goals and Centralize Data

    • Focus database design for a new technology/application to mature a capability or business service
    • Determine the requirements for an information systems project such as the business intelligence team initiating a new reporting project
    • Use entity-relationship diagrams used to analyze databases and resolve problems in logic or deployment.
    • Determine the level of detail you are modeling to: conceptual/logical/physical.
    • Gather and load the available data entity data into the iServer repository, predefined data fields to structure and guide data towards best practice analysis
    • Identify and review other data contents such as existing ERD diagrams, data matrices and third party data modeling tools
    • Use Orbus Software’s Data Modeler Connect service to sync tools such as Erwin, ER/Studio and import existing data models into iServer

    Week 1
    gather-information
    gather-information
    Week 2

    Week Two

    Assess Data and Define Modeling Standards

      • Survey stakeholders, data stewards, and other SMEs to fill in gaps and gather key information on data entities
      • Collate information in the iServer repository
      • Define modeling notation and templates to represent data modeling from the available viewpoints, including UML database notation (class diagram), crows foot database notation and IDEF1X database notation

      Week Three

      Model Your Data

        • Use your selected notation and create the data model.
        • Orbus to provide out of the box templates for your chosen notation.
        • The iServer/ Model Explorer will allow you to reuse objects on to the diagram.
        • Model the database design to create the relationships.

        Week 3
        model-your-data
        communicate-collaborate-and-socialize
        Week 4

        Week Four

        Communicate, Collaborate and Socialize

          • Share the diagrams to stakeholders through Portal / Office 365.
          • Prove that functional requirements have been met.
          • Present the data models to key stakeholders.
          • Outline next steps and priorities.

          Business Outcomes

           

          Following these steps enable the business to deliver several benefits including:

             
          • Ensuring functional requirements are met.  
            • Conceptual and Logical data models are used to prove that functional requirements are addressed. Functional requirements coming from the business are aligned to the goals of the company.  
          • Improved communication. 
            • A data model provides a focus for determining scope and offers something tangible to help business sponsors and developers agree over precisely what is included. Operational staff have visibility of development projects, and the models promote consensus among developers, customers and other stakeholders.

          • Quicker time to market by building software faster, catching errors early and automating tasks. 
          • Better documentation as models document important concepts and jargon, proving a basis for long-term maintenance.  
          • Risk is mitigated through a data model which estimates the complexity of software, and provides insight into the level of development effort and project threats. 

           

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