Data Modeling & Analysis of Information Needs

An enterprise data model is a very powerful communication tool for creating complex models using simple conventions that can be easily explained. This technique is independent of the mechanisms used to hold that information, such as paper forms or computers. As long as the business does not change, the model will remain accurate.  Renée Taylor Consulting recommends enterprise data modeling in order to plan strategic information system development, maximize data integration, and increase data integrity across an enterprise. We offer extensive enterprise data modeling consulting and support services, including data modeling training and workshops. We recommend that two important concepts to be considered when approaching data modeling: generic modeling and convergent modeling.

Generic Modeling

Data modeling can be performed in a passive way by modeling exactly what exists. A more active form of data modeling, commonly employed in mathematics and science, uses a generic model to predict something that was not previously known, or to provide for a circumstance that does not yet exist. Generic models are simpler and easier to understand than “mirror image” models, and they also answer more questions. The data modeler consistently looks for more simple and elegant solutions that will stand the test of time, are cheaper to implement and maintain, and that anticipate the changes that the organization will undergo. Generic models that have been successfully used by other organizations can be adopted as patterns. Such published patterns make useful starting-points or building blocks for a new data model. Many of our clients in the State of California have used our services to take advantage of generic model patterns in a number of areas such as human resources, cashiering, and demographics.

Convergent Modeling

Sometimes entities may at first appear to be different, yet with a closer look they are seen to have similar attributes and to behave in similar ways. For example, ORDER, REQUEST and PROPOSAL behave in very similar ways regarding fees, requirements and inventory needs.

If the data model could combine these different entities into one entity or supertype, then the functions to manipulate these entities could also be combined. Such combination of entities is called convergent modeling. The result is a reduction in the number of entities and programs, which then translates into applications that are cheaper to develop and maintain.

For large-scale projects, or enterprise data modeling, convergent modeling assists greatly in the integration and reusability of data.

Relational Data Modeling Training

Develop practical data modeling experience in using structured analysis techniques and standard (Oracle) notation for entity-relationship diagramming to document enterprise-wide data requirements. This class focuses on the skills of data analysis and data modeling, and is not a tools class. Data modeling problems of increasing complexity are given in class exercises and workshops, and modeled with pen and paper. Lectures are followed by training workshops, with hands-on (paper-based) data modeling exercises. Includes:

  • Introduction to Data Analysis & Modeling
  • Standards for Drawing Entity-Relationships
  • Hands-On Relational Data Modeling Exercises
  • Subtype vs. Arc Implementation Options
  • Quality Assurance Tips

Learn more about DM Trainings, here.

Enterprise data modeling is a technique that aids in:

  • Identifying related and overlapping information across multiple systems elements
  • Reducing data overlaps and inconsistencies and data integrity problems arising as a result
  • Creating an architecture for data interfaces and potential integration of data elements that can be mapped to database designs for future in-house IT projects and third-party purchases
  • Developing a direct route to an integrated, flexible information resource based on a clear understanding of information requirements expressed as data structures, interdependencies, and common functionality
  • Planning for high-quality systems that better meet the requirements of enterprise information processing, derived from business models agreed between managers, users, and developers
  • Initiating working practices in the organization to support the implementation of structured techniques for systems development and the use of supporting CASE tools
  • Improving productivity within IT by clarifying basic concepts at the senior management level and simplifying requirements or converging redundant development efforts
  • Enabling the scoping of information requirements for major information systems efforts, such as the acquisition of financial management software, new systems developments, and longer-term IT planning to integrate information across multiple current systems

Examples of RTC Data Modeling Engagements:

CA Department of Health Care Services

Developed an enterprise data management strategy for health information across the Department, and enterprise data models for provider management, member management, operations management, health care plan management, contractor management, business relationship management, and financial management. Developed a roadmap with implementation steps and timelines for improved data integrity and normalization across future systems.

CA Water Resources Control Board

Managed project to analyze requirements and create an enterprise-wide model of integrated data requirements for a major alternative procurement effort involving the redesign of multiple systems (SWIM II). Facilitated management meetings, risk and issue resolution and quality assurance of deliverables. Provided training to staff in advanced data modeling techniques.