Pipeline Data Modeling and Process
Data modeling
Once the GRM system and its components are defined, the requirements completed, and
business workflows established, we focus on the data model. A frequently asked question is
whether or not to normalize the data model. There are both advantages and disadvantages to
normalizing. In fact, there is no magic formula or standard answer. Highly normalized data
models will result in some performance degradation; however, zero normalization can result
in unnecessary duplication of data and less efficient use of the database. Normalization needs
to be evaluated and implemented with special consideration of the target system and its
requirements.
Logical Data model: An important prerequisite to logical data modeling is to have all the
business requirements and workflows defined. The logical data model addresses blocks of
information and how they relate based on established business workflows and constraints,
independent of the database choice. An Entity Relationship Diagram (ERD) is the initial step
in building the logical data model. There are data modeling tools available to assist in the
construction and maintenance of the ERD.
Physical Data model: Establishes full attribution of the model based on defined data
structures with consideration for database of choice, implementation platform, client/server
environment, data segmentation, user outputs, performance and other user needs.
Pre-developed Models
Today, leading software vendors and implementers offer pre-developed data models, which
include pre-configured workflows and interfaces to popular third-party software applications.
These “standardized” data models are the result of knowledge gained by vendors’ years of
experience designing and implementing the integrated workflows demanded by clients. The
advantages of a pre-configured model are many and include standardization, ease of
implementation, and a robust starting point from which to arrive at a unique data model that
meets your specific organizational needs.
Conclusion
Careful planning of the essential data model foundation is necessary to successfully
implement a GRM and achieve the many possible quantifiable benefits of GRM. Through a
properly developed data model, data can be efficiently exchanged between independent
information systems and applications and interfaces can be added according to your
technical, organizational, and financial priorities. The data model must match each
company’s business strategy – that is, there is not one data model that will ensure success.
However, there is a methodology to help determine the specific data model that will best
meet your company’s objectives.
