Introduction
Logical data and process modeling are two essential first steps in the development of information systems, for both transaction processing and decision support (data warehousing). But how do you
learn to be a modeler? How do you make sure the next information system you develop begins with the proper foundation?
The purpose of logical data modeling is to discover, analyze, define and standardize the discrete facts required by the business to conduct its activities. A data modeler must be concerned with
both correctly interpreting the information needs of the business and providing organization and reusability to the resulting data entities and attributes. To accomplish this purpose, a modeling
candidate must learn certain skills, techniques and rules, and apply those lessons rigorously and consistently. How best to learn is a matter of some debate, but there are some proven methods for
educating logical modelers that can be exploited and refined.
Many people learn modeling “on the job” some study a book or use computer-based education, others attend a public class, while still others learn by watching an experienced modeler
perform what seems like magic and attempting to imitate the practices of the mentor. Ultimately, the best way to learn and retain the knowledge and skills of data modeling is a combination of all
these methods. However, it is the order in which these methods are employed and the actual tools used that can separate a successful modeling educational experience from one that teaches the
student very little or nothing.
Basic Requirements
Data modeling education does not assume much technical systems experience; indeed, a novice can become an excellent logical data modeler with no programming or systems administration experience.
Many data modeling professionals agree that programmers and systems administrators are not well-suited to be data modelers since the skills needed for logical data modeling (abstract thinking,
conceptual design, user liaison, and communication, for example) are different from the skills developed in the programming and systems administration fields (concrete thinking, hardware and
software knowledge, and systems integration experience). Basic requirements for a logical modeling candidate would include the proven ability to think abstractly and conceptually, to gather
requirements from vague and often conflicting testimony, to demonstrate logical thought processes, and to communicate well, including–especially– to listen well.
Communication skills are essential for the data modeler, even with the proliferation of documentation software, because much of the job of logical data modeling involves the translating and
balancing of multiple user requirements and documenting the final results from the user perspective. A data modeler will usually present the completed model to an audience of users and database
professionals, and write clear and concise documentation to support models and craft appropriate definitions. Modelers will also listen to the user(s) describe requirements, and must cull the
important and relevant facts from the discourse.
Most novice modelers come from the business community. By taking experienced business people as modeling trainees, a company can capitalize on their business knowledge to assist in “filling in the
gaps” during modeling education. It is extremely helpful for a novice modeler to understand the business being discussed and analyzed so the student is able to concentrate on learning data
modeling.
The Learning Process
At the start of a novice modeler’s training period, he or she is usually should be assigned to a mentor, unfortunately this is not always the case. Ideally this mentor should be a very
experienced, modeler who has having had formal modeling training education and has also experienced having lived through “on the job” learning. If the mentor has also undergone some training in
the educational development process, to be able to understand the needs of a student and to respond appropriately to those needs, and to develop the inherent skills of the novice in conjunction
with those modeling techniques under study understands how to be a good teacher, then the novice will get an even better education.
Many organizations are accustomed to training by experience (“on the job”), and to some degree this “education” is a proper way to expand a person’s knowledge base in data modeling and
subsequent software development. However, most training by education from experience is predicated on a foundation of knowledge, and with modeling most novices have no foundation upon which to
build.
A better use of the student / novice’s initial time and energy could be spent studying a Computer-Based-Training program (CBT) that focuses on computer-based education teaching the concepts
elements of data and how understanding those elements in any business situation is “data modeling.”
Using the power and convenience of computer-based education is one of the better ways novice modelers can become exposed to the techniques used in discipline of logical modeling; experiential
training demonstrates method for using that basic education to solve actual modeling problems. The case situations presented in a well-developed CBT course should form a progression of degrees of
difficulty as well as ensure that the concepts of prior lessons are reinforced. For a novice modeler with little or no data modeling experience, beginning their course of study with such a CBT
course is advisable.
However, CBT courses are taken in a vacuum, and if the mentor with actual modeling projects does not fully reinforce the lessons taught in the course, making the time invested in the CBT that
education will not be fruitful. Each session of the computer-based education should be followed by an actual modeling mini-project for the student led by the mentor and drawn from an actual
modeling project for the organization. This activity has several purposes: 1) to provide concrete experiential learning of the concepts under study, 2) to demonstrate the business requirements that
are being modeled, and 3) to allow the students to solve modeling problems commensurate with their skill levels and to receive immediate results on their successful completion of the modeling
assignment. The experiential learning exercise should be progressively more difficult, and if at all possible should occur in actual modeling sessions with users. This setting will also introduce
the student to the facilitated sessions of data modeling discovery and will provide opportunity for the student to practice actual data requirements gathering with the mentor.
Most disciplines have formal courses that provide a student with the concepts and techniques of that discipline, and data modeling is no different. There are several good workshops/seminars that
can train a student in the concepts and techniques of data modeling. Modeling courses are designed to prepare the student to successfully gather requirements, execute the modeling techniques, and
document and discuss the models for users and technicians (programmers, DBA’s, etc.). These courses are most beneficial when they are coupled with the successful completion of CBT instruction
computer-based education and mentored-driven performance.
Courses of varying intensity and knowledge transfer are offered through training companies (Inteq Group, The Knowledge Exchange, etc.). Some universities, such as the University of Washington,
offer distance learning in Data Management or offer data management courses as part of their IS curriculum. (Do not confuse tool training with education in how to think about data clearly.)
Finding an appropriate course requires some diligence on the part of the student and their manager/mentor. To reduce some of the confusion and to assist in sorting through the details, the
International Data Management Association (DAMA) has an Educational Services function, where under which recognized vendors and instructors can display their course outlines and overviews of the
programs in a central location. Although DAMA International does not endorse any specific course(s), providing this central location of data management instructional information delivers a service
to the ever-expanding field of data management/data administration. To learn more about the DAMA Educational Services “clearing house,” visit the DAMA International web site at www.dama.org. Many data administration/data management professionals find the affiliation with a professional organization such as DAMA to be very beneficial in their
career and for continued education in this field. The DAMA I curriculum committee (part of the Educational Services function) is developing a comprehensive approach to IRM/DRM education, which will
include data modeling as part of its foundation.
Publications can assist the novice and experienced modeler in learning their trade. Some excellent books published about modeling books have been publsihed. Two are: The Data Modeling
Handbook, by Michael Reingruber; and Data Modeling by G. Lawrence Sanders. Magazines can offer the opportunity to refresh knowledge or start a learning process in a new area. Some
good data management magazines are DM Review – www.dmreview.com; Intelligent Enterprise – www.intelligententerprise.com is another excellent source for discussions of data management. With the proliferation of the World Wide Web, detailed
infofrmation can be found at the clik of a mouse. This publication, The Data Administration Newsletter – www.tdan.com offers practical articles on data
management in each quarterly edition.
Conclusion
As one of the most important steps in the development of information systems, logical data modeling require specific skills and knowledge that can come from a variety of sources and methods of
learning. Each situation will dictate the path(s) the student will follow, but a multi-disciplined approach offers the best opportunity to acquire the knowledge and skills necessary for a
successful data modeling career.
