In my last column, I described the data modeler's qualifications with respect to the construction and maintenance of the data models. Those skills enable the modeler to build a model that is structurally correct.
This month I will describe another important competency the data modeler must possess in the form of analytical skills. These skills enable the modeler to create a model that correctly represents the business environment and business needs, and they include business focus, scope management, information gathering, information synthesis, recognition of "good enough" and communication.
Business focus: Higher level data models need to represent the business rules, with the scope of the model for each increment dictated by the needs to be addressed by that project. To do that well, the data modeler should genuinely be interested in understanding the enterprise mission, values, priorities and strategies. This is critical because part of the analyst's job is to build a synergistic relationship with the business stakeholders for the project by merging the stakeholder's knowledge of the business with the modeler's understanding of business intelligence and approaches for leveraging the value of information.
The modeler should become familiar with information provided in the company's annual report, website, intranet and other sources. In addition, he or she should pursue and review documents that further describe key corporate actions, as well as the plans and actions of the major business areas being served. This provides the modeler with credibility to discuss business needs with stakeholders. Without the business focus, the modeler may simply go through the motions and create a model that stresses only the data structure. With the business focus, the modeler gains a greater understanding of the business and can then use that knowledge to create a better model, and perhaps more importantly, to help business stakeholders discover additional needs.
Information gathering: The data modeler needs to know multiple techniques for gathering information and where each is most appropriate. Some of the techniques that should be at his or her disposal are interviewing, facilitation, observation, prototyping and research. BI initiatives add a layer of complication for information gathering where it's wise to remember that the person defining the business needs may not know all the needs. Hence, information gathering sessions should familiarize the subjects with needs that have not yet surfaced, as well as those that have.
Scope management: During the information gathering process, stakeholders are likely to provide information and delineate needs outside the scope of the project. The modeler needs to recognize that these are outside the scope and determine how to handle them without discouraging the stakeholder. Options include explicitly including them in the scope, using the scope change management process or helping the business stakeholder understand that these are outside the scope and (if appropriate) encouraging him or her to request a scope change or propose a follow-up initiative. In addition, the modeler should determine how the model will be impacted if these needs are incorporated later and possibly include hooks (e.g., tangential entities without attributes) to minimize the rework when they are subsequently implemented.
Information synthesis: Ultimately, the modeler needs to decide what to include in the model. This requires a critical evaluation and verification of the information that has been gathered. The business accuracy of the model is dependent on the modeler's ability to ferret out reasonably complete information and sift through the information gathered to ensure that it is accurate and pertinent. Another very important aspect of information synthesis is the modeler's ability to draw conclusions and commit those conclusions to the model.
Recognition of "good enough": It is well-known among data modelers that the model will never be complete. The modeler must recognize that the model is a means to an end and understand what needs to be completed so that it can be handed off to the database administrators and programmers. Some items, such as refinements of definitions, don't impact the placement of attributes and entities and can be performed in parallel with the physical implementation steps.
Communication: Finally, the data modeler must be a good communicator. Verbal skills are critical for interacting with stakeholders in collecting information and helping them think outside the box; with designers, programmers and DBAs to help them understand nuances of the model and the reasons behind decisions made; and with supervisors and managers to communicate progress and issues that require their involvement. Written communication skills are critical for creating useful, meaningful and accurate information as well as documenting the key issues that require resolution.
These are the most critical analysis skills the modeler must possess. These augment knowledge about modeling techniques and tools described in my last column. Together, the two skill sets enable the modeler to develop a rich model that is not only structurally correct, but also accurately reflects known - and previously hidden - information about the business and its needs. I welcome your input if you believe there are additional critical skills that should be added to this list.
Register or login for access to this item and much more
All Information Management content is archived after seven days.
Community members receive:
- All recent and archived articles
- Conference offers and updates
- A full menu of enewsletter options
- Web seminars, white papers, ebooks
Already have an account? Log In
Don't have an account? Register for Free Unlimited Access