Contextual/Conceptual Data Modeling
Contextual and conceptual models can be the key to ensuring that IT projects support real business needs, and that those business needs are understood holistically.
Our approach ensures that IT projects have a clear model of the knowledge content of your business and its surroundings. We find that most modeling efforts spend too much time creating models and no time using the models. We quickly identify the areas of your information landscape that require modeling and focus on those areas using models to drive and guide the conversation about business needs. We are clearly capable of creating exhaustive models, but do not see this as a valid reason for doing it. We do not create exhaustive models unless they are clearly justified. Each project-level modeling effort will contribute a piece to the data architecture of the enterprise.
Creation of these models exposes hidden contradictions in the language of your business. We capture the semantics of the business as they apply to structured and unstructured information. We prepare these models so that they can support the transition from definition to design. These models allow a controlled and rapid transition from the problem space to the solution space.
Logical Data Modeling
Our logical modeling efforts are aimed at abstraction of business semantics into efficient, technology independent storage patterns. These patterns transition easily into physical models, once the storage technologies have been identified. They also enable future migration as needed, and the incorporation of unstructured data.
Physical Data Modeling
We apply model-driven, forward engineering methods on projects. We also provide reverse engineering in understanding legacy data sources.
Enterprise Repository Design
We have established a subject based generic model for enterprise repositories that is scalable and extensible. These models are ideal for incorporating new information into existing enterprises, for example, during M & A activity. Our models support multi-consumer and multi-tenant expansion.
Data Service Hub Design
We surround our generic subject area models with services that allow simplified access through the open data protocol.
In one recent case, various stakeholders had different views on the concept of "channel". For some, it was a group of customers, for others, it was a group of distributors, for others, it was a line of products, for others, it was a communication method, and one group was so entrenched in the legacy operational systems, that they would only describe it as a 2-digit field. Attempting to model these concepts resulted in a recognition that each concept was fine on its own, but many attempted solutions had failed to meet expectations, because of the ambiguous use of one word for five concepts. We guided the definition and clarification of these concepts, so that when business needs are formally communicated, these concepts are named appropriately. Executives and managers are quick to say, "I will just tell everyone how it is," but this approach rarely works. If semantics don't work for a particular group, they will not adopt it. We provide a model and external guidance to lend objectivity, but we allow the stakeholders to reach consensus. When it is their new definition or terminology, they adopt it easily.