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With the changing business environment there is an increasing demand for information, often on short notice. Although companies may be utilizing the latest technology for their hardware and software infrastructure, they realize that their data resources cannot support their information needs.

Frequently, they find that their data is poorly defined, poorly structured, and not well understood. As organizations gain an understanding of their data, they also come to find that it is redundant and inconsistent. It is at this point that companies become alarmed, especially as they realize their redundant, inconsistent data is being captured and proliferated at an exponential rate. In this environment, it is impossible to extract well-defined information and useful knowledge from the data source.

LABBLEE Corporation has worked with Government and Commercial Enterprises in establishing and maintaining Corporate Data Architectures. Architecture is defined as the "structure of the components, their relationships, and the principles and guidelines governing their design and evolution over time."

The Corporate Data Architecture provides principles for data that must be invariant over time. The purpose of these principles is to guide us to the correct decisions today and tomorrow, rather than to make finite decisions. A Corporate Data Architecture provides a common context for data that can be understood, and gradually integrated into the business environment.

LABBLEE Corporation had the lead role in establishing the environmental data portion of the Department of Defense (DoD) Corporate Data Architecture. LABBLEE facilitated sessions with environmental experts and information system technologists throughout the DoD to determine data requirements. The resultant architecture was then used in generating software application migration and development plans.

LABBLEE is currently working with another large Government Agency in establishing their initial Corporate Data Architecture. The methodology for developing the architecture was broken into three phases: Data Analysis, Data Migration, and Data Management.

The initial Data Architecture was developed in the Data Analysis Phase. The Data Analysis Phase consisted of gathering existing data resources, identifying major subject areas, synthesizing the subject area models from the existing data resources, and augmenting the subject area models with new concepts. Lastly, this phase of the process included the support of all related guideline and description documents. LABBLEE has now progressed with the Agency to work on the Data Migration Phase of the project.

For more information about LABBLEE's proven approach to developing data architecture models, contact us at: data@labblee.com.