Level: Intermediate

Uche Ogbuji (uche@ogbuji.net), Consultant, Zepheira, LLC

06 May 2008

Data is the lifeblood of the enterprise, and the best way to prepare for a development and integration project is to document the characteristics of the data that drive the target applications. Learn the key questions that an enterprise data architect should explore in order to effectively document the characteristics of relevant data and take the most important first step towards project success.

There are many software development methodologies, but all of them emphasize the importance of the analysis portion of the life cycle. During analysis, you look to clearly understand the problem and create use cases. You document requirements and user-acceptance criteria. You document the social and technological environment in which the software is to be used. One part of analysis that's critical to the success of many projects is data analysis. Perhaps you are implementing an enterprise resource planning (ERP) system. Perhaps you are adding sophisticated e-commerce features to an existing corporate Web site. Perhaps you are developing an innovative software as a service (SaaS) product. In any such project, whether you are developing a new application or integrating existing applications, it's essential to understand the nature and flow of the related data.

Developing and documenting the shared understanding of relevant data sets and data characteristics is your primary job as an enterprise data architect. If the project involves integrating existing systems, you have to do a comprehensive analysis of the existing data and governing business rules. Often there is precious little written down clearly, and you have to perform the analysis from scratch. If the project is creating entirely new components, you might have to create new data models. Either way, the key to getting the right answers about data is in asking the right questions. In this article I present a series of questions you can use to make sure you cover all aspects of data during analysis.

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