Data architects face many challenges on a day-to-day basis. Models and associated metadata are the only means by which complex data environments can truly be understood and managed. Without comprehension, it is impossible to manage data quality. A well-defined data architecture makes it possible to address all of the described challenges and is a foundation to improve data quality, master data management and data governance in general. With enterprise scale capabilities such as business glossaries, data dictionaries, reverse engineering, forward engineering and cross-organizational collaboration, data modeling tools are needed to address the challenges of data architecture not only for today, but also the future.
This whitepaper highlights five major data architecture challenges, and provides insights regarding how to address them with data modeling: The evolution of development, methodologies and culture, adapting to changing architecture, complex data environments, data quality, and business focus. The challenges described in the whitepaper have made data modeling and metadata management more important than ever.