Data architects face many challenges on a day-to-day basis. Models and associated metadata are the only means by which you can understand and manage complex data environments. Without comprehension, it is impossible to manage data quality. A well-defined data architecture makes it possible to address all the described challenges and is a foundation to improve data quality, master data management, and data governance. Enterprise capabilities include such as business glossaries, data dictionaries, reverse engineering, forward engineering and cross-organizational collaboration. With such enterprise scale capabilities, we need data modeling tools to address the challenges of data architecture not only for today but also in the future.
Read the whitepaper “Top 5 data architecture challenges” to learn more about the major data architecture challenges. Also, gain insights regarding how to address them with data modeling. Explore how the development, methodologies, and culture developed by 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.