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Enterprise Data Modeling for Business Intelligence Applications

In a recent data management trends report, 70% of respondents feel that their organization considers data a strategic asset. No matter where your data came from, legacy systems or acquisitions and mergers, there are core functions can help break down the data silos and better inform business decisions by utilizing data as a strategic asset.

Executing an effective business intelligence workflow requires effective data management. You need to know where you’ve been and what you have to know where you’re going. Ideally, a data architect with a focus on business intelligence will start with data models to document what data the organization has, where it resides, how it’s used, and who owns it to generate accurate analytics. It’s virtually impossible to navigate the complexities of the data landscape if you don’t have a data modeling tool to create representations that everyone can understand.

Even with less than half of the respondents reporting definitive engagement in a data modeling solution, the top use cases in enterprise environments for data modeling continue to trend upwards. Focus for organizations is more around metadata, data governance, and data lineage (the core functions of developing an effective business intelligence environment). Businesses are seeing an increased need for business process models and system architecture diagrams, with a requirement for improved data flow that is aligned with business objectives rising over 50%. Data modeling provides the foundation for processing data through an organization with usability in mind. This whitepaper explains how various data modeling functions can categorize and structure organizational data with applied metadata to bring value to business intelligence. By defining metadata characteristics and applying them as new information enters the organization, data lineage and governance can be maintained.

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