Data modeling is critical for effective information management in organizations. It standardizes and captures essential enterprise information to build a foundation for designing information systems. Data modeling ensures a consistent data view and proactively identifies information-related issues for new and existing projects.
The most pressing challenges of data modeling are clarifying business requirements, maintaining data accuracy, handling complex data, and adapting to evolving business requirements. However, data modeling is neglected in modern platforms, leading to data integrity, extraction, and classification challenges.
This further results in data redundancy and increased time on storage and computation. Businesses can ensure successful data modeling by prioritizing collaboration, communication, stakeholder involvement, data governance, and ongoing documentation while complying with evolving trends.
These approaches are pivotal in enabling organizations to effectively manage information that seamlessly aligns with the business objectives and technical requirements. The two fundamental data modeling approaches are:
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