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datagrading-ai.docx.pdf |
Introduction:
In today's data-driven world, businesses rely heavily on data to make informed decisions, predict trends, and gain a competitive edge. However, not all data is created equal. The quality of data can vary significantly, and consuming bad data can have detrimental effects on downstream processes and overall business performance. This is where data grading comes into play.
We will explore the concept of data grading, its significance, and how generative AI, like OpenAI, can be harnessed in a cloud environment to curate data effectively while also implementing a product-centric approach. Additionally, this approach aids in detecting discrepancies in metadata, master data, business rules, data duplication, and transaction data. Generative AI helps identify issues and provides recommendations with alerts so that product owners can address these issues at different stages of data processing—before processing, during processing, and after processing.
In today's data-driven world, businesses rely heavily on data to make informed decisions, predict trends, and gain a competitive edge. However, not all data is created equal. The quality of data can vary significantly, and consuming bad data can have detrimental effects on downstream processes and overall business performance. This is where data grading comes into play.
We will explore the concept of data grading, its significance, and how generative AI, like OpenAI, can be harnessed in a cloud environment to curate data effectively while also implementing a product-centric approach. Additionally, this approach aids in detecting discrepancies in metadata, master data, business rules, data duplication, and transaction data. Generative AI helps identify issues and provides recommendations with alerts so that product owners can address these issues at different stages of data processing—before processing, during processing, and after processing.