1、Unified Data Model:Applying Generative AI for Data UnificationINDUSTRIALWHITE PAPER2Contents1Executive Summary32Introduction33Problem Statement Overview:Midstream Oil&Gas Data Ecosystem Overview44Problem Statement Detail:Data Fragmentation45Proposed Solution:Generative AI-Driven Sensor Tag Mapping F

2、ramework5Design&Develop:Algorithm Flow6Step 1:Automated Data Ingestion&Normalization8Step 2:Semantic Tag mapping11Step 3:Anomaly Detection and Correction13Step 4:Intelligent Tag Recommendation14Step 5:Integration with Knowledge Graphs156Midstream Oil&Gas Example17Step 1:Automated Data Ingestion and

3、Normalization19Step 2:Semantic Tag Mapping20Step 3:Anomaly Detection and Correction22Step 4:Intelligent Tag Recommendation23Step 5:Integration with Knowledge Graphs247Conclusion273IntroductionCompanies face significant challenges in unifying fragmented data despite major investments in data manageme

4、nt infrastructure.Fragmented data estates often result in wasted time for domain and technical experts and millions of dollars spent without achieving scalable business value through a unified data layer.According to a recent McKinsey report,60%of data integration projects fail due to a lack of cohe

5、sive strategy and execution.60%of data integration projects fail due to a lack of cohesive strategy and execution,according to a recent McKinsey reportHowever,applying AI in the right quantities with a structured methodology can effectively address these challenges.While AI is not a silver bullet,it

6、 offers significant leaps at every stage of the data unification journey,reducing costs and project timelines by several magnitudes.A Generative AI-based approach significantly outperforms traditional rule-based mapping models by minimizing the effort and time required to build unified data models.R