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Data in Depth
Optimizing Salesforce's Data Cloud for Advanced AI Applications
Artificial Intelligence (AI) and machine learning technologies hold the keys to a wide array of innovative applications and solutions. Their capabilities are, however, largely dependent on the quality and accessibility of the data that feeds into them. Fragmented, isolated data spread across multiple systems can obstruct the path of AI in drawing holistic, insightful conclusions.
Often, organizations encounter the complexity of managing customer data that is dispersed across diverse platforms such as CRM systems, Enterprise Resource Planning (ERP) software, social media channels, email marketing tools, and various e-commerce platforms. The unification of this disparate data is a critical aspect that drives the accuracy and effectiveness of AI algorithms. This is the exact problem that Salesforce's Data Cloud is designed to address.
The Data Cloud compiles and synthesizes all customer data into comprehensive 360-degree customer profiles. This provides the robust and integrated intelligence AI engines need to uncover patterns and trends that are impossible to see in fragmented data.
Salesforce applies a four-step method to unify data through Data Cloud:
1. Configure and Connect - The platform extends its reach to various data sources, ranging from internal systems to external third-party data.
2. Ingest, Map, and Model - Raw customer data is imported into Data Cloud and then aligned with Salesforce's unified data model for ease of application.
3. Build Rules - Identity resolution rules reconcile data fragments across different sources to weave together complete customer profiles. This is a vital step that delivers the accurate, holistic data view that AI requires.
4. Segment and Activate - Once the unified data is validated, audience segments can be crafted in the Data Cloud, ready for application in diverse AI projects across domains.
With the implementation of Data Cloud, businesses can truly harness the full potential of AI. By unifying customer data from across disjointed systems into precise 360-degree profiles, they enable a new frontier of personalized and context-aware AI applications that were once considered unattainable.
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