Data Distiller 101
This Data distiller overview will demonstrate how to overcome common data distiller challenges as well as the key uses best practices for success.
Key Discussion Points
- Data distiller overview
- Data Distill FAQ’s and their solutions
- Key use cases
Key takeaways
Overview and Purpose of Data Distiller
Data Distiller is designed to provide an overview of its key use cases and customer solutions. It supports data architects, data engineers, data scientists, and marketing entities by enabling data segmentation, curation, and contextual data addition.
Primary Use Cases
The webinar highlighted five primary use cases for Data Distiller:
- Creating brand-centric data models for a South American retail company.
- Enriching next-best-offer emails with personalized data for a telecommunications company.
- Optimizing data for reporting and attribution modeling for a luxury retailer.
- Customizing insights for operational dashboarding.
- Leveraging AI and ML feature pipelines for training and scoring models.
Key Capabilities
Data Distiller offers SQL-based processing, scalable data management, Adobe-defined functions, automation and scheduling, monitoring and alerting, and integration with third-party tools for extended insights.
Data Transformation and Enrichment
Data Distiller allows for cleaning, shaping, manipulating, and enriching data. This includes standardizing data, reshaping data formats, augmenting data for granularity, and deriving additional attributes for downstream use.
Operational Dashboards and AI/ML Integration
Data Distiller enables the creation of real-time operational dashboards and supports AI/ML feature pipelines. This allows users to train models with Adobe data, score models, and integrate predictions back into Adobe Experience Platform for enhanced data-driven decision-making.
At a glance
Product: Experience Platform
Series: Adobe Customer Success Webinars
Role: Admin,Developer,Leader,User