Collaborative Data Platform Blueprint Engine for Marketing Automation
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Solution Overview
Problem
Existing data deployment schemes for distributed marketing programs are time-consuming and resource-intensive due to the complexity of managing large, heterogeneous data sets across various locations, requiring significant manual coordination and computation.
Innovation Solution
A collaborative data management and delivery platform with a blueprint engine that enables users to create customizable data deployment programs through a graphical user interface, allowing selection and hierarchical definition of data components, which generates a template for automated data deployment across multiple channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual coordination and computation are used to manage large heterogeneous data sets, then data deployment can be customized, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system segments the data deployment program into modular components including data sources, transformations, and delivery channels. Users can select and combine pre-defined modules through a graphical interface, eliminating the need for manual coordination of entire data pipelines from scratch. This modular segmentation reduces development time while maintaining customization flexibility.
Solution Approach 2:
The platform pre-organizes data sets, transformations, and delivery configurations into reusable templates and blueprints before the user needs them. Users can select from pre-built components rather than creating everything manually, significantly reducing program development time while still allowing customization through parameter adjustment and component selection.
2Adaptability or versatility
If manual coordination is used to manage data deployment, then flexibility in customization is maintained, but computing resource usage increases
Solution Approach 1:
The system provides self-service through automated data mapping, transformation generation, and deployment execution. The graphical interface automatically translates user selections into executable deployment configurations, eliminating the need for manual computation and reducing computing resource requirements compared to traditional manual coordination approaches.
Solution Approach 2:
The platform changes parameters by allowing users to adjust high-level configuration parameters through the graphical interface rather than manually configuring low-level system parameters. This parameter abstraction layer maintains deployment flexibility while reducing the computational complexity and resource usage required for data deployment management.
3Quantity of substance
If large heterogeneous data sets are managed, then comprehensive data availability is achieved, but system complexity increases
Solution Approach 1:
The graphical interface acts as an intermediary between users and the complex underlying data infrastructure. It abstracts the complexity of managing heterogeneous data sets by providing a simplified visual interface for data selection, mapping, and deployment configuration, while the system handles the complexity of data integration and coordination in the background.
Solution Approach 2:
The platform provides universal functionality by supporting multiple data sources, transformations, and delivery channels through a single unified interface. This multi-functional approach consolidates what would otherwise require multiple separate systems, reducing overall system complexity while maintaining comprehensive data availability.
Data Source
AI summary
Techniques for managing a collaborative data management and deployment platform and generating customizable blueprints for a data deployment program are disclosed. Techniques include receiving one or more parameters for generating the blueprint for the program, receiving a selection of one or more blueprint components satisfying the one or more parameters, and generating the blueprint template as a function of the selected components. In addition, one or more data sets for use in a program are obtained. A selection of the blueprint template is received. Objects of the selected blueprint are mapped to the one or more data sets. The selected blueprint is mapped to one or more third-party data channels.