Cross-Platform Big Data Analytics System for Advertising Campaigns
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Solution Overview
Problem
Current analytics tools for advertising campaigns are limited in their ability to efficiently analyze and understand the performance of cross-platform advertising efforts due to the vast volume and varied formats of data from multiple ad-serving companies and social media platforms, leading to inefficient budget utilization and revenue losses.
Innovation Solution
A system and method for cross-platform data analytics that includes a data sanitizing module for normalizing data from multiple advertising platforms, a transformation and storage engine for computing campaign measurements, a data-mart module for optimized storage and access, and a management user interface for client access, enabling unified and accelerated data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from multiple advertising platforms independently, then the volume of data increases providing more comprehensive campaign information, but the complexity of analyzing and integrating this data becomes almost impossible
Solution Approach 1:
The system segments the complex data analysis task into distinct functional modules: a data sanitizing module for normalization, a transformation and storage engine for computing measurements, a data-mart module for optimized storage, and a management UI module for access. Each module handles a specific aspect of data processing, making the overall system manageable despite handling trillions of records from multiple platforms.
Solution Approach 2:
The patent introduces intermediary components that mediate between diverse data sources and the analysis process. The data sanitizing module acts as an intermediary that receives data in various formats from different platforms and transforms it into a normalized format. The transformation and storage engine serves as another intermediary that computes campaign measurements from the normalized data, bridging the gap between raw multi-platform data and actionable insights.
2Adaptability or versatility
If each ad-serving company presents statistics in different formats, then each company maintains its own data standards, but the complexity of integrating and comparing data across companies increases
Solution Approach 1:
The data sanitizing module is designed with universal functionality to handle data from multiple advertising platforms in different formats. It implements a unified data model that can accommodate various input formats while outputting normalized data in a consistent format. This universal approach allows the system to integrate data from any platform without requiring platform-specific integration logic, reducing overall integration complexity.
3Quantity of substance
If trillions of records are gathered from multiple companies for the same campaigns, then comprehensive campaign coverage is achieved, but existing analysis tools become insufficient to process this volume
Solution Approach 1:
The patent transitions from traditional row-based data storage to a column-oriented data-mart structure optimized for analytical queries. This dimensional change in data organization enables efficient processing of trillions of records by allowing selective access to specific columns and enabling parallel processing operations. The transformation and storage engine further enhances productivity by pre-computing campaign measurements and storing them in an optimized format for rapid retrieval and analysis.
4Ease of operation
If campaign managers use existing tools to analyze gathered information, then some analysis can be performed, but the efficiency is severely limited due to data volume and format variations
Solution Approach 1:
The system implements self-service capabilities through the transformation and storage engine that automatically computes campaign measurements from the normalized data without requiring manual intervention. The engine autonomously processes the data, calculates key performance indicators, and stores results in the data-mart module. This self-service approach significantly improves analysis efficiency while maintaining ease of operation, as campaign managers can access pre-computed measurements through the management UI without needing to manually process the underlying trillions of records.
Data Source
AI summary
A system and method for performing cross-platform data analytics of advertising campaign information. The system comprises a data sanitizing module for receiving information related to at least one campaign from a plurality of advertising platforms and to produce a normalized dataset having data values that comply with a unified format; a storage and transformation (TS) engine for transforming data values in the normalized dataset into a format defined in a relaxed data schema, thereby resulting with a relaxed dataset, the TS engine is further configured to analyze the relaxed dataset to compute a plurality of campaign measurements of measurable data values included in the relaxed dataset; a data-mart module for storing the relaxed dataset together with the computed campaign measurements; and a management user interface (UI) module for allowing allow client devices access to data stored in the data-mart module, wherein the data-mart module is optimized for providing an accelerated data for data stored therein.