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Development of a scalable data integration, analytics, and activation platform for AdTech industry
  1. case
  2. Development of a scalable data integration, analytics, and activation platform for AdTech industry

Development of a scalable data integration, analytics, and activation platform for AdTech industry

lineate.com
Advertising & marketing
Media
Retail

Data Management Challenges in AdTech: Fragmentation, Compliance, and Real-Time Activation

The client faces significant challenges managing and activating vast amounts of first-party and third-party data across multiple sources and devices. Rapidly changing technological landscapes and increasing compliance requirements necessitate a unified, flexible, and automated data infrastructure that supports real-time analytics and audience activation to stay competitive.

About the Client

A mid-to-large scale digital advertising organization seeking to optimize its data management and targeting capabilities to enhance campaign performance and compliance.

Goals for Advanced Data Infrastructure and Activation Platform

  • Develop an integrated data unification system aggregating data from diverse sources and devices to provide a comprehensive consumer view.
  • Implement automated data mapping and classification processes for efficient data organization.
  • Integrate emerging data sources such as IoT devices and new digital platforms to enhance data collection.
  • Leverage AI and machine learning for predictive analytics, campaign optimization, and maintaining high data quality.
  • Build a robust data orchestration system to enable automatic, action-oriented responses based on data insights.
  • Ensure the solution can handle large-scale data processing with high accuracy, security, and compliance.

Core Functional System Features for Data Management and Activation

  • Data unification module that aggregates and consolidates data from multiple sources and devices.
  • Automated data mapping, classification, and organization processes.
  • Interfaces for ingesting data from emerging platforms like IoT and digital channels.
  • AI-powered predictive analytics for ad performance forecasting and campaign adjustments.
  • Data quality assurance mechanisms using machine learning and AI for continuous improvement.
  • Data orchestration engines that automate actions and responses based on insights.
  • Real-time data processing capabilities supporting large-scale data ingestion and analysis.
  • Secure, compliant data handling processes aligned with privacy regulations.

Technology and Architecture Preferences for Data Solutions

Data integration and automation platforms leveraging scalable architectures (e.g., microservices).
AI and machine learning frameworks for predictive analytics and data quality enhancement.
Real-time data processing technologies for high-volume ingestion and analysis.

External System and Data Source Integrations

  • First-party data sources such as customer demographics and behavioral data platforms.
  • Third-party data providers for enriched audience targeting.
  • Digital platforms, including IoT devices and emerging digital advertising channels.
  • Existing data management and activation tools to streamline workflows.

Key Non-Functional System Requirements

  • System scalability to support increasing data volume and new data sources.
  • High performance for real-time data processing and analytics.
  • Robust security and privacy compliance, including consent management.
  • High availability and 24/7 support for continuous operation.
  • Automated monitoring and alerting for system health and market opportunities.

Projected Business Outcomes of the Data Management Platform

The implementation of this integrated data platform is expected to significantly improve data accuracy, enable real-time audience activation, and enhance campaign effectiveness. It aims to accelerate time-to-market for targeted advertising, support compliance efforts, and increase revenue opportunities through better data utilization and automation.

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