Metadata-Driven Predictive Intelligence Platform for Real-Time Analytics
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Solution Overview
Problem
Traditional analytic development systems face challenges such as misalignment between technical and data science resources, long lead times due to data discrepancies between offline and online systems, and the need for custom software, which hinders real-time predictive analytics and data sharing across departments or entities.
Innovation Solution
A metadata-driven real-time predictive intelligence platform that allows businesses to define domains using metadata, eliminating the need for custom software, and enabling real-time predictive analytics by receiving entity events, executing component modules, and computing probabilistic predictions through a meta API and decision engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional offline analytics systems are used to build predictive algorithms, then data processing can be performed with sufficient computational resources, but the lead time from algorithm development to production deployment becomes excessively long (3-6 months)
Solution Approach 1:
The system segments the algorithm deployment process into independent containerized modules that can be developed, tested, and deployed separately. Each predictive algorithm is packaged as a self-contained unit with its own dependencies, allowing parallel development and faster iteration without requiring full system revalidation.
Solution Approach 2:
A metadata-driven intermediary layer is introduced between the offline analytics environment and the online transaction system. This metadata layer serves as a common language that automatically maps data schemas and algorithms between systems, eliminating the manual ETL and recoding processes that cause delays.
2Quantity of substance
If data is extracted, transformed and loaded (ETL) from production database to offline system for algorithm development, then sufficient data processing capability is achieved, but data discrepancies between offline and online systems cause multiple iterations and delays
Solution Approach 1:
Instead of extracting and transforming data to offline systems, the system creates lightweight copies of the necessary data processing logic and algorithms that can execute directly in the online environment. Containerized predictive models are deployed alongside the transaction system, ensuring they operate on the same data without ETL transformations.
Solution Approach 2:
The metadata-driven platform provides a universal interface that works across different data sources and algorithm types. The same metadata schemas and algorithm interfaces function whether processing transaction data, customer data, or product data, eliminating the need for system-specific data transformations.
3Measurement precision
If algorithms are written in statistical languages (R, SAS, MATLAB) and must be translated into business logic languages (Java, Python, C++), then mathematical precision is maintained, but the translation process requires extensive collaboration and creates delays
Solution Approach 1:
The system changes the fundamental parameter of algorithm representation from compiled code in specific programming languages to declarative metadata definitions. Algorithms are defined using high-level metadata parameters that describe their behavior and data requirements, which can be executed by the platform without translation to specific programming languages.
Solution Approach 2:
The metadata-driven platform provides self-service capabilities where algorithms defined in statistical languages can be automatically registered, validated, and deployed without manual translation. The platform's execution engine interprets the metadata definitions and automatically generates the necessary deployment artifacts, eliminating the need for business developers to manually recode algorithms.
4Productivity
If custom software is developed for each specific application or domain, then the system can be optimized for that particular use case, but data and analytics sharing across departments or entities becomes difficult or impossible
Solution Approach 1:
The platform provides a universal metadata-driven framework that can accommodate multiple applications and domains through configuration rather than custom code. The same core infrastructure supports fraud detection, credit risk assessment, marketing analytics, and other applications by loading different metadata definitions, enabling both optimization and sharing simultaneously.
Solution Approach 2:
The system transitions from static custom software to a dynamic configuration model where application behavior is determined by loadable metadata and algorithms. This allows the system to adapt to different domains by loading appropriate metadata definitions while maintaining a shared underlying platform, enabling both specialization and interoperability.
Data Source
AI summary
A real-time predictive intelligence platform comprises: receiving from a user through a meta API definitions for predictive intelligence (PI) artifacts that describe a domain of an online transaction system for least one business entity, each of the PI artifacts including types, component modules and behavior bundles; exposing an entity API based on the PI artifacts for receiving entity events from the online transaction system comprising records of interactions and transactions between customers and the online transaction system; responsive to receiving an entity event through the entity API, executing the component modules and behavior bundles to analyze relationships found between past entity events and metrics associated with the past entity events, and computing a probabilistic prediction and/or a score, which is then returned to the online transaction system in real-time; and processing entity event replicas using modified versions of the PI artifacts for experimentation.