Centralized Data Analytics Platform for Breaking Data Silos
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Solution Overview
Problem
Organizations face scattered data and analytical resources, leading to siloed data stores, suboptimal analytical results, and inefficiencies due to lack of a centralized, holistic data and analytics platform, resulting in suboptimal ML model efficacy and resource waste.
Innovation Solution
An advanced data and analytics management platform (ADAM) provides a centralized, vendor-agnostic solution for data science workbench and asset management, enabling seamless integration and productionalization of analytical assets across an organization, supporting various analytics tools and languages, and facilitating model deployment and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a centralized data and analytics management platform is implemented, then data utilization and predictive accuracy are improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The platform is divided into distinct functional modules including data ingestion module, data processing module, model training module, and deployment module. Each module handles specific tasks independently, allowing the system to achieve high predictive accuracy through specialized processing while managing overall complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediary components such as standardized data interfaces, centralized model repositories, and automated pipeline orchestration layers that mediate between different data sources, processing operations, and deployment targets. These intermediaries standardize interactions and reduce the complexity of integrating diverse analytical tools and data sources.
2Adaptability or versatility
If disparate analytical solutions are deployed across different teams, then organizational adaptability and tool diversity are improved, but data silos and resource duplication increase
Solution Approach 1:
The platform is designed with universal data interfaces and standardized protocols that allow multiple analytical tools and processing methods to operate within a unified framework. Different teams can deploy their preferred analytical solutions while the platform ensures seamless data sharing and integration, preventing data silos while maintaining tool diversity.
Solution Approach 2:
The patent combines previously分散 (dispersed) data resources, computational infrastructure, and model repositories into a centralized platform. This merging eliminates data silos by providing a unified data lake and shared model repository, while the platform's architecture preserves team autonomy by allowing multiple analytical approaches to coexist and interact.
3Duration of action of stationary object
If data is retained in conventional data warehouses for long-term storage, then data availability is improved, but analytical effectiveness and insights quality deteriorate
Solution Approach 1:
The platform implements dynamic data processing pipelines that continuously transform, enrich, and reprocess stored data rather than simply retaining it statically. Data in the data lake undergoes ongoing processing including feature engineering, model retraining, and pattern recognition, ensuring that even long-retained data generates fresh analytical insights and maintains high analytical effectiveness.
Solution Approach 2:
The system performs preliminary data processing, cleaning, and feature extraction in advance before analytical queries are executed. Data is preprocessed and stored in optimized formats with pre-computed features, so when data is retrieved from long-term storage, it is immediately ready for high-value analytical operations, maintaining analytical effectiveness despite extended retention periods.
Data Source
AI summary
A advanced data management platform, method, and system allowing for several users to remotely access a centralized database and use analytical tools, models, and algorithms to analyse and extract predictive information, trends and the like from data sets. Moreover, the data management platform, method, and system includes the option of allowing for data analysis and model training with anonymized data. This then further allows for the trained model to be applied on data sets located outside of the data platform and/or system to derive predictive information about that data set.