System for adaptive data blending across heterogeneous BI platforms for real-time decision making

Abstract

A system (100) for adaptive data blending across heterogeneous BI platforms for real-time decision making, comprising: (a) a data source abstraction module configured to establish standardised and secure connections with several different Business Intelligence (BI) platforms and convert their native data structures into a unified intermediate schema; (b) a real-time data ingestion and synchronisation module operable to continuously monitor and retrieve data updates from the BI platforms using event-driven mechanisms and configurable synchronisation policies; c) an adaptive data mapping and transformation module designed to intelligently harmonize, adapt, and semantically translate disparate data formats using rule-based logic and AI-assisted schema mapping; (d) a semantic context and metadata management module configured to maintain a unified data dictionary, track data sequence and maintain semantic consistency across platforms; (e) a cross-platform query orchestration engine designed to decompose common user queries, translate them into platform-specific query languages, execute them across the relevant BI platforms, and aggregate the results into a coherent output; (f) a decision intelligence and recommendation module that analyses the mixed data to generate real-time insights, alerts and contextual recommendations for decision support; and (g) a security, governance and compliance module configured to enforce access controls, maintain audit trails and ensure compliance with data protection regulations; h) the system provides seamless, real-time and secure data blending to enable consistent analytics and decision-making across heterogeneous BI environments.

Description

[0001] The present invention relates to the field of business intelligence (BI) and data integration technologies. In particular, it relates to a system for adaptive data blending across heterogeneous BI platforms. The invention enables seamless, real-time data harmonization to support dynamic and informed decision-making in diverse enterprise environments.

[0002] In modern enterprise ecosystems, organizations rely on multiple business intelligence (BI) platforms to analyze and visualize data for operational and strategic decision-making. However, these BI platforms often operate in silos, each with its own data formats, protocols, and access mechanisms. This fragmentation poses a significant challenge when companies need to integrate and analyze data across systems in real time. Traditional ETL (extract, transform, and load) processes are not designed for dynamic environments and often result in data latency, redundancy, and the loss of contextual relevance.

[0003] Furthermore, the need for real-time insights for agile decision-making in competitive markets has become crucial. Static dashboards and disjointed reporting tools are no longer sufficient for modern, data-driven organizations. Without a unified mechanism for adaptively merging data from disparate BI platforms, decision-makers struggle with delays, inconsistencies, and missed opportunities. There is a clear need for an intelligent solution that can harmonize diverse data sources without laborious manual configuration or scripting.

[0004] To solve these problems, the present invention presents an adaptive data blending system that intelligently synchronizes, transforms, and reconciles data from heterogeneous BI environments. This system enables real-time interoperability between disparate data tools, supports on-demand query translation, and ensures semantic consistency across different platforms. By automating the data integration process and optimizing it for dynamic business requirements, the invention significantly improves responsiveness, accuracy, and decision-making flexibility across the enterprise.

[0005] One goal of this disclosure is to enable real-time data integration across multiple heterogeneous BI platforms.

[0006] Another goal of this disclosure is to reduce manual effort through AI-driven data mapping and transformation.

[0007] Another objective of this disclosure is to ensure semantic consistency through centralized metadata and data sequence tracking.

[0008] Another goal of this disclosure is to support cross-platform queries with automatic query translation and orchestration.

[0009] Another objective of this disclosure is to improve decision-making through real-time insights and recommendations.

[0010] Another objective of this disclosure is to improve data accuracy and relevance by synchronizing live data streams.

[0011] Another objective of this disclosure is to maintain data security and compliance with integrated governance controls.

[0012] Another objective of this disclosure is to provide a scalable, modular architecture suitable for dynamic enterprise environments.

[0013] The present invention relates to an adaptive data blending system that combines data from multiple heterogeneous BI platforms. It enables seamless real-time integration of structured and unstructured data for dynamic analytics.

[0014] Another embodiment of the present invention is the use of a data source abstraction module to standardize access to different data environments. This abstraction eliminates the need for custom integrations and simplifies data interoperability.

[0015] Another embodiment of the present invention is a real-time ingestion and synchronization module that ensures continuous data updates. It uses event-driven triggers and intelligent synchronization to ensure data freshness and reduce latency.

[0016] Another embodiment of the present invention is that the system features an AI-powered transformation module that maps and harmonizes data across platforms. This enables accurate, context-aware consolidation without manual data preparation.

[0017] Another embodiment of the present invention is that the semantics and metadata module maintains a unified semantic layer that ensures consistency and traceability. It manages data sequencing and supports query resolution based on metadata.

[0018] Another embodiment of the present invention is a cross-platform query orchestration module that translates and executes user queries across BI platforms. The results are intelligently merged into a single, coherent output.

[0019] Another embodiment of the present invention is a decision intelligence module that analyzes mixed data in real time and provides insights, alerts, and intelligent recommendations, thereby improving operational and strategic decision-making.

[0020] Another embodiment of the present invention includes a robust security and governance framework that ensures regulatory compliance and data integrity. It provides access control, auditing, and regulatory compliance across the system.

[0021] The present invention relates to an adaptive data blending system that enables seamless integration and analysis of data across heterogeneous BI platforms in real time. It consists of specialized modules, including a data source abstraction module for standardized connectivity, a real-time ingestion module for continuous data updates, and an AI-driven transformation module for data harmonization. A semantic context and metadata management module ensures consistent interpretation across all sources. The system also includes a query orchestration module for executing cross-platform queries and a decision intelligence module that delivers real-time insights. Security, governance, and compliance are managed through a dedicated module that ensures secure and regulated data processing. Data source abstraction module:

[0022] This module is responsible for establishing secure and standardized connections to a variety of heterogeneous data sources and BI platforms, including cloud-based tools, on-premises databases, and API-driven services. It abstracts the underlying complexity of each source by creating metadata-driven connectors that map platform-specific data structures into a common intermediate schema. This abstraction enables consistent access and interaction across disparate environments and reduces the dependency on manual integration scripts. Real-time data entry and synchronization module:

[0023] This module continuously monitors and retrieves live data streams or batch updates from connected BI platforms. It uses event-driven triggers and incremental data collection techniques to ensure data freshness and consistency. Furthermore, it supports configurable synchronization policies that adapt to the performance and latency characteristics of individual data sources, enabling near-real-time data availability for downstream processing. Adaptive data mapping and transformation module:

[0024] This core module performs intelligent data mapping, alignment, and transformation based on semantic models, custom rules, and AI-based pattern recognition. It harmonizes data formats, resolves schema mismatches, and translates terminology across platforms using dynamic transformation templates. The module ensures that data from different sources is contextually correct and compatible, enabling consistent analysis without manual intervention. Module for managing semantic context and metadata:

[0025] This module manages the semantic layer and metadata repository for the entire system. It creates and maintains a unified data dictionary, tracks relationships between data records, and preserves lineage and data provenance. Semantic context helps ensure data remains meaningful and interpretable across all BI environments, enabling consistent queries, compatible visualizations, and traceable analyses. Cross-platform query orchestration module:

[0026] This module enables users to generate unified queries that are intelligently decomposed and translated into the native query languages ​​of the underlying BI platforms. It orchestrates query execution across different systems, aggregates the responses, and delivers a consolidated result to the end user. The module includes a translation engine, a query optimizer, and data merging capabilities that work in parallel to minimize latency and ensure accuracy. Decision intelligence and recommendations module:

[0027] This AI-driven module leverages harmonized and blended data to deliver real-time insights, alerts, and recommendations for decision support. It analyzes data trends, detects anomalies, and presents contextual suggestions based on predefined business logic or machine learning models. This module enhances the user's ability to make informed and timely decisions across departments by leveraging blended data from diverse sources. Security, Governance and Compliance Module:

[0028] This module enforces enterprise-grade security, access control, and data governance policies throughout the entire data processing process. It ensures compliance with data protection regulations, audits data access and transformation, and supports role-based permissions for sensitive information. This module can also be integrated into existing governance frameworks to provide a secure and compliant data blending infrastructure for all BI platforms.

[0029] The invention is explained again below with reference to the figure. It shows: Fig. : a system for adaptive data blending across heterogeneous BI platforms for real-time decision making.

[0030] Fig.illustrates a system for adaptive data blending across heterogeneous BI platforms for real-time decision-making. The system begins operation by leveraging the data source abstraction module, which establishes secure connections to various BI platforms and data sources and translates their unique data structures into a standardized intermediate format. Once the connection is established, the real-time data ingestion and synchronization module is activated, continuously checking for updates and retrieving live or batch data through event-driven mechanisms to ensure timely synchronization between the systems. The ingested data is then processed by the adaptive data mapping and transformation module, which intelligently matches schemas, harmonizes formats, and applies semantic normalization using AI-based logic and predefined transformation rules.As data flows, the Semantic Context and Metadata Management module builds and maintains a unified semantic layer, preserving metadata, lineage, and contextual relationships to enable consistent interpretation across platforms. As users initiate queries, the Cross-Platform Query Orchestration module dynamically decomposes and translates them into native queries for each source system, manages distributed execution, and merges the results into a single, coherent output. The processed and unified data is then analyzed by the Decision Intelligence and Recommendation module, which delivers real-time insights, alerts, and AI-driven recommendations to support actionable decisions.Throughout the entire process, the security, governance, and compliance module enforces data access controls, logs all operations for traceability, ensures compliance with regulatory standards, and safeguards data integrity and confidentiality across all modules. This orchestrated operation delivers seamless, secure, and intelligent adaptive data blending for real-time business decisions.

Claims

[1] A system (100) for adaptive data blending across heterogeneous BI platforms for real-time decision making, comprising: (a) a data source abstraction module configured to establish standardised and secure connections with several different Business Intelligence (BI) platforms and convert their native data structures into a unified intermediate schema; (b) a real-time data ingestion and synchronisation module operable to continuously monitor and retrieve data updates from the BI platforms using event-driven mechanisms and configurable synchronisation policies; c) an adaptive data mapping and transformation module designed to intelligently harmonize, adapt, and semantically translate disparate data formats using rule-based logic and AI-assisted schema mapping; (d) a semantic context and metadata management module configured to maintain a unified data dictionary, track data sequence and maintain semantic consistency across platforms; (e) a cross-platform query orchestration engine designed to decompose common user queries, translate them into platform-specific query languages, execute them across the relevant BI platforms, and aggregate the results into a coherent output; (f) a decision intelligence and recommendation module that analyses the mixed data to generate real-time insights, alerts and contextual recommendations for decision support; and (g) a security, governance and compliance module configured to enforce access controls, maintain audit trails and ensure compliance with data protection regulations; h) the system provides seamless, real-time and secure data blending to enable consistent analytics and decision-making across heterogeneous BI environments. [2] The system (100) of claim 1, wherein the data source abstraction module supports both structured and unstructured data from cloud, on-premises, and API-based sources. [3] The system (100) of claim 1, wherein the real-time data acquisition and synchronization module uses change data capture (CDC) techniques and configurable synchronization intervals. [4] The system (100) of claim 1, wherein the adaptive data mapping and transformation module uses machine learning algorithms to automatically detect schema mismatches and suggest transformation rules. [5] The system (100) of claim 1, wherein the semantic context and metadata management module is integrated with enterprise metadata repositories to ensure consistency with enterprise data standards. [6] The system (100) of claim 1, wherein the cross-platform query orchestration module includes a query optimizer to minimize execution time and ensure efficient parallel processing. [7] The system (100) of claim 1, wherein the decision intelligence and recommendation module includes predictive analytics and anomaly detection models to improve decision making. [8] The system (100) of claim 1, wherein the security, governance and compliance module supports role-based access control (RBAC) and automatic enforcement of policies for handling sensitive data. [9] The system (100) of claim 1, wherein the modules are deployed in a cloud-native microservices architecture to enable scalability, fault tolerance, and modular integration.

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