AI Data Integration for Post-Merger Entity Consolidation
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Solution Overview
Problem
Conventional systems for post-merger and acquisition (M&A) data integration face challenges such as lack of visibility, transparency, prescriptive analytics, and scalability, leading to inefficient integration and potential data loss due to manual processes and unsophisticated tools, which hinder seamless integration across diverse technology landscapes and business domains.
Innovation Solution
An AI-based data integration method and system that extracts metadata from multiple entities using text mining models and Natural Language Processing (NLP) rules, assigns similarity scores to user responses, and recommends data integration models through a recommendation engine, facilitating dynamic and scalable integration across various domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes and unsophisticated tools are used for M&A data integration, then implementation simplicity is maintained, but integration efficiency and scalability deteriorate
Solution Approach 1:
The patent replaces manual mechanical processes with automated AI-based systems. Text mining models automatically extract metadata from data sources, NLP rules automatically configure assessments, and AI processing models automatically assign similarity scores and recommend integration models, eliminating the need for manual intervention in these tasks.
Solution Approach 2:
The system performs self-service by automatically extracting metadata, configuring assessments, assigning similarity scores, and recommending integration models without human intervention. The AI-based processing model and recommendation engine autonomously analyze data and provide integration recommendations based on the configured parameters and metadata.
2Loss of information
If conventional tools are used for M&A integration, then ease of operation is maintained, but visibility and transparency deteriorate
Solution Approach 1:
The system provides feedback through the recommendation engine that analyzes assessment responses and similarity scores to generate integration model recommendations. This feedback loop ensures transparency by showing users how their responses are processed and what integration approaches are recommended, improving visibility into the integration process.
3Reliability
If manual data integration processes are used, then implementation simplicity is maintained, but data accuracy and authenticity deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically extracting metadata from data sources before the main integration process. Text mining models pre-process the data to identify and extract relevant metadata, which is then used to configure assessments and guide the integration process, ensuring data authenticity is established early.
Solution Approach 2:
Manual data verification and validation processes are replaced with automated AI-based processing models that analyze responses, calculate similarity scores, and validate data authenticity automatically, significantly reducing the time required while improving reliability.
4Measurement precision
If diverse technology landscapes are integrated without AI-based processing, then adaptability to different systems is maintained, but integration precision and similarity assessment deteriorate
Solution Approach 1:
The system achieves universality by designing a flexible assessment configuration that can adapt to different technology landscapes. The NLP rules and AI processing models are configured to handle diverse data formats and structures, enabling the same system to accurately assess similarity across different technologies while maintaining measurement precision.
Data Source
AI summary
This disclosure relates generally to data integration, and more specifically to artificial intelligence based data integration of entities post market consolidation. The method includes extracting, using one or more text mining models, metadata associated with at least one category of each of the participating entities of the deal, from data sources associated with the entities. The disclosed system dynamically configures an assessment for the at least one category based on the metadata by using a set of Natural Language Processing (NLP) rules. The assessment includes parameters associated with the data integration of the entities. Response to the assessment is obtained from users belonging to the entities. An artificial intelligence (AI) based processing model assigns a similarity score to the responses, where the similarity score is indicative of extent of match between distinct responses obtained from the entities. A recommendation engine recommends a data integration model based on the similarity score.