Business Analytics Configurator for Modular Architecture Design
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Solution Overview
Problem
Businesses face challenges in flexibly integrating data analytics into their processes due to complexity, expense, and the need for specialized expertise, making it difficult to deploy sophisticated data mining solutions without substantial investments in infrastructure and personnel.
Innovation Solution
A computer-implemented method and system that receives functional and nonfunctional requirements to produce first-level outputs, followed by a guided questionnaire to generate a client-specific solution reference architecture, components, and reference links, facilitating the design of architectural solutions within a business analytics and optimization framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If companies invest in sophisticated data analytics solutions including data mining tools and infrastructure, then data analytics capability is improved, but cost and complexity increase substantially
Solution Approach 1:
The patent introduces a data integration layer with pre-built connectors and adapters that act as intermediaries between source systems and analytics tools. This layer handles data extraction, transformation, and loading automatically, eliminating the need for companies to build complex custom integration infrastructure while maintaining reliable data analytics capability.
Solution Approach 2:
The patent segments the data analytics infrastructure into modular components: source systems, data integration layer with standardized connectors, data warehouse/lake, and analytics tools. Each segment can be independently configured and scaled, reducing overall system complexity while preserving analytical reliability.
2Reliability
If companies hire mining experts and specialized personnel to deploy data analytics solutions, then data analytics expertise is improved, but operational cost increases
Solution Approach 1:
The patent implements self-service capabilities through automated data integration workflows, pre-configured connectors, and user-friendly analytics interfaces. Business users can independently connect to data sources, select appropriate analytics functions, and generate insights without requiring specialized data mining expertise, thereby eliminating the need to hire expensive specialists.
Solution Approach 2:
The patent creates a universal data integration platform that handles multiple data sources, formats, and analytics functions through a single standardized interface. This multi-functional system replaces the need for multiple specialized personnel by enabling any user to perform various analytics tasks across different data sources.
3Measurement precision
If companies spend considerable time mapping and tuning between data and mining functions, then data analytics precision is improved, but time consumption increases
Solution Approach 1:
The patent pre-configures data connectors, transformation rules, and analytics models during system setup. Data mapping relationships and tuning parameters are established in advance through automated discovery processes, eliminating the need for time-consuming manual configuration when deploying analytics projects. Precision is maintained because the pre-configuration is performed by expert systems rather than rushed manual tuning.
Solution Approach 2:
The patent automatically adjusts data transformation parameters and analytics configuration based on the specific data source and target analytics function. The system dynamically optimizes mapping parameters, data types, and transformation rules without requiring manual intervention, thereby achieving high precision quickly through automated parameter optimization rather than time-consuming manual tuning.
Data Source
AI summary
A method and associated system for designing an architectural solution. Functional requirements and nonfunctional requirements are received. Responsive to receiving the functional requirements and nonfunctional requirements, first level outputs are produced. Further input is received through a guided questionnaire, wherein the guided questionnaire is based on the first level outputs. Responsive to receiving the further input, a list of client specific components and subcomponents, a client specific solution reference architecture, and a set of reference links are produced.