Unified Data Platform for Cross-Functional Training Optimization

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Solution Overview

Problem

Organizations face challenges in efficiently developing and updating training courses due to the difficulty in accessing and analyzing data from multiple cross-functional sources, leading to courses that may be too basic or not relevant to real-world scenarios, resulting in wasted resources and potential loss of customer interest.

Innovation Solution

A method that collects and manages structured and unstructured data from various sources, including sales, customer support, surveys, social media, and engineering data, using a big data analytics system to identify consumer issues and pain points, enabling the creation of targeted training courses and products that are customer-driven.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If data is collected from multiple cross-functional sources, then the completeness and relevance of training content is improved, but the complexity of data management and analysis increases

Engineering Contradiction:

Improvecompleteness of training contentVSAvoiddata management complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent combines multiple cross-functional data sources (sales, customer support, surveys, social media, engineering) into a unified data platform. This merging allows comprehensive collection of customer feedback and market information while managing complexity through integrated data structures and centralized processing systems.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent introduces intermediate processing layers including data normalization modules, analytics engines, and reporting systems that mediate between raw multi-source data and training content development. These intermediaries transform complex multi-source data into actionable insights for course creators.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Manufacturing precision

If comprehensive data analysis is performed to identify consumer issues, then the relevance of training courses to real-world scenarios is improved, but the time and resources required for analysis increase

Engineering Contradiction:

Improverelevance of training contentVSAvoidanalysis time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent implements preliminary data processing steps including pre-collection of feedback data from multiple sources, pre-processing of unstructured data, and pre-analysis of trends before actual training content development begins. This preliminary action reduces the time required during the main content creation phase.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces manual data analysis methods with automated analytics systems and algorithms that can process large volumes of multi-source data rapidly. This substitution of mechanical human analysis with automated computational systems significantly reduces analysis time while maintaining or improving precision.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS10614472B1Method and system for managing, accessing and analyzing data from multiple cross-functional sources

Publication Date: 2020.04.07 EMC IP HLDG CO LLC

AI summary

Experience data corresponding to offerings of an enterprise is collected from a plurality of data sources in a single data resource. A query of the experience data is received and run on a big data analytics system to determine one or more consumer issues from the experience data. The one or more consumer issues are identified to a user in results of the query.