Automated Data Standardization for Multi-Module Pricing Insights
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Solution Overview
Problem
Incomplete, unstructured, and unprocessed data sets hinder the generation of insights and visualization, leading to inefficient pricing strategies and decision-making in business settings, as they are difficult to analyze and visualize, especially when not formatted correctly for graphical user interfaces.
Innovation Solution
A system and method for automated and semi-automated data ingestion, processing, and visualization that ingests data from disparate sources, standardizes it, applies analytics, and generates visualizations, allowing users to interact with the data through a multi-module graphical user interface for optimized pricing insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data sets are left incomplete and unstructured, then data storage and processing time is reduced, but data analysis and visualization capability deteriorates
Solution Approach 1:
The system performs preliminary data standardization, cleaning, and structuring operations automatically during data ingestion, before analysis is required. This preprocessing converts raw unstructured data into standardized formats with consistent schemas, making subsequent analysis operations faster and more effective without requiring manual intervention at analysis time.
Solution Approach 2:
The data processing system automatically performs self-service operations including data validation, standardization, and transformation without human intervention. The system autonomously handles incomplete and unstructured data by applying predefined processing rules and algorithms, enabling the system to serve itself in preparing data for analysis and visualization.
2Reliability
If data sets are manually processed and structured, then data quality and completeness improves, but time and resource consumption increases
Solution Approach 1:
The system implements automated self-service data processing that performs validation, standardization, and quality assurance operations without manual intervention. Algorithms automatically detect and correct data quality issues, fill missing values using statistical methods, and ensure consistency across data sets, thereby maintaining high data quality while eliminating manual processing time and human resource requirements.
Solution Approach 2:
Manual mechanical data processing operations are replaced with automated computational systems. The system uses computer algorithms and processing engines to perform data validation, transformation, and quality assurance tasks that previously required manual human effort, thereby maintaining data quality standards while dramatically reducing processing time and eliminating human resource dependency.
3Loss of information
If complex data processing operations are applied, then data insight quality improves, but system complexity and computational resources increase
Solution Approach 1:
The complex data processing system is segmented into modular functional components including data ingestion modules, standardization modules, analysis modules, and visualization modules. Each module performs a specific function and can be independently configured and maintained. This segmentation allows the system to apply complex processing operations while managing system complexity through modular architecture, where each component handles a specific aspect of data transformation and analysis.
4Adaptability or versatility
If data is not standardized, then data storage flexibility is maintained, but data visualization and analysis capability deteriorates
Solution Approach 1:
The system dynamically changes data parameters including format, schema, and structure through automated standardization processes. Raw data with varying parameters is transformed into standardized formats with consistent parameter definitions, enabling effective visualization and analysis while the system maintains adaptability to handle diverse data sources through configurable transformation rules that preserve storage flexibility.
Data Source
AI summary
A method for data ingestion for a data visualization platform comprises receiving a plurality of data sets, generating and storing a merged data set based on the plurality of received data sets, and, receiving, via a graphical user interface, a first input comprising an instruction to perform a data standardization operation. The method comprises, in response to receiving the first input, applying a data standardization operation to the merged data set to process the merged data set to generate a standardized data set. The method comprises receiving, via the interface, a second input comprising an instruction to perform a data analytics operation, and responsively applying the data analytics operation to the standardized data set to generate insights data. The method includes receiving, via the interface, a third input comprising an instruction to perform a data visualization operation, and responsively generating one or more data visualizations based on the insights data.