Grid-Based Data Profiling with Error Indicators
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Solution Overview
Problem
The existing data viewing and editing tools for extract, transform, and load (ETL) processes are cumbersome and require specialized knowledge, making it difficult for data analysts to efficiently identify and correct errors in vast amounts of data.
Innovation Solution
A data profiling server extracts data from multiple sources, performs validation, and presents it in a grid format with error indicators and recommended transformations, allowing users to apply transformations without leaving the editing environment, while tracking transformation history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional data viewing and editing tools are used for ETL processes, then data can be viewed and edited, but the tools require specialized knowledge and are cumbersome to operate
Solution Approach 1:
The patent introduces an intermediary layer between the raw data and the user interface. This intermediary processes and transforms the data into a more user-friendly format, hiding the complexity of the underlying data structures and operations. The system acts as a mediator that translates complex ETL operations into simple, intuitive grid-based interactions for users.
Solution Approach 2:
The system provides self-service capabilities by automatically generating data profiles, detecting errors, and suggesting transformations without requiring users to have specialized knowledge. The automated error detection and transformation recommendation features allow the system to serve itself in identifying and correcting data issues, reducing the need for expert intervention.
2Productivity
If traditional data tools are used, then data can be processed, but the process is time-consuming and cumbersome
Solution Approach 1:
The system performs preliminary actions by automatically generating data profiles and identifying potential errors before the user begins working with the data. This pre-processing step saves time by preparing the data and highlighting issues in advance, so users don't need to manually examine and identify problems during their analysis workflow.
Solution Approach 2:
The system implements feedback mechanisms that automatically detect errors and provide recommendations for corrections. This real-time feedback loop allows users to quickly identify and fix data quality issues without manual inspection, significantly reducing the time required to process and validate data during ETL operations.
3Adaptability or versatility
If specialized knowledge tools are used for data transformation, then transformations can be applied, but users need specialized knowledge of data structures and query languages
Solution Approach 1:
The system provides self-service transformation capabilities by automatically analyzing data structures and recommending appropriate transformations. Users don't need to understand complex data structures or query languages because the system autonomously generates transformation suggestions based on the data profile and identified errors, making advanced transformations accessible to users with minimal technical knowledge.
Solution Approach 2:
The system changes the parameters of data transformation from requiring specialized knowledge to using simple, parameter-based controls. By transforming complex SQL queries and data structure manipulations into user-friendly parameters and options within the grid interface, the system maintains adaptability while dramatically improving ease of operation.
Data Source
AI summary
A data profiling server extracts data from multiple data sources and stores the extracted data in a target data store. Validation functions are performed on the extracted to determine whether the data conforms to validation rules associated with the target data store and/or business logic definitions. For data that does not conform to the validation rules, the data profiling server identifies recommended transformations to be applied to the data to correct any errors that cause the data to not conform with the validation rules. A data profiling application presents to a user the data extracted from the multiple data sources in a grid structure. The grid structure also includes error indicators identifying the data that does not conform to the validation rules and specifies the recommended transformations identified by the data profiling server.