Structured Cloud Data Analyzer for Spreadsheet Error Detection
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Solution Overview
Problem
Structured cloud data is often subject to input errors and variations that are not detected until they have already negatively impacted data quality, requiring significant programming resources or manual review by experienced employees, which can be costly and inefficient.
Innovation Solution
A method and system for a structured cloud data analyzer that compares data in different ranges of spreadsheet cells, determines the scope of formulas, and automatically generates review flags for inconsistencies, such as non-consecutive cells or shifted data locations, to identify and correct errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex formulas, macros, and programming solutions are deployed to avoid data errors, then data quality is improved, but programming resources and complexity increase significantly
Solution Approach 1:
The system enables structured cloud data to self-diagnose errors by automatically comparing actual data against expected patterns, constraints, and relationships defined in the data model, eliminating the need for complex external programming solutions
Solution Approach 2:
A data model acts as an intermediary layer between raw structured cloud data and analysis applications, providing automated validation and error detection without requiring complex programming in the applications themselves
2Reliability
If manual review by experienced employees is used to detect data errors, then data quality is improved, but time and resource costs increase
Solution Approach 1:
The system enables structured cloud data to self-diagnose errors automatically by comparing actual data against predefined data models, eliminating the need for manual review by experienced employees
Solution Approach 2:
Data models are configured in advance with expected patterns, constraints, and relationships, enabling automated error detection before data is processed by applications, rather than requiring post-hoc manual review
3Reliability
If data error detection is performed manually or with simple tools, then errors are identified, but detection occurs long after negative effects have occurred
Solution Approach 1:
Data models are configured in advance with expected patterns, constraints, and relationships, enabling automated error detection to occur proactively before data is processed by applications, rather than reactively after negative effects manifest
Solution Approach 2:
The system provides automated feedback loops that continuously monitor structured cloud data against data models and immediately identify deviations, enabling real-time error detection rather than delayed manual discovery
4Reliability
If comprehensive data validation is implemented, then data integrity is improved, but system complexity and processing overhead increase
Solution Approach 1:
The system segments validation logic into modular data models that can be independently configured and managed, reducing overall system complexity while maintaining comprehensive validation coverage
Solution Approach 2:
Data models serve as an intermediary layer that encapsulates validation logic, separating complexity from applications and providing a manageable interface for data integrity enforcement
Data Source
AI summary
Data in different, respective ranges of spreadsheet file cells is compared, and a scope of a formula determined with respect to selected cells of the ranges of cells, wherein the formula pulls input data from selected cells of one range of cells and either pulls input data or generates output data to selected cells of the other range of cells. A review flag is automatically generated in association with data in a flagged cell in response to determining: that the flagged cell is omitted from a consecutive plurality of input data rows or columns; that the selected formula input cells are not consecutive within one of the ranges of cells; and that a high percentage of data values in corresponding cell rows or columns match but that and a location of the flagged cell is shifted from a corresponding cell within the other range.