Analytics Tag Data Quality Scanning From Design-Generated Rules
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Solution Overview
Problem
Existing analytics tracking systems face challenges in ensuring consistent and accurate digital tagging due to inconsistencies in implementation, translation issues between analysts and developers, and the complexity of custom tagging attributes, leading to potential data validation failures and the collection of private identifying information or useless server calls, which are difficult to monitor efficiently.
Innovation Solution
A production tag feed and tag design document are used to compare expected tagging against actual results, employing a design file scanner script to convert tag design documentation into data quality rules stored in a data rule repository, with a dictionary data structure to cross-reference actual server calls and identify data quality issues, enabling root cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated data quality scanning is implemented, then data quality monitoring efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the data quality scanning process into distinct modular components: rule engine for policy definition, scanner engine for execution, dictionary generator for custom attribute handling, and reporting module for results. This segmentation allows each component to be independently developed, maintained, and scaled, resolving the contradiction by improving monitoring efficiency through automation while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediary components including a standardized data dictionary that mediates between custom tagging attributes and validation rules, and a rule engine that mediates between policy requirements and actual scanning operations. These intermediaries simplify the overall system complexity by providing standardized interfaces and abstraction layers, while enabling comprehensive automated monitoring.
2Measurement precision
If comprehensive tagging validation is performed, then data accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-generating data dictionaries from tag design documentation before validation occurs. The dictionary contains all expected custom attributes, data types, and valid values, allowing the scanner to perform rapid lookups during validation rather than complex real-time analysis. This preliminary preparation improves data accuracy through comprehensive validation while reducing processing time during actual scanning operations.
Solution Approach 2:
The patent changes parameters by transforming unstructured tag design documentation into structured data dictionaries with defined schemas, data types, and validation rules. This parameter transformation enables efficient automated validation by converting qualitative design requirements into quantitative checkable parameters, thereby improving data accuracy while maintaining fast processing speeds through structured data comparison.
3Adaptability or versatility
If custom tagging attributes are extensively used, then analytics capability is improved, but validation difficulty increases
Solution Approach 1:
The system implements a universal data dictionary structure that can accommodate any custom tagging attribute while maintaining consistent validation mechanisms. The dictionary schema provides a multi-functional framework that handles diverse data types (strings, numbers, dates, enums) through a unified validation approach. This universality enables extensive analytics capability through flexible custom attributes while reducing validation difficulty through standardized processing rules that apply across all attribute types.
Solution Approach 2:
The patent uses copying by generating standardized validation rules and data dictionaries automatically from tag design documentation templates. Instead of manually creating validation logic for each custom attribute, the system copies and adapts standardized schemas based on the documented tag specifications. This copying approach maintains analytics versatility through custom attributes while significantly reducing validation complexity through automated rule generation.
4Ease of manufacture
If manual tagging assessment is performed, then implementation simplicity is maintained, but monitoring coverage is insufficient
Solution Approach 1:
The system implements self-service by automatically generating data dictionaries and validation rules from tag design documentation without requiring manual intervention for each tag. The scanner engine autonomously executes validation against the generated rules and produces comprehensive reports. This self-service automation maintains implementation simplicity through a single automated process while dramatically improving monitoring coverage by validating all tags systematically rather than relying on limited manual assessment.
Data Source
AI summary
This disclosure is related to systems and methods for performing data quality scanning on analytics tags within electronic documents (e.g., web pages). Specifically, tag data quality rules are generated from tag design documents within an organization. A design file scanner script is used to automatically convert tag design documents with custom tagging elements into data quality rules in a dictionary data structure. Once generated, the tag data quality rules may be stored in a data rule repository and used at a later time for performing data quality checks on production tagging data. As such, the disclosed systems and methods may increase the quality of tag design documents and overall quality of custom tagging within the organization.