Code Data Versioning for Modular Analytics Pipelines
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Solution Overview
Problem
Current data analytics approaches require significant manual effort for data preparation and transformation, leading to inefficiencies and a lack of reuse of intelligence across different business use cases, resulting in lengthy development cycles and missed business opportunities.
Innovation Solution
A system and method for rapid development and deployment of reusable analytic code, utilizing a centralized environment for pre-processing steps and signal generation, allowing for modular integration with various platforms and reducing software development complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data scientists manually prepare and transform raw data for each use case, then data analysis can be performed, but significant time and effort are spent on tedious manual processes
Solution Approach 1:
The patent implements pre-processing pipelines that automatically prepare and transform data before it reaches analysts. Data is cleansed, validated, and structured in advance using automated workflows, eliminating the need for manual data preparation at the time of analysis. This preliminary action stores processed data in ready-to-use formats that can be quickly retrieved and analyzed.
Solution Approach 2:
The patent creates reusable data templates and processing patterns that can be copied and applied across multiple use cases. Once a data preparation workflow is established for one analysis, it can be replicated and adapted for similar analyses, avoiding redundant manual work. Templates for common data transformations and validation rules are stored and reused systematically.
2Adaptability or versatility
If intelligence is developed from scratch for each use case, then specific business needs are met, but there is no reuse of intelligence across different use cases
Solution Approach 1:
The patent implements a centralized intelligence repository that stores processed data, analytical patterns, and insights in a universal format accessible across multiple use cases. The system is designed so that intelligence developed for one business function can be retrieved and applied to other functions. Data models, transformation rules, and analytical frameworks are created as universal assets rather than use-case-specific artifacts.
Solution Approach 2:
The patent breaks down complex analytical workflows into modular, reusable components that can be independently developed and combined. Intelligence is segmented into discrete units such as data cleaning modules, transformation functions, and analysis templates that can be mixed and matched for different use cases. This modular approach allows rapid assembly of customized solutions from pre-built intelligent components.
3Reliability
If data scientists focus on manual data preparation tasks, then data quality can be maintained, but less time is available for actual analytics and business intelligence
Solution Approach 1:
The patent implements automated self-service data preparation systems that perform quality assurance tasks without human intervention. The system automatically validates data completeness, checks for anomalies, applies cleansing rules, and verifies data consistency using pre-configured quality standards. This self-service approach maintains rigorous data quality controls while freeing data scientists from manual quality checking tasks.
Solution Approach 2:
The patent replaces manual mechanical data preparation processes with automated computational systems. Algorithms and software agents perform data cleansing, transformation, and validation tasks that previously required manual effort. This substitution maintains data quality through systematic automated checks while dramatically increasing the speed and scale of data preparation, allowing data scientists to focus on higher-value analytical work.
Data Source
AI summary
Code and data versioning allows developers to work on code and data without affecting production code and data and without affecting the development activities of other developers. Code and data being worked on by a developer are associated with a task. The system automatically determines the dataset to use for a given development task from among a production dataset, a latest dataset, or a temporary dataset associated with the development task so that development code does not have to be modified to read from a specific dataset.