Self-Service Data Analytics via Canonical Data Binding
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Solution Overview
Problem
Current data analytics and machine learning libraries lack guidance on when and how to apply algorithms to specific business problems, requiring collaboration between data scientists and business analysts, which is disrupted by the cloud model's self-service approach that aims to reduce such dependencies.
Innovation Solution
A data analytics system that associates machine learning algorithms with canonical data and business-oriented questions, allowing users to select appropriate algorithms based on domain and data category, minimizing the need for manual adaptation and expert intervention by matching user datasets to predefined canonical data structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data analytics libraries provide a wide range of configurable algorithms, then algorithm versatility is improved, but ease of operation deteriorates due to lack of guidance on when and how to apply them
Solution Approach 1:
The patent introduces an intermediary layer between the user and the algorithms consisting of business questions and canonical data structures. This intermediary guides users by translating business problems into appropriate algorithm selections, eliminating the need for users to directly understand complex algorithm configurations while preserving access to diverse algorithms through the question-driven interface
2Measurement precision
If data analytics requires collaboration between data scientists and business analysts, then measurement precision is improved through expert knowledge, but device complexity increases due to multiple stakeholders
Solution Approach 1:
The patent enables business analysts to perform data analytics independently through a self-service interface driven by business questions. The system automatically matches user datasets to canonical structures and selects appropriate algorithms, eliminating the need for data scientist intervention while maintaining analysis quality through the structured question framework that encapsulates expert knowledge
3Ease of operation
If cloud analytics platforms enable self-service, then ease of operation is improved, but reliability deteriorates due to reduced expert intervention
Solution Approach 1:
The patent performs preliminary actions by pre-defining business questions with associated canonical data structures and algorithm mappings. This preparation work, done in advance by experts, embeds the necessary knowledge and validation rules into the system, allowing self-service users to reliably select appropriate algorithms without real-time expert intervention while maintaining analysis quality through the pre-validated question framework
Data Source
AI summary
Systems and methods of data analytics, which in various embodiments enable business analysts to apply certain machine learning and analytics algorithms in a self-service manner by binding them to generic business questions that they can be used to answer in particular domains. The general approach may be to define the application of an algorithm to solve specific problems (questions) for particular combinations of a business domain and a data category. At design time, the algorithm may be linked to canonical data within a data category and programmed to run with this canonical data set. At runtime, given a dataset and its category, and a business domain, a user may choose from the corresponding questions and the system may run the algorithm bound to that question.