GUI-Based Data Store List Generation and Management
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Solution Overview
Problem
The existing process of generating and managing data store lists is inefficient, requiring multiple iterations between analysts and query specialists, leading to delays and increased workload, as analysts often need to revise queries due to incorrect assumptions or unsuitable results, and may miss timely business opportunities.
Innovation Solution
A method and system for generating data store queries using a graphical user interface (GUI) tool that allows users to interactively define search criteria and job types, automatically generating and executing SQL queries against a data store, such as a Teradata warehouse, to produce desired lists and quick counts, reducing the need for query specialists and improving turnaround time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If analysts submit queries to query specialists for processing, then queries can be executed against the database to produce customer lists, but the process requires multiple iterations and causes delays
Solution Approach 1:
The system enables analysts to self-serve by directly submitting queries to the database without requiring query specialists. The automated query processing system handles query execution, result generation, and delivery independently, eliminating the need for human intermediaries and reducing iterative revisions.
Solution Approach 2:
An automated query processing system acts as an intermediary between analysts and the database. This system receives queries from analysts, processes them against the database, and returns results automatically, replacing human query specialists as the mediator and enabling near real-time processing.
2Productivity
If query specialists handle multiple analyst requests, then query processing capacity is maximized, but analysts experience delays and idle time
Solution Approach 1:
Analysts independently submit and track their own queries without relying on query specialists. The system provides automated query execution and result delivery, allowing analysts to proceed with their work immediately after submission rather than waiting for specialist processing.
Solution Approach 2:
The automated system enables continuous query processing without interruption or batching delays. Multiple queries are executed concurrently and independently, ensuring that each analyst receives results as soon as processing completes rather than waiting for a query specialist's schedule.
3Adaptability or versatility
If analysts revise queries to achieve desired list sizes, then query results meet business requirements, but additional iterations increase workload and delay results
Solution Approach 1:
Analysts independently adjust and resubmit queries with modified parameters to achieve desired results. The system automatically processes these revised queries without requiring query specialist intervention, allowing analysts to flexibly adapt queries while reducing procedural complexity.
Data Source
AI summary
Techniques for data store list generation and management are provided. A user supplies criteria for a list via a graphical user interface tool. The criteria are used to generate a query, and the query when executed against a data store produces results representing the list. The list may then be used for a variety of purposes.