Virtual Data Store System for CRM Memory Optimization
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Solution Overview
Problem
Current Customer Relationship Management (CRM) systems face inefficiencies due to the decay of customer data, leading to wasteful use of memory, processing, and network resources from storing and processing stale data, which results in inaccurate communication attempts and increased costs.
Innovation Solution
A virtual data store system that dynamically formulates verification queries, uses a proxy service for load balancing, generates confidence scores, and performs subsequent verification operations to ensure data accuracy, deleting stale data from memory to conserve resources and enhance communication efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored in CRM systems without verification, then memory resources are utilized, but data accuracy deteriorates and stale data accumulates
Solution Approach 1:
The system performs preliminary verification of data records against multiple online sources before storing them in the CRM system. By proactively checking data accuracy through confidence score generation and subsequent verification operations, the system prevents stale and inaccurate data from being stored, thus maintaining high data reliability while optimizing memory resource utilization.
Solution Approach 2:
The system implements a feedback mechanism where data records are continuously verified against online sources, confidence scores are generated, and verification results are used to determine whether to retain or delete records. This closed-loop feedback ensures data accuracy is maintained while preventing accumulation of stale data that would waste memory resources.
2Reliability
If verification operations are performed on all data records, then data accuracy is improved, but processing time increases
Solution Approach 1:
The system applies partial verification by first generating confidence scores for all data records, then performing subsequent verification operations only on records with low confidence scores. This selective approach ensures data accuracy for critical records while minimizing processing time by avoiding redundant verification of already reliable data.
Solution Approach 2:
The system changes the verification parameter from binary (verify/not verify) to a confidence score-based approach. By calculating confidence scores and using them to determine verification necessity, the system optimizes the balance between data accuracy and processing time, performing detailed verification only when needed.
3Quantity of substance
If stale data is retained in the system, then memory resources are occupied, but communication efficiency deteriorates
Solution Approach 1:
The system extracts and identifies stale data records through verification operations and confidence score analysis, then removes them from the CRM system. By taking out inaccurate and stale data, the system frees up memory resources and ensures that only valid, accurate data remains, thereby maintaining communication efficiency.
4Reliability
If multiple verification operations are performed, then data accuracy is improved, but device complexity increases
Solution Approach 1:
The system implements dynamic verification where the number and depth of verification operations adapt based on confidence scores. Records with high confidence scores undergo minimal verification, while those with low confidence scores trigger more rigorous subsequent verification operations. This dynamic approach maintains data accuracy while avoiding unnecessary complexity for reliable records.
Data Source
AI summary
A virtual data store system comprises a network interface and a processing device operable to access a source database of data records comprising data, wherein an entity is enabled to utilize the virtual data store system as a database. A verification query is formulated with respect to a data record, that stores an item of data, which is transmitted to online network sources via a proxy service. A query response is parsed and in response to determining that the item of data in the data record is potentially present in the parsed response, a corresponding confidence score is generated. If the confidence score is below a first threshold, a subsequent verification operation is performed. If the subsequent verification operation fails, the second item of data is deleted from memory to conserve memory resources. The data record may be accessed from the virtual data store and used to initiate a networked communication.