Live Query Charting System for Real-Time CRM Data
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Solution Overview
Problem
Conventional data processing systems struggle to provide real-time, two-dimensional charting capabilities for sales data analysis, relying on static data that may not represent the most recent information, which hampers efficient data visualization and reporting.
Innovation Solution
A charting system that builds on-demand queries to fetch real-time data from a CRM system, allowing for dynamic two-dimensional chart generation with clickable segments linked to individual opportunity data, eliminating the need for pre-calculation caching and enhancing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems export opportunity data into spreadsheets and use previously obtained data to plot graphs, then the data processing system can generate visual representations, but the data utilized in plotting graphs is static and may not represent the most recent data
Solution Approach 1:
The system transitions from static data visualization to dynamic real-time data visualization by implementing live queries that continuously fetch updated opportunity data from the CRM system. The charting system dynamically adapts to data changes without requiring manual updates or re-exports, ensuring the displayed information always reflects the current state of sales opportunities.
Solution Approach 2:
The charting system autonomously retrieves updated data through automated live queries triggered by data changes in the CRM system. Instead of requiring manual data export and re-processing, the system self-updates by automatically querying the CRM database and refreshing visualizations, eliminating human intervention in the data refresh cycle.
2Productivity
If the system builds on-demand queries to fetch real-time data, then real-time data visualization is enabled, but the system complexity increases
Solution Approach 1:
The charting system implements a universal query interface that handles multiple data dimensions and chart types through a single standardized mechanism. The live query infrastructure serves multiple purposes: initial data loading, incremental updates, filtering, and aggregation, eliminating the need for separate data collection and update systems for different charting scenarios.
Solution Approach 2:
The system introduces an intermediary layer between the CRM database and the visualization interface that manages live queries. This intermediary handles data retrieval, transformation, and caching strategies, abstracting the complexity of real-time data access from both the database and the charting logic, thereby simplifying the overall system architecture despite the added real-time capability.
Data Source
AI summary
A first request is received at a server for a view of a chart graph from a client device over a network. The first request includes a first view identifier (ID) identifying a first of the views and a first filtering parameter for filtering data to be associated with the first view. A second request is transmitted to a CRM system over a network to retrieve live data associated with one or more chart elements of the first view. The second request includes information identifying the first filtering parameter such that only live data satisfying the first filtering parameter is retrieved from the CRM system. In response to the live data received from the remote CRM system, the live data is transmitted to the client device, wherein the client device renders a chart graph based on the live data and displays the chart graph at the client device.