We implemented a streamlined data management system for Conversion Finder that centralized data from multiple sources into a single repository. This approach not only consolidated disparate data streams but also ensured that all data was readily accessible for analysis. By standardizing data processes, we enhanced data granularity and significantly improved data quality, allowing for more reliable and actionable insights.
The project empowered Conversion Finder with robust tools for strategic decision-making, enhancing their ability to analyze trends and performance metrics. As a result, they could respond more effectively to market dynamics and optimize their campaign strategies.
Before Implementation
Reliance on multiple Excel files and Google Sheets from inconsistent sources leads to inefficiencies and potential errors.
Inefficient data aggregation processes, causing duplicated work.
Lack of centralized documentation for existing data flows, making it difficult to track data from input to final use.
Key Improvements
Replaced pre-aggregated tables with a granular data structure for easier grouping, efficient modeling, and reduced redundancy.
Developed a monitoring dashboard, providing a segmented view of information.
Created documentation for data flow, allowing step-by-step tracking of data from the initial inputs to its use in spreadsheets.
Project Overview
1.
Data Integration
The project consolidated data from daily client submissions, CSV files, BigQuery, and other sources into a centralized data storage. For instance, data from daily exports from Paragon were combined and processed in BigQuery.
2.
Standardized Data Flow
Standardizing data flows and processes improved quality and reduced redundancy. This involved defining relationships between tables like "Leads," "Transfers," and "Retainers," ensuring consistency across data.
3.
Monitoring Dashboard
A monitoring dashboard was created to provide detailed insights into campaigns, vendors, and client performance.
4.
Process Automation
Processes were automated to reduce manual effort and potential errors. For instance, data extraction and transformation tasks were automated using scripts to move data from Gmail to BigQuery.
Python
BigQuery
Gmail API
SFTP
Technologies used
Jordan Soblick
CEO - Conversion Finder
"Working with datakimia was transformative for our data processes. They streamlined our data flow and improved visibility with dashboards. Highly recommended!"
