Streamlining Retail Data Management With Snowflake for Seamless Operations
Introduction to the need of Retail Data Management for a leading retail enterprise
A leading retail enterprise had their customer data, sales transactions, and inventory records scattered across multiple systems, limiting real-time insights and operational efficiency. They further struggled with fragmented data silos, slow reporting, and high infrastructure costs, making decision-making inefficient. With growing data demands, they needed a scalable cloud solution to unify data, improve accessibility, and enable data-driven decision-making.
Major Challenges
Develop a data pipeline to get the raw data from each platform into targeted tables in Snowflake.
Fetch the data from targeted tables based on platform, fiscal week, and network and compare the results with the source/raw data.
Create an excel file to compare the source data with the resulting data from the snowflake tables.
Generate a report on a weekly basis to highlight which platform, network, and date range have inaccurate or missing data issues.
Innovative Snowflake Solution for the Retail Business’s Impact and Growth
Queried a data pipeline to transform and load raw data from various platforms into the targeted tables in Snowflake.
Developed a query to group the activity and conversion data from the targeted Snowflake tables based on fiscal week and network for each platform.
Created an excel template to compare the source and targeted data based on fiscal week, network, and platform.
Fetched the sourced data directly from various platforms and inserted it into the excel template.
Run the developed query to get the grouped data from the targeted snowflake tables and load it in the excel template.
Created calculated fields in excel to compare the activity and conversion data between the source vs snowflake data and find out the data discrepancies in %.
Updated the source as well as the targeted data every week in excel to get the latest data.
Generate an excel report on a weekly basis to highlight the % data discrepancies for each network and fiscal week of the current fiscal year for each platform.
Exceptional Results catered using Snowflake for the Retail Enterprise
Data Accuracy
With the developed model, there is a 70% reduction in data inaccuracy and inconsistency leading to a more precise dataset.
Time Reduction
It ensures cost-effectiveness because it saves time and money by making sure that the datasets collected and used in processing are clean and accurate.
Easy Decision-Making
Due to reduction in data inaccuracy and inconsistency it is helpful for top management to make informed decisions.
Fighting Data Decay
Validating data can assist the organization in reducing the potential errors caused by data decay by identifying where data is missing, incomplete, inconsistent, and inaccurate.
