1-800-Flowers

While collaborating with a project team at 1-800-Flowers, we encountered a distinctive challenge. The team aimed to analyze specific data that wasn't readily accessible in their current database. They provided a 2-gigabyte CSV file, a dataset too vast for processing through standard methods like Excel. Despite the proficiency of our team members in Excel, the sheer volume of data rendered traditional processing techniques ineffective.

The project's core objective was to calculate the total miles traveled by packages from the point of purchase (purchaser's zip code) to the delivery location (recipient's zip code). This task necessitated a method capable of efficiently managing and analyzing large datasets, necessitating a bespoke solution.

To tackle this issue, I utilized Node.js to create an extensive codebase designed as a functional library. This library was engineered to process the CSV file line by line, executing asynchronous operations on each data entry. This approach allowed for the efficient parsing of data, enabling the referencing of locations and the use of an API to calculate distances between zip codes. By aggregating these distances, we were able to determine the total miles each order traveled within the dataset.

Following the data processing phase, I developed a visual representation to illustrate the distances packages traveled over the year. This visualization was instrumental in providing clear and immediate insights into the distribution patterns and played a crucial role in informing the strategic planning of future marketing campaigns.

This endeavor highlighted the utility of Node.js in handling and analyzing extensive datasets and showcased the importance of innovative data processing methods in deriving actionable insights. Transforming a cumbersome 2-gigabyte CSV file into a strategic resource underscored the critical role of technical expertise in addressing business challenges and enhancing the strategic planning process for 1-800-Flowers.