Product Design

DataSense

Nov 20, 2024

Project Overview

Context: This 24hr Project was developed for the 2024 NYU Product Management Competition (sponsored by PitchBook Data), and won 3rd Place.

Challenge: How might we design a lightweight data management solution that minimizes manual work, is intuitive for freelancers and small businesses, and remains cost-effective by eliminating unnecessary features?

Date: Nov 19th - Nov 20th 2024 (24hr Hackton)

Team: Yuris Luo, Yilin Wang, Alina Zeng, Eva He

Responsibilities: Wire-framing, Flow Design, Feature Design, Collaborative Research

View Figma Prototype

Problem Research

Current data entry processes are broken...

Persona

Sarah is a small Jewelry store Owner in NYC

Currently she is manually recording her sales information, consuming 1.6hr of her time daily.

Now she is considering switching to digital. However, the current selection of applications are often overly powerful and pricing.

Problem Statement

Freelancers and small businesses spend too much time on manual data management for clients, projects, and more. Current solutions are bloated and expensive, forcing them to pay for unneeded features.

How Might We

How might we design a lightweight data management solution that minimizes manual work, is intuitive for freelancers and small businesses, and remains cost-effective by eliminating unnecessary features?

Feature 1: Guided bulk data upload

Supports uploading multiple files simultaneously for efficient data management.

Benefit users from uploading multiple large databases simultaneously by drag and drop.

Feature 2: Automated data cleaning

Standardizes formats and corrects errors for seamless integration.

Feature 3: Data Structuring and Organization

Users can now process multiple files at once, automatically rematching field schemas to a standardized format.

They can also define relationships between fields in two sheets to generate a combined, unified sheet.

Feature 4: Entity Matching

Automatically matches entities like customer records, product details, and financial data with existing system data, reducing errors and improving consistency.