Labsheets is a spreadsheets tool for consolidating scientific data.
Scientists use labsheets to document their sample's information such as species, quantity, expiry date, etc. Each labsheet column has a data type such as text, number, and date.
After running experiments, scientists import CSV data into their labsheets to conduct analysis.
In their experiment process, scientists use 10+ different instruments and need to consolidate their results in one place.
Uh-oh!
Scientists are making CSV imports with missing data.
My team was being flooded with 100+ email complaints from customers about incomplete data in their labsheets.
The Problem
Scientists cannot map their CSV columns due to data type incompatibilities with the existing columns in their lab sheet.
My Impact
I designed a new AI data import tool within 5 days.
This AI data import tool can map CSV columns to existing columns, while also flag data errors in CSV columns.
V1 shipped to 100+ labs in a week
Cut down import time by 30%
Current State
The current column mapping process is extremely time consuming.
I went through the import csv process, audited the flow, and discovered these three core issues.
🔴 Mapping mistakes
Scientist made mistakes in manually mapping columns.
🔴 No new columns
Scientists can't create new columns out of their CSV.
🔴 No error warnings
No warnings about incompatible mappings.
CURRENT PROCESS
Challenge #1: Review column mappings
How might scientists review AI-suggested column mappings?
I planned to use AI to automatically map CSV columns to existing labsheet columns based on name and data type similarity. Based on the mapping, scientist can then decide to change the mapping, do not import the column or create a new column.
Initial Layout Explorations
VERSION #1
Reviewing mappings 1 by 1
High attention to detail.
Time consuming to review each pair at a large scale.
Too many actions scattered across UI.
CHOSEN
Reviewing mappings in a list
Scannable for review.
High scalability.
Leaving columns out of import
To address the need for selective data import, I introduced an action button to exclude specific columns. Initially, the option to deselect columns was unclear, so I refined it with a 'Don't Import' dropdown for better clarity.
VERSION 1
Selecting imported columns
CHOSEN
"Don't Import" dropdown option
Creating new columns
Currently, users are unable to create new columns in the import flow. I explored ways how might a scientists be able to create a new column and select its column type.
VERSION #1
Creating columns from a modal
Multi step flow.
Redundancy in entering name of column.
CHOSEN
Creating columns from a dropwdown menu
Lightweight UI.
Easily switch between all actions.
Challenge #2: Informing Data Errors
How might we inform scientists of data incompatibilities?
Structured and clean data is incredible important for scientists to minimize error and misinterpretations during the analysis phase. Thus, I scoped out adding warnings about rows that could be lost in the import process.
USER INSIGHT #1
"I want to know what's wrong with my CSV columns, so I can fix it in my CSV file, remove the column from import or change the mapping."
VERSION #1
Tagging columns based on data quality
See from high level data compatibilities
Unclear that these invalid data types are errors
Lack of differentiability with other mappings
CHOSEN
Separate tab with list of errors
Separate tab helps track the quanitty errors
More context behind what these errrors are.
REFLECTIONS
Grateful for the journey! Here's what I learned....
Quality means shipping fast
This was my first time working at a fast-paced and high-growth startup! I felt uncomfortable at first making a lot of product and design decisions at an accelerated pace. But, gradually, I learned that shipping fast and getting the design out there in the world allowed us to learn about what customers really think, get feedback, and iterate on the product, turning into something people love using.
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