ABOUT US - ABOUT US - ABOUT US
Our goal is to simplify data
analytics workflow by breaking
it down into smaller,
manageable tasks ...
Based on input from data analysts and scientists, we have identified distinct categories of tasks, that, together make up a typical analytics workflow.
categories of tasks in a typical data analytics workflow ...
% of people who report having difficulties with the task or find it repetitive and time consuming
Importing files
87%
This includes navigating complex project directories to find neccessary files, generating Pandas code for importing multiple files simultaneously, figuring out which tabs to load from Excel files as well as determining separators for CSV files
Preprocessing datasets
96%
Includes tasks such as viewing datasets eifficiently once they are imported as well as seeing their shape (e.g., head tail, shape commands). Other tasks include preprocessing datasets, such as filtering columns and/or rows, merging, concatenating datasets etc.
Visualizing results
90%
Most data professionals find it challenging to quickly build visualizations to share with their colleagues. Even more challenging is building dashboard that combine multiple visualizations.
This insight became the foundation of our modular approach, allowing us to create modules that target specific part of the analytics workflow, so that users can mix and match modules based on their needs.