Data Pipeline
A platform that empowers organizations to handle large-scale data operations with ease, supporting machine learning and analytical tasks without requiring advanced technical skills.
Timeline
2018
Company
Volantis Technology
Contributors
Nafi’ah Al-Khoir (Designer)
Ajeng Tri Astuti (Product Manager)
Cecilia Astrid (Engineering Lead)
Responsibility
Interface design
Interaction design
View work
https://volantis.io/platform/data-pipeline
01
Overview
Challenge
Volantis wanted to simplify all data processing, from cleaning data to training data with Machine Learning algorithm, with no code required and easy interaction.
What I did
Setting goals and objectives
Conduct stakeholders interview
Building personas
Conducting competitive research
Creating sitemap
Creating wireframes
High-fidelity design
Creating interaction flow
Tools
Sketch, Zeplin, Abstract
02
Goals and objectives
Objectives are an important focusing lens for use throughout the project. They spring from the client company’s overall business strategy, so the project objectives are in line with the strategic initiatives within the company.
What is the app about?
Volantis Data Pipeline is an app that simplify all data processing in a single canvas using drag-and-drop data pipeline tools. Volantis Data Pipeline covers all steps of data processing, such as cleaning raw data, aplying data processor such as joiner and filter, and training data with Machine Learning algorithms.
What are the goals of the app?
The goal of the website is to provide an easy way to build datasets and models aside from coding, so even people with less knowledge of programming can also learn to build datasets and models.
Who are users of the app?
Primary audiences: Data Scientist
Secondary audiences: IT Engineer
03
Research
Personas
Creating personas is very tricky because of company’s limited resource (including potential users). I had to interview a Data Scientist to get the idea of the users and their pain points.
Who are they?
Data scientists
Age: 20+
Gender: mixed
Family: mixed (single or married)
Education: computer science
How do they create models?
Create datasets by cleaning the data first then applying operators (joiner, pivot, filter) using Excel
Program the model using R or Python
Main goals
Clean data faster
Less effort to create a model (less coding)
Pain points
Datasets are scattered in various storage
Cleaning dataset takes too much time
Multiple apps are used to create pipeline, from cleaning data to data preprocessing (not handy)
Motivation
Storage datasets and connect it to create pipeline
Get datasets cleaned faster
Discover pretrained model to use in pipeline
Competitive research
Dataiku is seen as Volantis Data Pipeline’s biggest competitor. They have been in this Machine Learning industry since 2012 and keep refining their features along the way. One of their good points is how they represent each settings of the operators and their good user experience.
04
Deliverables
Sitemap
There are three main groups in Volantis Data Pipeline: input, connector, and output. I created sitemap to get better understanding regarding how pipeline works.
Wireframes
Before jumping into wireframes, I created the layout first by sketching on sketchbook as the early concepts of the product. Platform team will use the wireframe version to be attached in their Business Requirement Document, which later to be presented to stakeholders and asked for their approval to continue with the development.
High-fidelity designs
Designing for dark theme is a challenge for me, especially with Volantis’ colour palette. Putting colour after colour when I had the hand-sketches and wireframes beforehand wasn’t hard, and UI Kit that I’d made before got my mockups done quite fast.
Interaction flow
Sometimes prototyping doesn’t do justice to show the interaction of each screen. I tried to find a way how to represent each screen and their respective functions that can benefit for development team and decided to create interaction flow. From this, I got feedbacks from stakeholders faster and helped engineering team for the interaction of each component.
Nafi’ah Al-Khoir
Product Designer
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