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 data cleaning to training data with machine learning algorithms, 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 focal point throughout the project. They stem from the client company’s overall business strategy, ensuring that the project objectives align with the company’s strategic initiatives.
What is the app about?
Volantis Data Pipeline is an app that simplifies all data processing on a single canvas using drag-and-drop data pipeline tools. It covers all steps of data processing, such as cleaning raw data, applying data processors like joiners and filters, and training data with machine learning algorithms.
What are the goals of the app?
The main goal is to provide an easy way to build datasets and models without coding, so even people with limited programming knowledge can 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 was very tricky due to the company’s limited resources (including potential users). To address this, I interviewed our internal data scientists to gain a better understanding of how they work.
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 considered Volantis Data Pipeline’s biggest competitor. They have been in the machine learning industry since 2012 and continue to refine their features. One of their strengths is how they represent each setting of the operators and their strong user experience.
04
Deliverables
Sitemap
There are three main groups in Volantis Data Pipeline: input, connector, and output. I created a sitemap to gain a better understanding of how the pipeline works.
Wireframes
Before jumping into wireframes, I first created the layout by sketching on a sketchbook as the early concepts of the product. The platform team used the wireframe version to attach to their Business Requirement Document, which was later presented to stakeholders for approval to proceed with development.
High-fidelity designs
Designing for a dark theme was a challenge for me, especially with Volantis’ color palette. Adding color after color wasn’t difficult when I had the hand sketches and wireframes beforehand, and the UI kit I had created previously allowed me to complete my mockups quickly.
Interaction flow
I tried to find a way to represent each screen and its respective functions in a way that would benefit the development team, and decided to create an interaction flow. This helped me visualize the design better, and I received feedback from stakeholders more quickly, which in turn helped the engineering team with the interaction of each component.
05
Conclusion
Objectives are an important focal point throughout the project. They stem from the client company’s overall business strategy, ensuring that the project objectives align with the company’s strategic initiatives.
Outcome
TBD
Lesson learned
TBD
Next steps
TBD
Nafi’ah Al-Khoir
Product Designer
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