DATArt

Make art with your data, and share it with your team!

Comment

Inspiration

We were inspired by a common interest in abstract minimalist art, and by our experiences with our companies attempting to showcase KPI's with overwhelming number sets.

What it does

The project takes your business data and generates a single graphic that summarizes the distribution of your desired data filters based on volume in a visually interesting and intuitive way.

How we built it

We decided on our project through a team ideation session (focused on common problems) and a feasability-impact analysis. Our team's designer created an initial design [Figma], then the team had an iterative project approach in which our FE engineer worked on to create the product [Typescript] and the team continued to iterate on the design and functionality.

Challenges we ran into

  • Programming the math formulas for how to generate the art patterns based on the data.
  • Managing the data load and S3 bucket calls with limited resources (e.g. under S3 free tier, limiting the complexity of our data visualization).
  • Using S3 APIs to get data for the UI in an efficient way and parseable for art generations.
  • Finding a common data structure that is parsable but also free for any data source usage.
  • Setting up integration of Segment to S3 was time consuming and complicated for setting up permissions for Segment to write to our bucket.
  • Not able to fully harness he power of Segment in this timeframe.

Accomplishments that we're proud of

  • We're also proud to have been able to combine both science and art in a single product, creating something that is both useful and fun for business stakeholders.
  • Our team was spread across 3 time zones during this project and we are proud to have coordinated successfully to deliver an MVP within the timeline allotted.

Lessons learned

  • Learned about the software involved; about S3 more deeply, including how object storage works and the connection to access control and possible APIs for S3 and their XML structure.
  • Learned how Segment maps data from sources and destinations and different ways to ingest and process data.
  • Learned about different data visualizations and purposes and benefits.
  • Really learned about the importance of having an MVP with this project, especially given limited resources and time.
  • Time & practice presentations before recording.

What's next for DATArt

In the future, we aim to be compatible more types of data sources, this can include:

  • Advertising accounts
  • Data warehouses
  • A/B testing platforms

We will support more art complex and thought provoking art styles We will allow more seamless communication with Mailchimp and Slack integrations through Segment. With more resources and data source support, DATArt can be applied to more use cases beyond marketing -- like showing product sales and customer sentiment.

Share this project:

Updates