I’ve been developing data visualization dashboards using Dash for a bit now for a work project and I’m loving it.
However my management is asking me for justification to build dashboards using Dash instead of “off the shelf” dash boarding products like Tableu, Spotfire, or Splunk Enterprise Visualizations that would take less engineering time to set up. (Assuming budget isn’t an issue)
Some things I can think of would be the advantages of customization, as well as owning the code for version control purposes.
Can any of you Dash veterans help me out in coming up with reasons why you chose Dash over off the similar “off the shelf” commercial dashboard products for your project(s)?
Hi CallbackCat, it’s great to see your post about working with Dash on your data visualization project. Yeah the power of an “App” approach versus read-only dashboard tool like Tableau, PowerBI, Spotfire, etc is amazing. The question about “why Plotly Dash” often comes up when users contact us and I can share some personal perspective since working for Tableau (and other BI software companies) for many years.
Justification is easy because of these factors and feel free to contact me to help with the internal write-up!
1) Need for deeper analytics - data projects often need more than just a read-only dashboard, examples like running dynamic Python/R/Julia models or data calls, ability to write into the dashboard for numeric or commentary inputs, ability to save interactive views and print/subscribe PDFs. Plotly Dash Apps can do these things and other dashboard tools cannot!
2) Lower cost to scale - data analytics leaders always have a vision for “Analytics Everywhere” which means many users or consumers of the dashboards. When looking at user subscription costs for larger scale deployments, often the subscription price tag gets to high thousands/millions of dollars for simple dashboard consumers (and this stops projects!). Plotly Dash Enterprise is a platform priced on the number of data analyst/developer/scientists and provides unlimited applications and users of these apps. Dash is much more affordable to meet the Analytics Vision, as we cost a fraction of the other visualization/BI tools!
3) AI-ready - many companies are on a journey with advancing their analytics and data capabilities into AI/ML. Other dashboard tools need to have APIs or bridges to their Python/R/Julia models which makes for complicated and in many cases inadequate functionality. Dash is already there in Python/R/Julia programming language and can simply integrate to these advanced AI/ML and BI data models and have the benefit of being deployed by the analytics team without the need of IT or full-stack developers for the web interface.