Agile Data Science
Thoughtworks Technology Podcast
Thoughtworks
4.5 • 58 Ratings
🗓️ 23 August 2018
⏱️ 21 minutes
🧾️ Download transcript
Summary
Learn how agile disciplines are applied to the complexities of data science to demonstrate incremental value within intelligent systems and solutions. Join ThoughtWorks' CTO, Rebecca Parsons, and Principal Associate, Alexey Boas, as they interview agile data scientist David Johnston and ThoughtWorks' head of data science, Ken Collier. The result is a better understanding of how agile practices are adapted to the uncertainty of machine learning, and how data scientists fit within a cross-functional agile delivery team. -- https://www.thoughtworks.com/podcasts
Transcript
Click on a timestamp to play from that location
| 0:00.0 | Welcome to the ThoughtWorks podcast. My name is Rebecca Parsons. I'm the chief technology officer of ThoughtWorks and one of your hosts for today. |
| 0:11.8 | And I am Alexei Villasboas. I'm the head of Technology for Brazil. |
| 0:15.7 | Hi. My name is David Johnston. I'm a data scientist at ThoughtWorks. |
| 0:18.7 | And I'm Ken Collier. I'm the head of data science and engineering at ThoughtWorks. |
| 0:22.6 | So let me start by tossing a question to you. |
| 0:24.6 | Why is Agile Data Science important? |
| 0:28.6 | What is it about this combination of Agile and data science that is interesting for us? |
| 0:33.6 | I think the important part is that there is data science that is going on, but not being |
| 0:38.2 | done very well, very effective. It's not resulting in any kind of applications that are useful for |
| 0:42.2 | clients. So we're starting to sort of look at the delivery method and see what needs to change. |
| 0:47.7 | And the agile method of delivering software certainly is very effective for the kind of software |
| 0:51.4 | that we've been developing the last 20 and 30 years. But this new kind of software is quite different. It needs to be modified in a way to account for some of the differences in the applications that we're developing. The way it normally is a software app is you have the user as kind of the center of the world, right? You have user stories and UAT and user feedback. And it's because the applications are made for the user, right? |
| 1:12.2 | You have a UI, for example, in the web app and the user is going to be using that to do things. |
| 1:17.6 | Whereas the applications that we're developing now are more predictive, for example. |
| 1:20.6 | So the user may not have a large role, right? |
| 1:23.6 | It's the algorithm that is making the choices of what to do. |
| 1:26.6 | So these are very different. |
| 1:31.4 | You know, the agile delivery method needs some change to adapt to those kind of things. |
| 1:37.9 | So, Ken, you've got a lot of experience in applying agile techniques in somewhat related fields of agile data warehousing, business intelligence. How would you characterize how |
| 1:43.4 | things are different with data science |
| 1:45.7 | from the Agile perspective? |
| 1:47.1 | Sure. And in addition to data warehousing and business intelligence, I have a background in |
... |
Please login to see the full transcript.
Disclaimer: The podcast and artwork embedded on this page are from Thoughtworks, and are the property of its owner and not affiliated with or endorsed by Tapesearch.
Generated transcripts are the property of Thoughtworks and are distributed freely under the Fair Use doctrine. Transcripts generated by Tapesearch are not guaranteed to be accurate.
Copyright © Tapesearch 2026.