The Ultimate Guide on
Implementing Agile for Data Teams


From agile 101 for data teams to tips for improving your team productivity by 4x, here’s everything you need to know.

Featuring an exclusive learnings section sharing lessons from our data team on using the Scrum framework.



Here's what you can expect to learn in this ebook.

Chapter 1
Why are data projects so challenging?
Chapter 2
Is there a way to be better at planning data projects?
Chapter 3
What is Scrum and how does it work for data teams?
Chapter 4
How did Atlan do 2x in half the time with Scrum?
Chapter 5
What are the top learnings on implementing Scrum for data teams?
dots pattern

See what our data team has to say about the entire process and on life before and after scrum.

The underlying principle of the Scrum framework is simple. Just like a machine learns and makes better forecasts after every iteration, the Scrum framework says that repeating the same exercise again and again will help you get better at it by working on the weaker areas”


Shilpa Arora,

Principal Data Scientist


Would everyone believe in Scrum? Does everyone face problems with productivity? Is meeting daily sensible? Is gamification or the velocity calculation required? Is it worth the time investment?

We had all these questions in mind after we read about Scrum and wanted to implement it for our data team.


Himanshu Sikaria,

Head of Data Science


The common thread between all of our bad sprints was either shortcuts or not spending enough time prioritizing. Even for a team of 5, more than 8 hours go into the stand-ups and sessions every week. Sometimes we’d avoid investing this time to spend more time solving problems. However, it led to misunderstandings, miscommunication, bottlenecks and frustrations everywhere.


Prukalpa Sankar,


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