Fall 2019 Reston Azure DataFest: Big Data & Analytics Conference

October 11 | Microsoft Offices | Reston, VA
About Fall 2019 Reston Azure DataFest: Big Data & Analytics Conference

We are pleased to announce that registration is open for Fall 2019 Reston Azure DataFest: Big Data & Analytics Conference, a 1-day event on Friday, October 11, 2019, 9:00AM to 5:00PM at the Microsoft offices at 12012 Sunset Hills Road, Reston,VA., 20190. We will be in Room 3028, 3054, 3058, and 3062.

Conference Location

12012 Sunset Hills Road, Reston,VA., 20190

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Here's what we are talking about!
Making Agile Work for Data Teams: Building Better Backlogs for Data...
By Mathias Eifert
Making Agile Work for Data Teams: Building Better Backlogs for Data Products
By Mathias Eifert
Presentation time
October 11th
2:35p.m.
Register Now!

Want to help your data and analytics teams embrace Agile but don’t know where to start? Wondering why your data team seems to struggle with creating manageable yet valuable stories? Curious why Agile for data teams is a distinct challenge?


We’ll discuss reasons why data work is often structured more like a pyramid than the familiar “layer cake” and how full-stack vertical slices present significant challenges since they easily become too complex, interdependent, and unwieldy for fixed-length sprints. Traditional user stories also don’t accommodate the highly exploratory nature of advanced analytics and data science projects where end users lack awareness of value opportunities, while technical experts can’t easily predict what is actually possible.

Based on one of Agile’s first principles to obtain feedback on feasibility and end user value as quickly and systematically as possible, this session presents lessons learned across multiple teams from applying alternative approaches to Product Backlog Items for data products that support small, independent stories while still maintaining a value focus. We’ll discuss ways to decouple the technical stack through stubbing and gradual tightening of the Definition of Done, as well as Lean Startup concepts and hypothesis-driven development (HDD) approaches that allow for explicit experimentation and risk trade-offs towards relevant milestones such as model quality or performance in the context of extreme uncertainty.


Join us as we discuss some of the friction we can encounter trying to use Agile on data teams, as well as some validated ideas for meaningful solutions.

Making Agile Work for Data Teams: Building Better Backlogs for Data...
By Mathias Eifert

Want to help your data and analytics teams embrace Agile but don’t know where to start? Wondering why your data team seems to struggle with creating manageable yet valuable stories? Curious why Agile for data teams is a distinct challenge?


We’ll discuss reasons why data work is often structured more like a pyramid than the familiar “layer cake” and how full-stack vertical slices present significant challenges since they easily become too complex, interdependent, and unwieldy for fixed-length sprints. Traditional user stories also don’t accommodate the highly exploratory nature of advanced analytics and data science projects where end users lack awareness of value opportunities, while technical experts can’t easily predict what is actually possible.

Based on one of Agile’s first principles to obtain feedback on feasibility and end user value as quickly and systematically as possible, this session presents lessons learned across multiple teams from applying alternative approaches to Product Backlog Items for data products that support small, independent stories while still maintaining a value focus. We’ll discuss ways to decouple the technical stack through stubbing and gradual tightening of the Definition of Done, as well as Lean Startup concepts and hypothesis-driven development (HDD) approaches that allow for explicit experimentation and risk trade-offs towards relevant milestones such as model quality or performance in the context of extreme uncertainty.


Join us as we discuss some of the friction we can encounter trying to use Agile on data teams, as well as some validated ideas for meaningful solutions.