Making Agile Work for Data Teams: Writing Effective PBIs for Data Products

schedule Oct 15th 11:00 AM - 11:45 AM place Tiered Classroom people 15 Interested

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 we think Agile for data teams is a distinct challenge?

Data work is often structured more like a pyramid than the familiar “layer cake” metaphor due to the state of data infrastructure technology, common industry practices, and the heavy lift to integrate data before it can be analyzed and visualized. Prevailing Agile wisdom of cutting work into “vertical slices” thus presents significant challenges for Agilists working on data teams! Typical full-stack vertical stories in this environment can easily become too complex, interdependent, and unwieldy to fit into fixed-length sprints. Technical stories can encapsulate smaller work increments but risk becoming too abstracted from the customer’s core problems and trap the team in infrastructure work for too long. An additional impediment to traditional user stories is the highly exploratory nature of advanced analytics and data science projects where in many cases end users lack awareness of what kind of problems can even be solved and technical experts can’t initially predict which solutions will actually be possible.


This session presents successes and lessons learned from applying alternative story decomposition and writing techniques on several data products across multiple teams. Returning to one of the fundamentals of what makes Agile valuable, namely to obtain feedback on feasibility and end user value as quickly and systematically as possible, our approaches strive to ensure teams have small, independent stories while still maintaining a value focus. We discuss ways to decouple the technical stack through stubbing and gradual tightening of the Definition of Done. This technique accommodates the necessary foundational work in the background while also obtaining early feedback about the value of the eventual product delivery options. A second approach incorporates Lean Startup concepts and centers on replacing traditional user stories with testable hypothesis statements that allow for explicit experimentation and risk trade-offs towards relevant milestones such as model quality, performance, predictive reliability, etc. in the context of extreme uncertainty.


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

 
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Outline/structure of the Session

  1. Differences between software and data teams
  2. Types of data projects
  3. Why do traditional stories often not work?
  4. Going back to first principles - it’s all about feedback
  5. Alternative 1: Decoupling the stack
  6. Alternative 2: Solving problems before implementing solutions – Hypothesis-Driven Development and Lean Startup concepts
  7. Key takeaways

Learning Outcome

  • Understand the inherent differences between standard development projects and data development projects
  • Improve awareness of potential pitfalls when helping data teams break down work into manageable and meaningful increments
  • Become familiar with two alternative techniques for writing PBIs for data teams and when to use them

Target Audience

Anyone working with a data team in an Agile environment. Anyone curious about what makes data teams unique in an Agile space.

Prerequisite

  • Familiarity with Agile fundamentals
  • Experience with story writing and decomposition is a plus
schedule Submitted 5 months ago

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  • Phillip Manketo
    By Phillip Manketo  ~  4 months ago
    reply Reply

    Claire / Mathias... The committee reviewed this session and believe the abstract would benefit from additional details before a final decision is made, specifically:

    • What are the alternative techniques specifically for writing PBIs that you plan to speak to in your presentation (please provide as much detail or examples as possible)?
    • What are the potential pitfalls when helping data teams break down work into manageable and meaningful increments and how did your teams overcome them or what was learned in the process of doing so?

    Alternately, please attach a deck to the proposal detailing the above. Thanks in advance.

    • Clare Stankwitz
      By Clare Stankwitz  ~  4 months ago
      reply Reply

      Hi Phillip,

       

      Thank you for your comment. We have incorporated some additional details into an updated abstract. Please let us know if you think we need anything further.

       

      Best,

      Clare & Mathias


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