Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Data Science in the Balanced Team

Ian Huston
November 06, 2016

Data Science in the Balanced Team

First presented at PyCon Ireland 2016

The goal of a Balanced Team is to share ownership and responsibility for the success of a project between team members. Each team member has specific obligations to the team and a specific area of authority. Until recently, designers, product managers and developers were the usual team members considered. In this talk I explore how data scientists can function in a balanced team and discuss my experience working as a data scientist on balanced teams at Pivotal Labs with our global clients. I consider what obligations and authority a data scientist can provide as part of a balanced team and how this situation differs from the usual jack-of-all-trades type data science work. I outline specific examples where data science can help user centric design and product management, and where the practices of lean-startup and agile development can help accelerate analysis and data science. Based on my experience building data science driven products with a global bank and European car manufacturers, I describe what we tried, what worked and most importantly what didn’t.
If you are a data scientist or need to work with one, this talk will equip you to understand how data science can be an integral part of a balanced team.

Ian Huston

November 06, 2016
Tweet

More Decks by Ian Huston

Other Decks in Technology

Transcript

  1. Data Science in the
    Balanced Team
    Ian Huston

    View full-size slide

  2. Who am I?
    Data Scientist at Pivotal Labs
    @ianhuston
    http://www.ianhuston.net
    Started working with data as an academic...

    View full-size slide

  3. Balanced Team
    Shared Responsibility
    Act in service to the team
    Each role has obligations and an authority
    No more hero designer or PM as CEO
    More: Janice Fraser video
    http://tinyurl.com/jfraser
    http://www.slideshare.net/clevergirl/2015-balanced-teams-product-management-engineering

    View full-size slide

  4. Design in a Balanced Team
    “Empathizer-in-Chief”
    Obligations to the Team
    1. Understand the customer at an expert level
    2. Translate high-value needs into product
    3. Hone your craft
    4. Facilitate balance within the team
    One Important Authority
    Prioritize customer problems
    http://www.slideshare.net/clevergirl/2015-balanced-teams-product-management-engineering

    View full-size slide

  5. “[Y]ou need data thinking to be part of the culture and
    top of mind, not an after-thought.”
    https://medium.com/art-marketing/data-literacy-product-design-and-the-many-faced-god-cf8339e035a#.i8ry0xhqv

    View full-size slide

  6. Things that worked

    View full-size slide

  7. User Research + Data Exploration

    View full-size slide

  8. Enabling PM to understand DS

    View full-size slide

  9. Involving the whole team in data science
    process

    View full-size slide

  10. Data Science + Dev pairing

    View full-size slide

  11. Things that did
    not work

    View full-size slide

  12. Single Dev + DS backlog

    View full-size slide

  13. Arriving late to a project

    View full-size slide

  14. Being part of multiple teams

    View full-size slide

  15. Being (seen as) the magic bullet

    View full-size slide

  16. Data Science in the Balanced Team
    “The Voice of Data”
    Obligations
    1. Provide deep understanding about the available data and identify potential
    valuable uses/techniques.
    2. Guard against unjustified, unethical or inappropriate uses of data.
    3. Begin and continue collection of data to support future product goals.
    4. Facilitate balance in the team.
    One Important Authority
    Bring data to every product conversation

    View full-size slide

  17. Data Science in the
    Balanced Team Make data a central part of the
    product conversation.
    Provide data insights and
    possibilities in service to the team.
    Break down walls between data
    scientists and designers &
    developers.
    Data Science
    @ianhuston

    View full-size slide