RStudio Champions

Advocate for data science at your organization https://www.rstudio.com/champion/

- Build a business case
- Build a working relationship with IT

Transcript

1. RStudio Analytic
   Infrastructure Champions
   

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2. Advocating for Analytic Infrastructure
   ● What it is
   ● Why it matters
   ● Ideas for identifying leverage points
   

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3. Typology of Organizational Culture (Westrum, 2004)
   

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4. What is Analytic Infrastructure?
   All the HOW, WHERE, and with WHAT that goes into your daily data
   science work.
   

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5. Production is...
   CUSTOMER/USER FACING
   - Ready to use
   - Software that end users are using
   - An app that is live and available to the
   end user
   - Apps on our production server are
   available to our clients
   - Client facing
   Credibility
   AT SCALE
   - Scaled to a larger audience
   - Bulletproof, scalable, fails predictably
   - Live to 1000 of users with production
   vehicle data
   SERVICE LEVEL AGREEMENTS
   - Required for mission-critical operations;
   downtime affects the ability to serve customers
   - Deployed for end users to have continual
   access without performance issues
   ENVIRONMENTAL REQUIREMENTS
   - An area where validated applications are deployed in a locked down
   environment
   - The main part of a company that handles all process
   - Application or system operates effectively without much maintaining effects
   - A server or environment that runs the “final” applications that your ultimate
   end-users (often external customers) use to get stuff down
   DOCUMENTATION - TESTING &
   MONITORING
   - Creating apps that can reach a wider
   audience and are deployed/tested in a
   consistent manner
   - Running in a way that is stable to use,
   documented and monitored
   

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6. There are no easy answers
   There is no “typical workflow diagram”
   The standard architecture might not make sense for you
   There is no perfect deployment pipeline - everything is and
   should be evolving
   Don’t get caught up in
   the hype!
   

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7. Why does it matter?
   Lessons from the world of DevOps
   Tactical (dismissible) metric: code deployment lead time
   ● How long does it take you to get from raw materials (data) to some kind of finished
   product?
   ● How many teams do you have to traverse to make a real impact with the product of
   your work?
   

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8. Why does it matter?
   Lessons from the world of DevOps
   1. Your analytic infrastructure is what enables teams to deliver value through
   decreasing code deployment lead time
   2. It also dominates how daily work is performed
   

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9. Phoenix is the most important project in the company.
   Theyʼve spent $20M over three years. And yet, here she
   is, trying to help, and they wonʼt spend $5k on more disk
   space. And now she wonʼt get a Dev environment for five
   months!
   She buries her head in her hands and
   silently screams down at her keyboard.
   ...None of the meetings on her calendar seem interesting
   anymore. Itʼs just people complaining about waiting.
   Waiting for something. Waiting for someone. Everyone is
   just waiting. And she wants no part of it right now.
   

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10. “R Admin” - Analytic Administrator Role
    A data scientist who:
    Onboards new tools, deploys solutions, supports existing standards
    Works closely with IT to maintain, upgrade and scale analytic environments
    Influences others in the organization to be more effective
    Passionate about making R a legitimate analytic standard within the organization
    Nathan Stephens on the RViews Blog -
    Analytics Administration for R
    

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11. Challenges for the Analytic Admin
    Organizational
    ● Legitimacy
    ● Leverage
    ● Relationships
    Technical
    ● Experience
    ● Education
    ● Exposure
    

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12. Building a Business Case
    

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