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
2. Advocating for Analytic Infrastructure • What it is • Why it matters • Ideas for identifying leverage points
3. Typology of Organizational Culture (Westrum, 2004)
4. What is Analytic Infrastructure? All the HOW, WHERE, and with WHAT that goes into your daily data science work.
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
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!
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?
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
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.
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
11. Challenges for the Analytic Admin Organizational • Legitimacy • Leverage • Relationships Technical • Experience • Education • Exposure
12. Building a Business Case