Product Analytics

Our Mission & Values
We deliver quantitatively-based user insights to inform decision-making within the Foundation and the Wikimedia Movement. We strive to provide guidance, insights, and data that are:


 * ethically sourced & applied
 * reliable & valid
 * impact-oriented
 * accessible & digestible
 * inclusive & equitable
 * inspired & innovative

Product Analytics primarily supports teams within Product department, but we also support teams across the Foundation as well as community members in the Wikimedia Movement.

Our Work

 * Empowering others to make data-informed decisions through education and self-service analytics tools
 * Extracting insights from the Foundation's data repositories
 * Crafting key performance indicators (KPIs) and other metrics
 * Building dashboards for tracking success and health metrics
 * Design and analysis of experiments (A/B tests)
 * Ad-hoc analyses and machine learning projects
 * Developing tools and software for working with data

Structure
Each analyst is embedded into one team, which means they participate in the team's day-to-day activities and discussions so that they can provide proactive analysis support. Analysts are also assigned to additional teams and functional areas, which means they're the primary point of contact for requests from those teams, but generally don't join the team's day-to-day activities.

Depending on the team's capacity, it may also accept requests from others in the Wikimedia Foundation.

The team also reserves "10 percent time" to work on professional development. The team's manager is Kate Zimmerman, Head of Product Analytics, whose role involves developing an overall strategy for product analytics, prioritizing requests, managing capacity, and professionally developing members of the team.

Task management
We track our work in the Product-Analytics project on Phabricator. For our definitions of the task priority levels, see the project description.

Who's on the team?
Listed alphabetically by first name within each section

Leadership

 * Kate Zimmerman, Head of Product Analytics
 * Leading Better Use of Data program
 * Ask me about: Collaborating with Product Analytics, using data to inform product and business decisions, experiment design, decision science, applied stats

Team Members

 * Chelsy Xie, Data Analyst
 * Ask me about: R, search logs, traffic logs, eventlogging, Hive/SQL/Spark, experiment, machine/deep learning
 * Maya Kampurath, Data Quality Analyst (Contractor)
 * Ask me about:
 * Megan Neisler, Analyst
 * Ask me about: R, data visualization, reader metrics, technical writing
 * Mikhail Popov, Data Analyst
 * Ask me about: R, data visualization, search logs, traffic logs, Hive/SQL, Bayesian statistics, machine/deep learning, Bayesian networks & influence diagrams, time series analysis, Google Search Console
 * Morten Warncke-Wang, Data Analyst
 * Ask me about: R, machine learning, spatial (geographic) models, article quality, editor/editing/newcomer metrics, prior research on Wikipedia, and perhaps also time-series modeling (forecasting)
 * Neil Patel Quinn, Product Analyst
 * Ask me about: Python for data analysis, SWAP, editor metrics, new editor research

Honorary Members

 * Jason Linehan, Software Engineer
 * Ask me about: programming languages other than R, analytics infrastructure, randomness
 * Max Binder, Team Effectiveness Coach
 * Ask me about:

Guidelines for data and analysis requests
Please submit a ticket through Phabricator; our Product Analytics board has details and a template for submitting requests.

See also meta:Research:FAQ

How to contact us

 * Contact information for team members are available on their user pages (linked above).
 * Group mailing list: product-analytics@undefinedwikimedia.org

Data references, best practices, and reports

 * Onboarding notes for new team members
 * Wikimedia Product/Data dictionary
 * A/B Testing
 * Data Products (various deliverables such as reports, analyses, and datasets)

Documentation for tools we use

 * Phabricator (managing requests and tracking work)
 * Superset (WMF internal dashboards and reports)
 * Turnilo (WMF internal tool for pivoting and exploring data)
 * Event Platform (Various event stream distribution and processing systems we employ at WMF)