Product Analytics

About Us
Nurturing data-informed decision-making in Product since 2018-02-01.

Our Mission & Values
We deliver quantitatively-based user insights to inform decision-making within the Foundation and the Wikimedia Movement in order to support Wikimedia’s strategic direction toward service and equity.

We strive to provide guidance, insights, and data that are:

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

Product Team Support
Each analyst is a point person for a team, project, or program. Our goals are to maintain context and domain knowledge while also allowing for flexibility in analyst work assignments.

Teams that do not currently have an assigned point person are encouraged to submit requests through Phabricator. Depending on the team's capacity and organizational needs, we may also accept requests from others in the Wikimedia Foundation. The team reserves "10 percent time" to work on professional development.

The team's manager is Kate Zimmerman, Head of Product Analytics, who is responsible for developing an overall strategy for product analytics, prioritizing requests, managing capacity, and professionally developing members of the team.

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

 * Connie Chen, Data Analyst
 * Ask me about:
 * Jennifer Wang, Data Analyst
 * Ask me about: AHT/Comm tech metrics
 * 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 Shah-Quinn, Product Analyst
 * Ask me about: Python for data analysis, SWAP, editor metrics, new editor research
 * Shay Nowick, Data Analyst
 * Ask me about: Mobile metrics, Pydata and Jupyter Notebooks, cohort analysis

Honorary Members

 * Irene Florez, Data Analyst (Contractor)
 * Ask me about
 * Jason Linehan, Software Engineer
 * Ask me about: programming languages other than R, analytics infrastructure, randomness
 * Lani Goto, Technical Program Manager
 * Ask me about: team process, meetings, coordinating cross-team projects

Submitting Requests
Please submit a ticket through Phabricator; our Product Analytics board has details and a template for submitting requests.

Office Hours
Analysts host weekly office hours (details). Click here to view the calendar or schedule an appointment.

Data FAQs
See also meta:Research:FAQ#Where_do_I_find_statistics_about_a_specific_product_audience?

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

 * Comparison datasets
 * Data Dictionary
 * A/B Testing
 * Data Products (various deliverables such as reports, analyses, and datasets)
 * Analytics Infrastructure
 * Experiment Platform draft
 * Query style guide
 * Reporting Guidelines

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)
 * Piwik/Matomo (JavaScript tracking client used for wikimediafoundation.org and other smaller-scale sites)

Team references

 * Product Analytics Team norms
 * Working with Product Analytics
 * Chore Wheel
 * Onboarding notes for new team members
 * Offboarding

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