Our Mission & Values

We deliver quantitatively-based user insights to inform decision-making in support of Wikimedia’s strategic direction toward service and equity.

We strive to provide guidance, insights, and data that are:
Ethical • Trusted • Impactful • Accessible • Inclusive • Inspired

What We Do

Product Analytics contributes to the Wikimedia Movement through our work with Product teams and departments across the Foundation.

Our responsibilities include:

• Empowering others to make data-informed decisions through education and self-service analytics tools
• Helping set and track goals that are achievable and measurable
• Ensuring that Wikimedia products collect useful, high quality data without harming user privacy
• Extracting insights through ad-hoc analyses and machine learning projects
• Building dashboards and reports for tracking success and health metrics
• Designing and analyzing experiments (A/B tests)
• Developing tools and software for working with data, in collaboration with Data Engineering and Product teams.
• Addressing data-related issues in collaboration with teams like Data Engineering, Security, and Legal

Team Leadership

• Mikhail Popov, Data Science Manager

Team Members

• Connie Chen, Sr. Data Scientist
• Irene Florez, Data Scientist III
• Jennifer Wang, Staff Data Scientist
• Maya Kampurath, Sr. Analyst
• Megan Neisler, Sr. Data Scientist
• Morten Warncke-Wang, Staff Data Scientist
• Neil Shah-Quinn, Sr. Data Scientist
• Shay Nowick, Sr. Data Scientist

Submitting Requests

If you'd like to request data, analysis, or advice, create a task in Phabricator or send an email to product-analytics@wikimedia.org.

Requests are reviewed by Product Analytics and inform the direction and priorities of data projects. A team member will follow up about whether we’ll be able to work on your request.

Some questions may be suited to consultation hours; see Product Analytics Consultation Hours for more information and a link to book appointments.

Provide the following information to help us prioritize and respond to your request appropriately:

• Name for main point of contact and contact preference

We use Phabricator to track our work and provide progress updates. Please let us know if you would like us to follow up by other methods (e.g. email).

• What teams or departments is this for?

This helps us understand who will be using the analysis.

• What are your goals? How will you use this data or analysis?

This helps us understand the context and priority. What decisions do you need data to inform? Will you take different actions depending on the direction of the data? Do you want to share data publicly? Do you want to include data in a narrative or message (e.g. for PR, audience engagement, or fundraising)?

• What are the details of your request? Include relevant timelines or deadlines

Is there a date after which the analysis will no longer be useful? Please provide any timeline/relevant deadlines, requested formats, examples, links to documentation, or other information that would help us understand your request.

• Is this request urgent or time sensitive?

We try to reply to “Urgent” requests immediately and “Time sensitive” requests by the end of the workday. All other requests will be prioritized during our weekly triage.

Note: We use Phabricator to track our work, and by default tickets are publicly visible. If any part of your request is sensitive and should be kept confidential, let us know.

Consultation Hours

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

Data FAQs

See meta:Research:FAQ

Data references and reports

• Comparison datasets
• Data Dictionary (documents data sources, such as those available in Superset and Turnilo)
• Data Glossary (definitions for core metrics)
• Data Products (various deliverables such as reports, analyses, and datasets)
• Movement metrics
• ETL jobs

Guidelines and best practices

• Data access guidelines
• Query style guide
• Reporting Guidelines
• Dashboarding Guidelines

Documentation for tools we use

• Phabricator (managing requests and tracking work)
• Superset (WMF internal dashboards and reports)
  • Obtaining access to Superset/Turnilo, with explanation of LDAP/Developer Account terminology
• Hive
• Turnilo (WMF internal tool for pivoting and exploring data)
• Event Platform (Various event stream distribution and processing systems we employ at WMF)
• Matomo/Piwik (JavaScript tracking client used for wikimediafoundation.org and other smaller-scale sites)
• Wikimedia Infrastructure
• Google Search Console access

Team references

• Product Analytics Mission and Values
• Product Analytics Team norms
• Working with Product Analytics
• Chore Wheel
• Phabricator board
• Onboarding notes for new team members
• Offboarding
• Contingency Carousel
• Fun