Product Analytics
Product Analytics
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The Wikimedia Foundation's Product Analytics team has nurtured data-informed decision-making in the Product department since February 2018.
Our Mission & Values
[edit]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
[edit]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
Who is on the team
[edit]Listed alphabetically by first name within each section
Product Analytics is part of the Research and Decision Science group, led by Kate Zimmerman, Senior Director of Decision Science.
Team Leadership
[edit]- Mikhail Popov, Data Science Manager
Team Members
[edit]- Connie Chen, Sr. Data Scientist
- Irene Florez, Data Scientist III
- Jennifer Wang, Staff Data Scientist
- Krishna Chaitanya Velaga, Data Scientist III
- Megan Neisler, Staff Data Scientist
- Morten Warncke-Wang, Staff Data Scientist
- Shay Nowick, Sr. Data Scientist
Product Team Support
[edit]Analyst | FY24–25 | FY23-24 Embedded in… |
---|---|---|
Connie | De-embedded | Structured Content |
Irene | De-embedded | Campaigns-Product
Trust and Safety Product (Incident Reporting System, limited capacity) Wikipedia ChatGPT-plugin and other Future Audiences experiments |
Jennifer | Partially embedded in Web
Supporting Temporary Accounts (formerly IP Masking) |
Trust and Safety Product (IP Masking) |
Krishna Chaitanya | Partially embedded in Language and Product Localization (LPL) during Q1 while wrapping up support for Automoderator project.
Fully embedded in LPL for the remainder of the fiscal year. |
Language
Community-Tech (limited capacity) |
Megan | Partially embedded in Editing | Editing |
Morten | De-embedded
Supporting Metrics Platform |
Growth |
Shay | Fully embedded in Wikimedia (Mobile) Apps | Wikimedia Apps |
How to get help with data or analysis
[edit]Teams that we currently support
[edit]Teams who we have committed to supporting this fiscal year (see section above) can file tasks in Phabricator and tag product-analytics. Please do not specify the priority yourself, the team has its own prioritization framework. Product Managers should let the analyst assigned to their team know about the task and whether it is urgent. The analyst will work with the Product Manager and the Product Analytics team manager to prioritize the new task relative to other tasks they are or will be working on.
When making a request for data or analysis/report please provide the following information:
- What team/program is this request for?
- This is not always clear just from your Phabricator username (especially if you're making the request on someone else's behalf) and not everyone adds their team's/program's tag to the task.
- What are you requesting?
- Describe your need, don't prescribe our approach. If you are aware of technical details which may be helpful to the analyst, feel free to share them, but please leave the process of figuring out the approach to the analyst.
- What is the problem you're trying to solve?
- This helps us understand the context / remind us of the context – “the why” of a request. This also helps us identify if the request as stated needs refinement to truly solve the problem.
- What decision will you make or action will you take with the deliverable?
- This improves our understanding of “the why” of a request and helps inform how we approach the task from start to finish. This also guards us against spending time creating something that goes unused or pulling numbers for numbers' sake.
- Additional details
- See section below for suggestions.
Additional request details
[edit]The following are some suggestions of additional details to include
- If there is a different point of contact for the request, who is it?
- If there will be a deliverable (e.g. a dataset, a report), what format would you like it in?
- Please also include any examples, links to documentation, or other information that would be help us understand your request.
- Is there a date after which the analysis will no longer be useful?
- 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.
Others
[edit]Other staff may refer to the Research and Decision Science request process on Office wiki for ad-hoc requests. We currently do not accept requests from people outside the Wikimedia Foundation.
Advice and consultations
[edit]Some questions may be suited to consultation hours hosted by everyone on the team. Analysts host weekly.
Project proposals and collaboration invitations
[edit]If you would like to talk with us about a large project (e.g. a potential annual plan project) or receiving long-term/dedicated support for your team or project, please contact the team manager.
How to contact us
[edit]Group mailing list: product-analyticswikimedia.org
Team references
[edit]- Team mission and values
- Team norms
- Data Products (various deliverables such as reports, analyses, and datasets)
- Working with Product Analytics
- Chore Wheel
- Onboarding notes for new team members
- Research and Decision Science documentation and materials for new data practitioners
- Includes guidelines, best practices, documentation on tools we use
- Offboarding
- Contingency Carousel
- Fun
All sub-pages of Product Analytics
[edit]- Chore Wheel
- Comparison datasets
- Consultation Hours
- Contingency Carousel
- Dashboarding Guidelines
- Data Products
- Data Products/fawiki metrics summary
- Data Products/ptwiki intervention impact report
- Data Products/ptwiki metrics summary Jun2022
- Event Platform recommendations
- Event logging
- Fun
- Mission and Values
- Movement metrics
- Offboarding
- Offsites
- Offsites/2018-11-Onsite
- Onboarding
- Reporting Guidelines
- Style guide
- Superset Access
- Team norms
- Wiki comparison suggestions
- Working with Product Analytics