The Product Data Insights (formerly known as "Product Analysis") group consists of a team of product analysts. This group reports to the Senior Director, Product Monetization and serves as a functional analytics team to support the GitLab Product division and product data-related analysis across GitLab.
In addition to supporting the Product division, the Product Data Insights team is an active contributor to the GitLab Data Program. As part of the Research & Development (R&D) Data Fusion Team, the product analysts also work closely with members from the central Data team. In addition, the Product Data Insights team is part of the Functional Analytics Center of Excellence (FACE), along with other functional analytics groups across the GitLab Data Program.
Read more about what we do at GitLab on our Direction page.
Product Data Insights is a small (but mighty) team. In order to support the Product division, each analyst is assigned to one or more sections or teams to support.
The Product Data Insights group works in two-week iterations, which dictate how and when we plan and prioritize work. Iterations start on Thursdays and end on Wednesdays.
You can see our current iteration here.
For all Product Data Insights requests, please create an issue in the
Product Data Insights project,
apply the Team::PDI and product data insights labels, and follow the guidelines below.
All data issues with the Team::PDI label will appear on the Product Data Insights board.
Please select the appropriate template based on your type of request and answer as many of the questions as you can. The more information and context we have up front, the faster we are able to triage and begin work on the issue.
| Request Type | Template |
|---|---|
| Ad hoc / Default request | Ad Hoc Request |
| Create new or update existing PI chart | PI Chart Help |
| Experimentation analysis | Experiment Analysis Request |
| Iteration planning | Iteration Planning |
In order to be considered for the upcoming iteration, please open all issues by EOD Monday before the next iteration begins. We understand that urgent matters come up, but please try to adhere to the submission due date for any planned work.
The PDI team takes three different priorities into account when scheduling work, each captured by a scoped label:
section-priority::pm-priority::pdi-priority::Section priority is ultimately determined by section leaders and product leadership. It should align with overall product investments and business impact of the work. Analysts have default guidelines to assign section priority, which then becomes part of the prioritization discussion with section leadership.
Section priority labels will be added by product analysts or section leadership.
| Label | Definition |
|---|---|
section-priority::1 |
Work supporting high-impact, high-investment stages or groups; cross-stage analysis |
section-priority::2 |
General work supporting the section; help with PI charts; etc |
section-priority::3 |
Low-impact work; "nice-to-haves" |
Most issues will fall under section-priority::1 and section-priority::2.
PM (Product Manager) priority captures the relative priority of the issue compared to any other issues that the PM (or their group) have open in the backlog (if applicable). In general, the issues that are more immediately actionable and impactful to the company KPIs should be higher in priority.
We ask that PMs apply a pm-priority:: label to issues to indicate relative priority of the
request.
| Label | Priority |
|---|---|
pm-priority::1 |
High and/or urgent |
pm-priority::2 |
Medium |
pm-priority::3 |
Low, non-urgent |
PDI priority is owned by the individual analyst. The Senior Manager, Product Data Insights will help refine priority based on importance and capacity. The team will work with the Senior Director, Product Monetization and/or Product leadership on trade-offs (if needed).
Section priority and PM priority are both inputs in determining PDI priority. However, the scope of the team's work extends beyond section support (ex: cross-functional initiatives), and therefore there are other considerations, as well.
PDI priority labels will be added by the Product Data Insights team as a part of triage and planning.
| Label | Priority |
|---|---|
pdi-priority::1 |
High / Urgent Priority Any analysis requests that are required to be completed within the current iteration. All requests that have Priority 1 should have a direct KPI and/or OKR that will be affected by the analysis. |
pdi-priority::2 |
Medium Priority This is where most requests would fall into. This can be any net-new analysis, reporting (dashboard creation), or exploratory analysis that is needed for decision making. |
pdi-priority::1 |
Consultant: This is for any analysis that does not have a direct action as a result of the analysis and/or other low-level, non-urgent requests that can be placed in an analyst's backlog. |
Most issues will fall under pdi-priority::2 and pdi-priority::3.
Final commitment and prioritization will occur during the iteration planning meeting, which takes place the day before an iteration begins (every other Wednesday). The team will consider new and existing issues, along with issues in progress. When selecting issues for the next iteration, the team considers the following:
Analysts will use the different priority labels (as defined above) as inputs into planning.
Each issue is assigned a weight based on estimated time commitment.
If a single issue has a weight greater than the length of the iteration (2 weeks), it should be broken into smaller units of work. (This could also be an indicator that the issue should be converted to an epic).
| Weight Value | Estimated time to complete |
|---|---|
| 1 | < 1 hour |
| 2 | 2 hours |
| 3 | 4 hours |
| 5 | 1 day |
| 8 | 2-3 days |
| 13 | 1 week |
| 21 | 2+ weeks |
| 34 | 1+ month |
Product Data Insights defines velocity as the amount of work (measured in issue weight) completed by the team within a given iteration. While we recognize that this is an imperfect measurement (partially-completed issues and undocumented work are not accounted for), it is a rough gauge of team output.
We aim to only commit to work we believe can be completed within the 2-week iteration. As such, we will commit to less than our recent velocity and leave a buffer to account for urgent issues and interruptions. To start, each analyst will leave a buffer of ~2 days worth of work (an estimate based on the recent volume of unplanned work). High-priority issues exceeding the allotted buffer will have a material impact on our ability to complete planned work, so please plan ahead if you know that you will need assistance from the Product Data Insights team.
As GitLab team members, we are encouraged to take PTO and observe public holidays in order to maintain a healthy work-life balance. Analyst capacity should be adjusted based on the number of days they are working in the iteration.
Product Data Insights groups work into three different buckets. During planning, analysts will aim to maintain the following breakdown:
section-priority::1 labelsection-priority::2 or section-priority::3 labelsThe distribution of work will vary from iteration to iteration, but the 50/25/25 breakdown is the target state.
If an urgent matter comes up, please open an issue and tag your analyst contact (and/or @cbraza).
Please include why the issue is urgent, when it is needed by, what it will inform or how it will
be used, and who is the intended audience.
If you have not heard from the tagged analyst within 1 business day* (or earlier if the issue
requires a faster turn-around), please send a message in #data
and feel free to tag @cbraza.
*Please keep in mind that we work across different time zones
Please keep the following in mind when working with the Product Data Insights team:
Scope creep is a problem everyone faces. Please keep in mind that team capacity is a zero-sum game, so scope creep in one issue can mean that we are unable to complete other work planned for that iteration.
Additional scope (change in requirements, additional follow-ups, etc) that adds a material amount of work* to an issue will need to be captured as net-new work in a new issue. The new issue will then go through the normal prioritization and planning process. The best way to avoid scope creep is to have thorough, complete requirements in the issue when you initially open it. The issue templates should help guide you to include all relevant information.
*The threshold for a "material amount of work" is to be determined by the analyst working on the issue.
If an issue is blocked and it requires additional work* to diagnose or troubleshoot (ex: a data issue is uncovered), a new issue should be opened, assigned a weight and priority, and linked to the original blocked issue. The new issue can be added to the current iteration without going through the formal planning process at the analyst's discretion, but this can impact our ability to complete all issues in the iteration.
*The threshold for "additional work" is to be determined by the analyst working on the issue.
Experimentation analysis issues are naturally blocked by the experiment actually running (we have to wait until we have sufficient data in order to perform the analysis). In order to enable a more accurate measure of velocity, we will divide the work into 2 separate issues*:
*At this time, PMs should continue to open a single issue and analysts will separate accordingly.
Please open an issue for all Product Data Insights requests. Requests made via comments in Google Drive are extremely difficult to track, and Slack history is gone after 90 days. In addition, these requests are not planned or accounted for in team velocity. Your informal request might mean that we are unable to complete work we committed to for another stakeholder.
By keeping a formal record of requests (via issues), we are able to identify frequently asked questions (which could lead to building a self-service solution) and quickly replicate past work.
The Product Data Insights team is tasked with supporting the entire Product organization, in addition to other product-related data needs across GitLab. As such, team capacity can be limited as we grow towards our target gearing ratio. However, limited capacity should not stop GitLab team members from opening issues for Product Data Insights, it simply means that lower-priority requests will have to wait until resources are available. As the group grows, so will our ability to turn around requests in a shorter period of time.
In addition to opening an issue, there are several other methods to engage with the team, both sync and async.
In order to support more PMs across GitLab, the Product Data Insights team offers office hours. Office hours are held every other Wednesday, alternating between and . While our primary stakeholders are PMs across the organization, all GitLab team members are welcome to join.
The intent of office hours is to give PMs faster access to the team and get support for smaller tasks, brainstorming, and data self-service. More formal requests that answer more complex questions are captured in issues and go through a more rigorous, structured prioritization process.
The agenda is first-come, first-served. Walk-ins/drop-ins are always welcome, but if possible, please add your name and topic (or question) before office hours begins. This allows the team time to review new agenda items ahead of time. If we are unable to cover a topic on the agenda, it will be pushed to the following meeting.
Stakeholders are welcome to leverage office hours to discuss and define new issues, which can help reduce async back-and-forth communication in the issue itself.
Office hours are intended for smaller bodies of work, brainstorming, and assistance with data self-service. Here are some examples of topics for office hours:
I am interested in launching an experiment to see if we can increase adoption of Secure.
I am trying to do an analysis on the relationship between users with SSO enabled and invite acceptance rate.
I wrote a query to calculate xMAU for namespaces that converted from a trial to a paid plan.
JOIN correct?Note: We are not able to accommodate all code reviews in the scope of office hours. Please limit this type of topic to specific aspects of a query, whether you are using the correct data source, etc.
I am looking to make some updates or enhancements to this existing dashboard.
In the last key meeting, you presented an analysis on early trial adoption.
I am going to open an issue for a new analysis.
If the topic you bring is too broad to be addressed during office hours, we can discuss the work and will redirect you to open an issue.
What is the difference between topics for office hours and formal data requests?
Office Hours is intended to help PMs with smaller tasks, provide a venue for brainstorming, and help folks looking to learn more about data self-service. The benefit is that the agenda is first-come, first-served, the prioritization process is bypassed, and the wait time is minimal.
Formal data requests and larger bodies of work are captured in issues in the Product Data Insights project. They can help answer more complex questions, but go through more robust intake and planning processes. As such, there is a longer turn-around time given team size and capacity.
What if I don't know if my topic is best suited for office hours or whether I need to open an issue?
Feel free to ask your analyst partner (if applicable) or in #data. When in doubt, come to office hours and the team can discuss there.
@product-analysts - Notifies the entire Product Data Insights team@randdanalyticstriage - Notifies the entire Product Data Insights team and the Data
team's R&D Fusion group, per the Enterprise Data Triage Program@functional-data-analysts - Notifies the entire Product Data Insights team and other functional
analysts across the GitLab Data Program@gitlab-data/product-analysts - Notifies the entire Product Data Insights team