The following page may contain information related to upcoming products, features and functionality. It is important to note that the information presented is for informational purposes only, so please do not rely on the information for purchasing or planning purposes. Just like with all projects, the items mentioned on the page are subject to change or delay, and the development, release, and timing of any products, features or functionality remain at the sole discretion of GitLab Inc.
As part of our overall 3 year Strategy GitLab is striving to build a Customer Centric DevOps platform through Strong Data Insights. In order to empower our customers to achieve their goals, and ultimately enable GitLab to ship a world class DevOps product, we must provide the necessary data and reporting so all teams within the business can identify opportunities, mitigate risks, and make the right decisions. By providing robust and accurate reporting we can reduce the cycle time from when we release a change, to when we know its impact to overall product usage, and customer experience, helping all of GitLab reach our goals through operational efficiencies, R&D allocation/investment, development priority, etc.
To accomplish this, we aim to leverage our deep expertise, tied with investments in a strong technical foundation to enable all teams within GitLab to produce, analyze, and report on product data by FY23-Q4. Like many top software/SaaS companies we are moving to be a product-led and data-driven organization. Through a partnership with our Data Team and collaboration with Product and Engineering we are cultivating a strong data focused culture within GitLab, driving to support our 30 year BHAG company goal of "becoming the most popular collaboration tool for knowledge workers in any industry".
In order to build the best DevOps product we can, and provide the most value for our customers, we need to collect and analyze usage data across the entire platform so we can investigate trends, patterns, and opportunities. Insights generated from our Product Intelligence program enable GitLab to identify the best place to invest people and resources, what categories to push to maturity faster, where our UI experience can be improved and how product changes effect the business.
We understand that usage tracking is a sensitive subject, and we respect and acknowledge our customers' concerns around what we track, how we track it, and what we use it for. To protect and empower our users the Product Intelligence team follows these guiding principles when developing solutions and picking technologies:
Transparency
We will always be transparent about what we track and how we track it. In line with our company's value of Transparency, our tracking source code and documentation will always be public.
User Access and Control
We will provide a simple and easily accessible Privacy Control Center, where users can view what they have opted-in to, what is being tracked, and update privacy settings.
GitLab's single application approach to DevOps creates a product that is both wide and deep, encompassing a large collection of features used by many teams within an organization, which are composed of different types of users.
That depth/breadth makes it exceedingly complex to properly map out and understand how our diverse customer set is using the product and gaining value.
We currently are unable to provide GitLab the required data to identify opportunities and make the right decisions against them.
GitLab's MVC approach to product development introduces frequent changes to the product stages and what data is available, making historical trend analysis difficult
For more information on Product Intelligence, you can checkout our Product Intelligence Guide which details a high-level overview of how we make data usable, the Collection Frameworks we leverage, our Metrics Dictionary, and much more!
Product Intelligence provides the necessary frameworks, tooling, and expertise to help us build a better GitLab. Naturally we sit in the middle of many projects, initiatives and OKRs at GitLab. In order to provide clarity and realistic expectations to our stakeholders and customers we practice ruthless prioritization(per Product Principle #6), identifying what is above the line, what is below, and what is unfunded and not possible for us to action on in a given timeline.
Product Intelligence top priority is to build out the infrastructure and analytics frameworks for our Product and Engineering Teams to instrument analytics throughout the GitLab platform, provide guidance on best practices and enforce standardization.
To learn how we set these at the Product level checkout Product Performance Indicators.
Teams Involved: Sales, Customer Success, Product, Product Intelligence, Data
We are working with Sales and Customer Success to document the metrics they need so we can map out a path to delivering them over time.
Why This Is Important:
As GitLab has grown, and our analytics tracking has expanded we are finding areas where the integrity and accuracy of our data needs to be reviewed. Product Intelligence is working with internal stakeholders and teams to resolve 14 Data Integrity issues in Q1FY2021.
Teams Involved: Product, Product Intelligence, Data, Infrastructure, Sales, Customer Success
Product Intelligence Responsibilities:
Recent Progress:
What's Next:
Why This Is Important:
Teams Involved: Product Intelligence, Data, Product Managers
Product Intelligence Responsibilities:
Recent Progress:
What's Next:
Why This Is Important:
Teams Involved: Product Intelligence, Data, Infrastructure, Product
Product Intelligence Responsibilities:
Recent Progress:
What's Next:
Why This Is Important:
The Data Availability cycle times are currently a 51 day cycle and our exports of the Versions DB are currently done manually every 14 days according to this schedule In order for all this instrumentation to be as valuable as possible we need to reduce the amount of time it takes to generate and be accessible by the business.
Teams Involved: Product Intelligence, Data, Infrastructure, Product
Product Intelligence Responsibilities:
Recent Progress:
What's Next:
Teams Involved: Product Intelligence, Data
Why This Is Important:
Currently Usage Ping is not segmented by pricing tier, which means for any SaaS free / paid account segmentation cannot be done using Usage Ping. Instead, as a work around, the data team is using the Postgres database import and manually recreating all Usage Ping queries in Sisense. A single usage ping on SaaS takes 32+ hours to generate, for group level metrics, we need to run this 1 million times which is only feasible if it's done in the data warehouse.
Product Intelligence Responsibilities:
Recent Progress:
What's Next:
Why This Is Important:
Product Intelligence Responsibilities:
Teams Involved: Product Managers, Product Intelligence, Data
Recent Progress:
Process defined for implementing product performance indicators
In order to support 100% of DevOps groups have Predicted GMAU (or Paid GMAU) we are working with the PMs of each group that still needs to finalize their GMAU metrics so the Data Team can apply the Predicted GMAU formula to their dashboards.
What's Next:
-
Why This Is Important:
Product Intelligence Responsibilities:
Teams Involved: Sales, Customer Success, Product Intelligence, Data
Recent Progress:
What's Next:
Why This Is Important:
Teams Involved: Sales, Customer Success, Product Intelligence, Data
Recent Progress:
What's Next:
As Product Intelligence sits in the center of many of GitLab's initiatives and OKRs, there are items we want to work on, but are unable to prioritize with current resources and team. These items are still important and will provide value, but are below the priority line for us at this time:
| Resource | Description |
|---|---|
| Product Intelligence Guide | A guide to Product Intelligence |
| Usage Ping Guide | An implementation guide for Usage Ping |
| Snowplow Guide | An implementation guide for Snowplow |
| Metrics Dictionary | A SSoT for all collected metrics from Usage Ping |
| Privacy Policy | Our privacy policy outlining what data we collect and how we handle it |
| Implementing Product Performance Indicators | The workflow for putting product performance indicators in place |
| Product Intelligence Direction | The roadmap for Product Intelligence at GitLab |
| Product Intelligence Development Process | The development process for the Product Intelligence groups |
