| Category | Value Stream Management |
|---|---|
| Stage | Manage |
| Group | Analytics |
| Maturity | Minimal |
| Content Last Reviewed | 2020-07-06 |
Thanks for visiting the direction page for Value Stream Management in GitLab. This page is being actively maintained by the Product Manager for the Analytics group. If you'd like to contribute or provide feedback on our category direction, you can:
Over the years, software has become more powerful and specialized; however, the teams who create, build, deliver, and analyze the performance of products are more disconnected, despite the increasing time spent on updates and status reports. In order to scale and speed up the delivery of quality products, silos have to be broken but the business, product managers and engineers still use different tools glued together by imperfect integrations. We strongly believe that having a single application for the entire software development lifecycle is already a huge step forward, but we are only just starting to explore ways to surface valuable insights and recommendations, which will help organizations increase transparency and productivity across teams and connect the business with engineering.
Value Stream Mapping in Software Development focuses on understanding and measuring the value added by the flow of activities in the software development lifecycle. In the time of technological disruption we are in, success will be largely dependent on the ability of enterprises to define, connect and manage software and business value streams. Often, this will coincide with a culture shift requiring many enterprises to adapt the way they work. At GitLab, we are making a small step towards connecting the business with engineering by using issues and MRs with labels associated with OKRs, for example.
Our first attempt at helping organizations get a better understanding of their flow was the introduction of Value Stream Analytics for all customers that follow the GitLab Flow. Value Stream Analytics got a lot of attention and we quickly understood that while we are striving to define best practices, different organizations have different value streams and workflows and we need to support the ability to define and measure these customized workflows in GitLab in order to move the industry forward and serve our customers better. As of June 2019, Value Stream Analytics is officially part of our VSM solution.
We are building GitLab with the goal of having teams manage their entire development lifecycle and we believe that an integrated solution will enable teams to be faster and more efficient. However, you cannot measure what you cannot see, so we want to make it easy for Engineering Leadership to quickly get a unified view of the end-to-end flow of how software value is created by their teams. We believe we can capture the vast majority of the processes metadata within GitLab since we already have tools for ideation, planning, design, development, testing, code review, security testing and release.
It's important that data and performance is evaluated with a focus on outcomes. Whether we succeed in enabling customers to visualize the bottlenecks in their process can be objectively measured by faster time to production. According to the 2018 DORA report, Elite DevOps performers take less than an hour to get from code committed to production, while low performers - anywhere between one and six months. We will show you trends of how well you are doing over time plotted against industry benchmarks or your own organizational targets. Being fast, however should not come at the cost of wasteful coding practices creating a lot of quality and instability issues. High and elite performers in DevOps show high correlation between faster time to production and rare, quickly identified and resolved issues. We will provide KPIs coming from our monitoring stage such as MTTD, MTTR to provide the full picture of the health of your value stream.
Through our product, we have worked with a vast host of engineering teams, which has helped us identify common patterns of success and failure in DevOps. We are planning to encode this knowledge and given the data we enable users to store in their instance, we would like to automatically highlight process bottlenecks and generate recommendations on how to minimize their impact. We would also like to enable customers to set targets for the different stages of their process, which we can follow up on with reminders and suggestions.
Recommendations will be ranked in order of importance by the value they bring in reducing time to production and increasing quality. Through your feedback on the quality of those recommendations and industry developments, we will continue to improve our algorithm to provide more tailored suggestions.
Any process improvement effort must begin with a clear understanding of the current process. As of GitLab 12.9, we have added the ability for teams to map their process with customizable stage definitions. We are enhancing this capability to support multi-team repositories in 13.2 and 13.3 with new value stream filtering options and support for multiple group-level value stream maps.
By providing customers with the ability to define their processes at different levels of granularity and to calculate the time it takes to complete each stage of the process, users should be able to quickly spot where their relative bottlenecks are. However, in order to get to the bottom of a problem, we need to allow users to drill-down. This is why we will build a library of configurable chart widgets, which users could quickly add to their dashboard to gain quick insights of possible causes of slow-moving stages in their process. Read more about how configurable Dashboards, Reports and our Metrics Library complement our Value Stream Mapping capability at our DevOps Reports category direction page.
Today Value Stream Analytics feature allows users to see key performance characteristics of their proccess such as median time spent in each stage of the process. We also list the specific issues or merge requests from which calculations are made allowing users to explore sources of delay. Our next step is to shift from offering a retrospective analysis of the value stream, to showing a visualization of the work flowing through the value stream so that sources of waste can be spotted and addressed in real time. Recommendations draw the user's attention to specific corrective actions which, if taken, would reduce process lead time and cycle times. Teams may add process targets or budgets so as to further focus improvement activities and track progress.
We believe that the insights generated by value stream analysis will have their greatest impact in experiences which move beyond the retrospective analysis offered by many value stream solutions, to bring those insights into the everyday workflows. The fundamental insight of the lean movement is that our instincts about how to optimize a process are generally wrong. We tend to focus efforts at process improvement on value added activities, rather than looking for the waste to eliminate in the non-value added steps which constitute the largest portion of our processes. Furthermore, we tend to optimize locally given the limits of our own line-of-sight, rather than optimizing for flow across the many different areas of our organizations involved in delivering value to customers through the value stream. By embedding value stream insights into experiences such as the issues and merge request pages, release planning experiences and personal work queues, we expect to amplify the impact of value stream thinking for the teams and organizations that rely on GitLab.
During the minimal and viable states of the category, we aim to cater to the needs of Development Team Leads (primarily) and Product Managers (secondarily) in their efforts to improve velocity and predictability of their value streams. As we build out the category, Department Leads and Senior Executives will have one place where they can oversee the progress of their teams and identify best practices and negative patterns that inspire improvements across their organizations.
This category is currently Minimal. The next step in our maturity plan is achieving a Viable state of maturity.
For VSM to be Viable, we must have a way of measuring time across the full value stream - it should be a sum of each component of value stream analytics. It should give users something valuable out of the box without running into data errors.
TaskTop is exclusively focused on Value Stream Management and allows users to connect more than 50 tools together, including Atlassian's JIRA, GitLab, GitHub, JamaSoftware, CollabNet VersionOne, Xebia Labs, and TargetProcess to name a few. Tasktop serves as an integration layer on top of all the software development tools that a team uses and allows for mapping of processes and people in order to achieve a common data model across the toolchain. End users can visualize the flows between the different tools and the data can be exported to a database for visualization through BI tools.
While we understand that not all users of GitLab utilize all of our stages, we believe that there is already a lot of information across planning, source code management and continuous integration and deployment, which can be used to deliver valuable insights.
We are starting to build dashboards, which can help end users visualize a custom-defined value stream flow at a high level and drill down and filter to a specific line of code or MR.
CollabNet VersionOne provides users with the ability to input a lot of information, which is a double edged sword as it can lead to duplication of effort and stale information when feeds are not automated. It does however, allow a company to visualize project streams from a top level with all their dependencies. End users can also create customizable reports and dashboards that can be shared with senior management.
Plutora Analytics seem to target mainly the release managers with their Time to Value Dashboard. The company also integrates with JIRA, Jenkins, GitLab, CollabNet VersionOne, etc but there is still a lot of configuration that seems to be left to the user.
Targetprocess tries to provide a full overview of the delivery process and integrates with Jenkins, GitHub and JIRA. The company also provides customizable dashboards that can give an overview over the process from ideation to delivery.
Although GitPrime doesn't try to provide a value stream management solution, it focuses on productivity metrics and cycle time by looking at the productivity of a team. It exclusively uses only git data.
Naturally, Azure is working on adding analytics that can help engineering teams become more effective but it's still in very early stages. It has also recently acquired PullPanda.
Similarly to GitPrime, Code Climate focuses on the team and uses git data only.
Similarly to GitPrime, Gitalytics focuses on the team and uses git data only.
XebiaLabs' analytics are predominantly focused on the Release Manager and give useful overviews of deployments, issue throughput and stages. The company integrates with JIRA, Jenkins, etc and end users can see in which stage of the release process they are.
CloudBees DevOptics is focused on giving visibility and insights to measure, manage and optimize the software delivery value stream. It allows comparisons across teams and integrates with Jenkins and Jira and SVM /VCS solutions.
CA Agile Central combines data across the planning process in a single integrated page with custom applications available to CA Agile Central users. The applications can be installed in custom pages within CA Agile Central or on a dashboard.
Forrester's New Wave: Value Stream Management Tools, Q3 2018 uncovered an emerging market with no leaders. However, vendors from different niches of the development pipeline are converging to value stream management in response to customers seeking greater transparency into their processes.
Forrester’s vision for VSM includes:
Additional functionality, requested by clients includes:
