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authorGitLab Bot <gitlab-bot@gitlab.com>2020-10-05 16:54:15 +0300
committerGitLab Bot <gitlab-bot@gitlab.com>2020-10-05 16:54:15 +0300
commitbe834a25982746ffd85252ff502df42bb88cb9d5 (patch)
treeb4d6a8ba0931e12fac08f05abea33a3b8ec2c8a2 /doc/user/analytics/productivity_analytics.md
parentee925a3597f27e92f83a50937a64068109675b3d (diff)
Add latest changes from gitlab-org/gitlab@13-5-stable-eev13.5.0-rc32
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diff --git a/doc/user/analytics/productivity_analytics.md b/doc/user/analytics/productivity_analytics.md
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--- a/doc/user/analytics/productivity_analytics.md
+++ b/doc/user/analytics/productivity_analytics.md
@@ -13,7 +13,7 @@ Track development velocity with Productivity Analytics.
For many companies, the development cycle is a black box and getting an estimate of how
long, on average, it takes to deliver features is an enormous endeavor.
-While [Value Stream Analytics](../project/cycle_analytics.md) focuses on the entire
+While [Value Stream Analytics](../analytics/value_stream_analytics.md) focuses on the entire
Software Development Life Cycle (SDLC) process, Productivity Analytics provides a way for Engineering Management to drill down in a systematic way to uncover patterns and causes for success or failure at an individual, project, or group level.
Productivity can slow down for many reasons ranging from degrading code base to quickly growing teams. In order to investigate, department or team leaders can start by visualizing the time it takes for merge requests to be merged.