array(67) {
  ["page"]=>
  int(0)
  ["insightsarticles"]=>
  string(40) "from-siloed-data-to-foundational-clarity"
  ["post_type"]=>
  string(16) "insightsarticles"
  ["name"]=>
  string(40) "from-siloed-data-to-foundational-clarity"
  ["error"]=>
  string(0) ""
  ["m"]=>
  string(0) ""
  ["p"]=>
  int(0)
  ["post_parent"]=>
  string(0) ""
  ["subpost"]=>
  string(0) ""
  ["subpost_id"]=>
  string(0) ""
  ["attachment"]=>
  string(0) ""
  ["attachment_id"]=>
  int(0)
  ["pagename"]=>
  string(0) ""
  ["page_id"]=>
  int(0)
  ["second"]=>
  string(0) ""
  ["minute"]=>
  string(0) ""
  ["hour"]=>
  string(0) ""
  ["day"]=>
  int(0)
  ["monthnum"]=>
  int(0)
  ["year"]=>
  int(0)
  ["w"]=>
  int(0)
  ["category_name"]=>
  string(0) ""
  ["tag"]=>
  string(0) ""
  ["cat"]=>
  string(0) ""
  ["tag_id"]=>
  string(0) ""
  ["author"]=>
  string(0) ""
  ["author_name"]=>
  string(0) ""
  ["feed"]=>
  string(0) ""
  ["tb"]=>
  string(0) ""
  ["paged"]=>
  int(0)
  ["meta_key"]=>
  string(0) ""
  ["meta_value"]=>
  string(0) ""
  ["preview"]=>
  string(0) ""
  ["s"]=>
  string(0) ""
  ["sentence"]=>
  string(0) ""
  ["title"]=>
  string(0) ""
  ["fields"]=>
  string(3) "all"
  ["menu_order"]=>
  string(0) ""
  ["embed"]=>
  string(0) ""
  ["category__in"]=>
  array(0) {
  }
  ["category__not_in"]=>
  array(0) {
  }
  ["category__and"]=>
  array(0) {
  }
  ["post__in"]=>
  array(0) {
  }
  ["post__not_in"]=>
  array(0) {
  }
  ["post_name__in"]=>
  array(0) {
  }
  ["tag__in"]=>
  array(0) {
  }
  ["tag__not_in"]=>
  array(0) {
  }
  ["tag__and"]=>
  array(0) {
  }
  ["tag_slug__in"]=>
  array(0) {
  }
  ["tag_slug__and"]=>
  array(0) {
  }
  ["post_parent__in"]=>
  array(0) {
  }
  ["post_parent__not_in"]=>
  array(0) {
  }
  ["author__in"]=>
  array(0) {
  }
  ["author__not_in"]=>
  array(0) {
  }
  ["search_columns"]=>
  array(0) {
  }
  ["ignore_sticky_posts"]=>
  bool(false)
  ["suppress_filters"]=>
  bool(false)
  ["cache_results"]=>
  bool(true)
  ["update_post_term_cache"]=>
  bool(true)
  ["update_menu_item_cache"]=>
  bool(false)
  ["lazy_load_term_meta"]=>
  bool(true)
  ["update_post_meta_cache"]=>
  bool(true)
  ["posts_per_page"]=>
  int(100)
  ["nopaging"]=>
  bool(false)
  ["comments_per_page"]=>
  string(2) "50"
  ["no_found_rows"]=>
  bool(false)
  ["order"]=>
  string(4) "DESC"
}

DATA

From Siloed Data to Foundational Clarity

Part two of Unify's Four-Part Blog Series: The Engineering Analytics Journey

Written by Bob Bloem, Managing Director at Unify Consulting

In our last post, we discussed the challenge of “dark data”—the valuable but disconnected information locked within your engineering and HR systems. The first step on the maturation journey is to move from being blind to having a baseline understanding. The solution isn’t to generate more data but to synthesize what you already have.

At Unify Consulting, we partner with clients to do just this. The methodology is straightforward but transformative, focusing on two foundational connections:

  1. Connect People to Work: First, we integrate your HRIS data (the “who”) with your project management system like Jira (the “what”). This simple step provides immediate clarity on how your talent is allocated across different projects, initiatives, and value streams.
  2. Connect Work to Output: Next, we link your project management tickets to your version control system like GitLab (the “how”). This traces a business requirement from its inception as an idea all the way to its delivery as code, allowing you to measure true end-to-end cycle time.

With this unified data model in place, we can apply established industry frameworks like DORA to analyze performance objectively. This foundational clarity allows you to spot systemic bottlenecks, understand team workload, and finally have a data-driven conversation about performance and velocity. For the first time, you can see not just that the team is busy, but how their work flows toward completion.

This initial visibility is a powerful first step. However, mature organizations quickly learn that speed alone is not enough. In our next post, we’ll discuss the critical evolution from measuring velocity to balancing it with quality.

 

Missed Part One of Unify’s Four-Part Blog Sereies: The Engineering Analytics Journey? Read it here