Hero image representing a simplified, abstract, generic taxonomy structure

Content Ecosystem (CES) — Case Study Narrative

Overview

The Content Ecosystem (CES) is a large-scale, headless content repository of over 10,000 articles, videos, and images sourced from internal authors and licensed partners. Every asset entering CES needed to be tagged accurately and consistently using a unified taxonomy to support search, personalization, and downstream content delivery across multiple enterprise portals.

As a taxonomist embedded in a scrum team — and drawing on my background in UX and library science — I governed, tuned, and operationalized the Unified Taxonomy that powered CES. My work spanned taxonomy design, metadata governance, model tuning, automation, reporting, and UX improvements to internal tools.

Over two years, I took ownership of a strong but undocumented taxonomy that had been dormant for nearly a year, developing the automation, tuning workflows, and governance structures that allowed it to operate reliably at enterprise scale.

Context & Problem Space

Before this work began, CES relied on a Unified Taxonomy comprised of seven vocabularies created by a previous taxonomist. The Topics taxonomy was triggered through an AEM workflow, with the goal of automatically applying Topic tags at content ingestion. The remaining vocabularies were applied through a structured Excel export-and-reimport process managed by the technical product manager, referred to internally as the “Bulk Tool,” which compensated for AEM’s lack of a native bulk update feature.

While workable at smaller scale, this hybrid approach became increasingly difficult to sustain as the content library grew.

The challenges were significant:

  • Manual tagging was slow and error-prone; tagging 300 assets took approximately one month.
  • Metadata inconsistencies led to mistagged, over-tagged, or untagged content.
  • The taxonomy lacked documented governance, allowing drift and duplication over time.
  • Internal tools were built for developers rather than authors, creating UX and accessibility issues.
  • Downstream systems depended on accurate metadata, increasing operational risk.
  • The business required a taxonomy model that could scale, automate tagging, and support governance without disrupting existing workflows.

My Role

I served as both taxonomist and UX designer, responsible for:

  • Leading ongoing development and governance of the Unified Taxonomy in Semaphore, with primary responsibility for Unified Taxonomy – Topic
  • Designing and tuning the topic model that powered automated tagging
  • Creating and governing metadata standards for 10,000+ assets
  • Building reporting workflows to identify mistagged or over-tagged content
  • Automating a 35-step Excel routine using CoPilot Agents to accelerate reporting
  • Designing UX improvements for internal authoring tools and workflows
  • Creating the governance process for taxonomy intake, evaluation, approval, and deployment
  • Training authors and strategists on metadata best practices and tooling

This hybrid role allowed me to bridge UX, taxonomy, and data — designing systems that were both technically sound and human-centered.

Challenges

Several constraints shaped the work:

  • Scaling governance of an existing Unified Taxonomy within a growing enterprise content ecosystem
  • Navigating hybrid metadata workflows: workflow-triggered Topics tagging and structured Excel-based reimport processes
  • Tooling constraints within AEM requiring coordinated workarounds
  • Governing and tuning the Unified Taxonomy in Semaphore
  • Developing hands-on expertise in Semaphore through self-directed learning while supporting live workflows
  • High-stakes metadata accuracy, with errors propagating across multiple enterprise portals
  • UX improvements constrained by technical and taxonomy implementation sequencing

These challenges required systems thinking, technical fluency, and a deep understanding of both user needs and metadata behavior.

Approach

I approached this work as an ongoing governance and scalability effort within an existing Unified Taxonomy. My primary focus was Unified Taxonomy – Topic, where accuracy and consistency had the greatest downstream impact, and where partial automation was being explored.

Because metadata application relied on a hybrid process—workflow-triggered Topics tagging in AEM and a structured Excel export-and-reimport process (“Bulk Tool”) for other vocabularies—I prioritized repeatable practices that reduced risk, improved consistency, and supported future automation.

1. Assessing and Strengthening the Unified Taxonomy

Rather than building a taxonomy from scratch, I began by analyzing and validating the existing Unified Taxonomy model in Semaphore to understand how it functioned in practice.

  • Reviewed how the vocabularies comprising the Unified Taxonomy operated together within the Unified Taxonomy
  • Mapped term relationships and overlaps across vocabularies
  • Identified inconsistencies and opportunities for refinement
  • Modeled refinements and adjustments in Semaphore
  • Helped ensure controlled vocabularies (Topics, Audience, Purpose, Language, Content Type) functioned cohesively

This work strengthened the structural integrity of the Unified Taxonomy and created a clearer foundation for consistent tagging and future automation.

2. Manual Tagging → Automated Tagging

Early in the project, I manually tagged 300 assets — a process that took approximately one month. This experience revealed:

  • The true scale of the problem
  • The need for automation
  • The importance of accurate model tuning

Once the topic model was configured, I could trigger the AEM workflow to tag content in minutes, freeing me to focus on higher-value work such as tuning and governance.

3. Model Tuning & XML Optimization

Model tuning became a core part of my role:

  • Analyzed tagging accuracy
  • Identified un‑tagged, mis‑tagged, and over‑tagged assets
  • Adjusted term weights
  • Edited XML rules in Semaphore
  • Maintained a .75 threshold to control over‑tagging

This iterative tuning improved tagging precision and reduced the need for manual corrections.

4. Reporting & Automation

To monitor tagging quality, I developed a 35-step Excel routine that:

  • Identified un‑tagged assets
  • Flagged over‑tagged fragments
  • Detected syntax errors
  • Calculated monthly metadata health metrics

The routine took about 15 minutes per file — until I automated it.

Using CoPilot Agents, I transformed the routine into an automated workflow that completed the same analysis in seconds. This dramatically improved reporting speed and allowed for more frequent monitoring.

5. Governance Process Design

  • I created the governance process for the Unified Taxonomy, including:
  • Intake of candidate terms
  • Tracking and documentation
  • Research and validation
  • SME card sorts
  • Approval workflows
  • Updates to Semaphore
  • Testing new terms in AEM
  • Weight calibration for automated tagging

This governance model ensured long-term consistency and controlled vocabulary growth.

6. UX Improvements to Internal Tools

Because the tools used to manage metadata were originally built for developers, I provided UX guidance to improve:

  • Field labeling and grouping
  • Accessibility
  • Navigation
  • Error handling
  • Author/developer mode separation (removing “easter egg” interactions)

These improvements reduced friction for authors and made the system more usable.

Outcomes & Impact

Operational Impact

  • Reduced tagging time for large content sets from approximately one month of manual work to minutes via workflow-triggered automation
  • Improved metadata consistency across 10,000+ assets
  • Reduced over-tagging, syntax errors, and manual correction cycles through model tuning
  • Established repeatable reporting processes, enabling proactive detection of tagging issues
  • Improved downstream content delivery accuracy across enterprise portals

Team Impact

  • Developed job aids and documentation supporting authors working in AEM and, when appropriate, the structured Excel reimport process (“Bulk Tool”)
  • Conducted frequent in-person training sessions, increasing author confidence in applying and managing metadata
  • Served as a bridge between technical and non-technical teams, translating taxonomy and tooling concepts into accessible guidance
  • Provided documentation leveraged by engineering leadership for cross-team communication
  • Supplied developers with clear, actionable requirements for tool and workflow improvements

Organizational Impact

  • Strengthened the Unified Taxonomy as a foundational enterprise system asset
  • Reduced operational overhead through workflow automation and improved governance
  • Established a governance model supporting sustainable vocabulary growth
  • Improved metadata quality supporting search, personalization, and content reuse initiatives
  • Partnered with content strategy to develop a plan for broader enterprise adoption of AEM and Semaphore

Reflection

The Content Ecosystem project marked a pivotal moment in my career — where my UX background and library science training converged into a hybrid practice of taxonomy, metadata, and systems design.

It reinforced several strengths:

  • Designing clarity within complex content systems
  • Bridging UX and information science
  • Creating scalable metadata models
  • Automating workflows to reduce operational burden
  • Leading governance for long-term sustainability
  • Improving internal tools for non-technical users

It also reinforced the importance of sequencing UX work early — and navigating situations where technical implementation precedes design.

Ultimately, this project solidified my identity as a systems thinker who thrives at the intersection of UX, taxonomy, and data.