From Writers to Engineers: The Evolution of Product Content Engineering

 Beth Lemesany and Dawn Bunting, of ServiceNow, noted that we weren't just just supporting content, but engineering the systems behind it. Writers wear a lot of different hats. Important to have clarity in your role. 

Do you troubleshoots content issues, build content, use scripts? Do you consider how content scales, or work with engineers or tool admins? You might be an engineer already. 

When yo rebrand to an engineer, there is less context shifting, get to focus more on strategy and scale, getting pulled into the right conversations, and get built-in efficiencies. 

Product Content Engineering engineered conversion from wiki, built DITA-OT transforms, and supported writers with DITA training. 

Eventually moved from support to tools, become proactive instead of reactive. Started to think about scaling. Generated a classification map for search taxonomy. Created automated tests. Exposed to "engineering" without yet calling it that. 

Leared to think like engineers, started to deliver like them. Didn't just level up, but re-orged up. Learned to think in terms of projects, automation, scalability, and maintainability. Team had a PM, which helped in visibility. 

As scope grw, so did structure. Identifies type of work through mandatory ticketing. Split work into categories, such as support work, project work, and technical debt (later adding technical wealth), and leaned into agile, which allowed planning for capacity. That allowed respect for capacity, allowing us to say "no" more often, and allowing the ability to advocate for headcount. 

Best practices provide consistency and efficiency. Architecture diagram creates visibility and collaboration to help understand how the systems work, creating shared understanding and clarity and accountability. A change management framework protects the user experience, creates alignment across teams, and scales. Recurring meetings and ceremonies allow for structure and focus, helps catch and fix real problems, and makes work easier to sustain. Coding best practices keeps code maintainable, supports collaboration, and reduces risk. 

When we faced resistance and skepticism, we turned it around by building credibility through delivery.  

Scaling is harder than solving. That's why you give the work structure.

How We Live in the Shadows: Taxonomy in Large Wikis

 Jeffrey Scattini, Haley Helgesen, and Lindsay Bachman, of Crunchyroll, talked about a taxonomy in highly unstructured environments. 12 years without a standard IA and multiple knowledge repositories. Much info locked in people's heads. Acquired companies, none with doc people, folded in many doc environments. 

Current IA lift: 16,0000 pages across 70 spaces. Teams organized own content, and were attached to putting content where it made sense for them at the time. Widespead interest in findability, searchability, and relevance. 

Confluence: hero and villain. Inherently amorphous. 

Confluence operates as a "folksonomy." A collaborative classification system of applying user-created keywords to describe and categorize information. 

Designed and implemented a standardized hierarchical taxonomy, applied across all business units. It needs to serve a range of users. To be accessible to all teams, needed  to hear from all teams. 

Started with a card sorting exercise. This identified common patterns and rationalities, which helped created a taxonomic structure with primary, secondary, and tertiary labels. 

A parent-child label schema is the standard backend of a taxonomy. It reinforces the taxonomic language by using consistency and repetition. It fosters inter-document relationships. 

Implemented with a phased approach. Started with content analysis.  Then implementing based on what found in space reorganization. Content audit used Google Sheets with info about pages. Analysis of each page included whether it should be archived, retained, or reviewed, and its place in the taxonomy.

The Wizard of Docs: Finding the Magic Behind Taxonomy-Driven Documentation

 Scott Hudson, Sr. Mgr, Content Architecture, and Eliot Kimber, Sr. Staff Content Engineer, opened their presentation with a song about taxonomy using a very Wicked/Wizard of Oz theme. 

The land of documentation chaos (before taxonomy) customers were Dorothy, lost in a confusing landscape, no clear path, no consistency, really needed a Yellow Brick Road. 

How do we help Dorothy? How do we classify content in a way that works for both authors and customers? The goals: Better navigation, fully classified, and good governance. 

Classification has three major components: automated classification, authored classification, and governance.  The automated part is assisted by AI, but the authored is the human oversight. 

DITA SubjectScheme maps define consistent terms, define authoring consistency, and enable powerful navigation. It's not magic, but a structure, scalable solution. 

Without a structured taxonomy, content becomes the Wicked Witch, powerful but misunderstood. Customers struggle with findability, duplicate search results, and inconsistency. 

It takes time to properly classify content. Start small, then scale. Balance automation and human input. Governance is key. 

Formal taxonomy of subjects for products and features, user roles and personas, and other domains as needed. All managed by a corporate taxonomy group and applies to all content. It includes a formal change process and is accessible via REST API.

Markup requirements include capturing taxonomic classification in topics, using taxonomy ID to connect to master taxonomy, enabling reliable automatic updating of documentation source, and using built-in element types and attributes. An audit trail tracks classification actions over time.

A Journey for Your Content: DITA for Learning Environments

 Martina Schmidt and Barbara Kalous, of NINEFEB, stated that technical documentation is a high-quality, versatile information product that is more than a payment trigger and a must for policy compliance. It's support for service personnel and an information basis for training materials. 

Training materials are often inefficient. They last too long and little is retained. 

The vision is to break information silos of technical documentation and training. But the silos are very solid. So what does it take?

Fist, a shared data pool. Knowledge objects in a standardized XML scheme, such as DITA. The ability to manage media. Learning target formats are necessary. 

Theory without practice is sterile. Practice without theory is blind. 

DITA is good for creating structured technical documentation. Uses a topic approach for optimum reuse. Allows for a wide range of publications.  A topic contains all elements required to define a complete unit of information that addresses a single subject or answers a single question. Good for tech docs, but good for training? 

DITA 1.3 learning and training adds additional maps, interactions, and topic types specific for learning presentations.

Designing Curricular Transformation in Technical Communication Through Industry and Academia

 Rebekka Andersen, from the University of California, Davis, and Davis Carlos Evia, from Virginia Tech, started a multi-disciplinary research project to understand how content roles are involved in industry. 

It is a complicated love story between industry and academia in technical communication. But there's also a disconnect between industry and academia. They are not speaking the same language. And a relationship between two complicated entities will always be...complicated. 

Where are all the qualified college graduates? Al;so, perception that college graduates are not interested in the professions. TC in academia is "service' course, like a required course, undergraduate majors & minors, certificates, extension programs and advanced degrees. 

Many programs still do not offer instruction in structured authoring, content modeling, and other topics relevant in today's content world. 

Many students are unaware of the tech content discipline. Many also don't see "content" curriculum in their programs. Most students are not prepared to work in the content discipline. When students see content, they expect TikTok and Instagram, not DITA and structuring. 

Our kind of content goes by many names, including intelligent content, omnichannel content, technical content, and it is structurally rich, semantically aware, reusable, reconfigurable, adaptable, abnd rhetorically effective. 

Computer science is the future of academia, bit to the detriment of other disciplines. 

"Industry" is where he jobs are. What we call "content organizations." Managers report a disinterest in or lack of qualifications for working in highly technical environments. Entry-level content-focused position are becoming more hybrid, requiring background in both writing and programming. 

Delivering Targeted and Relevant Online Information

 Jonatan Lundin, of Excosoft, focused on content delivery and making sure content delivered is relevant. 

Relevant content delivery is being able to deliver the right content for the right role at the right time.

Products will require a "digital product passport" in the EU in the coming years to help deliver correct content for products in the future. 

Today's user has access to so much content, but most is not relevant. Delivering relevant content means hiding content that is not relevant to them. When users don't get content filtered, they have to find relevant content themselves, which means having to judge whether what they find is relevant and useful. 

Four user challenges when searching and reading:

1. Select a relevant information source.
2. Express the information eed to the selected source.
3. Assess the relevance of retrieved information.
4. Comprehend the retrieved information.

 So how to achieve relevant content delivery? An example is serial number-specific documentation. 

Content needs to be written and structured to support filtering. But it needs to be easy to understand. Structured authoring is the key. Content needs metadata. Content needs to be delivered in a portal that supports filtering.

The Power of Collaboration: Transforming Content Development for the Modern User

 Bravya Aggarwal, CEO & Founder of zipBoard, a content review and approval platform, started by saying that teams that collaborate effectively are 5 times more likely to achieve high performance. Often, content teams need to collaborate with people not on their direct team. Distance makes things even harder. 

Collaboration matters because user demand rapid, personalized, and engaging content. Collaboration can help meet that demand. Diverse--and expert--stakeholder expertise ensures insightful content. 

Challenges in content development include communication silos and disconnected workflows, slow and disorganized review cycles, and repetitive efforts. In addition, there is difficulty in managing feedback across multiple versions, confusion between internal and external feedback, and inefficient transition from feedback to actionable tasks. 

Good collaboration accelerates content production and refinement, encourages diverse perspectives, and aligns teams. 

Principles of effective collaboration include clear and centralized communication, asynchronous capabilities, and actionable feedback with clear accountability. Accountability happens in two phases, one by the end of the review, second when receiving feedback, as in who will make the fixes or changes. 

Important to make room for different working styles. 

AI has potential in automating routine tasks. It frees collaborators for strategic, creative input. But AI won't replace the role of people 

Tips to enhance collaboration include scheduling regular check-ins, implement clear feedback guidelines, and use visual aids and templates to standardize feedback.  Establish a dedicated review cycle with clear timelines and responsibilities, and document and share best practices and lessons learned, and do the latter across teams.