Land and Risch discussed how LLM-powered solutions can be harnessed to overcome the challenges of content overload and duplication. They explored how GenAI technologies can be leveraged to automatically identify and eliminate duplicate or near-duplicate content, enabling organizations to streamline their repositories and retain only the most relevant and valuable materials. 

The talk included real-world case studies from Enterprise Knowledge, illustrating how GenAI has been successfully deployed to help our clients analyze and act on insights about the current state of their content. 

The LavaCon Conference on Content Strategy and Technical Communication Management will be held October 27-30 in Portland, Oregon. EK will be sponsoring and presenting a session at the conference. 

The post Getting Control of Your Content: AI Solutions to Streamline and Optimize Your Digital Assets appeared first on Enterprise Knowledge.

What Makes Content AI-Ready?

Content that is ready for AI is:

• High quality: The tried and true “garbage in/garbage out” applies to AI-ready content. Reduce the volume of content by cleaning out content that is not current or is duplicate. 
• Relevant and accurate: Ensure all content has a purpose. Is the content aligned to business strategy or task completion? Is the information presented correct and reliable?
• Contextualized: Modeling your knowledge domain establishes a foundational relationship between your content strategy and business strategy, providing context to enrich the meaning AI derives from content.
• Semantically rich: Metadata and contextual tags provide essential context like purpose, audience, and relationships to other content guiding LLMs to interpret content accurately, ensuring AI-driven interactions are meaningful and relevant.
• Structured and standardized: Structured content is explicit, relevant, and clear in its relationships, providing additional context to LLMs.
• Governed: Content governance includes standardization, quality control, and proper metadata tagging, ensuring that once content is AI-ready it stays that way over time.

Download the AI-Ready Content Accelerator Brochure

What is the AI-Ready Content Accelerator?

Working with EK’s content strategy, artificial intelligence, and semantic solutions experts will help your team:

• Analyze the current state of your content and metadata for factors affecting AI readiness.
• Develop and prioritize use cases to validate the content model’s impact on AI outcomes.
• Model the structure and relationships of prioritized content to increase context and compatibility with GenAI solutions.
• Transform and enrich prioritized content.
• Test the improved content model for impact on prioritized AI use cases.
• Develop a strategy and roadmap to move your content model and content operations toward an AI-ready target state.

Key Deliverables

• AI-Ready Content Primer: Technically rigorous guidance about what makes content AI-ready as well as best practices for improving content along all of the factors which improve AI readiness.
• AI-Ready Content Analysis Report: Technically rigorous report of the current state of your prioritized content and its current AI-Readiness with tactical data points to enable content cleanup.
• Knowledge Model: A conceptual map of key business concepts which informs the design of your MVP content model.
• MVP Content Model: Content structure and relationships at the right level of technical rigor for your prioritized use cases.
• Content Operations Expansion Plan: As a companion to the content model, the expansion plan provides key insights into how to scale the MVP content model and supporting content processes.

Key Outcomes

• Reliable, high quality content that is validated for relevancy, currency, deduplication, accuracy, and semantic richness.
• Data to demonstrate the impact of high quality content on prioritized AI use cases.

How it Works

The post AI-Ready Content Accelerator appeared first on Enterprise Knowledge.

Enterprise Knowledge (EK) will have a significant presence, both presenting and sponsoring, at the LavaCon Conference on Content Strategy and Technical Communication Management, which takes place October 27-30 in Portland, OR.

The theme of this year’s event is Content as a Business Asset: Reducing Costs, Generating Revenue, and Improving the Customer Experience Through Better Content.

Four of EK’s experts will be presenting a main conference session and a pre-conference webinar. EK’s experts will share thought leadership that highlights EK services at the intersection of: 

• Content strategy and operations, including AI-enabled content analysis, and
• The semantic layer, part of EK’s solution for dynamic content assembly and content personalization.

Guillermo Galdamez, Principal Consultant, and Nina Spoelker, Senior Analyst, will jointly present Out of Many, One: Building a Semantic Layer to Tear Down Silos. Galdamez and Spoelker will provide practical, proven guidance on how to break down knowledge silos using a semantic layer and streamline the delivery of content. 

Paula Land, Principal Consultant for Advanced Content, and Elliott Risch, Semantic AI Consultant, will present a pre-conference webinar on the topic of AI-assisted content analysis titled Getting Control of Your Content: AI Solutions to Streamline and Optimize Your Digital Assets. The webinar will take place Tuesday, September 24, at 10:00 a.m. Pacific.

In addition to speaking roles, EK will be a sponsor of the event with an exhibit booth, where you can meet the EK speakers and register to win signed copies of Land’s book, Content Audits and Inventories: A Handbook for Content Analysis, 2nd Edition. Find us at Booth 14 in the exhibit area.

Visit the conference site for more information and to register for the conference. 

The post Enterprise Knowledge Sponsoring and Speaking at LavaCon 2024 appeared first on Enterprise Knowledge.

Paula Land, Principal Consultant for Content Strategy and Operations and Elliott Risch, Semantic AI Consultant, will present a pre-conference webinar on the topic of AI-assisted content analysis titled Getting Control of Your Content: AI Solutions to Streamline and Optimize Your Digital Assets. 

Land and Risch will delve into how LLM-powered solutions can be harnessed to overcome the challenges of content overload and duplication. They will explore how GenAI technologies can be leveraged to automatically identify and eliminate duplicate or near-duplicate content, enabling organizations to streamline their repositories and retain only the most relevant and valuable materials. 

The talk will include real-world examples from Enterprise Knowledge, illustrating how GenAI has been successfully deployed to help our clients analyze and act on insights about the current state of their content. 

The webinar will take place Tuesday, September 24, at 10:00 a.m. Pacific. The event is free but registration is required.

Register for the webinar

The post Paula Land and Elliott Risch Presenting Webinar for LavaCon 2024 appeared first on Enterprise Knowledge.

Paula Land, Principal Consultant for Content Strategy and Operations at Enterprise Knowledge, and Elliott Risch, Semantic AI Consultant, will be delivering a presentation on Forging a Next Generation Content Lifecycle at the ConVEx IDEAS virtual conference. Land and Risch will explore GenAI’s transformative potential to enhance the content lifecycle: from planning and creation to publishing and optimization.  

They will highlight how content professionals can achieve greater efficiencies and enhance their content operations using GenAI’s capabilities at each phase of the content lifecycle.

Tuesday, September 10, 2024 at 9:00 a.m. PDT / 12:00 p.m. EDT

Register for the event

The post Paula Land and Elliott Risch to Present on Forging a Next Generation Content Lifecycle at ConVEx IDEAS Conference appeared first on Enterprise Knowledge.

In this post, I share the factors that affect whether your content is ready for the semantic layer, how those factors are assessed (including nuances related to some sample use cases), and the steps to remediate the issues and gaps found in the content audit

Content Analysis Defined

Content analysis, or content auditing, is the process of assessing content against a set of defined, measurable criteria and the needs of the business and the content’s audiences. The analysis also considers the content operations surrounding the planning, creation, production, and distribution of the content to all the systems that consume it, whether those be web content management systems, knowledge portals, digital asset management systems, enterprise search, or AI-enabled features. 

When conducted in advance of the integration of content into a semantic layer—a structured representation of data, content, knowledge assets, and the relationships among them–content analysis becomes a powerful tool for preparing content for understanding, categorization, and enrichment.

The outcome of an audit is insight into the content’s quantity, quality, and structure, and a set of recommendations for content improvement. When the semantic layer adds context and metadata to that analysis of the content, you have a content source that is well-optimized to support whatever your organizational needs may be. Examples of how content integrated into a semantic layer can be leveraged include

• knowledge discovery, as in a semantic search program or knowledge portal
• content generation–for example, the automated creation of reports or development of chatbots 
• content recommenders that use the rich data about the content combined with user behavior to suggest relevant content.

Factors Affecting Content Readiness

When preparing content for a semantic layer, the quality of your overall content ecosystem is essential. Factors such as content duplication, recency, and availability will affect the content’s readiness. Within that ecosystem, targeted content will need to be assessed along several dimensions to ensure effectiveness and usability:

• Structure: Content that has been modeled and structured in its source system allows for easier categorization and relationship identification.
• Semantic richness: Through techniques such as semantic tagging and entity extraction, the content can be enriched with metadata that describes its context, topics, and entities.
• Presence and quality of metadata: Metadata, including tags, keywords, and annotations, is vital in describing the content’s context, meaning, and relationships.
• Consistency and standardization: Content should adhere to consistent formatting, naming conventions, and data standards to ensure interoperability and ease of integration within the semantic layer. Consistency facilitates accurate data interpretation and enables effective knowledge extraction.
• Content quality: Content that is well-written to be current, accurate, and useful affects not only the end user experience but is critical for reliable semantic analysis and inference.
• Volume and complexity: The amount of ingested content can affect the performance and scalability of consuming systems.

By addressing these aspects of content preparation, you can enhance the effectiveness and value of the semantic layer, enabling more accurate, intelligent, and context-aware knowledge representation and discovery.

Auditing Content for Readiness

Designing a content audit to assess the readiness of content for ingestion into a semantic layer involves taking a structured approach to evaluating the content. General principles for auditing content begin with setting objectives and scope, including the metrics by which you will measure success; inventorying and categorizing the content sources for the semantic layer; assessing content structure; determining how well understood, available, and consistently used metadata is across content sources; analyzing domain-specific content; and evaluating the overall quality of the content.

When auditing content readiness for different use cases involving a semantic layer—such as knowledge discovery, chatbot development, content recommendation engine development, or content generation—you will need to tailor the steps to address each application’s unique requirements and goals. Here’s how you might frame the audit steps differently for each use case:

General Principles for All Use Cases

• Flexibility: Each step must be adapted to the specific needs and characteristics of the use case. EK worked with the marketing operations team at a global telecommunications company to define the structure of products, the relationship between product components, and the taxonomy necessary to traverse those relationships. The product content model now enables the intelligent assembly of sales collateral, as well as multi-channel publishing to multiple user experiences including sales enablement portals, marketing websites, mobile applications, and social media.
• User-centric approach: Consider the end-user experience and how content will be consumed or interacted with. For example, EK recently worked with a global software vendor who needed to deliver more personalized, timely, and relevant release notes of upcoming product changes in a continuous implementation, continuous delivery (CI/CD) environment to both internal and external end-users. EK focused on developing a comprehensive content model supporting structured and componentized release note content, improving user experience (UX) interactions, and leveraging the organization’s taxonomy to filter the content for more personalized delivery. We leveraged human-centered design practices and facilitated a series of focus groups across the teams of content authors, marketing, technical SMEs, and executive leadership to define the current state of content authoring processes and content management and ensure cross-team alignment on the target state for authoring, content management, and structured content model design. EK carefully considered the stakeholder requirements in our delivery of the solution.
• Feedback loops: Implement robust feedback mechanisms to continuously improve content readiness and alignment with the semantic layer’s goals. EK worked with a bioscience technology provider who needed EK’s help to bridge the gap between product data and marketing and educational content to ultimately improve the search experience on their platform. EK incorporated ML, knowledge graphs, taxonomy, and ontology to redefine the user experience, allowing users to discover important content through an ML-powered content discovery system, yielding suggestions that resonated with their needs and browsing history. EK’s flexible approach allowed for open dialogue and iterative development and value demonstration, ensuring that the project’s progression aligned closely with the evolving needs of our client.

Taking Action on Your Audit

The outcome of your audit, as stated above, will be a set of issues to address that were uncovered by the analysis. To address those issues and ensure that your content is well-prepared for integration into the semantic layer and optimized for various AI-enabled applications, follow these steps.

1. Optimize content structure: For easier categorization and better integration within the semantic layer, reorganize and reformat content to adhere to standardized formats like JSON, XML, or DITA. Break down large content pieces into smaller, reusable components that can be individually tagged for targeted, dynamic assembly and delivery. For example, you might convert a set of technical manuals from PDF format to a structured content model that breaks out individual topics for reassembly and reuse in multiple contexts.
2. Ensure consistency and standardization: Improve consistency and interoperability of content by developing and enforcing content standards and naming conventions across the content domains. Implement governance as part of your content operations, by creating a style guide, training content creators, and providing templates that automatically enforce these standards.
3. Enhance metadata quality: To improve searchability and context-awareness in consuming systems, add missing metadata and improve existing entries. Use tools for semantic tagging and entity extraction to automatically generate and add metadata tags (for example, author names, publication dates, keywords, and abstract summaries for a set of articles) where they are missing.
4. Increase semantic richness: To enhance automated reasoning and inferencing capabilities, add detailed annotations and semantic tags to content. Use a natural language processing (NLP) tool to identify and tag entities and relationships in a database of content. Add these annotations directly into the content’s metadata fields.
5. Improve content quality: Achieve higher reliability and accuracy by implementing a quality assurance program that includes regular audits to review content and plan for updates. To make this process more efficient, consider using a content management tool that scans content to find and correct errors and inconsistencies and identifies duplicated or outdated content. 
6. Optimize scalability and performance: Enable efficient and reliable content operations that manage ingestion and processing by scaling up and optimizing tool capabilities and refine workflows for handling large volumes of content. 

• Implement iterative improvements: Enhance content readiness and alignment with semantic layer requirements on an ongoing basis. Establish a continuous improvement cycle based on feedback and audit findings. Set up regular review meetings with content teams to assess progress and adjust strategies. Analyze user feedback and audit reports. Track improvements and report status to content stakeholders.

Summary

Incorporating a semantic layer into content management and distribution is a transformative approach to addressing the organizational challenges of handling vast amounts of information. As outlined above, the process begins with a comprehensive content analysis or audit to evaluate the readiness of content for integration into the semantic layer. This step is critical for enhancing the content’s structure, semantic richness, metadata quality, consistency, and overall quality.

By enriching content with metadata and contextual information, organizations can significantly boost the capabilities of consuming systems such as AI-enabled chatbots, knowledge management systems, recommendation engines, and enterprise search. The audit process identifies gaps and provides insights into the content’s quantity, quality, and structure, along with actionable recommendations for improvement.

Key factors such as structure, semantic richness, metadata quality, consistency, content quality, and scalability play vital roles in ensuring the effectiveness of content within the semantic layer. Properly structured and componentized content enriched with detailed metadata ensures accurate categorization and relationship identification, facilitating automated reasoning and inferencing.

Addressing these aspects during content preparation optimizes the content for semantic enrichment and enhances downstream distribution in relevant contexts. This leads to more accurate, intelligent, and context-aware knowledge representation and discovery, ultimately maximizing the content’s potential in the semantic layer.

Follow these audit steps and address the outlined factors to unlock your content’s full potential to drive better decision-making, improve user experiences, and achieve greater operational efficiency.

Looking for expert assistance with your content audit project? Contact us.

The post Analyzing and Optimizing Content for the Semantic Layer appeared first on Enterprise Knowledge.

Wednesday, June 19, 2024 at 9:00 a.m. PDT / 12:00 p.m. EDT

Content auditing, the qualitative aspect of content analysis, is typically thought of as less about tools and more about human evaluation. By harnessing AI tools, however, content strategists can enhance the speed, accuracy, and depth of their audits, allowing the humans to focus on high-level strategic tasks. In this presentation, Paula will talk about how AI technologies can empower content strategists to identify patterns, analyze data, and make informed decisions, ultimately leading to more effective content strategies.

Paula Land is Principal Consultant for Advanced Content at Enterprise Knowledge and author of Content Audits and Inventories: A Handbook for Content Analysis.

Click here to register for the event and learn more about Content Strategy at EK here.

The post Paula Land to Speak at Content Strategy Mastermind Event appeared first on Enterprise Knowledge.

The Solution

To address the challenge of carbon emissions created due to content storage, EK designed a web application that uses AI technologies to identify duplicate content and evaluate options for handling it. The application dashboard displays aggregate statistics on the presence and type of duplicate content, allowing users to make a decision as to whether it should be retained, updated, archived, or deleted. This provides a clear view into duplication and its connection to CO2 emissions, promoting a cultural shift among employees to increase awareness about carbon footprint and the role they play in contributing to a wider sustainability strategy.

One of the striking aspects of this effort was the sheer magnitude of the IM team’s content collection, estimated at 500 million documents and many petabytes of information. The EK team worked with our stakeholders to gather and prioritize requirements and build the application with this scale in mind, including a number of key components:

• Identification of content storage locations;
• Indexer to crawl the content collection;
• Data pipeline to convert content to a vector database that allows for closer examination;
• AI model to continuously identify duplicate content within designated content locations; and
• Method of showing scope and impact of duplicate content to the end user.

The technical team leveraged Azure Open Source AI and Power BI to design a prototype dashboard to quantify duplicate unstructured and semi-structured assets based on scans, indexes, and queries. EK also used reusable code wherever possible to further minimize computing power and carbon emissions. Leveraging the metadata and textual content, AI-based analysis can rate the likeness of other information previously indexed. 

The team created a value statement and strategic roadmap that will continue to provide guidance to the company on continued expansion of their Green IM tool. It included consideration of the complexities of their environment for topics like scaling, rollout, and customer footprint growth, underscored with the importance of change management as a critical component. As part of the roadmap, the team was also able to identify opportunities to selectively introduce proactive and reactive automation in order to help users reduce their content duplication throughout the course of their normal day, such as warning notifications when uploading content that meet a certain similarity threshold with an existing content item in a given repository (proactive) or enabling system triggers to remove duplicative content through the dashboard interface itself (reactive). The ultimate future state goal, as identified in the roadmap, is to enhance the web application and make it actionable in supporting push-of-a-button content deletion through the application itself, promoting a pure “don’t make me think” content experience and further behavioral change.

The EK Difference

EK leveraged our history with the company, our understanding of their strengths and challenges, and a balanced team of technical and strategy subject matter experts (SMEs) to proactively propose the idea of a “green” application, based on their enterprise effort to reduce greenhouse gas emissions.

The EK team also developed a customized scorecard to evaluate the pilot’s success and ensure alignment to the company’s strategic objectives, including measurable factors like:

• The ability to calculate carbon footprint with at least 80% accuracy;
• 75% of non-technical users report being able to use the application on their first try with minimal training; and
• A solution that abides by all architectural and security requirements established by the technical team.

This comprehensive understanding of the challenge, paired with the delivery of a tailored mechanism for assessing the proof of concept’s success, enabled EK to perfectly position the organization to take on similar efforts in the future.

The Results

EK identified potential pathways to quickly address carbon emissions within the IM team, with an initial focus on reducing the amount of duplicate content within their repositories. 

In partnership with the client, EK identified a pilot set of 226 million of the company’s approximately 500 million total documents to prove out the concept.

With a 15% target deduplication rate this company has the potential to remove over 34,000 kilograms of CO2 from the environment through a reduction in physical server usage, directly supporting their objective to remove greenhouse gas emissions from operations that they are capable of controlling.

Beyond providing tangible, quantifiable statistics upon which to build a business case for larger ESG initiatives, this initiative also provided the IM team with a repeatable framework for running similar “green” efforts in the future, as well as faster and more accurate decision making through less clutter and quicker access to content.

In doing so, EK was able to demonstrate the technical and business viability of an AI-driven content deduplication tool. The pilot use of this tool demonstrated the accurate identification of duplicates based on conversion to a vector database and AI modeling to identify – and fine tune – duplicate content.

 

Download Flyer

 

The post AI-Augmented Content Analysis to Remediate Duplicate Content appeared first on Enterprise Knowledge.

Your content isn’t just words on a page; it’s a vital business asset driving your marketing, documentation, communications, customer support, and knowledge management. 

Beyond the text, it’s about optimizing the systems that manage and deliver content for optimal usefulness, relevance, and impact. To ensure that your content aligns with current business goals and user needs and stays future-ready, you need to understand how your content is created, structured, and managed, what your users are engaging with, and how that content performs.

Content Analysis is the practice and process of analyzing the current state of content and content systems against business objectives and user goals to deliver practical, usable outcomes that enable informed decision-making and support the ROI of content investments.

What We Do

• Requirements gathering: Content stakeholder focus groups and interviews to understand business priorities and user goals for the content.
• Content current state analysis: Evaluation of the content quality and performance against business goals and user needs.
• Content ecosystem current state analysis: Evaluation of the environment in which content is created and managed to inform recommendations for improved tools, processes, and workflows.
• Target state vision: Applying what we’ve learned about business priorities, user goals, and content to create a vision for fully realized content.
• Roadmap: An actionable plan to improve content and achieve the target state, prioritizing those items that will demonstrate ROI most efficiently.

Download the Content Analysis Brochure

Why Content Analysis?

Content analysis is conducted in support of a variety of content initiatives. The outcomes you’re trying to achieve will drive the factors by which content and content systems are assessed. These initiatives may include:

• AI readiness: Assess the consistency of structure and completeness of content and metadata in order to assess AI readiness
• Knowledge management: Assess content duplication and discoverability across complex repositories and systems  
• Search optimization: Evaluate structure, markup, and keywords to enhance search queries
• Dynamic content: Analyze the semantically meaningful components of content which would best enable publication of content in multiple end-user experiences
• Content transformation: Quantitative and qualitative analysis of content types, structure, and metadata to enable transformation from unstructured to structured content

Outcomes of a Content Analysis

At the end of a content analysis, you will have

• Shared understanding, vision, and alignment: Content stakeholders across the enterprise understand and champion the role of content in meeting business and user objectives 
• Report of findings: Written report that documents the outcomes of the audit, including trends, opportunities, and gaps 
• Actionable plan and roadmap: Detailed plan for content and content process improvement and optimization
• Content Governance Plan: Processes to improve accountability and lifecycle management of content to ensure that it is updated and maintained, guaranteeing usability and accuracy
• Preparedness for advanced content capabilities: Skills and repeatable processes to support current and future initiatives, including AI and composable content

How Does It Work?

The post Content Analysis appeared first on Enterprise Knowledge.

People

The People factor is the one that is most important to first address in any content management effort, it involves the individuals and underlying culture that surround content management within an organization. People can include content creators, editors, and publishers, and culture can include the general attitudes and views that employees hold when creating and managing content. Common challenges our clients face when facing the People factor of content management include: 

• People are overly reliant on technologies and content repositories to store their information and documents without considering user-centric design elements that are needed for the technology to work effectively; 
• There are no defined roles, responsibilities, or processes in place for maintaining, organizing, and updating content. 
• There are no systems of recognition or reward for those who make meaningful efforts to create and maintain content to encourage good behaviors and adherence to best practices; and 
• The common cultural attitudes towards content are not recognized or seen as having an impact on the organization as a whole. 

These challenges can make it difficult for organizations to keep their content up to date, applicable, and organized. It also means that employees rarely take the initiative to organize their own content as it isn’t promoted across all levels of the organization. In order to help alleviate these challenges, I often recommend the following: 

• Promote the maintenance of content by clearly communicating the benefits to content creators, managers, and consumers;
• Provide staff with the opportunities and guidelines to integrate content and knowledge management into their daily tasks in order to ensure  collective buy-in and active upkeep of content;
• Facilitate working sessions, meetings, and leverage other communication methods to help people recognize that content repositories are places to foster organizational learning and maintain institutional knowledge, even among colleagues that they don’t interact with on a day to day basis, not just giant folders in which to throw documents for storage purposes;
• Define roles and assign clear responsibilities to employees for creating and managing their content;
• Train employees so that they understand what they are expected to do to perform content management and how they can do it successfully; and 
• Create a recognition system such as an easily accessible dashboard that shows who has updated content most recently. This can visualize the work people have done and encourage other users to participate, especially those who want to be recognized for the work they are doing within a new system to tag, upload, or share content, and need to know that their work contributes not only to their own success, but to the success of others.

It is important for organizations to provide their staff with better opportunities to manage their content and change their attitudes towards content management. For example, a principal revenue collection agency was facing challenges keeping their content up to date and easy to find, which resulted in staff frustrations and slow response times by call agents. EK helped to design a plan that helped to standardize the way information is captured and managed in SharePoint, as well as better provide staff with the training and abilities to maintain their old and newly created content which resulted in greater efficiency and decreased in time finding applicable content during high-pressure calls from citizens.

Content

In order to implement proper content management, an organization must know the type of content that lives within all their KM systems, or within the systems they prioritize for change. The term “content” can apply to a wide array of information, and any knowledge that is written, stored, and readily accessed (such as knowledge articles, blogs, podcasts, presentations, etc). The content factor can feel the most daunting due to the following challenges: 

• Organizations do not know what content exists, where it exists, or whether it is up to date;
• There are no content creation and editing processes and procedures in place, and if they do exist, staff are not trained or aware of the proper processes; 
• There is no standardized approach as to which metadata needs to be completed to describe, structure, and manage content in a consistent manner; and
• Content management is not embedded in daily practices and is often seen as an extra task for employees, meaning it is consistently deprioritized.

Organizations need structures in place to maintain and govern their content. Without it, content enters the cycle of being created, duplicated, becoming outdated, and then remaining in the system in which it was originally placed. The following solutions can begin to address an organization’s content management challenges:

• Conduct an in-depth content inventory and analysis to identify and map the content critical to the organization. This analysis should be highly detailed and cover many areas of the organization and topics in order to give those on any content management initiative an accurate starting point. This can also lead to the prioritization of content that needs to be addressed first in following content management initiatives; 
• Design and implement content types, which can lead to structural guidelines and easy content creation for employees, ensuring that content stays consistent. 
• Designing and implementing a taxonomy, which can be used to tag content the same way throughout the enterprise and allow content to be more searchable and findable; and 
• Create and socialize content governance guidelines that include processes for editing, uploading, and publishing content. These guidelines should include a process to check content on a regular basis for accuracy and consistency. 

Conducting content analyses and inventory assessments are important to help organizations what they’ve got, what’s working, and what’s not. For instance, a leading Fortune 500 Company had an excess of outdated, siloed, and hard to find content, which put an increased burden on staff trying to find the content to do their jobs. EK performed a detailed and extensive content inventory across the organization’s main content repositories in order to assess their state of content. As a result of the content inventory, EK was able to provide the organization with short- and long-term actions they can take to mitigate the risk of duplicate and outdated content and ensure that staff have confidence that they are accessing the right content at the point of need.

Technology

The last piece to the effective implementation of content management is technology. Although technology can be the most appealing or tangible element of content management, it’s important to view it as an enabling factor rather than an end unto itself. Without the previously discussed foundations in place to support people, culture, and content, your organization will likely not realize the full benefit of new tools. Technology should enable and empower people to do their day to day work while ensuring they can seamlessly maintain their content and apply best practices, while avoiding common KM technology mistakes. 

Organizations often face the following technology challenges: 

• Different parts of the organization prefer different tools and systems, and there are no overarching guidelines in place for selecting and implementing new technologies; 
• Technologies used by the organization are siloed and don’t interact with one another. If a change is made to content in one system, the same content housed in a different system remains unchanged; 
• Employees don’t have a common or integrated content management system, so information is either siloed into limited access folders or stored on personal computers and file systems; and 
• Employees use different technological tools on a day to day basis, but these tools are not integrated well with proper content management guidelines and practices.

Organizations should take an integrated approach to technology, investigating the dynamic ways in which they can integrate search, content management, metadata management, and a taxonomy, which will give them an extremely robust system and foundation upon which content can be built and accessed. Technologies enable content improvement and allow the cultural and content changes being implemented to be successful. The following solutions can help organizations successfully integrate technology with content management strategies:

• Consolidating systems or creating new avenues for information to flow between existing systems;
• Implement a “Headless” Content Management System (CMS) that is focused on the creation and accessibility of content despite the source platform;
• Integrating repositories with an enterprise search interface that makes finding content intuitive and user-friendly, helping overcome content silos; 
• Define and apply usage metrics and other analytics, which can help provide constant feedback to content teams and help them learn ways to improve the accessibility and usability of content;
• Implement additional solutions that will integrate content repositories through a metadata hub that creates a “single source of truth” for all content and  allows content to be surfaced and reused; and
• Designing artificial intelligence solutions based on ontologies and knowledge graphs to improve content findability and discoverability by enabling content recommendations, chatbots, and other advanced semantic features. 

Integrating content management with new or existing technologies at organizations is an important step in the content management process. The learning team for an international retailer was struggling to search for, find, and deliver learning content to in-store associates due to not having a standardized taxonomy. EK partnered with the organization to assess the current state and define the target state of the retailer’s content management maturity, which ultimately resulted in a fully customized, iterative, task-based content management strategy, implementation roadmap, and KM systems architecture to help the learning team achieve their target state. The KM systems architecture design featured recommendations to leverage new and existing technologies including a metadata management hub, taxonomy management system, knowledge graph, and search engine. This plan results in greater technical capabilities and content management maturity for this organization and will rapidly improve their staff’s efficiency and their content’s findability. 

The path to creating a robust technological environment for content varies depending on the organization, but the philosophy behind it should always remain the same: technology exists to amplify content efforts, provide structure and support processes, and help reinforce good content management behaviors. 

Conclusion

If implemented right, an organization can expect to see the following outcomes from a content management initiative: 

• Content becomes findable and the systems are easy to navigate;
• Out of date content is updated regularly or is removed until it can be updated, reviewed, and/or deleted;
• Employees know what the systems are used for and are confident in the accuracy of content and the information they find within the systems;
• Staff know their roles regarding content, and have a collective buy in to the benefits of proper content system management;
• Systems are integrated with one another, allowing for expanded accessibility and usability of content;
• Content is consolidated where it can be accessed by the people who need to access it, and content that is intended to be restricted access follows similar or even the same governance rules to maintain consistency across a system and ultimately the enterprise. 

Understanding the changes that need to be made to the workstreams of People, Content, and Technology can allow an organization to work towards a more mature and robust content management environment. This will promote employee satisfaction, reduce time searching for content, and create a more effective and collaborative work environment at any organization. If you’re interested in starting on the path to more effective content management, implementing a content management system, or have questions, contact us at EK.

The post Three Important Factors to Consider for Implementing Effective Content Management appeared first on Enterprise Knowledge.