Getting Control of Your Content: AI Solutions to Streamline and Optimize Your Digital Assets
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The document discusses AI solutions for managing content overload, emphasizing the importance of content governance, deduplication, and optimization to enhance user experience and reduce operational costs. It outlines case studies demonstrating how organizations have successfully implemented AI technologies to improve content accessibility, categorization, and avoid duplication, leading to significant improvements in efficiency and accuracy. The document also highlights the need for a structured approach to content management that incorporates metadata and strategic oversight.
Getting Control of Your Content: AI Solutions to Streamline and Optimize Your Digital Assets
- 1. Getting Control of Your Content: AI Solutions to Streamline and Optimize Your Digital Assets September 24, 2024 Paula Land Principal Consultant, Advanced Content Enterprise Knowledge Elliott Risch Semantic AI Consultant Enterprise Knowledge
- 2. ENTERPRISE KNOWLEDGE ⬢ Principal Consultant, Advanced Content, Enterprise Knowledge ⬢ Consulting since 2006 ⬢ Author of Content Audits and Inventories: A Handbook for Content Analysis, 2nd Edition ⬢ Instructor in the Master’s Degree in Content Strategy Program, FH Joanneum, Graz, Austria Paula Land
- 3. ENTERPRISE KNOWLEDGE ⬢ Technical Consultant, Semantic AI Solutions, Enterprise Knowledge ⬢ Background designing and implementing enterprise semantic layers, as well as laboratory automation research ⬢ Recovering Academic; Professor of Mathematical Logic and Philosophy Contact me erisch@enterprise-knowledge.com https://www.linkedin.com/in/modusponens/ Elliott Risch
- 4. ENTERPRISE KNOWLEDGE Agenda The Business Value of Managing Content Overload Case Studies Looking Ahead: AI Readiness
- 5. ENTERPRISE KNOWLEDGE Meet Enterprise Knowledge 10 AREAS OF EXPERTISE ● KM STRATEGY & DESIGN ● TAXONOMY & ONTOLOGY DESIGN ● AGILE, DESIGN THINKING & FACILITATION ● CONTENT & DATA STRATEGY ● KNOWLEDGE GRAPHS, DATA MODELING, & AI Clients in 25+ Countries Across Multiple Industries HEADQUARTERED IN ARLINGTON, VIRGINIA, USA GLOBAL OFFICE IN BRUSSELS, BELGIUM ● ENTERPRISE LEARNING ● INTEGRATED CHANGE MANAGEMENT ● ENTERPRISE SEARCH ● CONTENT AND DATA MANAGEMENT ● ENTERPRISE AI 80+ EXPERT CONSULTANTS AWARD-WINNING CONSULTANCY KMWORLD’S ● 100 COMPANIES THAT MATTER IN KM (2015-2023) ● TOP 50 TRAILBLAZERS IN AI (2020-2023) INC MAGAZINE ● THE 5000 FASTEST GROWING COMPANIES (2018-2023) ● BEST WORKPLACES (2018-2019, 2021-2023) WASHINGTONIAN MAGAZINE’S ● TOP 50 GREAT PLACES TO WORK (2017) WASHINGTON BUSINESS JOURNAL’S ● BEST PLACES TO WORK (2017-2020) ARLINGTON ECONOMIC DEVELOPMENT’S ● FAST FOUR AWARD – FASTEST GROWING COMPANY (2016) VIRGINIA CHAMBER OF COMMERCE’S ● FANTASTIC 50 AWARD – FASTEST GROWING COMPANY (2019, 2020) Top Implementer of Leading Knowledge and Data Management Tools 500+ Thought Leadership Pieces Published
- 6. THE BUSINESS VALUE OF MANAGING CONTENT OVERLOAD
- 7. Relevance: Users may encounter different versions of the same content, leading to uncertainty. User Impact Accessibility: Content that is not regularly reviewed may fail to meet accessibility standards, leading to potential legal liabilities and a poor user experience. Currency: Outdated or inaccurate information affects user trust in information. Personalization: Personalizing content becomes more challenging when there are multiple versions to manage and coordinate. Quality: Without clear guidelines or regular audits, content quality can vary significantly, leading to inconsistency in messaging, branding, and user experience. Consistency: Different versions of the same content can confuse users and weaken brand identity.
- 8. Business Cost and Risk Increased costs: Maintaining and updating multiple versions of the same content requires additional resources, increasing operational costs. Compliance and legal risks: Ensuring all versions of content comply with legal and regulatory requirements is challenging, increasing the risk of non-compliance. Outdated or unvetted content may not comply with current regulations, creating legal risks. Storage issues: Storing duplicate content consumes more storage space, potentially increasing costs and complicating data management. Lack of governance: Without clear governance policies, content management becomes ad-hoc, leading to inconsistencies in content creation, review, publication, and retirement.
- 9. ENTERPRISE KNOWLEDGE Operational Considerations Content sprawl: The proliferation of content without strategic oversight leads to "content sprawl," where there is more content than the team can effectively manage or utilize. Discoverability: Without a centralized repository or effective metadata, teams struggle to locate specific content pieces, leading to duplication of efforts, wasted resources, and reduced efficiency. Disorganized content repositories: Content scattered across multiple platforms, systems, or storage locations makes it hard to maintain a cohesive view of the content landscape. Content management complexity: Managing multiple versions of the same content creates confusion and inefficiencies within content management systems, making it harder to keep content up-to-date and accurate. Archiving challenges: Lack of a structured process for archiving or decommissioning content results in the retention of irrelevant or outdated content, which can overwhelm systems and complicate content searches. Reduced efficiency: Searching for content or verifying its relevance can consume significant time, reducing the team's overall productivity and ability to focus on creating high-value content.
- 10. Benefits of Content Clean-Up and Deduplication Improved User Experience Content is relevant, current, trustworthy. Users don’t encounter different versions of the same information. Personalizing content for specific audience segments is more accurate. Accurate Search Results Search results are relevant and information meets user search intent. Simplified Content Management Clean, consistent, unique content is simpler to keep up to date and accurate in a content management system. Cost Reduction Reducing duplicate content reduces the operational costs of storing, maintaining, and updating multiple versions of the same content.
- 11. APPLYING AI TO CONTENT OVERLOAD
- 12. Applying AI to the Problem of Content Overload AI is uniquely suited to address the challenges of content overload due to its advanced capabilities in automation, pattern recognition, and data-driven decision-making. With AI, organizations can harness powerful tools to: ● Identify and eliminate redundancies AI can automatically detect duplicate or near-duplicate content, reducing clutter and ensuring repositories are streamlined. This enables teams to focus on the most relevant and valuable content. ● Enhance content categorization and tagging By analyzing the content’s structure, topics, and usage patterns, AI can improve the accuracy and consistency of categorization and tagging, making content more accessible and aligned with organizational goals. ● Deliver actionable insights AI-driven analytics can reveal trends and patterns in content consumption, guiding decisions on what content to keep, update, or retire. This helps organizations maintain an organized, up-to-date, and highly relevant content library. Through case studies, we will explore how these capabilities have been effectively applied to help clients manage their content more strategically and efficiently.
- 13. Take action Review Prepare the content Identify the relevant content Designing a Solution Focus on the exact content needing transformation and define the essential adjustments. Utilize metadata to swiftly find relevant content, streamline it into precise sections, and categorize efficiently. Seamlessly apply intelligent AI-driven updates while preserving a clear comparison to the original. Deliver a refined review experience where humans approve changes, fueling continuous improvement. Integrate updates with precision, optimize metadata, and seamlessly push content to production while managing archival effortlessly. Define the scope of problem
- 14. CASE STUDIES
- 15. THE CHALLENGE At a large, US-based investment and insurance company, staff were spending extraneous amounts of time finding information through the organization’s search experience. Data and content were largely inaccessible due to duplication, lack of application of metadata, and siloed systems, teams, and processes. The organization recognized a need to automate and improve their data, content, and metadata strategies to improve the findability of information and more accurately respond to their customers’ inquiries. THE SOLUTION The information architecture included a taxonomy/metadata that standardized the language to facilitate data classification, describing the quality of content in search results, and takes a user-facing form as interactive reporting/analytics process. All content migrated to or appearing in the search and reporting platforms leveraged the new knowledge base layered with semantic meaning through the semantic data model. THE RESULTS EK established a solution that: Reduced outdated and inaccurate information by 90+ % Accuracy Rating Established a strategy and procedure to reduce manual cleanup and deduplication. EK developed an enterprise information architecture and data layer to serve as the nexus between structured data, content, and a semantic search experience. Previously siloed information connected and tagged with ESTABLISHED: Enterprise information architecture. ENRICHED: Knowledge model to content through auto-classification. APPLIED: Leveraged enriched content to surface duplicative or irrelevant content and support content cleanup. @EKCONSULTING 45%
- 16. Take action Review Prepare the content Identify the relevant content Deconstructing the Solution We set clear content audit goals… …to enhance migration efficiency and search performance. We conducted an inventory, quantified the content, and defined clear parameters to remove redundancies and outdated material. We designed and implemented a taxonomy… …within an AI-driven semantic solution, running a one-time auto-tagging process that drastically reduced the need for manual metadata entry. We leveraged improved metadata… …to accelerate content analysis and deduplication. Clean-up actions were defined, and we updated or archived content as necessary, streamlining the repository. We standardized the content structure… …by designing a comprehensive content model, ensuring ongoing governance processes to sustain quality. The content we kept was transformed into the new model, while autotagging ensured accurate metadata in the new system. We optimized search relevance… …by tuning results to take full advantage of the improved content structure and metadata. Finally, we developed an intuitive, filter-driven search experience, enhancing the user’s ability to find relevant content quickly. Define the scope of problem
- 17. Content Analysis & Deduplication Outcomes f Total number of content items assessed 49,937 Content items requiring review for clean-up 9,777 Content items identified for automatic archival 22,583 45% 80% 90% Reduction in outdated or inaccurate information Accuracy rating for information classification and tagging Reduction of manual cleanup and deduplication Analysis Outcomes
- 18. THE CHALLENGE In alignment with the UN Paris Agreement, a global oil and gas company pledged to become Carbon Net Zero with emissions by 2050. EK worked with the company’s Information Management (IM) team to identify some challenges contributing to the company’s increased emissions. These challenges included: ⬢ Proliferation of duplicative content; ⬢ High barrier to entry to reduce duplication proactively (e.g., linking existing content requires more effort than making copies); and ⬢ Collaboration software unintentionally builds silos and promotes content duplication due to a lack of visibility and awareness. THE SOLUTION EK identified a few potential pathways to quickly address carbon emissions within the IM team, but settled on reducing duplicate content within their repositories. EK developed an AI-Augmented Content Analysis tool to crawl the content collection and convert the content to a vector database enabling analysis. An AI model was leveraged to identify duplicate content and a dashboard visualized the duplicate content to incentivize remediation. LEVERAGED: AI to rapidly assess content and identify duplication. DEVELOPED: Dashboard to quantify and visualize duplicate content. SOCIALIZED: Strategic roadmap for continued expansion of the Green IM tool. THE RESULTS EK established a solution that: Documents Deduplicated 75 % KG of CO2 reduced from the environment EK implemented an AI-Augmented Content Analysis to Remediate Duplicate Content. Users able to use the application on their first try with minimal training 33 Million 34K
- 19. Review Prepare the content Identify the relevant content Deconstructing the Solution We identified content storage locations… …and scoped the audit to a pilot set of 226 million documents out of the company’s total 500 million. Audit parameters were established to guide the process and ensure comprehensive coverage. We built an indexer to crawl the content… …collection, creating a data pipeline that converted the content into a vector database. This enabled us to run scans, indexes, and queries that returned lists of duplicate unstructured and semi-structured content. We developed a dashboard… …that presented aggregate statistics on the presence and types of duplicate content. The client team then reviewed the data to decide whether content should be retained, updated, archived, or deleted. We implemented a GenAI model… …that continuously identified duplicate content across designated locations, while also uncovering opportunities for proactive and reactive automation to help users reduce duplication on an ongoing basis. Define the scope of problem
- 20. Deduplication Outcomes f Documents indexed and evaluated 226 million 33 million 34,000 k Documents deduplicated Outcomes Analysis 34kg KG of CO2 removed from the environment
- 21. THE CHALLENGE A leading federal research organization identified a significant challenge in managing its vast amounts of unstructured data. These challenges included: ● The firm's data landscape was cluttered with dark data, including project documents, proposals and research papers, some “classified” government information. ● Data was scattered across various platforms such as shared drives, email servers, and cloud storage solutions. ● This led to inefficiencies in data access, increased risk of exposing sensitive data, and difficulties in complying with federal regulations. THE SOLUTION IMPLEMENTED: Data pipelines – to connect and extract data siloed in different systems. ENABLED: Hybrid content classification based on predefined sensitivity rules BUILT: BI dashboard to provide system administrators a clear view into dark data THE RESULTS EK established a solution that: Documents scanned and analyzed by AI 30k The client needed a more flexible and sophisticated approach to automatically identify classified information and evolving categories of sensitive content specific to the organization buried in enterprise data assets. The solution leveraged an ML model to aid in the identification and labeling of potentially sensitive data across the enterprise, reducing risk and increasing productivity.
- 22. Review Prepare the content Identify the relevant content Deconstructing the Solution We pinpointed and unified content… …from scattered repositories, bringing clarity and order to the organization’s data landscape. We engineered powerful data pipelines… …to seamlessly connect and extract siloed data. Using a hybrid of cutting-edge AI/ML models and precision-driven methods, we crawled enterprise data, uncovering critical insights. We strategically classified data assets… …by sensitivity, criticality, and usage. Sensitive content with overly permissive access was flagged, enabling immediate adjustments to protect confidential information. The hybrid classification system empowered the organization to spot and control overshared sensitive content. We created an intuitive BI dashboard… …that gave system administrators crystal-clear visibility into dark data, enabling swift remediation of sensitive content access and safeguarding critical data across the enterprise. Define the scope of problem
- 23. KEY TAKEAWAYS
- 24. Initial Human Input The human defines a task or provides data to the AI model. AI Model Output The AI provides an initial output based on the input provided. Human GenAI Human Review and Refinement The human reviews and revises the output to meet specific needs or corrects any deficiencies. Resubmission to AI Model The human resubmits the refined output for further AI processing. AI Model Final Output The AI delivers a final output based on the human's refinements. End of Interaction Conclude the interaction, achieving the desired result.
- 25. AI Readiness of Content Develop a content operations model that addresses the relationships, the structure, the clean up, and the governance of the content. Model the knowledge domain Audit, clean up, and de-duplicate content Add structure and standardization Componentize the content Build governance policies and workflows Enrich with metadata
- 26. ENTERPRISE KNOWLEDGE Nurture the Foundations Strategize with precision Define the path forward Audit with clarity Illuminate content strengths and gaps Reaping the Benefits Harvest the Potential Generate with insight Amplify creativity with augmented intelligence Curate with care Maintain excellence through automated refinement Make Purposeful Progress Stay intentional We drive the strategy, not the machines Honor thy time Be wise stewards of your most valuable asset
- 27. ENTERPRISE KNOWLEDGE See you at LavaCon! Stop by Booth 14 in the Exhibit Area and say hello! Webinar attendees can get $250 off in-person tuition using the discount code EK24 https://lavacon.org
- 28. Q&A Thank you for listening.
- 29. Contact Us Paula Land, Principal Consultant, Advanced Content https://www.linkedin.com/in/paulaland/ Elliott Risch, Semantic AI Consultant https://www.linkedin.com/in/modusponens/ pland@enterprise-knowledge.com erisch@enterprise-knowledge.com X