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Leveraging KM as the Foundation for AI

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This presentation from Joe Hilger, Founder and COO of Enterprise Knowledge was presented at the KM Showcase 2020 in Arlington, VA on March 5th. The presentation addresses why knowledge management is the foundation for successful artificial intelligence. Hilger provides reasoning and examples for why taxonomy, content strategy, governance, and KM leadership are foundational requirements for organization's pursuing recommender systems, chat bots, and much more. Lastly, he defines Knowledge Artificial Intelligence and provides a brief overview of knowledge graphs.

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Leveraging KM as the Foundation for AI

  1. 1. LEVERAGING KM AS THE FOUNDATION FOR AI 2020 KM Symposium March 5, 2020 @EKCONSULTING
  2. 2. KNOWLEDGE TO AI
  3. 3. Highly internalized, gained through professional, educational, and personal experience that has not yet been recorded or captured. Knowledge that has been made visible by capturing, recording, or embedding it in databases, documents, and processes. Structured Easy for systems and machines to read and process. More difficult for human users to understand without underlying context. Unstructured Generally easy for human users to read and understand, but more difficult for machines to use and process. Tacit Explicit FORMS OF KNOWLEDGE
  4. 4. FIND CAPTURE ACT KM LIFECYCLE Knowledge and Information Management (KM) efforts exist on the same spectrum, with knowledge moving from tacit to explicit, and then content moving from unstructured to structured. The most effective KM efforts are those that are driven by business value and end user needs. KM can offer immediate value while laying the foundation for advanced search, customized knowledge experience, and artificial intelligence. @EKCONSULTING
  5. 5. BUSINESS OUTCOMESKM OUTCOMES ▪ Improved findability and discoverability of content. ▪ Less time waiting, searching, and recreating knowledge. ▪ Increased use and reuse of information. ▪ Decreased knowledge loss. ▪ Improved organizational awareness and alignment. ▪ Culture of knowledge sharing. ▪ Enhanced quality, availability, and speed of learning. ▪ Increased awareness of, and connection to, experts. ▪ Improved productivity. ▪ Decreased cost (and cost avoidance). ▪ Increased employee satisfaction and retention. ▪ Faster and better up-scaling of employees. ▪ Improved customer satisfaction and retention. ▪ Improved execution. ▪ Improved sales. ▪ Increased collaboration and innovation. ▪ Future readiness. @EKCONSULTING
  6. 6. KM FOUNDATIONS FOR AI WHY KM MATTERS CONTENT CLEANUP CONTENT GOVERNANCE TAXONOMY DESIGN CONTENT TYPES KNOWLEDGE SHARING CULTURE KM LEADERSHIP @EKCONSULTING
  7. 7. TAXONOMY DESIGN WHAT IS TAXONOMY DESIGN? WHAT IS THE VALUE TO AI? Classification of an organization's knowledge and information for the purposes of findability and discoverability. An effective business taxonomy will span an enterprise’s information, users, and potential needs as it is usable, intuitive, and natural. Ensure faceting works and different types of content from different sources can be seamlessly integrated. Provide a way for the machine to understand an organization’s vocabulary. Taxonomies are the building blocks to Ontologies, which are foundational for building relationships and context. Ensures an organization can effectively capture, manage, and present their growing store of information. Provides structure to unstructured information. Informs metadata and content tagging efforts. WHAT IS THE INDEPENDENT VALUE? @EKCONSULTING
  8. 8. CONTENT CLEANUP WHAT IS CONTENT CLEANUP? WHAT IS THE INDEPENDENT VALUE? WHAT IS THE VALUE TO AI? The review and cleaning of outdated, obsolete, incorrect, and duplicative content. Determine if content should be maintained as-is, updated, archived, or removed. Cleaned and enhanced content, with tags, ensures the right content surfaces for the end user and is weighted appropriately. AI requires training material. An organization needs to provide quality training material so that it can get to a high quality machine. Improved KM is worthless without the right information. Increases productivity as employees spend less time shifting through inaccurate and/or not actionable content. Decreases risk as employees are presented with accurate content.“Only 20-30% of all content stores should be maintained during a content migration.” @EKCONSULTING
  9. 9. CONTENT TYPES WHAT IS A CONTENT TYPE? WHAT IS THE INDEPENDENT VALUE? WHAT IS THE VALUE TO AI? The process of defining common or standard templates for the types of content users interact with within a specific system. Includes the definition of text fields and description of what information should be found in each field as well as the definition of which metadata fields apply to a specific template. Allows AI to surface appropriate elements of content in the most flexible manner possible. Enables different types of content to be mapped intelligently. Enables granular tagging for greater relationship identification. Standardizes and optimizes the way content is captured and presented, ensuring it remains “fresh” and accurate to best serve the needs of the organization. Decreases the amount of time required to create new content. Increases the readability and digestibility of content. @EKCONSULTING
  10. 10. CONTENT GOVERNANCE WHAT IS CONTENT GOVERNANCE? WHAT IS THE INDEPENDENT VALUE? WHAT IS THE VALUE TO AI? A common set of standards and processes that are designed to maintain and consistently improve the content over time. An effective governance plan includes: - Business Case - Roles and Responsibilities - Policies and Procedures - Communication, Education, and Marketing Ensures accuracy of the content. Ensures the accuracy of the ontology model. Enables the appropriate controls and flexibility to serve evolving needs, user behaviors, and clearer user understanding. Enables ongoing content management and continuous improvement of content over time. Increases the effectiveness of the organization’s content strategy efforts in order to meet its business objectives. @EKCONSULTING
  11. 11. KM LEADERSHIP WHAT IS KM LEADERSHIP? WHAT IS THE INDEPENDENT VALUE? WHAT IS THE VALUE TO AI? Enables and promotes knowledge processes such as knowledge creation, transfer, and dissemination, and creates environment/culture/ atmosphere which stimulates and facilitates active engagement in these processes. With KM Leadership, an organization can better strategize, budget, and champion for the implementation of the KM elements foundational for actionable AI. Promotes the acceptance of a new culture, drives adoption, and integrated change. Promotes the value of sound and systematic knowledge sharing by modeling best-practices, signaling the strategic importance of the investment. Encourages, recognizes, and rewards knowledge sharing in support of the sustainment of KM efforts. @EKCONSULTING
  12. 12. KNOWLEDGE SHARING CULTURE WHAT IS A KNOWLEDGE SHARING CULTURE? WHAT IS THE INDEPENDENT VALUE? WHAT IS THE VALUE TO AI? An organization that builds knowledge sharing into the day-to-day behaviors and actions of its workforce. Includes user-centric processes and concepts, such as Gamification, to ignite collaboration and learning. Surfaces the newly shared knowledge in a form that is easy to find and discover. All of the right knowledge is seeded in the right place to add the value. Knowledge must be captured to be related and surfaced. Faster knowledge dissemination. Keeps knowledge workers engaged, increases productivity and innovation, and fosters continuous learning and growth. Rewards good behavior. @EKCONSULTING
  13. 13. FIVE LEVELS OF KAI KNOWLEDGE ARTIFICIAL INTELLIGENCE (KAI) IS THE APPLICATION OF ARTIFICIAL INTELLIGENCE CONCEPTS AND THEORIES TO ENSURE THE APPROPRIATE CAPTURE, MANAGEMENT, AND PRESENTATION OF THE FULL SPECTRUM OF KNOWLEDGE AND INFORMATION. @EKCONSULTING
  14. 14. FIVE LEVELS OF KAI ANSWER ▪ Basic query/response capabilities. ▪ Action-oriented search, chatbots, and voice assistants. @EKCONSULTING
  15. 15. FIVE LEVELS OF KAI RECOMMEND ▪ Recommendations based on user behavior, tags, analytics, and a combination thereof. ▪ “Push” capabilities to go beyond finding and enabling discovery. @EKCONSULTING
  16. 16. FIVE LEVELS OF KAI ▪ Combination of different types, formats, and sources of content into a contextualized whole. ▪ Integrates parts and wholes of individual content types in order to provide a cohesive answer. COMBINE @EKCONSULTING
  17. 17. FIVE LEVELS OF KAI INFER ▪ Goes a step beyond Combine and introduces programmed decision-making logic into the mix. ▪ Triggered based on intelligence or prompts in order to build new content that doesn’t just answer a query, but answers a need/want. ▪ Results in the creation of new content, highly customized for a specific user or case. @EKCONSULTING
  18. 18. FIVE LEVELS OF KAI CONT. ▪ Incorporates predictive capabilities to spot trends, identify potential risks and opportunities, and provide actionable guidance leveraging a complete view and understanding of the knowledge and information that exists. ▪ Generates and surfaces the right knowledge before you even know you need it. ▪ Spots demographic trends to fill gaps and needs before they exist. ADVISE @EKCONSULTING
  19. 19. THE FUTURE SOLUTION + ONTOLOGY KNOWLEDGE GRAPHS = ARTIFICIAL INTELLIGENCE @EKCONSULTING
  20. 20. KNOWLEDGE GRAPHS Another word for graphs are “networks” Nodes Edges • A knowledge graph: a network of the things we want to describe and how they are related • We construct a semantic model since we want to capture and generate meaning with the model Google’s knowledge graph is a popular use case “The application of graph processing and graph DBMSs will grow at 100 percent annually through 2022 to continuously accelerate data preparation and enable more complex and adaptive data science.” – Gartner’s Top 10 Data and Analytics Technology Trends for 2019 @EKCONSULTING
  21. 21. WHAT IS A KNOWLEDGE GRAPH Content Sources Subject Predicate Object Project A hasTitle Title A Person B isPMOn Project A Document C isAbout Topic D Document C isAbout Topic F Person B IsExpertIn Topic D … … … Business Ontology Graph Database Enterprise Knowledge Graph Business Taxonomy Person B Project A Document C Person F Topic D Topic E @EKCONSULTING
  22. 22. BENEFITS OF A KNOWLEDGE GRAPH Understanding Context Relationships between information allows gives us a better understanding of how things fit together so that search can be more precise. Structured and Unstructured Information Graphs allow for the integration of structured and unstructured information so that users can search for data and content at the same time. Natural Language Search Graphs store information the way people speak. Integrating a graph into your search makes natural language search easier to implement. Aggregation Graphs allow for aggregation of information from multiple disparate solutions so that search results can display information that exists in multiple locations and formats. @EKCONSULTING
  23. 23. HOW DO I GET STARTED Educate the organization on how AI can and should work. Investigate the products and tools that you have access to that can automate information management. Inventory your content to understand what you have and how to organize it. Start small and then grow. @EKCONSULTING
  24. 24. WE’LL BE ANSWERING QUESTIONS NOW Q A& THANKS FOR LISTENING Q & A SESSION @EKCONSULTING
  25. 25. CONTACT US JOE HILGER JHILGER@ ENTERPRISE-KNOWLEDGE.COM 571-436-0271 ZACH WAHL ZWAHL@ ENTERPRISE-KNOWLEDGE.COM 571-800-9803

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