AI and Big Data in KM
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The document discusses using artificial intelligence and big data in knowledge management. It covers extracting knowledge from data through information architecture and data curation. It then discusses utilizing AI to deliver knowledge through chatbots using natural language processing, predicting trending knowledge areas, and personalizing knowledge delivery. The goal is to provide knowledge management that is dynamic, accurate, and personalized through leveraging AI technologies.
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AI and Big Data in KM
- 1. SIKM May 18, 2021 Artificial Intelligence & Big Data In Knowledge Management Copyright © 2019 A.J. Rhem & Associates, Inc. 1
- 2. Agenda Copyright © 2021 A.J. Rhem & Associates, Inc. 2 About Anthony Rhem & AJ Rhem & Associates, Inc. Introduction: KM, AI, and Big Data Extracting Knowledge Organizing & Optimizing Your Data Utilizing AI in the Delivery of Knowledge
- 3. About Anthony J. Rhem Anthony J. Rhem, PhD.: serves as the CEO/Principal Consultant of A.J. Rhem & Associates, Inc., a privately held Knowledge Management, Artificial Intelligence, and System Integration Consulting, Training and Research firm located in Chicago, Illinois. Anthony has over thirty (30) years of experience in information technology with a focus in Knowledge Management and Artificial Intelligence. A published author, educator, and researcher; Anthony has presented the application and theory of Software Engineering Methodologies, Knowledge Management, Artificial Intelligence, Information Architecture, and Big Data at universities and conferences in the US, Europe, Asia and Australia. Copyright © 2021 A.J. Rhem & Associates, Inc. 3
- 4. Introduction 4 Copyright © 2021 A.J. Rhem & Associates, Inc.
- 5. KM, AI, and Big Data 5 THE EMERGING CHALLENGE FOR ORGANIZATIONS IS TO DERIVE MEANINGFUL INSIGHTS FROM AVAILABLE DATA AND RE-APPLY IT INTELLIGENTLY. KNOWLEDGE MANAGEMENT PLAYS A CRUCIAL ROLE IN EFFICIENTLY MANAGING THIS DATA AND DELIVERING IT TO THE END USERS TO AID IN THE DECISION-MAKING PROCESS. KNOWLEDGE MANAGEMENT (KM) AND BIG DATA WITHIN ORGANIZATIONS, THE ADVANCEMENT OF SEARCH TECHNOLOGIES IS MAKING AN IMPACT. THE “FINDABILITY” OF INFORMATION AND KNOWLEDGE WITHIN LARGE AMOUNTS OF UNSTRUCTURED DATA CONTRIBUTE TO THE ABILITY TO DISSEMINATE AND REUSE THE KNOWLEDGE OF THE ENTERPRISE. AI THROUGH PREDICTIVE ANALYTICS AND KNOWLEDGE FLOW OPTIMIZATION WILL PROVIDE A DYNAMIC, ACCURATE AND PERSONAL DELIVERY OF KNOWLEDGE. AI WILL ENABLE ANALYZING AND SYNTHESIZING DATA TO DERIVE MEANINGFUL KNOWLEDGE. Copyright © 2021 A.J. Rhem & Associates, Inc.
- 6. KM, AI, and Big Data 6 BIG DATA IS UNIQUELY POSITIONED TO TAKE ADVANTAGE OF AI ANALYTICS, KM PROCESSES AND PROCEDURES. THESE PROCESSES AND PROCEDURES ENABLE KM TO PROVIDE A RICH STRUCTURE TO ENABLE DECISIONS TO BE MADE ON A MULTITUDE AND VARIETY OF DATA. THE TOTAL PICTURE IS WHAT SHOULD DRIVE DECISIONS”. KM ENABLES ORGANIZATIONS TO TAKE THE TOTAL PICTURE BIG DATA PROVIDES. LEVERAGING AI TOOLS THAT PROVIDE PROCESSING SPEED TO BREAK UP THE DATA INTO SUBSETS FOR ANALYSIS WILL EMPOWER ORGANIZATIONS TO MAKE DECISIONS ON THE VAST AMOUNT AND VARIETY OF DATA AND INFORMATION BEING PROVIDED. Copyright © 2021 A.J. Rhem & Associates, Inc.
- 7. Extracting Knowledge: Organizing & Optimizing Your Data 7 Copyright © 2021 A.J. Rhem & Associates, Inc.
- 8. Extracting Knowledge from Data starts with: Information Architecture Information Architecture: Information architecture (IA) focuses on organizing, structuring, and labeling content in an effective and sustainable way. The goal is to help users find information and complete tasks. To do this, you need to understand how the pieces fit together to create the larger picture, how items relate to each other within the system – Usability.gov 8 Copyright © 2021 A.J. Rhem & Associates, Inc.
- 9. Information Architecture Classifications & Hierarchies Labels & Indexing Navigation Search 9 INFORMATION ARCHITECTURE CONNECTS PEOPLE TO YOUR DATA Copyright © 2021 A.J. Rhem & Associates, Inc.
- 10. Three Major Parts of Information Architecture Users • Users: Information needs, audience types, expertise, tasks • [How to Use the Information] Content • Content: Document types Objects, structure, attributes, Meta-information • [How to describe the information] Context • Context: Business models & goals, corporate culture, resources • [Where information is used] 10 Copyright © 2021 A.J. Rhem & Associates, Inc.
- 11. IA Optimizes Knowledge Flow • An understanding of how knowledge flows throughout your organization is essential for delivering KM • Using IA: Connecting explicit knowledge to the tacit knowledge holders creates a holistic view of knowledge. • Tacit knowledge holders • workers who are experienced with executing certain tasks, developing a solution, working in a specific industry, practice area or company while leveraging the stored knowledge. 11 Connect Collect Store Use/Reuse Learn Create New Store New Copyright © 2021 A.J. Rhem & Associates, Inc.
- 12. IA: Data Curation for Big Data Analytics • IA effects Big Data Analytics through Content (Data) Curation • Content (Information & Knowledge) Curation is an essential step to enabling Big Data Analytics to be effective • Curate the content adding the necessary metadata (descriptive, structural, administrative) and categorization elements aligning it to the domain taxonomy (apply Information Architecture). • Content curation provides the methodological and technological data management support to address data quality issues maximizing the usability of the data. 12 Copyright © 2021 A.J. Rhem & Associates, Inc.
- 13. Content Curation Process 13 Copyright © 2021 A.J. Rhem & Associates, Inc.
- 14. Utilizing AI to Deliver Knowledge 14 Copyright © 2021 A.J. Rhem & Associates, Inc.
- 15. AI in KM Knowledge Delivery • AI plays an important part to KM by elevating how the delivery of knowledge occurs to the people who need it. AI is used to scale the volume and effectiveness of knowledge distribution. AI KM 15 Copyright © 2021 A.J. Rhem & Associates, Inc.
- 16. AI Powered Delivery of Knowledge To provide KM, the delivery of knowledge must be • Dynamic • Dynamic knowledge is constantly updated adhering to your organization’s content (information and knowledge) lifecycle management processes. This also includes the experts who can provide insights about the knowledge. The Dynamic component of knowledge reflects your organization’s brand, tone and evolves over time. • Accurate • The Accurate component of knowledge is identified as the authoritative source and authoritative voice for that subject matter. This knowledge is accepted by your organization as the “source of truth”. • Personal. • The Personalized component of knowledge answers the questions that the users of your knowledge are seeking. Personalized knowledge is tailored to what the individual need to make a decision and presented in a way that is tailored to the device and applications the users are leveraging to access answers to their questions. Personalized knowledge is facilitated by how knowledge flows throughout your organization. 16 Copyright © 2021 A.J. Rhem & Associates, Inc.
- 17. AI Powered KM: Chatbots w/ Natural Language Processing (NLP) Personalize knowledge – through specialized knowledge assistants (bots) working together with users, knowledge can be made available in a personalized way depending on the needs of the consumer. Chat Bots will provide value for all knowledge workers in the various business areas along critical decision-making points with personalization of the delivery of knowledge. Copyright © 2020 A.J. Rhem & Associates, Inc. 17
- 18. Copyright © 2020 A.J. Rhem & Associates, Inc. 18 Predict trending knowledge areas/topics that your knowledge workers needs Identify which targeted knowledge will resonate with your knowledge workers based on real- time engagement and content consumption Personalize knowledge based on individual preferences Improve content decisions by leveraging machine learning around what content will be best suited to address the situation AI will make search and its search products more relevant, precise and efficient. AI through intents will be able to better know what content your knowledge workers need. Intents will provide a better understanding of what a person is looking for by better understanding the intended use of the content. Chatbots w/ Natural Language Processing (NLP): will provide cognitive capabilities to understand, interpret and manipulate human language that will enable the bots to anticipate the needs, attitudes and aspirations of users to aid in decision making and improve outcomes, all geared to achieve substantial business value. AI will power the delivery of Knowledge by providing the ability to :
- 19. AI in KM Predicting Trending Knowledge • Predict trending knowledge areas/topics that your knowledge workers needs • Leverages supervised learning algorithms that will learn over time. • Supervised learning allows the algorithm to make inferences from labeled data. 19 Copyright © 2021 A.J. Rhem & Associates, Inc.
- 20. Discussion 20 Copyright © 2021 A.J. Rhem & Associates, Inc.
- 21. Anthony J. Rhem, PhD. CEO/Principal Consultant 205 North Michigan Ave., Suite 810 Chicago, Illinois 60601 Phone: 312-396-4024 email: tonyr@ajrhem.com Website: www.ajrhem.com Blog: http://knowledgemanagementdepot.com/ Latest Book: Copyright © 2019 A.J. Rhem & Associates, Inc. 21
Editor's Notes
- Put into practice, Information Architecture (IA) connects people to content (Information & Knowledge) by incorporating: Classifications and Hierarchies Labels and Indexing To improve Navigation and Search
- Interacting with stored knowledge in repositories and connecting this explicit knowledge to the tacit knowledge holders brings together a holistic view of knowledge. Tacit knowledge holders include the knowledge workers who are experienced with executing certain tasks, developing a solution, working in a specific industry, practice area or company while leveraging the stored knowledge.
- Deep learning algorithms run data through several “layers” of the algorithm, each layer passes a simplified representation of the data to the next layer. This allows the algorithm to learn more as it passes through each layer. The ability to process large numbers of features makes deep learning very powerful when dealing with unstructured data.