Knowledge Management Unit 1 PDF

KNOWLEDGE MANAGEMENT Unit 1 Presentation of the Contemporary Knowledge Management Economy Agenda Course intro What is knowledge? Knowledge in the era of Artificial Intelligence Explicit and codified knowledge Knowledge creation at the level of the individual, group and organiza...

KNOWLEDGE MANAGEMENT Unit 1 Presentation of the Contemporary Knowledge Management Economy Agenda Course intro What is knowledge? Knowledge in the era of Artificial Intelligence Explicit and codified knowledge Knowledge creation at the level of the individual, group and organization Current trends and implications in the management of knowledge Knowing what knowledge to harness: business strategy in knowledge management Assessing or measuring knowledge and its value, impact and importance Engineers and Entrepreneurs in knowledge management Summary A picture containing drawing, light Description automatically generated Assignments *Formative assignments may be required to be submitted prior to summative assessments completion. Rules Attendance: Students are expected to arrive on time and attend all classes. If students arrive more than 15 minutes late, they may not be allowed to join the class until the class break and will be marked as absent (A) for the full class. 5 missed classes are permitted; the 6th absence is a failure. If you have a diagnosed medical condition which impacts your learning, you must communicate this with your Counsellor and Wellbeing Coordinator (Julieta Garcia Arias: [email protected] ) prior to the semester beginning. No Smartphones in class (only for online class activities, if you do not have your laptop with you)! Charge your laptop in advance! Use your laptops only for making notes and fulfilling the class tasks during the class hours! Only bottled water!​ Use trash cans! http://apocalypse-how.com/images/no%20expectations.jpg http://3.bp.blogspot.com/_aRY-8dSGN2Y/TIawaKbKa1I/AAAAAAAAAGY/tgw10btE2cA/s1600/reality-expectation-gap.png Expectations (1) 1. I expect that I… 2. I expect that my colleagues… I expect that I… 3. I expect that the instructor… What is knowledge? Knowledge Images - Free Download on Freepik Definition of knowledge Difference Between Knowledge and Truth | Compare the Difference Between Similar Terms information skills, and understanding that you have gained through learning or experience (Cambridge Dictionary, 2023) A visual history of human knowledge|Manuel Lima |TED2015 https://www.ted.com/talks/manuel_lima_a_visual_history_of_human_knowledge#t-460610 Formulating knowledge Data-Information-Knowledge in 3 minutes or less, KnowledgeMT: https://www.youtube.com/watch?v=sIjSY05JE9Q&t=121s&ab_channel=KnowledgeMT Explicit vs. Tacit (codified, implicit) knowledge Explicit knowledge Tacit knowledge knowledge that can be easily articulated and communicated. is not easily communicated because it is deeply rooted in employee experience or in a corporation’s culture. the type of knowledge that competitive intelligence activities can quickly is more valuable and more likely to lead to a sustainable competitive identify and communicate. advantage than is explicit knowledge because it is much harder for competitors to imitate. it is relatively easy to learn and imitate another company’s core competency or capability if it comes from explicit knowledge. Explicit vs. Tacit Knowledge SECI model (1) This model explains how tacit and explicit SECI model of Knowledge creation. knowledge are converted into organizational knowledge. The SECI model distinguishes four knowledge dimensions socialization, externalization, combination, and internalization which together form the acronym "SECI". Video: https://www.youtube.com/watch?v=Rr02SdqmY2A (Nonaka & Takeuchi, 1996) SECI model (2) Socialization Externalisation Combination Internalization Tacit to Tacit Tacit to Explicit Explicit to Explicit Explicit to Tacit sharing tacit knowledge face-to- publishing, articulating knowledge organizing, integrating knowledge, receiving and application by an face or through experiences combining different types of individual explicit knowledge social interaction as tacit to tacit knowledge is crystallized, thus explicit knowledge is collected explicit knowledge becomes part of knowledge transfer allowing it to be shared by others, from inside or outside the an individual's knowledge and can and it becomes the basis of new organization and then combined, be an asset for the organization knowledge edited or processed to form new knowledge meetings and brainstorming concepts, images, and written building prototypes the ability to see connections and documents recognize patterns and the capacity to make sense between fields, ideas, and concepts. (Nonaka & Takeuchi, 1996) Knowledge creation at the level of the individual, group and organization Knowledge creation according to the SECI model is about continuous transfer, combination, and conversion of the different types of knowledge, as users practice, interact, and learn. Furthermore, for this interplay to be most fruitful, it is important to support unstructured work environments in areas where creativity and innovation are important. (Nonaka & Takeuchi, 1996)  http://oaks.nvg.org/w/bbloom.jpg “A person knows about knowing, thinks about thinking” Benjamin Bloom (1913-1999) (1956) The Johari Window, 1955 (1) The Johari Window, 1955 (2) Video: https://www.youtube.com/watch?v=9TUTc3h01oA Knowledge Capital Factor of production Knowledge Capital Components: Human Capital Relational Capital Structural Capital (Kenton, 2023) Improving Knowledge Capital Hiring a diverse pool of individuals with different educational and professional backgrounds Employee development, including training and continuing education Research and development (R&D) Innovation Incentives and other benefits, such as scholarships, family assistance, and bonuses Work-life balance Ways to encourage and foster collaboration (Kenton, 2023) Human Capital All aspects, that humans possess, including the knowledge, skills, abilities, and attitudes that enable them to function in society and to produce needed goods and services. (Becker, 1964) Human capital cannot be owned by the company.  1 2 1.1 1.2 1.2.1 1.2.2 1.2.3 (Kucharčíková, 2009) 1.2.2 (Kucharčíková, 2009) Intellectual Capital Brainpower Know-how Knowledge Processes A source of competitive advantage. (Bassi, 1997) Structural Capital Hardware Software Databases Organizational structure Patents http://www.acquisio.com/wp-content/uploads/2013/01/brand-trademark-ppc-bidding.jpg Trademarks Everything else of organizational capability that supports those employees’ productivity Everything that gets left behind at the office when employees go home Provides customer capital, the relationships developed with key customers (Bontis, 2001)  1 2 1.1 1.2 1.2.1 1.2.2 1.2.3 (Kucharčíková, 2009) Function of Knowledge Management ‘to guard and grow knowledge owned by individuals, and where possible, transfer the asset into a form where it can be more readily shared by other employees in the company’ (Brooking, 1998) To enable and encourage knowledge sharing… Management must understand where and in what forms knowledge exists. They must then provide the right forums for knowledge to be shared. For tacit knowledge this implies a particular emphasis on informal communication, while for explicit knowledge this implies a focus on a variety of IT systems. Management must create/design the right environments, processes, and systems that provide the means and willingness for it to take place. To create a suitable work environment… Allow new knowledge to be created through: interaction practice, and experimentation. To provide systems that support the work process… These can be software or other systems that facilitate communication or brainstorming use of cross-functional project teams group of experts from different parts of the organization, led by a "generalist" project leader that facilitates the creation of bridges between communities of practice. Trends and implications in knowledge management 1. The era of the monolithic, single enterprise content management system has come to an end 2. Digital immortality will be possible via AI knowledge graphs 3. AI-augmented analytics will be mainstream 4. Blockchain excels with identity 5. Filling the gap between RPA and AI with intelligent process automation (Wells & Simone, 2018) 1. The era of the monolithic, single enterprise content management system has come to an end Many different person types exist within the company, and their requirements to interact with, produce, and consume content all vary widely. Not forcing all users to a single Enterprise Content Management (ECM) interface or Intranet. Corporate intranets that are heavy and have low adoption rates will meet their demise in the future. While these programs have good intentions, execution is poor leading users to adopt non- sanctioned tools to do their jobs. (Wells & Simone, 2018) Stephane Donze, CEO of AODocs “To help combat bad use of IT, CIOs will focus on solutions for social collaboration inside an organization, like a chatbot, that provides both a strong user experience while ensuring compliance with existing processes and regulation.”  Semantic Models for Constructing Knowledge Graphs | by Giuseppe Futia | Towards Data Science 2. Digital immortality will be possible via AI knowledge graphs The combination of artificial intelligence and semantic knowledge graphs will be used to transform the works of scientists, technologists, politicians, and scholars into an interactive response system that uses the person’s actual voice to answer questions. (Wells & Simone, 2018)  Example Semantic knowledge graph Episodic knowledge graph Every arrow represents a (subject, predicate, object) triple, with the annotation of the arrow denoting the respective predicate. The triple (Ban Ki-moon, SecretaryOf, UN) (Ma et al., 2018)  Jans Aasman - DATAVERSITY Jans Aasman, CEO of Franz “AI digital persons will dynamically link information from various sources (such as books, research papers, notes and media interviews) and turn the disperse information into a knowledge system that people can interact with digitally. These AI digital persons could also be used while the person is still alive to broaden the accessibility of their expertise.” Jans Aasman - DATAVERSITY 3. AI-augmented analytics will be mainstream Human brains are not wired to evaluate millions of data combinations at sub second speeds, but machine learning is literally built for this problem and the perfect solution. Business leaders and data analysts are better understanding that AI is not going to replace jobs, but augment them, and it is expected that in the next years, most data analysts will have the power of data science at their fingertips without the need to write code. (Wells & Simone, 2018) Example 4. Blockchain excels with identity Blockchain and cryptographic technologies will allow users to own and control their data, and for data to be trusted by third parties they choose to interact with. This will have profound implications for digital identity and will remove significant friction from real-world transactions (such as applying for jobs or financial products). In a shifting job landscape, blockchain-verified digital identities will allow people to build valuable career capital as they gain digital credentials representing their skills, qualifications, and work experiences. (Wells & Simone, 2018) Gaurav, V. (2020). Everything you need to know about the basics of Cryptography! LinkedIn: https://www.linkedin.com/pulse/everything-you-need-know-basics-cryptography-gaurav-verma/ 5. Filling the gap between RPA and AI with intelligent process automation Robotic Process Automation (RPA) has been one of the hottest areas of tech in the last 2 years; freeing resources up to focus on higher value activities. But it has fundamental limits, it’s only effective with repetitive processes and it cannot impact workflows involving unstructured content which makes up over 80% of data in most enterprises. At the same time, Artificial Intelligence (AI) and machine learning are seen as too esoteric; requiring too much data science expertise, too much hand-holding, too much uncertainty and risk about ROI. Companies will look to bridge the gap in the following years, between the horsepower of RPA and the intellect of AI/machine learning through what may experts are calling “intelligent process automation.” (Wells & Simone, 2018) Knowing what knowledge to connect: business strategy in knowledge management Implementation of a Successful KM Program 1. Create a Top 3 Objectives List of challenges and opportunities which your KM program will address These objectives align business direction with program goals. For each of the Top 3 Objectives, list the specific actions which can be readily communicated to the organization. This will allow everyone to understand exactly what will be done, what they are expected to do, and what’s in it for them. 2. Provide answers to questions about people, process, and technology This information defines who will participate, which processes will be required, and how tools will support the people and processes. 3. Define the KM Strategy for your KM program These are specific steps which will be taken to implement the program, thus translating the Top 3 Objectives into action. (Garfield, 2014) Assessing or measuring knowledge and its value, impact and importance It is extremely difficult to create any measure of knowledge sharing that will show an absolute one to-one correlation between a knowledge-sharing action and a business result. Much like measuring the success of training and development programs, measuring the impact of knowledge sharing requires correlation and some assumptions. To truly understand the impact of knowledge sharing and reuse, an organization must first understand the baseline business or process performance before beginning KM efforts. If you do not know where the starting line is, how can you say what your time is at the finish line? Measures should help to manage the implementation of the KM initiative. Measurement of KM Projects: a Practical Guide for Librarians | Legal Information Management | Cambridge Core Engineers and Entrepreneurs in knowledge management Re-engineering is in a way reframing the mechanics of business and knowledge management is giving it a living organic touch. Dynamically changing and improving organizations like an evolving self- adapting species, gives a more intelligent corporate mind than an electric shock of re-engineering. While re-engineering infers to a one shot change to achieve maximum increase in efficiency, knowledge management implies a continuous and ongoing review of schemas to achieve not only short term, but also long term, sustained growth in productivity with efficiency. When entrepreneurs try to decide about the future steps which enable development, they should try to see the bigger picture and direct all ships to one course: knowledge. One thing is certain, if you’re hungry for knowledge and you constantly work on expanding your own and your team’s skillset, your business will be successful. However, all that knowledge you gather needs to be managed properly. (Kyupova et al., 2009) KNOWLEDGE MANAGEMENT AND INNOVATION | Dr Kondal Reddy Kandadi | TEDxUniversityofBolton https://www.youtube.com/watch?v=DNUwZctwwhw The Future of AI | Peter Graf | TEDxSonomaCounty https://www.youtube.com/watch?v=KKYfxyKNPCc Summary What is knowledge? Explicit and codified knowledge Knowledge creation at the level of the individual, group and organization Knowledge in the era of Artificial Intelligence Current trends and implications in the management of knowledge Knowing what knowledge to harness: business strategy in knowledge management Assessing or measuring knowledge and its value, impact and importance Engineers and Entrepreneurs in knowledge management  Sources 1. Bassi, LJ 1997, 'Harnessing the power of intellectual capital', Training & Development, 51, 12, p. 25, MasterFILE Premier, EBSCOhost 2. Becker, G. S., Human Capital – A Theoretical and Empirical Analysis, with Special Reference to Education. 3rd Edition. Chicago. The University of Chicago Press, Ltd. 1993. – 392pp. 3. Bontis, N., Assessing Knowledge Assets: A Review of the Models Used to Measure Intellectual Capital. IJMR. 2001, - pp. 41-60. http://www.business.mcmaster.ca/mktg/nbontis/ic/publications/IJMRBontis.pdf 4. Brooking, A. (1998) Corporate Memory: Strategies For Knowledge Management 1st ed. Cengage Learning EMEA. 5. Kucharčíkova, A., Human Capital – Definitions and Approaches. Human Resources Management & Ergonomics Vol. 5 No. 2/2011, pp. 60-70. 6. Nonaka, L., Takeuchi, H., & Umemoto, K. (1996). A Theory of Organizational Knowledge Creation. International Journal of Technology Management, 11, 833-845. 7. Wheelen, Thomas L. & Hunger, J. David. (2017). Concepts in Strategic Management and Business Policy: Globalization, Innovation and Sustainability. 15th edition Published 21 Nov 2017 by Pearson. 8. Ma, Y., Tresp, V. & Daxberger, E. A. (2018). Embedding models for episodic knowledge graphs. J. Web Semant. 59, 100490 10.1016/j.websem.2018.12.008 9. Wells, J. & Simone, S. (2018). AI and Machine Learning: 9 Predictions for 2019. Database Trends and Applications. https://www.dbta.com/Editorial/News-Flashes/AI-and-Machine- Learning-9-Predictions-for-2019-129046.aspx 10. Garfield, S. (2014). Identifying the Top 3 Objectives for a KM Program. LinkedIn: https://www.linkedin.com/pulse/20140915160122-2500783-identifying-the-top-3-objectives-for-a-km- program/ 11. Soulejman Janus, S. (2016). Becoming a Knowledge-sharing Organization. A Handbook for Scaling Up Solutions through Knowledge Capturing and Sharing. International Bank for Reconstruction and Development / The World Bank. https://openknowledge.worldbank.org/server/api/core/bitstreams/4f2fd0d1-6295-5c4e-a2f7-da60c65c95fc/content 12. Russell, H. (2017). Measurement of KM Projects: A Practical Guide for Librarians. Legal Information Management, 17(2), 109-118. doi:10.1017/S1472669617000238 13. Kyupova, B., Rees, S.J. & Penev, K. (2009). Knowledge Management Applied To Business Process Reengineering. International Conference on SOFTWARE, SERVICES & SEMANTIC TECHNOLOGIES. https://www.researchgate.net/publication/229028911_Knowledge_Management_Applied_To_Business_Process_Reengineering Thank you! Dr. Olga Leontjeva Professor [email protected] euruni.edu