| CODE | MGT2320 | ||||||||
| TITLE | Knowledge Management and Big Data in Organisations | ||||||||
| UM LEVEL | 02 - Years 2, 3 in Modular Undergraduate Course | ||||||||
| MQF LEVEL | 5 | ||||||||
| ECTS CREDITS | 4 | ||||||||
| DEPARTMENT | Business and Enterprise Management | ||||||||
| DESCRIPTION | The study-unit will introduce KM to the students by: briefly describing the history and evolution of KM; discussing the main taxonomies of knowledge (tacit vs explicit), knowledge resources, the knowledge based view (KBV) of the firm, KM life cycles/models and KM processes and enablers with focus on 福利在线免费 Technology (IT) as one of the most important enablers for KM (including big data). It will proceed with a review of the SMART model: Start with strategy, Measure Metrics and Data, Apply Analytics, Report results, Transform. This model allows for a better understanding of how big data interacts with business, and how the use of such data helps in creating value-added to businesses. The lectures will be complemented with case studies. Study-unit Aims: The aim of this study-unit is to make students aware of the importance of knowledge as a resource for organisations and to introduce Knowledge Management (KM) as the tool that helps to unlock the knowledge potential of an organisation, ultimately resulting in a more improved and effective decision-making process. It also aims at making the students aware of the role of big data as an enabler of KM. The study-unit also aims to make students aware and understand the importance of big data in today鈥檚 business environment. It shall provide a review of how to track the performance of a strategy using metrics and analytics. In a nutshell the study-unit will provide students with a general review on how to gather data, assess it, and interpret it. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Distinguish clearly between the main knowledge typologies (tacit vs explicit) and the different types of knowledge resources; - Describe the role of people, processes and technology in KM; - Define the SECI model in relation to knowledge creation; - Identify the important KM enablers and KM processes; - Describe how to set up a strategy in a way that helps inform the types of metrics to collect; - Recognise what are the difference sources of analytics and forecasting models available; - Identify how to create a 鈥渕etrics dashboard鈥�; - Using metrics and analytics to generate a 鈥渟tory鈥� to internal and external stakeholders. 2. Skills: By the end of the study-unit the student will be able to: - Apply KM to business situations; - Manipulate big data to help discover and create knowledge. Main Text/s and any supplementary readings: Main Texts: - Hislop, D., Bosua, R. and Helms, R. (2018), Knowledge Management in Organizations: A Critical Introduction, 4th Edition, UK: Oxford University Press. - Marr B. 2015. Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance. Wiley: U.K. Supplementary readings : - Edwards M, Edwards K. 2019. Predictive HR Analytics: Mastering the HR Metric. Kogan Page, Limited: - Glass R, Callahan S. 2014. The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat - Competitors, and Boost Profits. Wiley: Edwards M, Edwards K. 2019. Predictive HR Analytics: Mastering the HR Metric. Kogan Page, Limited: U.K. - Glass R, Callahan S. 2014. The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors, and Boost Profits. Wiley: U.K. - Marr B. 2016. Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. Wiley: U.K. - Marr B. 2017. Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things. Kogan Page: U.K. - Siegel E, Davenport TH. 2013. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. Wiley: U.K. - Stephenson D. 2018. Big Data Demystified: How to use big data, data science and AI to make better business decisions and gain competitive advantage. Pearson Education Limited: U.K. |
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| STUDY-UNIT TYPE | Lecture | ||||||||
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| LECTURER/S | David Baldacchino |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2025/6. It may be subject to change in subsequent years. |
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