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MIS 475 : Chapter 11 : Knowledge , Management , Business intelligence , and analytics

by: Winn

MIS 475 : Chapter 11 : Knowledge , Management , Business intelligence , and analytics MIS 475

Marketplace > Marshall University > MIS 475 > MIS 475 Chapter 11 Knowledge Management Business intelligence and analytics
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In multiple choices and practice questions
Managing and Using Information System
Class Notes
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This 5 page Class Notes was uploaded by Winn on Tuesday April 5, 2016. The Class Notes belongs to MIS 475 at Marshall University taught by in Spring 2016. Since its upload, it has received 8 views.

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Date Created: 04/05/16
Chapter 11 : Knowledge , Management , Business intelligence , and analytics Explaining Important More Important 1) Knowledge management, business intelligence, and business analytics : a) Knowledge management : the processes necessary to generate, capture, codify and transfer knowledge across the organization to achieve competitive advantage. _ Individuals are the ultimate source of organizational knowledge. b) Business intelligence (BI) : the term used to describe the set of technologies and processes that use data to understand and analyze business performance. - The management strategy used to create a more structured approach to decision making based on facts that are discovered by analyzing information collected in company databases. c) Business analytics : the term used to refer to the use of quantitative and predictive models and fact-based management to drive decisions. ( subset of BI ). d) Intellectual property : - Intellectual capital is defined as knowledge that has been identified, captured and leveraged to produce higher-value goods or services or some other competitive advantage for the firm. - Intellectual property allows individuals to own their creativity and innovation in the same way that they can own physical property. - 4 types : patents for inventions, trademarks for brand identity , designs for product appearance, and copyrights for literary and artistic material, music, films , sound recordings , broadcasts and software. 2) Data, information and knowledge : a) Data : specific, objective facts or observations, such as “inventory contains 45 units” b) Information is defined by Peter Drucker as “data endowed with relevance and purpose” c) Knowledge is a mix of contextual information, experiences , rules and values , valuable information from the human mind; includes reflection, synthesis , context. - Knowing what : based on assembling information and eventually applying it. - Knowing how - Knowing why 3) Tacit versus explicit knowledge : a) Tacit knowledge : first described by Michal Polyani - Personal, context-specific, and hard to formalize and communicate. - Experiences , beliefs and skills. - Entirely subjective and is often acquired through physically practicing a skill or activity. b) Explicit knowledge : knowledge that can be easily collected, organized , and transferred through digital means such as a memorandum or financial report. - Objective, theoretical and codified for transmission in a formal, systematic method using grammar, syntax and the printed word. c) Knowledge conversion strategies : often of interest in the business environment. d) Socialization : the process of sharing experiences ;it occurs through observation, imitation and practice. 4) Knowledge management processes : - Knowledge generation: all activities that discover “new” knowledge, whether such knowledge is new to the individual , the firm or the entire discipline. - Knowledge capture: continuous processes of scanning, organizing, and packaging knowledge after it has been generated. - Knowledge codification is the representation of knowledge in a manner that can be easily accessed and transferred. - Knowledge transfer: transmitting knowledge from one person or group to another, and the absorption of that knowledge. 5) Competing with business analytics : - Hard to duplicate - Uniqueness - Adaptability - Better than competition - Renewability 6) Components of business analytics : 7) Big data : Big Data is the term used to describe techniques and technologies that make it economical to deal with very large datasets at the extreme end of the scale. 8) Social Analytics or social media analytics were created to address this issue and as expected, many vendors began offering packages that provided these tools.


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