COIS13013 Business Intelligence- Knowledge Management

Answers

1. Knowledge management refers to the process which creates, gathers, organizes and shares the knowledge in the organisation. IT is an approach which aims at using the knowledge in the best possible way. Knowledge management is very important for the organisation in the global business environment (Amalia, 2009). It enables business in better decision making and make it easy for the business to find relevant resources. It helps the organisation in reusing the knowledge and expertise and it also avoids mistakes. It helps the organisation in achieving its objectives and the costs are reduced which enhances the profits of the organisation. Human Knowledge can build up strong pillars for the multinational companies and helps them in surviving the tough competition in the industry. It helps in surviving the volatility and in finding out right solutions for the business (Garfield, 2014).

Technology plays a key role in managing the knowledge management programs. It accelerates the speed of knowledge creation and transfer. Technologies facilitate knowledge management and support the systems of Knowledge management. The technologies which are used to support Knowledge management includes Artificial intelligence, Internet based systems, Business Intelligence, Knowledge portals and many more (Hislop, 2013). Artificial intelligence is used for acquiring knowledge and creating networks. Business Intelligence manipulates the data and prepare for the best performance and security of the business. It helps business in decision making after considering the knowledge and enhances the policies of the business (Jelenic, 2011). Knowledge portals makes simple for navigating towards the desired information. It helps in managing knowledge by supporting and collaborating it within the process. The internet based systems supports the knowledge creation in the organisation and helps in sharing that knowledge within the organisation and its various departments. It supports cooperative work in the organisation (Venters, 2010).

2. Data analytics can be understood as the process which examines the sets of data to derive conclusions about the information gained with the help of systems and software’s. Data analytics is mainly used by the commercial industries so that the organisations could make wiser decisions and in lesser time to verify or disapprove the hypothesis and theories. Text analytics is also known as Text mining which can be referred to the analysis of quality information and trends which helps in deriving potential insights and it helps in solving the business problems. Where Data analysis includes larger category and various types of data which also includes text analysis, Text analysis on the other hand, is a narrow category where only trends and patterns are analysed for gaining insights (Moreno and Redondo, 2016).

Data analytics is very useful for supporting business decisions. It enhances the speed of operations and increases efficiency of the business processes (EY, 2014). It could be used for taking future decisions and it also provides a competitive edge to the business which can be seen through many examples. Three leading companies of the world like Google uses data analytics to make decisions which helps in bringing ultimate success and profitability to the business. Google uses People analytics to help the HR department of the company to make decisions for analysing the performance of its employees. The second example can be taken of Amazon which uses the data in the form of recommendations given by its customers to take further saes decisions. The third example can be taken of airline companies which use data to track the customer’s information, luggage and future intentions to travel to provide them loyalty offers and to optimizing their operations. It helps in analysing the customer’s behaviours and take further actions which drives the sales of the company year after year.

3. The Decision Support Systems are the systems which helps business in taking the decisions. It is the set of computer programs and data which helps in the decision making activities of the organisation. It collects and compiles the information in raw form and then analyses it to solve the problems and make final decisions (Liu, et al., 2015). On the other hand, Knowledge based decision support system involves an Expert system which uses artificial intelligence and helps in taking the decisions for the business. Here the knowledge is used in making decisions making. The Decision support system which includes a component known as Knowledge is Knowledge based decision support system. It helps managers in making the strategic actions so that the best can be decided of the future of the business (Özbayrak and Bell, 2003).

Knowledge Based Decision Support systems have become famous in recent years and are used in the business organisations for many applications. Knowledge Based Decision Support systems are used in the fields of Medicine & Research and Development to improve the diagnosis and to reduce medical errors. The second example can be taken of the application of Knowledge Based Decision Support systems in the field of Environmental management where the issues of environment are resolved. For e.g. water and waste management practices and pollution management. The third example can be considered of the application of Knowledge Based Decision Support systems in establishing relationship with the suppliers and customers. It can be done by considering the recommendations and providing the services as desired by the customers. It is also helpful in developing and maintaining internal relationships and external relationships. It also helps in meeting the challenges of the volatile market (Liu, et al., 2015).

4. The decision support systems are used to solve variety of problems and help the service providers in making good decisions for the business. In Decision Support Systems databases and human machines are used combined to make scientific decisions. The market economy of today is consumer driven and in this economy, service oriented decision support systems are required so that the services can be managed effectively and response can be given rapidly (Wheeler, et al., 2010).

Three service models which provide the foundation to service oriented DSS can be Data Driven DSS, Communication driven DSS and Knowledge Driven DSS. In Data driven DSS the access is received on the internal and external data of the company. In Communication driven DSS, the network and communication technologies are used so that better decisions can be made. It also helps groups as a whole to make collaborative decisions. The Knowledge driven DSS provide the expertise to the business to solve specialised problems and the actions are recommended to the managers so that future decisions can be made (Iwai and Aoyama, 2011). There can be certain examples for these service models which are discussed below:

Data Driven DSS: Accessing the database of the particular sector or city to make better decisions and to provide them effective services. Like collecting the data in the city to predict the number of deaths in the next month in the city.

Communication driven DSS: For example Netmeeting by Microsoft which helps the groups to connect, communicate with each other sitting at different places and then to take effective group decisions for the companies.

Knowledge driven DSS: For example, MYCIN which is a reasoning program which assists in diagnosing the blood disease in the patients (Demirkan and Delen, 2013).

References

Amalia, M 2009, ‘Knowledge Management: A Case of a Multinational Company Subsidiary in Indonesia’, Institute for Development Policy and Management.

Demirkan, H and Delen, D 2013, ‘Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud’, Decision Support Systems, 55(1), pp.412-421.

EY 2014, ‘Big data Changing the way businesses compete and operate’, Insights on governance, risk and compliance.

Garfield, S 2014, ‘15 Knowledge Management Benefits’, LinkedIn.

Hislop, D 2013, Knowledge management in organizations: A critical introduction, Oxford University Press.

Iwai, A and Aoyama, M 2011, ‘Automotive cloud service systems based on service-oriented architecture and its evaluation’, In Cloud Computing (CLOUD), 2011 IEEE International Conference on (pp. 638-645). IEEE.

Jelenic, D 2011, ‘The Importance of Knowledge Management in Organizations – With Emphasis on the Balanced Scorecard Learning and Growth Perspective’, International Conference 2011.

Liu, S, Hudson Smith, M, Tuck, S, Pan, J, Alkuraiji, A and Jayawickrama, U 2015, ‘Where can knowledge-based decision support systems go in contemporary business management-a new architecture for the future’, Journal of Economics, Business and Management, 3(5), pp.498-504.

Moreno, A and Redondo, T 2016, ‘Text Analytics: the convergence of Big Data and Artificial Intelligence’, IJIMAI, 3(6), pp.57-64.

Özbayrak, M and Bell, R 2003, ‘A knowledge-based decision support system for the management of parts and tools in FMS’, Decision Support Systems, 35(4), pp.487-515.

Venters, W 2010, ‘Knowledge management technology-in-practice: a social constructionist analysis of the introduction and use of knowledge management systems’.

Wheeler, N, Funk, T, Raffuse, S, et al. 2010, ‘A New Decision Support System Based On A Service-Oriented Architecture’, Presented at the 9th Annual CMAS Conference, Chapel Hill, NC.