Relationship Between Knowledge Management And Decision Making
Relationship between knowledge management and decision making
In today’s complex and turbulent environment, knowledge management has become increasingly important in decision making. Unlike in the past where organizations employed consultants or experts to aid with the decision making process, these actors have today been replaced by knowledge managers and decision making is increasingly being supported by decision support systems with built in knowledge base (Gamble 2001).
In this view, this paper examines the relationship between knowledge management and decision making. There is no universally accepted definition of the term ‘Knowledge management’. However, in this context, it will be used in reference to the strategies and practices used by an organization to capture, store and distribute knowledge that is either embodied in individuals or embedded in the process and practices of the organization (Holsapple 1995).
As noted by Joshi (2001), knowledge management has important implications on decision making in an organization. Effective KM should support the process of decision making and strategic planning. For example, knowledge management plays a major role in the planning phase of a project. Based on the current information, forecasters guide decision makers in making complex decisions in the business world characterized by increased risks and uncertainty. The entire decision making endeavour is made based on the outcome of forecasting, a knowledge intensive activity (Mohammed & Jalal 2011). Knowledge management is thus important in tactical decision making.
Knowledge management in organizations is supported by information technology. That is, Knowledge Management Systems rely on routines programmed in the logic of computational machinery (Malhotra 2004). The expertise and experiences of employees are stored in computerized databases. Both the tacit and explicit knowledge are stored in computerized databases and software programs for re-use in future (Malhotra 2004). In fact, most of the knowledge management experts acknowledge that technology contributes around 15% of the solution (Gamble 2001). However, technology in itself is not sufficient. Of great importance are the people with knowledge. People are the main determinant of the success or failure of knowledge management.
But still, managing knowledge is no easy task. As suggested by Karlin & Taylor (1998), acquiring knowledge is not the real problem that organizations face, rather the main challenge is the lack of skills to manage such knowledge in order to ensure effective decisions. It is a major challenge to capture knowledge such as data, information and experiences from individuals that possess them and to use such ingredients and transform them into knowledge that would enhance decision making (Mohsen et al. 2011)
Practical examples where knowledge management guide decision making
A perfect case where knowledge management can guide decision making is in the PC market. Given the competitive environment which has resulted in diminishing margins in the PC markets, Dell may need to shift focus to hosting services (Malhotra 2004). To do so more effectively, Dell would first have to harvest knowledge through experimentation, adaptation and innovation (Malhotra 2004). Then it would need to redefine both the business and customer value propositions.
Another area where knowledge management has proven to be useful in decision making is the banking sector. Due to increase in competition and the growing integration of financial institutions, most banks are increasingly targeting at improving on customer satisfaction in order to continue to thrive. As such, the process of knowledge creation, storage and distribution has become essential such that banks have assigned specialized personnel to manage these critical processes (Mohsen et al. 2011).
Knowledge management in banks is particularly evident in the fields of risk management, performance management, customer relationship management and marketing management (Jayasundara 2008). Banks have invested heavily in knowledge management systems such as Decision Support Systems, Data Mining and Data warehouses (Jayasundara 2008). Through such systems, banks have been able to improve and attain more efficient results in decision making.
According to a survey by Reuters, it was found that 90% of the companies that deployed a KM solution had more efficient results in decision making (Malhotra 2001). The survey also revealed that 81% of the companies that deployed a KM solution experienced an increase in their productivity (Malhotra 2001). A similar study by Lui & Young (2007) in the manufacturing sector showed that global manufacturing businesses utilized knowledge management systems such as Enterprise Resource Planning (ERP), Product Life Cycle Management (PLM) and Customer Relations Management to enhance their manufacturing decisions.
Given the vital role that knowledge management plays in decision making, it is not surprising to find many organizations transforming knowledge from being an abstract concept to a tangible and manageable one (Oduoza 2010). But, whilst there is a general agreement that knowledge management enhances the decision making process and leads to worthwhile decisions, there are certain instances where such systems can fail.
Why knowledge management systems may fail?
Where knowledge management information systems are seen an end in themselves, failure is guaranteed. ‘Knowledge’ and ‘information’ have different meanings. Knowledge resides in the user and happens only through the processing, analyzing and filtering of data via human brain (Liew 2007). On the other hand, information refers to refined data that can be re-used (Liew 2007). The two are not the same yet many organizations fail to understand the difference and become frustrated when significant investments in technology fail to deliver the expected results (Paprika 2001).
In order to harvest employee knowledge and to turn it into corporate knowledge that can be widely shared, strategic thinking and planning must come into play. Without a strategic plan or a guiding strategy for increasing margins, knowledge management information systems are bound to fail. For example, if the technology department is only department mandated with a knowledge management initiative, then such systems are unlikely to deliver the expected outcomes.
To ensure the success of knowledge management systems, it is important to foster an environment that allows for knowledge sharing. Yet most organizations are still defined by hierarchical structures that do not support interdepartmental collaboration (Paprika 2001). Creating an organizational culture that supports sharing of knowledge is important to avoid such systems from failing.
Also, too much focus on IT-based knowledge management may impair a firm’s capacity for knowledge creation (Malhotra 2000). Solutions often tend to specify the ‘minutiae of machinery’, ignoring the human psychology of how people in the organization acquire, share and create knowledge (Malhotra 2000). Such constrained and restricted perspective of knowledge management can be detrimental on a firm’s learning and adaptive capabilities (Malhotra 2000).
In fact, it becomes more problematic in a dynamic environment that requires multiple interpretations and ongoing evaluation (Malhotra 2000). In order to address this weakness inherent in IT-based knowledge management, it is equally important to focus on the synergy of innovation and human creativity. Nonetheless, the process of decision making is a knowledge intensive activity. Explicit knowledge that is obtained from repositories and the tacit knowledge that is obtained through a one on one interaction between a manager and an employee can be used to support decision making.
Gamble, P.R., 2001. Knowledge management: a state of the art guide. Kogan Publishers
Holsapple, C.W., 1995. ‘Knowledge management in decision making and decision support’. The international Journal of knowledge Transfer and Utilization, vol.8 (1), pp.5-22
Jayasundara, C.C., 2008. Knowledge Management in Banking Industries: uses and opportunities.
Joshi, K.D., 2001. ‘A framework to study knowledge management behaviours during decision making’. Journal of the University Librarians Association of Sri Lanka, Vol. 12, PP.68-79.
Karlin, S., and Taylor, H. 1998. An Introduction To Stochastic Modeling. Orlando, Fla.: Harcourt
Lehaney, B., 2004. Beyond knowledge management. Idea Group Inc
Liew, A., 2007. ‘Understanding data, information, knowledge and their inter-relationships’. Journal of knowledge Management Practice, vol.8 (2)
Malhotra, Y., 2004. ‘Why Knowledge Management Systems FailEnablers and Constraints of Knowledge Management in Human Enterprises’. In: Michael E.D. Koenig & T. Kanti Srikantaiah (Eds.), Knowledge Management Lessons Learned: What Works and What Doesn’t, Information Today Inc. American Society for Information Science and Technology Monograph Series, 87-112.
Malhotra, Y., 2001. Expert Systems for Knowledge Management: Crossing the Chasm between Information Processing and Sense Making. Expert Systems With Applications, 20,1, 7-16.
Malhotra, Y., 2000. ‘From information management to knowledge management: beyond the ‘hi-tech hidebound’ systems’. In: K. Srikantaiah & M.E.D. Koenig (eds), knowledge management for the information professional. Medford, N.J., Information Today Inc., pp.37-61
Mohammed, W. and Jalal, A., 2011. ‘The influence of knowledge management system (KMS) on enhancing decision making process (DMP)’. International Journal of Business and Management, vol.6 (8)
Oduoza, C.F., 2010. Decision support system based on effective knowledge management framework to process customer order enquiry, UK.
Paprika, Z.Z., 2001. Knowledge management support in decision making. Budapest, Hungary