Software development can be one of the most complex activities within the information technology (IT) field. Software development can also be one of the most costly endeavors as well. Development projects are rarely finished on time or within budget. “…the discipline of software development is immature. Schedule and budget overruns are typical, low quality and functionally never delivered are other signs of immaturity” (Baskerville & Pries-Heje, 1999, p.26).
Carnegie Mellon’s Software Engineering Institute (SEI) developed the Capability Maturity Model (CMM) to provide a systematic methodology to help firms identify and improve the maturity level of their developmental processes. The core model is based on five levels of maturity: initial, repeatable, defined, controlled, ad optimizing. Each level is comprehensively defined by characteristics moving from the chaotic, intuitive, qualitative, quantitative, and feedback states of development.
SEI’s CMM method provides a solid benchmark for development assessment. However, not all firms have embraced the model. “But critics believe that CMM structures may encourage a rigid bureaucracy that can stifle creativity and innovation, and demoralize the workforce. Highly competitive and innovative software developers exemplified by Borland, Claris, Apple, Symantec, Microsoft, and Lotus did not plunge into the CMM along with early adopters (Bach, 1994b)” (Baskerville & Pries-Heje, 1999, p.28). Developments within knowledge management have offered new possibilities in CMM utilization.
A small Danish company, Proventum, develops highly sophisticated e-commerce websites. The strength of their organization came from a highly fluid and innovative style of programming development. “Proventum rapidly grew to 20 employees during its first year, including project managers, designers, programmers, and database specialists” (Baskerville & Pries-Heje, 1999, p.32). For three years the entrepreneurial spirit nurtured a cultural of innovation and informality. As with many entrepreneurial firms, Proventum ran into serious growing pains. “…during their third year the typical problems of software development organizations began to threaten the firm’s survival. Amid the chaotic development milieu, disputes arose over how to run projects and which technologies were important, and programmers began to quit. The firm dwindled to twelve employees. Proventum seemed unable to repeat or build on its success” (Baskerville & Pries-Heje, p.33).
The Proventum case is an excellent example illustrating two fundamental knowledge management processes: “linkage between knowledge management and organizational goals, and an active organizational, behavioral and technical knowledge infrastructure (Davenport et al. 1998)” (Baskerville & Pries-Heje, p.35). Proventum was able to turn their organization around utilizing a hybrid CMM – KM approach.
Reference
Baskerville, R., & Pries-Heje, J. (1999, Spring). Knowledge Capability and Maturity in Software Management. The DATA BASE for Advances in I
Wednesday, November 4, 2009
Wednesday, October 28, 2009
One of the Deadliest Sins of Knowledge Management in Practice: Developing a Shared Context
I have selected the fourth deadly sin of knowledge management, “Not Understanding that a Fundamental Intermediate Purpose of Managing Knowledge is to Create Shared Context” (Fahey & Prusak, 1998, p.268). The evolution of Decision Support Systems (DSS) truly illustrates the importance in developing a shared context across the organization. “Making a good decision starts with having or gathering the right information upon which to base a decision” (Remus & Kottemann, 1986, p. 17). An organization depends upon sound decision-making practices in order to compete. Decision Support Systems (DSS) have evolved to serve as a valuable tool in augmenting the decisional process.
“Since the 1970s, Decision Support Systems (DSS) have been conceived to fill the gap by helping decision makers in solving basically unstructured or semi-structured problems. It has only partially fulfilled its goal because decision-making is basically done through human cognitive process. Pure computer automation processing is inadequate to meet this dynamic process which involves both computer automation processing and cognitive processing” (Lee, 1989, p. 123). The development of business intelligence (BI) and subsequent knowledge management (KM) are not replacements for human cognition; they are available resources for the decision maker in order to generate a more “informed” decision. Given the dominate role of human cognition within BI and KM development, it is absolutely imperative that organizations understand how competing decisional context can lead to poor decision-making. “In the absence of shared context, individuals’ differing perspectives, beliefs, assumptions, and views of the future are most likely to collide and thus immobilize decision making” (Fahey & Prusak, 1998, p. 268).
One solution in attempting to create a universally shared decisional context might be found through the evolution of neural networks. As Stephen Haag relates, there are essentially two types of neural networks, self-organizing and back propagation (Haag, 2004). The primary difference between the two is in how they “learn”. The self-organizing neural network searches for patterns and possible correlations in vast pools of data. “Self-organizing neural networks often form part of data-mining tools for data warehouses” (Haag, 2004, p. 202). Back propagation neural networks are trained, much like a child, where several examples and scenarios are inputted. The key issue lies in, “As the neural network is learning to differentiate between good and bad, the weights (of various strengths) change. The flow of information to the output layer also changes. After you have fed the system enough examples, the weights stabilize, and the neural network then consistently classifies portfolios correctly” (Haag, 2004, p. 202).
A neural network’s reliability can be enhanced via training algorithms that will serve as mechanisms of evolutionary calibration. “A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated” (Maghami, 2000). Essentially a developer would utilize a training algorithm, like the one above, in conjunction with the input of multiple constraints, parameters, and variables. Over time, a neural network’s validity would increase raising the prominence of a shared decisional context.
References
Fahey, L., & Prusak, L. (1998, Spring). The Eleven Deadliest Sins of Knowledge Management. California Management Review, 40(3), 265-276.
Haag, S. Cummings, M. & McCubbery, D. (2004). Management Information Systems for the Information Age (Fourth Edition ed.). New York, NY: McGraw-Hill/Irwin.
Lee, D. T. (1989, September). An Overview of Intelligent Decision Systems. Journal of Information Technology, 4(3), 123-135.
Remus, W. E., & Kottemann, J. E. (1986, December). Toward Intelligent Decision Support Systems: An Artificially Intelligent Statistician. MIS Quarterly, 10(4), 402.
“Since the 1970s, Decision Support Systems (DSS) have been conceived to fill the gap by helping decision makers in solving basically unstructured or semi-structured problems. It has only partially fulfilled its goal because decision-making is basically done through human cognitive process. Pure computer automation processing is inadequate to meet this dynamic process which involves both computer automation processing and cognitive processing” (Lee, 1989, p. 123). The development of business intelligence (BI) and subsequent knowledge management (KM) are not replacements for human cognition; they are available resources for the decision maker in order to generate a more “informed” decision. Given the dominate role of human cognition within BI and KM development, it is absolutely imperative that organizations understand how competing decisional context can lead to poor decision-making. “In the absence of shared context, individuals’ differing perspectives, beliefs, assumptions, and views of the future are most likely to collide and thus immobilize decision making” (Fahey & Prusak, 1998, p. 268).
One solution in attempting to create a universally shared decisional context might be found through the evolution of neural networks. As Stephen Haag relates, there are essentially two types of neural networks, self-organizing and back propagation (Haag, 2004). The primary difference between the two is in how they “learn”. The self-organizing neural network searches for patterns and possible correlations in vast pools of data. “Self-organizing neural networks often form part of data-mining tools for data warehouses” (Haag, 2004, p. 202). Back propagation neural networks are trained, much like a child, where several examples and scenarios are inputted. The key issue lies in, “As the neural network is learning to differentiate between good and bad, the weights (of various strengths) change. The flow of information to the output layer also changes. After you have fed the system enough examples, the weights stabilize, and the neural network then consistently classifies portfolios correctly” (Haag, 2004, p. 202).
A neural network’s reliability can be enhanced via training algorithms that will serve as mechanisms of evolutionary calibration. “A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated” (Maghami, 2000). Essentially a developer would utilize a training algorithm, like the one above, in conjunction with the input of multiple constraints, parameters, and variables. Over time, a neural network’s validity would increase raising the prominence of a shared decisional context.
References
Fahey, L., & Prusak, L. (1998, Spring). The Eleven Deadliest Sins of Knowledge Management. California Management Review, 40(3), 265-276.
Haag, S. Cummings, M. & McCubbery, D. (2004). Management Information Systems for the Information Age (Fourth Edition ed.). New York, NY: McGraw-Hill/Irwin.
Lee, D. T. (1989, September). An Overview of Intelligent Decision Systems. Journal of Information Technology, 4(3), 123-135.
Remus, W. E., & Kottemann, J. E. (1986, December). Toward Intelligent Decision Support Systems: An Artificially Intelligent Statistician. MIS Quarterly, 10(4), 402.
Saturday, October 24, 2009
Review of 11 Deadliest Sins of Knowledge Management
As Fahey and Prusak (1998) noted, “A core tenet of any organizational learning project is that without detecting and correcting errors in “what we know” and “how we learn,” an organization’s knowledge deteriorates, becomes obsolete, and can result in bad decisions” (p. 265).
Eleven Deadliest Sins of Knowledge Management:
1. Not developing a working definition of knowledge.
2. Emphasizing knowledge stock to the detriment of knowledge flow.
3. Viewing knowledge as existing predominantly outside the heads of individuals.
4. Not understanding that a fundamental intermediate purpose of managing knowledge is to create shared context.
5. Paying little heed to the role and importance of tacit knowledge.
6. Disentangling knowledge from its uses.
7. Downplaying thinking and reasoning.
8. Focusing on the past and the present and not the future.
9. Failing to recognize the importance of experimentation.
10. Substituting technology contact for human interface.
11. Seeking to develop direct measures of knowledge (Fahey & Prusak, 1998).
Davenport and Prusak (2000) observed, “The knowledge project manager should have a good sense of his or her customer, the customer’s satisfaction, and the productivity and quality of services offered. However, the project managers in our study did not find it useful in most cases to describe the detailed process steps used in knowledge management” (p. 157). While a detailed mapping of processes in developing knowledge may not be practical, this does not mean to say that a process perspective is unimportant.
As Deborah Miller (2005) observed, “Knowledge, however, is indeed a process; collectively living and constantly evolving in and applied by the minds of knowers. Value is added to data to transform it into information, which, through knowledge-creating activities among people becomes knowledge. Knowledge leads to decision and action, and it continues to develop over time. Knowledge can judge new information and circumstances based on what is already known; and, as it responds, it can advance itself forward; continuously improving (Davenport & Prusak, 2000)” (Miller, 2005). In essence, we must look at our existing process environment and identify and address areas where the value of information is positively or negatively influenced.
References
Davenport, T. H., & Prusak, L. (2000). Working Knowledge: How Organizations Manage What They Know. Boston. Massachusetts: Harvard Business School Press.
Fahey, L., & Prusak, L. (1998, Spring). The Eleven Deadliest Sins of Knowledge Management. California Management Review, 40(3), 265-276.
Fore, S. (2005, September 15). 11 Deadliest Sins of KM Discussion Thread. Retrieved September 18, 2005, from http://sylvan.live.ecollege.com/ec/crs/default.learn?CourseID=2261698&Survey=1&47=2576689&ClientNodeID=984646&coursenav=1&bhcd2=1127069706
Green, G. (2005, September 15). 11 Deadliest Sins of KM Discussion Thread. Retrieved September 18, 2005, from http://sylvan.live.ecollege.com/ec/crs/default.learn?CourseID=2261698&Survey=1&47=2576689&ClientNodeID=984646&coursenav=1&bhcd2=1127069706
Miller, D. (2005, September 15). 11 Deadliest Sins of KM Discussion Thread. Retrieved September 18, 2005, from http://sylvan.live.ecollege.com/ec/crs/default.learn?CourseID=2261698&Survey=1&47=2576689&ClientNodeID=984646&coursenav=1&bhcd2=1127069706
Eleven Deadliest Sins of Knowledge Management:
1. Not developing a working definition of knowledge.
2. Emphasizing knowledge stock to the detriment of knowledge flow.
3. Viewing knowledge as existing predominantly outside the heads of individuals.
4. Not understanding that a fundamental intermediate purpose of managing knowledge is to create shared context.
5. Paying little heed to the role and importance of tacit knowledge.
6. Disentangling knowledge from its uses.
7. Downplaying thinking and reasoning.
8. Focusing on the past and the present and not the future.
9. Failing to recognize the importance of experimentation.
10. Substituting technology contact for human interface.
11. Seeking to develop direct measures of knowledge (Fahey & Prusak, 1998).
Davenport and Prusak (2000) observed, “The knowledge project manager should have a good sense of his or her customer, the customer’s satisfaction, and the productivity and quality of services offered. However, the project managers in our study did not find it useful in most cases to describe the detailed process steps used in knowledge management” (p. 157). While a detailed mapping of processes in developing knowledge may not be practical, this does not mean to say that a process perspective is unimportant.
As Deborah Miller (2005) observed, “Knowledge, however, is indeed a process; collectively living and constantly evolving in and applied by the minds of knowers. Value is added to data to transform it into information, which, through knowledge-creating activities among people becomes knowledge. Knowledge leads to decision and action, and it continues to develop over time. Knowledge can judge new information and circumstances based on what is already known; and, as it responds, it can advance itself forward; continuously improving (Davenport & Prusak, 2000)” (Miller, 2005). In essence, we must look at our existing process environment and identify and address areas where the value of information is positively or negatively influenced.
References
Davenport, T. H., & Prusak, L. (2000). Working Knowledge: How Organizations Manage What They Know. Boston. Massachusetts: Harvard Business School Press.
Fahey, L., & Prusak, L. (1998, Spring). The Eleven Deadliest Sins of Knowledge Management. California Management Review, 40(3), 265-276.
Fore, S. (2005, September 15). 11 Deadliest Sins of KM Discussion Thread. Retrieved September 18, 2005, from http://sylvan.live.ecollege.com/ec/crs/default.learn?CourseID=2261698&Survey=1&47=2576689&ClientNodeID=984646&coursenav=1&bhcd2=1127069706
Green, G. (2005, September 15). 11 Deadliest Sins of KM Discussion Thread. Retrieved September 18, 2005, from http://sylvan.live.ecollege.com/ec/crs/default.learn?CourseID=2261698&Survey=1&47=2576689&ClientNodeID=984646&coursenav=1&bhcd2=1127069706
Miller, D. (2005, September 15). 11 Deadliest Sins of KM Discussion Thread. Retrieved September 18, 2005, from http://sylvan.live.ecollege.com/ec/crs/default.learn?CourseID=2261698&Survey=1&47=2576689&ClientNodeID=984646&coursenav=1&bhcd2=1127069706
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