Managing a Business/MS-07
Hello Mr. Leo,
Kindly help me answer the following questions:
1. Discuss Data warehousing and data mining. How are they helpful?
2. Write short note on Knowledge management.
3. Discuss some characteristics that must be possessed by "information".
Looking forward to your reply.
1.Discuss Data warehousing and data mining. How are they helpful?
"A Data Warehouse is a repository of integrated information, available for queries and analysis. Data and information are extracted from heterogeneous sources as they are generated....This makes it much easier and more efficient to run queries over data that originally came from different sources."
IT is a repository of an organization's electronically stored data. Data warehouses are designed to facilitate reporting and analysis.
The data warehouse focuses on data storage. However, the means to retrieve and analyze data, to extract, transform and load data, and to manage dictionary data are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. An expanded definition for data warehousing includes tools for business intelligence, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata.
Data that gives information about a particular subject instead of about a company's ongoing operations.
Data that is gathered into the data warehouse from a variety of sources and merged into a coherent whole.
All data in the data warehouse is identified with a particular time period.
Data is stable in a data warehouse. More data is added but data is never removed. This enables management to gain a consistent picture of the business.
Benefits of data warehousing
Some of the benefits that a data warehouse provides are as follows:
• A data warehouse provides a common data model for data, regardless of the data's source. This makes it easier to report and analyze information than it would be if multiple data models from disparate sources were used to retrieve information such as sales invoices, order receipts, general ledger charges, etc.
• Prior to loading data into the data warehouse inconsistencies are identified and resolved. This greatly simplifies reporting and analysis.
• Information in the data warehouse is under the control of data warehouse users so that, even if the source system data is purged over time, the information in the warehouse can be stored safely for extended periods of time.
• Because they are separate from operational systems, data warehouses provide fast retrieval of data without slowing down operational systems.
• Data warehouses facilitate decision support system applications such as trend reports (e.g., the items with the most sales in a particular area within the last two years), exception reports, and reports that show actual performance versus goals.
• Data warehouses can work in conjunction with and, hence, enhance the value of operational business applications, notably customer relationship management (CRM) systems.
data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
Although data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost.
For example, one Midwest grocery chain used the data mining capacity of Oracle software to analyze local buying patterns. They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information in various ways to increase revenue. For example, they could move the beer display closer to the diaper display. And, they could make sure beer and diapers were sold at full price on Thursdays.
Data, Information, and Knowledge
Data are any facts, numbers, or text that can be processed by a computer. Today, organizations are accumulating vast and growing amounts of data in different formats and different databases. This includes:
• operational or transactional data such as, sales, cost, inventory, payroll, and accounting
• nonoperational data, such as industry sales, forecast data, and macro economic data
• meta data - data about the data itself, such as logical database design or data dictionary definitions
The patterns, associations, or relationships among all this data can provide information. For example, analysis of retail point of sale transaction data can yield information on which products are selling and when.
Information can be converted into knowledge about historical patterns and future trends. For example, summary information on retail supermarket sales can be analyzed in light of promotional efforts to provide knowledge of consumer buying behavior. Thus, a manufacturer or retailer could determine which items are most susceptible to promotional efforts.
Dramatic advances in data capture, processing power, data transmission, and storage capabilities are enabling organizations to integrate their various databases into data warehouses. Data warehousing is defined as a process of centralized data management and retrieval. Data warehousing, like data mining, is a relatively new term although the concept itself has been around for years. Data warehousing represents an ideal vision of maintaining a central repository of all organizational data. Centralization of data is needed to maximize user access and analysis. Dramatic technological advances are making this vision a reality for many companies. And, equally dramatic advances in data analysis software are allowing users to access this data freely. The data analysis software is what supports data mining.
What can data mining do?
Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data.
With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.
For example, Blockbuster Entertainment mines its video rental history database to recommend rentals to individual customers. American Express can suggest products to its cardholders based on analysis of their monthly expenditures.
WalMart is pioneering massive data mining to transform its supplier relationships. WalMart captures point-of-sale transactions from over 2,900 stores in 6 countries and continuously transmits this data to its massive 7.5 terabyte TERADATA data warehouse. WalMart allows more than 3,500 suppliers, to access data on their products and perform data analyses. These suppliers use this data to identify customer buying patterns at the store display level. They use this information to manage local store inventory and identify new merchandising opportunities. In 1995, WalMart computers processed over 1 million complex data queries.
The National Basketball Association (NBA) is exploring a data mining application that can be used in conjunction with image recordings of basketball games. The ADVANCED software analyzes the movements of players to help coaches orchestrate plays and strategies. For example, an analysis of the play-by-play sheet of the game played between the New York Knicks and the Cleveland Cavaliers on January 6, 1995 reveals that when Mark Price played the Guard position, John Williams attempted four jump shots and made each one! Advanced Scout not only finds this pattern, but explains that it is interesting because it differs considerably from the average shooting percentage of 49.30% for the Cavaliers during that game.
By using the NBA universal clock, a coach can automatically bring up the video clips showing each of the jump shots attempted by Williams with Price on the floor, without needing to comb through hours of video footage. Those clips show a very successful pick-and-roll play in which Price draws the Knick's defense and then finds Williams for an open jump shot.
How does data mining work?
While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought:
• Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.
• Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities.
• Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining.
• Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes.
Data mining consists of five major elements:
• Extract, transform, and load transaction data onto the data warehouse system.
• Store and manage the data in a multidimensional database system.
• Provide data access to business analysts and information technology professionals.
• Analyze the data by application software.
• Present the data in a useful format, such as a graph or table.
Different levels of analysis are available:
• Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.
• Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution.
• Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID) . CART and CHAID are decision tree techniques used for classification of a dataset. They provide a set of rules that you can apply to a new (unclassified) dataset to predict which records will have a given outcome. CART segments a dataset by creating 2-way splits while CHAID segments using chi square tests to create multi-way splits. CART typically requires less data preparation than CHAID.
• Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k 1). Sometimes called the k-nearest neighbor technique.
• Rule induction: The extraction of useful if-then rules from data based on statistical significance.
• Data visualization: The visual interpretation of complex relationships in multidimensional data. Graphics tools are used to illustrate data relationships.
What technological infrastructure is required?
Today, data mining applications are available on all size systems for mainframe, client/server, and PC platforms. System prices range from several thousand dollars for the smallest applications up to $1 million a terabyte for the largest. Enterprise-wide applications generally range in size from 10 gigabytes to over 11 terabytes. NCR has the capacity to deliver applications exceeding 100 terabytes. There are two critical technological drivers:
• Size of the database: the more data being processed and maintained, the more powerful the system required.
• Query complexity: the more complex the queries and the greater the number of queries being processed, the more powerful the system required.
Relational database storage and management technology is adequate for many data mining applications less than 50 gigabytes. However, this infrastructure needs to be significantly enhanced to support larger applications. Some vendors have added extensive indexing capabilities to improve query performance. Others use new hardware architectures such as Massively Parallel Processors (MPP) to achieve order-of-magnitude improvements in query time. For example, MPP systems from NCR link hundreds of high-speed Pentium processors to achieve performance levels exceeding those of the largest supercomputers.
One of the key issues raised by data mining technology is not a business or technological one, but a social one. It is the issue of individual privacy. Data mining makes it possible to analyze routine business transactions and glean a significant amount of information about individuals buying habits and preferences.
Another issue is that of data integrity. Clearly, data analysis can only be as good as the data that is being analyzed. A key implementation challenge is integrating conflicting or redundant data from different sources. For example, a bank may maintain credit cards accounts on several different databases. The addresses (or even the names) of a single cardholder may be different in each. Software must translate data from one system to another and select the address most recently entered.
A hotly debated technical issue is whether it is better to set up a relational database structure or a multidimensional one. In a relational structure, data is stored in tables, permitting ad hoc queries. In a multidimensional structure, on the other hand, sets of cubes are arranged in arrays, with subsets created according to category. While multidimensional structures facilitate multidimensional data mining, relational structures thus far have performed better in client/server environments. And, with the explosion of the Internet, the world is becoming one big client/server environment.
Finally, there is the issue of cost. While system hardware costs have dropped dramatically within the past five years, data mining and data warehousing tend to be self-reinforcing. The more powerful the data mining queries, the greater the utility of the information being gleaned from the data, and the greater the pressure to increase the amount of data being collected and maintained, which increases the pressure for faster, more powerful data mining queries. This increases pressure for larger, faster systems, which are more expensive
Data Mining [ commercial]
Data are any facts, numbers, or text that can be processed by a computer. Today, organizations are accumulating vast and growing amounts of data in different formats and different databases.
Data mining consists of five major elements:
-Extract, transform, and load transaction data onto the data warehouse system.
-Store and manage the data in a multidimensional database system.
-Provide data access to business analysts and information technology professionals.
-Analyze the data by application software.
-Present the data in a useful format, such as a graph or table.
DATA MINING ADVANTAGES
-provide operational or transactional data such as, sales, cost, inventory, payroll, and accounting
-provide nonoperational data, such as industry sales, forecast data, and macro economic data
The patterns, associations, or relationships among all this data can provide information.
-Information can be converted into knowledge about historical patterns and future trends.
DATA MINING HELPS TO DEVELOP
-Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.
-Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities.
-Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining.
-Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes.
-A data mining provides a common data model for data, regardless of the data's source. This makes it easier to report and analyze information than it would be if multiple data models from disparate sources were used to retrieve information such as sales invoices, order receipts, general ledger charges, etc.
-Prior to loading data into the data warehouse inconsistencies are identified and resolved. This greatly simplifies reporting and analysis.
-Information in the data warehouse is under the control of data warehouse users so that, even if the source system data is purged over time, the information in the warehouse can be stored safely for extended periods of time.
-it facilitate decision support system applications such as trend reports (e.g., the items with the most sales in a particular area within the last two years), exception reports, and reports that show actual performance versus goals.
-it can work in conjunction with and, hence, enhance the value of operational business applications, notably customer relationship management (CRM) systems.
-Easy access to frequently needed data
-Creates collective view by a group of users
-Improves end-user response time
-Ease of creation
-Potential users are more clearly defined
-cost of the development/ maintenance.
free/noncommercial data mining systems?
FREE / NONCOMMERCIAL data mining software represents a new trend in data mining
IN research, education and industrial applications, especially in small and medium enterprises (SMEs).
With FREE / NONCOMMERCIAL software
-an enterprise can easily initiate a data mining project using the most current technology.
-Often the software is available at no cost,
-allowing the enterprise to instead focus on ensuring their staff can freely learn the data mining techniques and methods.
- ensures that staff can understand exactly how the algorithms work by examining the source codBes, if they so desire,
-and can also fine tune the algorithms to suit the specific purposes of the enterprise.
However, diversity, instability, scalability and poor documentation can be major concerns in using open source data mining systems.
2. Write short note on Knowledge management.
Knowledge is defined variously as
(i) facts, information, and skills acquired by a person through EXPERIENCE or EDUCATION; the theoretical or practical understanding of a subject,
(ii) what is known in a particular field or in total; facts and information or
(iii) awareness or familiarity gained by experience of a fact or situation.
Knowledge acquisition involves complex COGNITIVE processes: perception, learning, communication, association and reasoning. The term knowledge is also used to mean the confident understanding of a subject with the ability to use it for a specific purpose.
At Knowledge Praxis, we define knowledge management as a business activity with two primary aspects:
• Treating the knowledge component of business activities as an explicit concern of business reflected in strategy, policy, and practice at all levels of the organization.
• Making a direct connection between an organization’s intellectual assets — both explicit [recorded] and tacit [personal know-how] — and positive business results.
In practice, knowledge management often encompasses identifying and mapping intellectual assets within the organization, generating new knowledge for competitive advantage within the organization, making vast amounts of corporate information accessible, sharing of best practices, and technology that enables all of the above — including groupware and intranets.
That covers a lot of ground. And it should, because applying knowledge to work is integral to most business activities.
Business strategies related to knowledge management
As you explore other explanations of knowledge management — a good starting point — you’ll detect connections with several well-known management strategies, practices, and business issues, including
• Change management
• Best practices
• Risk management
A significant element of the business community also views knowledge management as a natural extension of "business process reengineering," .
There is a common thread among these and many other recent business strategies: A recognition that information and knowledge are corporate assets, and that businesses need strategies, policies, and tools to manage those assets.
The need to manage knowledge seems obvious, and discussions of intellectual capital have proliferated, but few businesses have acted on that understanding. Where companies have take action — and a growing number are doing so — implementations of "knowledge management" may range from technology-driven methods of accessing, controlling, and delivering information to massive efforts to change corporate culture.
Opinions about the paths, methods, and even the objectives of knowledge management abound. Some efforts focus on enhancing creativity — creating new knowledge value — while other programs emphasize leveraging existing knowledge.
NOW THE QUESTION IS ?
Aren’t we managing knowledge already? Well, no. In fact, most of the time we’re making a really ugly mess of managing information. In practice, the terms information and knowledge are often used interchangeably by business writers.
Let’s choose a simple working definition and get on with it:
Knowledge has two basic definitions of interest.
1.The first pertains to a defined body of information. Depending on the definition, the body of information might consist of facts, opinions, ideas, theories, principles, and models (or other frameworks). Clearly, other categories are possible, too. Subject matter (e.g., chemistry, mathematics, etc.) is just one possibility.
2.Knowledge also refers to a person’s state of being with respect to some body of information. These states include ignorance, awareness, familiarity, understanding, facility, and so on.
There are many thoughtful and thought-provoking definitions of "knowledge" — including the important distinctions
LIKE '' DATA,INFORMATION,KNOWLEDGE, AND WISDOM'' .
Nevertheless, there is a good, sensible, functional definition, and it is sufficient for our purposes.
There are two kinds of knowledge distinction between
1.explicit knowledge (sometimes referred to as formal knowledge), which can be articulated in language and transmitted among individuals, and
2.tacit knowledge (also, informal knowledge), personal knowledge rooted in individual experience and involving personal belief, perspective, and values.
In traditional perceptions of the role of knowledge in business organizations, tacit knowledge is often viewed as the real key to getting things done and creating new value. Not explicit knowledge. Thus we often encounter an emphasis on the "learning organization" and other approaches that stress internalization of information (through experience and action) and generation of new knowledge through managed interaction.
It doesn’t matter whether a written procedure or a subject matter expert provides a solution to a particular problem, as long as a positive result is achieved. However, observing how knowledge is acquired and how we can apply knowledge — whether tacit or explicit — in order to achieve a positive result that meets business requirements … that’s a different and very important issue.
Why we need knowledge management now
Why do we need to manage knowledge? This list identifies some of the specific business factors, including:
• Marketplaces are increasingly competitive and the rate of innovation is rising.
• Reductions in staffing create a need to replace informal knowledge with formal methods.
• Competitive pressures reduce the size of the work force that holds valuable business knowledge.
• The amount of time available to experience and acquire knowledge has diminished.
• Early retirements and increasing mobility of the work force lead to loss of knowledge.
• There is a need to manage increasing complexity as small operating companies are trans-national sourcing operations.
• Changes in strategic direction may result in the loss of knowledge in a specific area.
• Most of our work is information based.
• Organizations compete on the basis of knowledge.
• Products and services are increasingly complex, endowing them with a significant information component.
• The need for life-long learning is an inescapable reality.
In brief, knowledge and information have become the medium in which business problems occur. As a result, managing knowledge represents the primary opportunity for achieving substantial savings, significant improvements in human performance, and competitive advantage.
It’s not just a Fortune 500 business problem. Small companies need formal approaches to knowledge management even more, because they don’t have the market leverage, inertia, and resources that big companies do. They have to be much more flexible, more responsive, and more "right" (make better decisions) — because even small mistakes can be fatal to them.
Roadblocks to adoption of knowledge management solutions
There have been many roadblocks to adoption of formal knowledge management activities. In general, managing knowledge has been perceived as an unmanageable kind of problem — an implicitly human, individual activity — that was intractable with traditional management methods and technology.
We tend to treat the activities of knowledge work as necessary, but ill-defined, costs of human resources, and we treat the explicit manifestations of knowledge work as forms of publishing — as byproducts of "real" work.
As a result, the metrics associated with knowledge resources — and our ability to manage those resources in meaningful ways — have not become part of business infrastructure.
But it isn’t necessary to throw up one’s hands in despair. We do know a lot about how people learn. We know more and more about how organizations develop and use knowledge. The body of literature about managing intellectual capital is growing. We have new insights and solutions from a variety of domains and disciplines that can be applied to making knowledge work manageable and measurable. And computer technology — itself a cause of the problem — can provide new tools to make it all work.
We don’t need another "paradigm shift" (Please!), but we do have to accept that the nature of business itself has changed, in at least two important ways:
Knowledge work is fundamentally different in character from physical labor.
The knowledge worker is almost completely immersed in a computing environment. This new reality dramatically alters the methods by which we must manage, learn, represent knowledge, interact, solve problems, and act.
You can’t solve the problems of Information Age business or gain a competitive advantage simply by throwing more information and people at the problems. And you can’t solve knowledge-based problems with approaches borrowed from the product-oriented, print-based economy. Those solutions are reactive and inappropriate.
Applying technology blindly to knowledge-related business problems is a mistake, too, but the computerized business environment provides opportunities and new methods for representing "knowledge" and leveraging its value. It’s not an issue of finding the right computer interface — although that would help, too. We simply have not defined in a rigorous, clear, widely accepted way the fundamental characteristics of "knowledge" in the computing environment.
Knowledge management: a cross-disciplinary domain
Knowledge management draws from a wide range of disciplines and technologies.
• Cognitive science. Insights from how we learn and know will certainly improve tools and techniques for gathering and transferring knowledge.
• Expert systems, artificial intelligence and knowledge base management systems (KBMS). AI and related technologies have acquired an undeserved reputation of having failed to meet their own — and the marketplace’s — high expectations. In fact, these technologies continue to be applied widely, and the lessons practitioners have learned are directly applicable to knowledge management.
• Computer-supported collaborative work (groupware). In Europe, knowledge management is almost synonymous with groupware … and therefore with Lotus Notes. Sharing and collaboration are clearly vital to organizational knowledge management — with or without supporting technology.
• Library and information science. We take it for granted that card catalogs in libraries will help us find the right book when we need it. The body of research and practice in classification and knowledge organization that makes libraries work will be even more vital as we are inundated by information in business. Tools for thesaurus construction and controlled vocabularies are already helping us manage knowledge.
• Technical writing. Also under-appreciated — even sneered at — as a professional activity, technical writing (often referred to by its practitioners as technical communication) forms a body of theory and practice that is directly relevant to effective representation and transfer of knowledge.
• Document management. Originally concerned primarily with managing the accessibility of images, document management has moved on to making content accessible and re-usable at the component level. Early recognition of the need to associate "metainformation" with each document object prefigures document management technology’s growing role in knowledge management activities.
• Decision support systems. According to Daniel J. Power, "Researchers working on Decision Support Systems have brought together insights from the fields of cognitive sciences, management sciences, computer sciences, operations research, and systems engineering in order to produce both computerised artifacts for helping knowledge workers in their performance of cognitive tasks, and to integrate such artifacts within the decision-making processes of modern organisations." That already sounds a lot like knowledge management, but in practice the emphasis has been on quantitative analysis rather than qualitative analysis, and on tools for managers rather than everyone in the organization.
• Semantic networks. Semantic networks are formed from ideas and typed relationships among them — sort of "hypertext without the content," but with far more systematic structure according to meaning. Often applied in such arcane tasks as textual analysis, semantic nets are now in use in mainstream professional applications, including medicine, to represent domain knowledge in an explicit way that can be shared.
• Relational and object databases. Although relational databases are currently used primarily as tools for managing "structured" data — and object-oriented databases are considered more appropriate for "unstructured" content — we have only begun to apply the models on which they are founded to representing and managing knowledge resources.
• Simulation. Knowledge Management expert Karl-Erik Sveiby suggests "simulation" as a component technology of knowledge management, referring to "computer simulations, manual simulations as well as role plays and micro arenas for testing out skills."
• Organizational science. The science of managing organizations increasingly deals with the need to manage knowledge — often explicitly. It’s not a surprise that the American Management Association’s APQC has sponsored major knowledge management events.
That’s only a partial list. Other technologies include: object-oriented information modeling; electronic publishing technology, hypertext, and the World Wide Web; help-desk technology; full-text search and retrieval; and performance support systems.
Categorization of knowledge management approaches
The term "knowledge management" is now in widespread use, having appeared in the titles of many new books about knowledge management as a business strategy, as well as in articles in many business publications, including The Wall Street Journal. There are, of course, many ways to slice up the multi-faceted world of knowledge management. However, it’s often useful to categorize them.
In a posting to the Knowledge Management Forum, two ''tracks'' have been identified - of knowledge management:
• Management of Information. To researchers in this track, "… knowledge = Objects that can be identified and handled in information systems."
• Management of People. For researchers and practitioners in this field, knowledge consists of "… processes, a complex set of dynamic skills, know-how, etc., that is constantly changing."
There is a three-part categorization: (1) mechanistic approaches, (2) cultural/behavioristic approaches, and (3) systematic approaches to knowledge management.
Mechanistic approaches to knowledge management
1. Mechanistic approaches to knowledge management are characterized by the application of technology and resources to do more of the same better. The main assumptions of the mechanistic approach include:
• Better accessibility to information is a key, including enhanced methods of access and reuse of documents (hypertext linking, databases, full-text search, etc.)
• Networking technology in general (especially intranets), and groupware in particular, will be key solutions.
• In general, technology and sheer volume of information will make it work.
Assessment: Such approaches are relatively easy to implement for corporate "political" reasons, because the technologies and techniques — although sometimes advanced in particular areas — are familiar and easily understood. There is a modicum of good sense here, because enhanced access to corporate intellectual assets is vital. But it’s simply not clear whether access itself will have a substantial impact on business performance, especially as mountains of new information are placed on line. Unless the knowledge management approach incorporates methods of leveraging cumulative experience, the net result may not be positive, and the impact of implementation may be no more measurable than in traditional paper models.
2. Cultural/behavioristic approaches to knowledge management
Cultural/behavioristic approaches, with substantial roots in process re-engineering and change management, tend to view the "knowledge problem" as a management issue. Technology — though ultimately essential for managing explicit knowledge resources — is not the solution. These approaches tend to focus more on innovation and creativity (the "learning organization") than on leveraging existing explicit resources or making working knowledge explicit.
Assumptions of cultural/behavioristic approaches often include:
• Organizational behaviors and culture need to be changed … dramatically. In our information-intensive environments, organizations become dysfunctional relative to business objectives.
• Organizational behaviors and culture can be changed, but traditional technology and methods of attempting to solve the "knowledge problem" have reached their limits of effectiveness. A "holistic" view is required. Theories of behavior of large-scale systems are often invoked.
• It’s the processes that matter, not the technology.
• Nothing happens or changes unless a manager makes it happen.
Assessment: The cultural factors affecting organizational change have almost certainly been undervalued, and cultural/behavioristic implementations have shown some benefits. But the cause-effect relationship between cultural strategy and business benefits is not clear, because the "Hawthorne Effect" may come into play, and because we still can’t make dependable predictions about systems as complex as knowledge-based business organizations. Positive results achieved by cultural/behavioristic strategies may not be sustainable, measurable, cumulative, or replicable … and employees thoroughly "Dilbertized" by yet another management strategy may roll their eyes. Time will tell.
3. Systematic approaches to knowledge management
Systematic approaches to knowledge management retain the traditional faith in rational analysis of the knowledge problem: the problem can be solved, but new thinking of many kinds is required. Some basic assumptions:
• It’s sustainable results that matter, not the processes or technology … or your definition of "knowledge."
• A resource cannot be managed unless it is modeled, and many aspects of the organization’s knowledge can be modeled as an explicit resource.
• Solutions can be found in a variety of disciplines and technologies, and traditional methods of analysis can be used to re-examine the nature of knowledge work and to solve the knowledge problem.
• Cultural issues are important, but they too must be evaluated systematically. Employees may or may not have to be "changed," but policies and work practices must certainly be changed, and technology can be applied successfully to business knowledge problems themselves.
• Knowledge management has an important management component, but it is not an activity or discipline that belongs exclusively to managers.
Where do we stand at the moment, and where do we go from here?
Knowledge management has already been embraced as a source of solutions to the problems of today’s business. Still it has not been easy for this "science" to construct for itself that royal road of self validation. On the contrary, I believe that it is still, at least for the majority of the practitioners and their customers, in the stage of blind groping after its true aims and destination.
Knowledge Management is any process or practice of creating,
acquiring , capturing, sharing, and using knowledge, wherever
it resides , to enhance learning and performance in organizations.
Knowledge Management involves transforming knowledge resources
by identifying relevant informing and then disseminating it so that
learning can take place. KM strategies promote the sharing of
knowledge by linking people with people, and by linking them
to information so that they can learn from documented experiences.
KM could be categorized as
-embedded in technologies, rules, organizational procedures.
-encultured in organization values, beliefs,stories, understandings.
-embedded in practical activities, skills, competences of members.
-embraced as conceptual understanding / cognitive skills.
-creating data warehouse
-developing/ using decision support sytems
-creating/ developing groupware systems like emails,lotus notes etc.
HOW DO WE GO ABOUT
Conducting a knowledge audit
What is a knowledge audit?
The term ‘knowledge audit’ is in some ways a bit of a misnomer, since the traditional concept of an audit is to check performance against a standard, as in financial auditing. A knowledge audit, however, is a more of a qualitative evaluation. It is essentially a sound investigation into an organisation’s knowledge ‘health’. A typical audit will look at:
• What are the organisation’s knowledge needs?
• What knowledge assets or resources does it have and where are they?
• What gaps exist in its knowledge?
• How does knowledge flow around the organisation?
• What blockages are there to that flow e.g. to what extent do its people, processes and technology currently support or hamper the effective flow of knowledge?
The knowledge audit provides an evidence-based assessment of where the organisation needs to focus its knowledge management efforts. It can reveal the organisation’s knowledge management needs, strengths, weaknesses, opportunities, threats and risks.
What are the benefits?
Among the key benefits of a knowledge audit are:
• It helps the organisation clearly identify what knowledge is needed to support overall organisational goals and individual and team activities.
• It gives tangible evidence of the extent to which knowledge is being effectively managed and indicates where improvements are needed.
• It provides an evidence-based account of the knowledge that exists in an organisation, and how that knowledge moves around in, and is used by, that organisation.
• It provides a map of what knowledge exists in the organisation and where it exists, revealing both gaps and duplication.
• It reveals pockets of knowledge that are not currently being used to good advantage and therefore offer untapped potential.
• It provides a map of knowledge and communication flows and networks, revealing both examples of good practice and blockages and barriers to good practice.
• It provides an inventory of knowledge assets, allowing them to become more visible and therefore more measurable and accountable, and giving a clearer understanding of the contribution of knowledge to organisational performance.
• It provides vital information for the development of effective knowledge management programmes and initiatives that are directly relevant to the organisation’s specific knowledge needs and current situation.
Some examples of situations in which a knowledge audit can be beneficial include:
• you are about to embark on creating a knowledge management strategy and so need to establish exactly ‘where you are now’
• people are having difficulty in finding the information and knowledge they need to make key decisions
• useful sources of information and knowledge are frequently stumbled across by accident
• there is duplication of information and knowledge gathering activities across different departments or teams, and hence duplication of costs
• questions are being raised about the value of knowledge management systems, initiatives or investments
• when findings from research and development are not making their way into practice quickly enough.
How do I go about it?
There are a wide variety of approaches to conducting a knowledge audit, with varying levels of coverage and detail. As a general rule, most knowledge audits will involve some or all of the following:
Identifying knowledge needs
The first step in most knowledge audits involves getting clear about precisely what knowledge the organisation and the people and teams within it need in order to meet their goals and objectives. A knowledge audit provides a systematic way of finding this out to some level of detail. Common approaches taken to collating this information include questionnaire-based surveys, interviews and facilitated group discussions, or a combination of these. In asking people about knowledge needs, it is important to provide a point of focus, as ‘knowledge’ can be seen as being quite conceptual and therefore difficult to articulate. To get around this, and to ensure that you are concentrating on vital knowledge, invite people to think about their goals and objectives, and the core processes, activities and decisions that they perform in the course of their day-to-day work. You might ask them to also consider their main problems and challenges, and how might faster access to better knowledge help them in that regard.
It is always beneficial to begin a knowledge auditing process with identifying knowledge needs. This enables you to then use your understanding of these needs to guide the rest of the auditing process, and therefore be sure that you are focusing on the knowledge that is important to the organisation.
Drawing up a knowledge inventory
A knowledge inventory is a kind of stock-take to identify and locate knowledge assets or resources throughout the organisation. It involves counting and categorising the organisation’s explicit and tacit knowledge. In the case of explicit knowledge, this will include things like:
• What knowledge we have – numbers, types and categories of documents, databases, libraries, intranet websites, links and subscriptions to external resources etc?
• Where the knowledge is – locations in the organisation, and in its various systems?
• Organisation and access – how are knowledge resources organised, how easy is it for people to find and access them?
• Purpose, relevance and ‘quality’ – why do these resources exist, how relevant and appropriate are they for that purpose, are they of good ‘quality’ e.g. up-to-date, reliable, evidence-based etc?
• Usage – are they actually being used, by whom, how often, what for?
In the case of tacit knowledge, the inventory will focus on people and look at things like:
• Who we have – numbers and categories of people
• Where they are – locations in departments, teams and buildings
• What they do – job levels and types
• What they know –academic and professional qualifications, core knowledge and experience
• What they are learning – on the job training, learning and development.
The knowledge inventory gives you a snapshot of your knowledge assets or resources. By comparing your inventory with your earlier analysis of knowledge needs, you can begin to identify gaps in your organisation’s knowledge as well as areas of unnecessary duplication. This is also explored in greater detail in the next step.
Analysing knowledge flows
While an inventory of knowledge assets shows what knowledge resources your organisation has, an analysis of knowledge flows looks at how that knowledge moves around the organisation – from where it is to where it is needed. In other words, how do people find the knowledge they need, and how do they share the knowledge they have? Again, the knowledge flow analysis looks at both explicit and tacit knowledge, and at people, processes and systems:
• The relative focus in this stage is on people: their attitudes towards, habits and behaviours concerning, and skills in, knowledge sharing and use. This will usually require a combination of questionnaire-based surveys followed up with individual interviews and facilitated group discussions.
• In terms of processes, you will need to look at how people go about their daily work activities and how knowledge seeking, sharing and use are (or are not) part of those activities. In most organisations, there will be pockets of good knowledge management practice (though they may not be called knowledge management). You will also need to look at what policies and practices currently affect the flows and usage of information and knowledge, for example are there existing policies on things like information handling, records management, web publishing? Are their other wider policies and practices that, while not directly related to knowledge management, act as enablers or barriers to good knowledge practice?
• On the systems side, some assessment is needed of key capabilities that will be used in any recommended actions or solutions. This includes the technical infrastructure: information technology systems, content management, accessibility and ease of use, and current actual levels of use. In short, to what extent do your systems effectively facilitate knowledge flows, and help to connect people with the information and other people they need.
• An analysis of knowledge flows will allow you to further identify gaps in your organisation’s knowledge and areas of duplication; it will also highlight examples of good practice that can be built on, as well as blockages and barriers to knowledge flows and effective use. It will show where you need to focus attention in your knowledge management initiatives in order to get knowledge moving from where it is to where it is needed.
Creating a knowledge map
A knowledge map is a visual representation of an organisation’s knowledge. There are two common approaches to knowledge mapping:
The first simply maps knowledge resources and assets, showing what knowledge exists in the organisation and where it can be found
The second also includes knowledge flows, showing how that knowledge moves around the organisation from where it is to where it is needed.
Clearly the second approach provides the most complete picture for the knowledge auditor. However, the first is also useful, and in some organisations is made available to all staff to help people locate the knowledge they need.
Are there any other points I should be aware of?
• Be clear about your purpose. The knowledge audit is not a quick or simple process, and so the time and effort required needs to be justified by a clear purpose and a set of actions that will be taken as a result of what the audit reveals.
• When conducting a knowledge audit, bear in mind the widely-accepted statistic that around 80% of an organisation’s knowledge is tacit, hence beware of focusing too much time and energy on explicit knowledge and not enough on tacit knowledge.
• The ease or difficulty that you have in gathering and collating the information you need as part of the audit process is itself a good indicator of the status of your current knowledge management capabilities.
• If you decide to commission a knowledge audit from external consultants, be aware that the quality and depth of work that comes under the general banner of ‘knowledge auditing’ varies quite. Many vendors use the term ‘knowledge audit’ to describe what is in fact an information audit – which will only look at explicit knowledge. Auditing tacit knowledge is probably where the greater challenge lies, and is hence the area in which expert help is likely to be most valuable.
KNOWLEDGE MANAGEMENT PLANNING& DEVELOPMENT
Step 1: Analyzing existing infrastructure
Focus on the following:
1. Understanding the role of your companys existing networks, intranet, and extranets in knowledge management. You will analyze, leverage, and build upon data mining, data warehousing, project management, and decision support system (DSS) tools that might already be in place.
2. Understanding the knowledge management technology framework and its components.
3. Considering the option of using knowledge servers for enterprise integration, and performing a preliminary analysis of business needs that match up with relevant knowledge server choices.
4.Integrating existing intranets, extranets, and GroupWare into your knowledge management system.
5.Understanding the limitations of implemented tools and identifying existing gaps in your company’s existing technology infrastructure.
6.Taking concrete steps to leverage and build upon existing infrastructural investments.
Step 2: Aligning knowledge management and business strategy
Focus on the following:
1.Shift your company from strategic programming to strategic planning.
2.Move your systems design practices and business decisions away from the seemingly rigorous, fallacious notion of making predictions using extrapolations from past data. You must shift this critical decision making dependency on knowledge that is both within and outside your company.
3.Perform a knowledge based SWOT (strengths, weaknesses, opportunities, and threats) analysis and create knowledge maps for your own company, your main competitors, and your industry as a whole.
4.Analyze knowledge gaps and identify how knowledge management can fill those gaps. Do a cost benefit analysis to prioritize filling such gaps.
5.Determine whether a codification or personalization focus is better suited for your com¬pany
6.Balance exploitation, exploration, just in time and just in case delivery supported by your KM system.
7.Before you can design your knowledge management system, determine the right diagnostic questions to ask.
8.Translate your strategy KM link to KM system design characteristics. You must articulate a clear strategy KM link and incorporate the critical success factors
9.Mobilize initiatives to help you "sell" your KM project internally.
10.Diagnose and validate your strategy KM link, and use it to drive the rest of the design process.
Step 3: Designing the knowledge management architecture and integrating existing infrastructure
Focus on the following:
1.Comprehend various components of the knowledge info-structure
2.Identify internal and external knowledge source feeds that must be integrated
3.Choose IT components to find, create, assemble, and apply knowledge
4.Identify elements of the interface layer: clients, server, gateways, and the platform
5.Decide on the collaborative platform: Web or Lotus Notes?
6.Identify and understand components of the collaborative intelligence layer: artificial intelligence, data warehouses, genetic algorithms, neural networks, expert reasoning systems, rule bases, and case based reasoning
7.Optimize knowledge object molecularity with your own company in mind
8.Balance cost against value added for each enabling component
9.Balance push[provision] and pull based[ demand ]mechanisms for knowledge delivery
10.Identify the right mix of components for searching, indexing, and retrieval
11.Create knowledge tags and attributes: domain, form, type, product/service, time, and location tags
12.Create profiling mechanisms for knowledge delivery
13. Validate your choices
Step 4: Auditing and analyzing existing knowledge
Focus on the following:
1. Develop a Knowledge Growth framework to measure process knowledge.
2. Identify, evaluate, and rate critical process knowledge on an 5 OR 7 point scale system.
3. Select an audit method out of several possible options.
4. Assemble a preliminary knowledge audit team.
5. Audit and analyze your companys existing knowledge.
6. Identify your companys Knowledge spot.
7. Choose a strategic position for your knowledge management system that is in line with
the strategic gaps identified in step 2.
STEP 5: DESIGNING THE KNOWLEDGE MANAGEMENT TEAM
In the this step on the KM road map, you create the knowledge management team that will design, build, implement, and deploy your companys knowledge management system.
Focus on the following:
1. Identify key stakeholders: IT, management, and end users; manage their expectations.
2. Identify sources of requisite expertise.
3. Identify critical points of failure in terms or unmet requirements, control, management
buy in, and end user buy in,
4.Balance the knowledge management team’s constitution
organizationally, strategically, and technologically.
5.Balance technical and managerial expertise that forms a part of this team.
6.Resolve team sizing issues.
STEP 6: CREATING THE KNOWLEDGE MANAGEMENT SYSTEM
Focus on the following issues in this step:
1. Customize the details of the several layers of the knowledge management architecture to your own company.
2. Understand and select the components required by your company: integrative repositories, content centers, knowledge aggregation and mining tools, the collaborative platform, knowledge directories, the user interface options, push delivery mechanisms, and integrative elements.
3. Design the system for high levels of interoperability with existing IT investments; optimize for performance and scalability.
4.Understand and execute repository life cycle management.
5.Understand and incorporate the several key user interface (UI) considerations.
6.Position and scope the knowledge management system to a feasible level where benefits exceed costs.
7.Make the build or buy decision and understand the tradeoffs.
8.Future proof the knowledge management system so that it does not "run out of gas" when the next wave of fancy technology hits the market.
This step integrates work from all preceding steps so that it culminates in a strategically oriented knowledge management system design.
STEP 7: DEVELOPING THE KNOWLEDGE MANAGEMENT SYSTEM
Once you have created a blueprint for your knowledge management system (step 6), the next step is that of actually putting together a working system.
Focus on the following:
1.Develop the interface layer. Create platform independence, leverage the intranet, enable universal authorship, and optimize video and audio streaming.
2.Develop the access and authentication layer. Secure data, control access, and distribute control.
3.Develop the collaborative filtering and intelligence layer, using intelligent agents and collaborative filtering systems. We took at options to buy intelligent agents versus easy and free tools that can be used to build your own.
4.Develop and integrate the application layer with the intelligence layer and the transport layer.
5.Leverage the extant transport layer to take advantage of existing networks that are already in place in your company.
6.Develop the middleware and legacy integration layer to connect the knowledge management system both to true legacy data and “ recent," inconsistent legacy data repositories and databases left behind by custom systems that your company needs to retire for reasons of cost or lack of functionality.
7.Integrate and enhance the repository layer.
STEP 8: PiLOT TESTING AND DEPLOYMENT USING THE RDI METHODOLOGY
A large scale project such as a typical knowledge management system must take into account the actual needs of its users. Although a cross functional KM team can help uncover many of these needs, a pilot deployment is the ultimate reality check.
Focus on the following:
1.Understand the need for a pilot knowledge management system deployment, and evaluate the need to run one; if it is needed, select the right, nontrivial, and representative pilot project
2.Identify and isolate failure points in pilot projects
3.Understand the knowledge management system life cycle and its implications for knowledge management system deployment.
4.Eliminate all wastages.
5.Understand the scope of knowledge management system deployment
6.Use the RDI methodology to deploy the system, using cumulative results driven business releases
7.Decide when to use prototypes, and when not to use them
8.Convert factors to processes
9.Create cumulative results driven business releases by selecting releases with the highest payoffs first
10.Identify and avoid the traps in the RDI[ results driven increment] methodology
STEP 9: THE CKO, REWARD STRUCTURES, TECHNOLOGY, AND CHANGE
Focus on the following:
1.Understand the role of a chief knowledge officer and decide if your company
decides not to appoint a CKO, who else can best play that role?
2.Organize the four broad categories of the CKO's or knowledge manager’s responsibilities. To do so, you must understand the CKO's technological and organizational functions.
3.Enable process triggers for knowledge management system success.
4.Plan for knowledge management success using the knowledge manager as an agent for selling knowledge needs.
5.Manage and implement cultural and process changes to make your knowledge management system as well as your knowledge management strategy succeed.
STEP 10: METRICS FOR KNOWLEDGE WORK
The tenth step measuring return on knowledge investment (ROKI) must account for both financial and competitive impacts of knowledge management on your business.
Focus on the following:
1.Understand how to measure the business impact of knowledge management, using a set of lean metrics
2.Calculate returns on investment (ROI) for knowledge management investments
3.Decide when to use benchmarking as a comparative knowledge metric
4.Evaluate knowledge management ROI using the Balanced Scorecard (BSC) method
5.Use quality function deployment for creating strategic knowledge metrics
6.Identify and stay clear of the seven common measurement pitfalls, and identify what not to measure
7. Review and select software tools for tracking complex metrics.
THERE ARE FEW ORGANIZATIONS, WHO HAVE USED KNOWLEDGE
MANAGEMENT TO GROW/ SHINE TOO.
-IBM [ USA ]
-MOTOROLA [ USA ]
-LEVERS BROS. [ UK ]
3. Discuss some characteristics that must be possessed by "information".
7.Closely aligned to business 8.objective of a company
9.Communicated in a simple and right format
11.Robust in terms of producing information that is suitable for both internal decision making and external reporting
Understandability-----user specific quality
Primary qualities--RELEVANCE/ RELAIABILITY
-predictive value/feedback value/timeliness
-variability/neutrality /representative falth
CONSISTENCY ---materiality consistent