To survive in the information era, organisations need to understand current business
activity. Realisation that elements for future success may be found within the
organisations’ commercially related data, has aroused the concept of Business
Intelligence [BI] within the KM domain. Business Intelligence equips knowledge
workers to quickly spot trends within business, financial and market data; this knowledge
can then be applied to enable better decision-making strategies. Knowledge of the
internal organisation, which leads to decision support, can also be categorised under the
Although, emphasis has been afforded to commercial data for some time, ensuring that it
was adequately captured and stored, it is only recently that the realm of Business
Intelligence has grown exponentially. This is due to technological development within
the BI field. High powered statistical and visualisation tools are applied to commercial
data offering the analyst fast access and insight to vast quantities of data in summarised
format. Tools within the BI stream include Data Warehousing and Data Marts, Data
Mining tools, and Modelling and Prediction tools.
Data Warehousing can be defined as,
‘any collection of summarised data from various sources, structured
and optimised for query access using OLAP (on-line analytical
processing) query tools.
Organisations typically contain two sets of data, namely, operational and informational.
The essence of Data Warehousing is to provide an architectural model to control the flow
of data from operational systems to decision support environments.
By interrogating Data Warehouses by Data Mining tools, such as Intelligent Miner and
Clementine Data Mining System, data patterns, classifications and associations can be
determined, leading to the optimisation of current organisation information assets and the
formation of new relationships between customers, suppliers and internal processes.
From this marketing strategies can be tailored to attract certain types of consumers, thus
encouraging profit maximisation or cost minimisation by optimising inventory
Knowledge Management encourages organisations to broaden their use of Data
Warehousing and Mining. KM highlights the fact that all employees in knowledge-based
organisations need to make decisions based on increasingly complex datasets (Offsey,
1997). By understanding the data components that form the basis of the organisation,
employees can make better forecasting decisions based on past business trends, can bring
better products to market in a more timely manner and can improve overall company
performance (IBM, 1996).
Intelligent Support Systems
Intelligent Support Systems [ISS] such as Decision Support Systems [DSS] and
Executive Information Systems [EIS] are suites of tools that enable managers to make
better decisions when faced with unstructured or semi-structured problems.
A DSS can be stand-alone or integrated with existing systems such as Transaction
Processing Systems [TPS] or Management Information Systems [MIS]. They can
support individual decision-making or group decision-making through Group Decision
Support Systems [GDSS] for independent, interrelated and/or organisational problems. A
DSS has five main functions that facilitate managerial decision-making, namely, model
building, ‘what-if’ analysis, goal seeking, risk analysis and graphical analysis.
Group Decision Support Systems are designed to operate in the same way as Decision
Support Systems. Huber, (1983), states,
‘Group Decision Support Systems are computer-based information
systems that enhance group decision making by facilitating the exchange
and use of information by group members, and interactions between the
group and the computer, to formulate and solve unstructured problems’
The purpose of GDSS is to foster an environment conducive to decision making where
both introverted and extroverted group members have equal opportunity to contribute.
Anonymity offers all participants the freedom to be open, creative and innovative without
fear of repercussions. As everyone can ‘talk’ at the same time, the system generates and
processes ideas in parallel. At the end of the meeting the system has created a ‘memory’
The field of Artificial Intelligence [AI] has been around since the late 1970s. However,
the emergence of Knowledge Management has evoked a renewed interest in AI
techniques. This is being fuelled by developments in the underlying technologies, and
the realisation that human knowledge and capabilities can be replicated to knowledge-
based systems [KBS]. AI technology offers organisations the opportunity to capture
individual and collective knowledge that can be codified and used to extend the
organisation’s knowledge base.
Laudon and Laudon, (1998) define Artificial Intelligence as,
‘the effort to develop computer-based systems that behave like humans,
with the ability to learn languages, accomplish physical tasks, use a
perceptual apparatus, and emulate human expertise and decision
Although successful AI systems are based on human expertise they are limiting and
should only be used to extend the powers of experts, not substitute them.
One AI technique applicable to the KM domain is that of Expert Systems, a point noted
by Liebowitz, (1998),
‘Expert systems should be an integral part of a Knowledge
Management System. Capturing expertise and putting it online in
terms of online pools of expertise or web-based interactive knowledge
centres is critical to the potential success of Knowledge Management’
Business decision-making often depends on the identification and application of
knowledge and expertise to problem solving situations. Wiig, (1997) supports this point,
‘The major impact of knowledge-based system applications in support
of Knowledge Management has been to deliver knowledge to the point-
of-action where the most accurate information on the situation
normally is presented, analysis is performed, decisions are made, and
the opportunity to serve the business in a timely manner is best’
Often it is difficult to elicit expert knowledge from those who hold it at the necessary
moment. Liebowitz, (1998) claims that Expert systems overcome this barrier,
‘Expert systems are an ideal technology for capturing, preserving, and
documenting knowledge, especially in today’s environment where
organizations are reengineering, downsizing, and losing senior
managers. Expert systems are useful for building the institutional
memory of the organization before this intellectual capital is lost’
To construct an Expert System and build ‘corporate memory’, knowledge engineers elicit
expertise from domain experts. This information is then organised and structured so it
can be explicitly stored and retrieved when required.
This idea is displayed in Post Office Consulting as part of their knowledge toolkit. ‘3E
Interviews’, that is Entry, Exit and Expert, are carried out within the organisation in an
attempt to capture knowledge. Recently the Expert interview was conducted on the
project manager of the Singapore Distribution Office project. Expertise from this project
was captured in an attempt to apply lessons learnt from this project when the new London
Distribution office is constructed at Heathrow Airport. By capturing the knowledge of an
expert and applying it to a similar project, re-invention of the wheel may be avoided.
Another Expert System technique applied for Knowledge Management is that of Case-
Based Reasoning in Workflow applications.
Workflow refers to a standardised sequence of tasks that occur within a company and
have been established to improve efficiency (Pompili, 1996). Many organisations are
employing workflow technology to respond to both internal and external requests for
information faster. The current thinking behind this approach is of a ‘one stop shop’. As
organisations become more customer-focused, relevant up-to-date organisational
information must be provided to enquirers with immediate effect. This applies to both
external and internal requests for information. If the knowledge worker is expected to
gain competitive advantage for the organisation, any information required must be made
available immediately, else the decision making process will be interrupted.
A common workflow application is a Help-desk. Help-desks provide technical support to
customers through the automation of procedures for entering, tracking and resolving
queries (Pompili, 1996). In addition, Frequently Answered Questions [FAQ’s] can be
included on a help-desk package to provide expertise and technical support to the user in
a first-pass attempt to solve problems prior to human intervention. Action Workflow
Metro and Webflow are tools suited to this purpose.