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Chapter 2 • Foundations and Technologies for Decision Making 87

scanning. Web browsers provide useful front ends for a variety of tools, from OLAP to data
mining and data warehouses. Data sources can be internal or external. Internal sources may
be accessible via a corporate intranet. External sources are many and varied.
Decision support/BI technologies can be very helpful. For example, a data ware-
house can support the intelligence phase by continuously monitoring both internal and
external information, looking for early signs of problems and opportunities through
a Web-based enterprise information portal (also called a dashboard). Similarly, (automatic)
data (and Web) mining (which may include expert systems [ES], CRM, genetic algorithms,
neural networks, and other analytics systems) and (manual) OLAP also support the intel-
ligence phase by identifying relationships among activities and other factors. Geographic
information systems (GIS) can be utilized either as stand-alone systems or integrated with
these systems so that a decision maker can determine opportunities and problems in a
spatial sense. These relationships can be exploited for competitive advantage (e.g., CRM
identifies classes of customers to approach with specific products and services). A KMS
can be used to identify similar past situations and how they were handled. GSS can be
used to share information and for brainstorming. As seen in Chapter 14, even cell phone
and GPS data can be captured to create a micro-view of customers and their habits.
Another aspect of identifying internal problems and capabilities involves monitoring
the current status of operations. When something goes wrong, it can be identified quickly
and the problem can be solved. Tools such as business activity monitoring (BAM), busi-
ness process management (BPM), and product life-cycle management (PLM) provide such
capability to decision makers. Both routine and ad hoc reports can aid in the intelligence
phase. For example, regular reports can be designed to assist in the problem-finding
activity by comparing expectations with current and projected performance. Web-based
OLAP tools are excellent at this task. So are visualization tools and electronic document
management systems.
Expert systems (ES), in contrast, can render advice regarding the nature of a prob-
lem, its classification, its seriousness, and the like. ES can advise on the suitability of a
solution approach and the likelihood of successfully solving the problem. One of the
primary areas of ES success is interpreting information and diagnosing problems. This
capability can be exploited in the intelligence phase. Even intelligent agents can be used
to identify opportunities.
Much of the information used in seeking new opportunities is qualitative, or soft.
This indicates a high level of unstructuredness in the problems, thus making DSS quite
useful in the intelligence phase.
The Internet and advanced database technologies have created a glut of data and
information available to decision makers—so much that it can detract from the quality
and speed of decision making. It is important to recognize some issues in using data and
analytics tools for decision making. First, to paraphrase baseball great Vin Scully, “data
should be used the way a drunk uses a lamppost. For support, not for illumination.” It
is especially true when the focus is on understanding the problem. We should recognize
that not all the data that may help understand the problem is available. To quote Einstein,
“Not everything that counts can be counted, and not everything that can be counted
counts.” There might be other issues that have to be recognized as well.

support for the Design Phase
The design phase involves generating alternative courses of action, discussing the criteria
for choices and their relative importance, and forecasting the future consequences of
using various alternatives. Several of these activities can use standard models provided by
a DSS (e.g., financial and forecasting models, available as applets). Alternatives for struc-
tured problems can be generated through the use of either standard or special models.

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