A Case Study and Tutorial in Using IT Knowledge Based Tools Part 1: Decision Support Discussion
Written By: E. Robins
Published On: May 2001
A Case Study and Tutorial in Using IT Knowledge Based Tools
1: Decision Support Discussion
Most business managers, whether vendors, vendor clients or implementers,
are unaware of the fundamental capabilities that knowledge based decision
support can provide to minimize project risk for all sides of technology
utilization. Given that over 90% of IT projects fail on first attempt,
according to the Standish Group, more thought and research on the evaluation
and selection process is needed. Many of these failures - some 30% - are
the direct result of poor selections, and represent upward of $30 billion
in wasted investment annually.
the vendor side, the challenge of educating the potential client of their
offerings results in long sales cycles, meticulous and numerous RFI responses,
and potential for a mismatch, resulting in projects that can go awry.
These failed projects do not bode well for the vendor, since the sales
cycle costs can only rise, and their reputation can suffer. Consequences
can be more severe for the client where it can, in extreme cases, lead
to business failure. For implementers, the issue is similar: having inadequate
information for the implementation phase means an inability to properly
plan and execute the implementation, or for a consultant to assist the
end client in making proper technology decisions. Implementers (which
can be internal IT departments as well as consultants) can also find that
decision-maker indecision leads to lengthened sales cycles, missed opportunities,
and risk of competitive intrusion. The root cause of this indecision is
an inability of the implementer to give confidence to the stakeholders
of their choice of solution.
sides need thought and research to build data and process information
in a meaningful context, which takes time and costs money for all participants
going through the selection process. But without spending time, thought,
research and money there is increased business risk to all.
cut away from this devil-and-the-deep-blue-sea conundrum means looking
under the hood of evaluation and selection practices, to determine if
there are better ways of moving through them. There is certainly room
to ask the fundamental question of whether the current practice of RFI
/ RFP processes, among other internal organizational procedures, are adequate
to the task of selecting complex systems. The record indicates there is
much room for improvement.
essence, for complex selections, the human-machine combination has to
work together to drive the solution. Both have to be understood and complement
each other in the process. It is easy for the human to be overwhelmed,
or simply run out of time, and the machine interface and engine to be
inadequate to the task. However, the results must benefit the process
if human and machine can function effectively together to process information
and avoid the pitfalls of past selection processes.
the second part of this article, we shall follow a simplified process
as an illustration. This method was used by the author to conduct the
selection on a personal device assistant (PDA). Though a PDA is far less
complex than an ERP system, processes and procedures enabling narrowing
down of solutions, and avoiding dissatisfaction, while taking on assessed
risks, are part of the process embedded in Knowledge Based Selection methods.
This Note: This is a two part note with Part 1 containing a discussion
of the use of an IT Knowledge Based selection tool as part of a Decision
Support System selection process. Part 2 is a tutorial which illustrates
using such a system to select the personal device assistant (PDA).
of Decision Support System
Traditionally, DSSs (Decision Support Systems) have largely been used
for internal corporate support. However, there is a growing trend to combine
DSS and knowledge bases for product and project evaluation that can benefit
all sides, leading to a methodology called Knowledge Based Selection.
Getting the methods and the technology right is important. This is the
main focus of TECs value proposition through its research and tools development
the benefits from using knowledge driven selection processes requires
two key components: accurate data and a clear process enabling stakeholders
to navigate through the data to get to a solution. If the system provides
these capabilities, the benefits can include:
products down to a shortlist. Vendors benefit from not pursuing unfruitful
clients, and clients benefit because the short list is usually reduced
to a manageable size
the end client business scenario early on enables a better fit and more
rapid narrowing of product match
of issues and negotiation perspectives are brought to the front, enabling
more efficient and productive negotiations
the construction of scenarios and measuring scenario performance to
reach the final decision
confidence in the decision among the client team
buy-in to a decision at the client site, easing political obstacles
to the selection
the solution implementers to be better aware of the challenges
vendors to be aware of product gaps with client needs
expectations of the implementation results to be realistic
better implementation planning
a more reliable and realistic outcome
future project discussions between the vendor and client to be processed
more effectively, since past data is intact and in a form that is reusable
and can be updated easily.
result of the exercise is a decision in which the business risk is minimized
and stakeholders have reached consensus, and mutual understanding in the
minimum time. Implementers are more thoroughly aware of the issues they
face, and the vendor has a better customer relationship as a result, leading
to potentially more future business.
Based Selection is a Tool
The last thing a knowledge based selection method does is to make the
decision for you. It is a tool that is part of a process, not the complete
process. However, it can be a very revealing component essential in the
overall quality of the result. The method consists of ways to rationally
input information, and then to evaluate the information according to the
value requirements of the stakeholders. Hence a component in the mix is
a knowledge base to store information, and an evaluation engine then enables
the stakeholders to navigate through it and estimate each solution's value
and risk in the context of stakeholder requirements.
processes within the evaluation engine must enable at least five things:
the inclusion of the value proposition to the end user
a narrowing down to a few solutions with high probability of delivering
the differences among the lead solutions to better understand the business
tradeoffs that may have to be made
a better and deeper understanding of the selected solution
confidence that the selected solution will meet at least minimum needs
past few years, many decision support tools and systems (DSSs) have appeared
on the Internet. In IT, most of these are based on 'value trees'. Value
trees are intended to accurately measure the degree of worth of a solution
to a business case, and if done correctly, will reflect the value proposition
of a solution to the required solution. From
the science perspective, most online DSS systems are simple and rarely
provide the insight needed in making major decisions, suffering from at
least one of five major flaws in IT solutions selection:
do not adequately represent the value propositions that need to be met
for each business case.
- The depth
of information (content) is inadequate.
- The processing
is done from a features and functions level, not a business objectives
and required capabilities level
- The processes
to uncover and make sense of content are inadequate.
client match methods lacks appropriate internal processes. This often
leaves many good niche players swamped by larger organizations who can
claim they 'do everything': niche players do some things very well,
and generally should be considered in particular business scenarios.
the human-machine combination has to work together to drive the solution.
Both have to be understood and complement each other in the process. It
is easy for the human to be overwhelmed, or simply run out of time, and
the machine interface and engine to be inadequate to the task. However,
the results must benefit the process if human and machine can function
effectively together to process information and avoid the pitfalls of
past selection processes.
Part 1 of a two-part discussion of using an IT Knowledge Based tool in
a Decision Support Process. Part 2 is a tutorial, which illustratively
discusses using such a tool to select a personal device assistant (PDA).