DSS News
                     D. J. Power, Editor
              August 18, 2002 -- Vol. 3, No. 17
         A Bi-Weekly Publication of DSSResources.COM

  Check Larry English's article "Ten mistakes to avoid if 
   your data warehouse is to deliver quality information" 


* DSS Wisdom
* Ask Dan! - Is ETL software needed to build a model-driven DSS?
* Report from AMCIS 2002 - Dallas
* What's New at DSSResources.COM
* DSS News Releases


DSS News is sent to more than 850 subscribers from 50 
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DSS Wisdom

According to Graham Allison, the Cuban Missile Crisis and technology 
advances prompted a number of changes in military decision making. 
Allison writes "Advances in the technology of communications made it 
possible for political leaders in the White House to talk directly with 
commanders of destroyers stationed along the quarantine line. Advances 
in the technology of mass destruction created the possibility that acts 
by men on a single destroyer in that quarantine line could rapidly 
escalate to bring death to millions of Americans.  Thus the governmental 
leaders had both the capability and the incentive to reach out beyond 
the traditional limits of their control.  Maps in the 'Situation Room' 
in the basement of the White House tracked the movement of all Soviet 
ships.  The members of the ExCom knew each of the ships by name and 
argued extensively about which should be stopped first, at what point, 
and how.  Sorenson records 'the President's personal direction of the 
quarantine's operation ... his determination not to let needless 
incidents or reckless subordinates escalate so dangerous and delicate a 
crisis beyond control.'  Thus, for the first time in U.S. military 
history, local commanders received repeated orders about the details of 
their military operation directly from political leaders -- contrary to 
two sacred military doctrines.  This circumvention of the chain of 
command and the accompanying countermand of the autonomy of local 
commanders created enormous pain and surface friction. (p. 127-128)"

from Allison, G. T., Essence of Decision: Explaining the Cuban Missile 
Crisis, Boston, MA: Little, Brown and Co, 1971.


Get Dan Power's new book, Decision Support 
Systems: Concepts and Resources for Managers, at


Ask Dan!
by Daniel J. Power

Is ETL software needed to build a model-driven DSS?

Extract, transform and load (ETL) tasks are part of building many
types of DSS, including some model-driven DSS.  BUT, the ETL software 
developed for creating and refreshing large data stores from 
transaction, enterprise resources planning (ERP) and/or operating 
systems is NOT needed for model-driven DSS. 

In the Ask Dan! column titled "What is ETL software and how is it 
related to DSS?" (DSS News 08/04/2002), the basics of this type of 
software are discussed.  The article by Larry English published at 
DSSResources.COM on  August 11, 2002 also has a discussion of ETL and 
data quality.  This column trys to clarify what data is needed for 
model-driven DSS and how it is obtained.

Model-driven DSS use complex financial, simulation, and/or optimization 
models to provide decision support. The needed data sets are usually 
small, and certainly much smaller than the 500 megabyte-5 terabyte data 
stores common with data-driven DSS. 

In some model-driven DSS, the user enters ALL of the data needed by the 
system.  The DSS performs data validation and data storage.  The data 
entry may be 5-15 parameter values, text or other inputs. No data is 
imported from a source system. For example, most of the model-driven 
decision aids at DSSResources.COM have users input all of the data 
required by the model.  To try the Cost/Benefit Analysis decision aid 
designed by D. J. Power and programmed in javascript by A. P. Power, 
check URL 

In other model-driven DSS, a time series of data on one or more 
variables needs to be imported into the DSS.  The data set may be 1000 
to even 10,000 values.  It is common to perform extract and transform 
tasks to create the data.  A report or data set is exported from a 
source system.  Then because the data set is small the data is usually 
cleaned up and formatted in a text editor or in a desktop application 
like Excel. Excel is often a useful tool for creating small data sets 
for use with a model-driven DSS.  The data set can then be incorporated 
into a spreadsheet-based DSS built using Excel or Lotus 123 or imported 
into another DSS development environment.

Larger data sets are used for some specialized model-driven DSS, but the 
size of the data set remains modest compared to data marts and data 
warehouses.  For example, Radical Logistics sells transportation 
software for calculating distances and rates. Data on "thousands of 
shipments" is used for the analysis and the data needs to be verified 
and cleaned up for correct ZIP codes and mileages.  The Radical 
Logistics software help with ETL, analysis and decision support.

Another common type of model-driven DSS uses a small number of data 
values from an external database that is needed for the analysis by the 
DSS user.  The user defines the analysis and inputs some parameter 
values. For example, many model-driven investment DSS extract data from 
a historical stock market database. The Intrinsic Value per Share 
Calculator at extracts earnings and price information from a 
general purpose database of stock information and the user inputs 
assumptions about interest rates for "What if?" analysis.

Finally, some model-driven DSS need very large data sets to create a 
visual simualation that the DSS user can interact with.  These data sets 
are created and data may be imported from video files, maps and other 
sources.  For example, DaimlerChrysler has a Virtual Reality Center to 
analyze and understand digital models and simulation results. The data 
used is not ERP or transaction data.  The extract, transform and load 
tasks are very different from those associated with data warehousing, 
business intelligence and data-driven DSS.

So ...  As the data needs of a model or models in a model-driven DSS 
increases, it becomes more likely that specialized software will be 
needed to help the DSS developer create the specific decision support 
data store. The software used to extract, transform and load the data 
depends on the data, the DSS development environment and the preferences 
of the developer.


English, L. P., "Ten mistakes to avoid if your data warehouse is to 
deliver quality information", DSSResources.COM, 08/11/2002.

Power, D. J. "What is ETL software and how is it related to DSS?", DSS 
News, 08/04/2002.


Report from AMCIS 2002 - Dallas

by Dan Power

AMCIS 2002 in Dallas had over 125 sessions. More than 300 papers were 
presented and there were 17 panels and tutorials. The SIG DSS business 
meeting was well attended and membership forms for SIG DSS should be 
available in September. Contact SIG DSS secretary/treasurer Ramesh 
Sharda, So what were the highlights? Well ... the 
sessions on decision support and data warehousing! Keith Lindsey and 
Mark Frolick (Univ. of Memphis) discussed current issues in Data 
Warehousing.  Cliff Ragsdale (Virginia Tech) chaired a panel on 
"Spreadsheet-based DSS Curriculum Issues".  Cliff provided examples of 
how he uses spreadsheets and Visual Basic for Applications to teach DSS 
concepts. Paul Bergey (NC State) and I also discussed our teaching 
approaches. A session on Web-based DSS identified some interesting 
issues. In the final DSS paper session on Sunday morning, Steve Alter 
and Roger Pick presented different views of DSS.  Alter presented a work 
system view of DSS that stimulated an interesting discussion and 
comments. I heard positive comments on a number of other sessions, but 
my highlights reflect only those sessions I could attend.  Thanks to the 
mini-track co-chairs for DSS - H. Bhargava, D. Power, M. Warkentin, and 
for data warehousing - K. Dowling, D. Schuff, and R. St. Louis.

The Tex-Mex Fiesta on Saturday night was "big" even by Texas standards. 
I'm sorry to report however, my opportunities to meet and chat weren't 
as frequent as in Boston last year. Perhaps that will change next year. 
Plans are in the works for AMCIS 2003, August 4-6, 2003 in Tampa, FL. 
The conference theme is "Navigating the Torrents of Technology".

See you in Tampa.


What's New at DSSResources.COM

08/11/2002 Posted article by English, L. "Ten mistakes to avoid if your 
data warehouse is to deliver quality information", DSSResources.COM, 
08/11/2002, URL


DSS News Releases - August 5 to August 15, 2002

Complete news releases can be found at DSSResources.COM.

08/15/2002 A.D.A.M., Inc. and Subimo partner to create comprehensive 
health care decision tools for consumers.

08/13/2002 Comshare(R) and Symmetry offer risk-free program to evaluate 
analysis services. 

08/12/2002 Radical Logistics has rapid adoption rate for new 
transportation modeling solution.

08/08/2002 Intergraph's research program encourages innovation in the 

08/08/2002 Roche Italy selects MicroStrategy Business Intelligence 

08/08/2002 Reminder: Americas Conference on Information Systems 2002 in 
Dallas, TX August 9-11, 2002.

08/07/2002 Plumtree offers $75,000 portal package. 

08/07/2002 Managers are unable to communicate value ... and it costs 
their shareholders big-time.

08/06/2002 Hospitals reduce medical errors and improve care with 
handheld technologies.

08/06/2002 New business planning products challenge investment doldrums.

08/05/2002 Reminder: The Data Warehousing Institute World Conference, 
Las Vegas, NV, August 18-23, 2002. 

08/05/2002 Primus Knowledge Solutions announces PrimusĀ® Quick Resolve - 
A new product that accelerates the productivity of tier-one agents. 


          You can read 786 DSS News releases in
          the Subscriber Zone at DSSResources.COM.


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