DSS News
                 D. J. Power, Editor
         February 25, 2007 -- Vol. 8, No. 4

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* Ask Dan: What are the features of a data-driven DSS?
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Ask Dan!

What are the features of a data-driven DSS?

by Dan Power

 This column is installment #2 about the features of the five types
of computerized decision support systems (Power, 2002). The focus is
on data-driven DSS. 

 Recall that features are identifiable capabilities or properties. A
specific decision support system implementation will not necessarily
have all of the features associated with a general category of DSS,
but a comprehensive list of features can help in classifying and
understanding computerized DSS. Just as we find with the facial
features of people some data-driven DSS features are more pronounced
than others in a specific system. In general, a number of major
features are shared by such systems.

 Many identifiable features are found in powerful, data-driven DSS
built using business intelligence and performance management
software, report and query tools, OLAP tools, executive information
system software, and data warehouse appliances and applications. Over
the years, the development of very large database storage systems,
multidimensional databases, parallel database systems, graphical user
interfaces and the Internet have expanded the technical possibilities.

 Data-driven DSS emphasizes access to and manipulation of a
time-series of internal historical company data, real-time
operational data and sometimes external data. Most current systems
emphasize historical company data. Simple file systems accessed by
query and retrieval tools provide the most elementary level of
functionality. Data warehouse systems that allow the manipulation of
data provide additional functionality. Data-driven DSS with On-line
Analytical Processing (OLAP) capabilities provide the highest level
of decision support linked to analysis of large collections of
historical data or streams of real-time data. Early versions of
Data-Driven Decision Support Systems were called Data-Oriented
(Alter, 1980) or Retrieval-Only DSS by Bonczek, Holsapple and
Whinston (1981). 

 One of the first data-driven DSS was built using an APL-based
software package called AAIMS, An Analytical Information Management
System. It was developed from 1970-1974 by Richard Klaas and Charles
Weiss at American Airlines (cf. Alter, 1980). AAIMS had a command
language user interface with capabilities that are still common in
data-driven DSS. Users could display data based upon criteria, make
simple calculations, design reports and tables, plot scatter
diagrams, calculate statistics, and create new commands. AAIMS also
included data management capabilities. AAIMS was primarily used for
ad hoc reporting and to build specific applications for budget
consolidation, corporate performance monitoring and revenue yield

 Research on Executive Information Systems (Watson et al., 1991)
expanded the features managers expect from data-driven DSS. A major
advance in technical capabilities of data-driven DSS occurred in the
early 1990s with the introduction of Online Analytical Processing
(OLAP) software. The term OLAP was coined in 1993 by E. F. "Ted"

 The key to a successful data-driven DSS is having easy and rapid
access to a large amount of accurate, well-organized multidimensional
data. Codd et al. (1993) argued OLAP systems were characterized by a
"multidimensional conceptual view", link to a variety of data
sources, easy for users to access and understand, "provide multiuser
support," "provide intuitive data manipulation", provide flexible
reporting, and provide analytical capabilities.

 The following is an alphabetical list of major features of
data-driven DSS from a user's perspective:

 1) Ad hoc data filtering and retrieval. The system helps users
systematically search for and retrieve computerized data, filtering
is often done using drop down menus, queries are often predefined,
and users have drill-down capabilities. Users can often change
aggregation levels, ranging from the most summarized to the most
detailed (drill-down).

 2) Alerts and triggers. Some systems help users establish rules for
email notification and for other predefined actions.

 3) Create data displays. Users can usually choose among displays
like scatter diagrams, bar and pie charts, can often interactively
change the displays, may be able to animate historical data on charts
or other representations, and may be able to playback historical data
in a time sequence.

 4) Data management. Users have limited "working storage" for a data
subset, users can sometimes group data or change data formats. In
some systems users can request changes to master data definitions and
data models.

 5) Data summarization. Users can view or create pivot tables and
cross tabulations. Users can create custom aggregations and calculate
computed fields, totals and subtotals. A pivot table summarizes
selected fields and rows of data in a table format. In a pivot table,
a user can view data from different perspectives and include various
fields in the table. Users can view a slice of the data or drill-down
for more detailed data from a summarized value in a table.

 6) Excel integration. Many data-driven DSS let users extract and
download data for further analysis, some systems allow users to
upload data for analysis in a user's "working storage".

 7) Metadata creation and retrieval. Users should be able to add
metadata to analyses and reports they create and temporarily change
labels and descriptive information stored as metadata. Metadata is an
explanation of the data in a DSS data store. It provides a context for
decision support and helps users understand the data in a system. Some
metadata is used to label screen displays and create report heading.

 8) Report design, generation and storage. Users can often
interactively extract, design and present information in a formal
report with tables, text, pie charts, bar charts, and other diagrams.
Once the user has created a format for a report, it can be saved and
reused with new data. Reports can often be distributed using print,
Web pages and PDF documents.

 9) Statistical analysis. Users can calculate descriptive statistics
to summarize or describe data, create trend lines and "mine" the data
for relationships.

 10) View predefined data displays. Data-driven DSS often have
displays created by the DSS designer. A system for operational
performance monitoring often includes a dashboard display. The term
is a metaphorical reference to an automobile's dashboard. The display
integrates information from multiple sources/metrics into gauges and
dials that resembles the dashboard of an automobile. A system for
more long-term strategic performance monitoring may include a
scorecard. A scorecard is a table displaying performance metrics and
it may include indicators like arrows or a stoplight display. Bar and
pie charts, scatter plots and two and three dimensional maps may also
be used in predefined data displays.

 11) View production reports. DSS designers may create and store
predefined, periodic reports as part of a data-driven DSS for users
to easily access.

 Please note: Decisions made using data-driven DSS can be adversely
affected by factors unrelated to the actual data so as part of the
design of such systems careful consideration must be given to how
data is framed and displayed. 

 Overall, with a data-driven DSS managers can access a single version
of the truth, perform their own analyses, have access to reliable,
consistent and high-quality information, make better informed
decisions, and have more timely information. To achieve these results
we need to build an appropriate DSS data store, create a user
interface with desired features, institute effective data governance
and insure consistent data gathering. Also, managers need to be
willing to share and integrate data across the enterprise! In
general, we should start a development effort by focusing on the
decision support capabilities and features we need and want in a new
data-driven DSS.

 As always your comments, questions and suggestions are welcomed.


 Alter, S.L., Decision Support Systems: Current Practice and
Continuing Challenge. Reading, MA: Addison-Wesley, 1980. 

 Bonczek, R. H., C. W. Holsapple, and A. Whinston. Foundations of
Decision Support Systems. Academic Press, 1981. 

 Codd, E.F., S.B. Codd and C.T. Salley, "Providing OLAP (On-Line
Analytical Processing) to User-Analysts: An IT Mandate", 
E.F. Codd and Associates, 1993 (sponsored by Arbor
Software Corporation). 

 Groves, S., "OLAP: The Panacea for the Ills of Management
Information Systems?" November 12, 1998, URL .

 Charter of comp.databases.olap, last updated: May 1, 1995, .

 Pendse, N., "What is OLAP?" Business
Application Research Center

 Rogalski, S. and Spyro Karakizis, "Business Intelligence Benefits:
Don't Forget the Revenue!" DM Review Magazine January 2004, URL 

 Watson, H.J., R.K. Rainer, and C. Koh, “Executive Information
Systems: A Framework for Development and a Survey of Current 
Practices,” MIS Quarterly, (March, 1991), pp. 13-30. 


        Check the new Glossary at DSSResources.COM


 DSS Conferences

 1. DAMA +Meta-Data, Boston, March 4-8, 2007. Check .

 2. ISCRAM 2007, May 13-16, 2007 Delft, The Netherlands. 
Check .

 3. AMCIS 2007, Americas Conference on Information Systems,
Keystone, CO USA, August 9-12, 2007. SIG DSS mini-tracks.
Check .

 4. DaWaK 2007, 9th International Conference on Data
Warehousing and Knowledge Discovery, Regensburg, Germany,
September 3-7, 2007. Full papers due: April 13, 2007.
Check .


       Purchase Dan Power's DSS FAQ book 
 83 frequently asked questions about computerized DSS 


What's New at DSSResources.COM

 02/17/2007 Posted an article by Mark Demarest, "Knowledge
Management: An Introduction". Check the articles page.

 02/12/2007 Posted DSS Time Line. Check Decision Support Systems Time
Line page.

 02/06/2007 Posted a new DSS glossary capablity developed in PHP and
MySQL. Check the DSS Glossary page.


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