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            March 11, 2007 -- Vol. 8, No. 5

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Ask Dan!

What are the features of a document-driven DSS?

by Dan Power

This column is installment #3 about features of the five types of
computerized decision support systems (Power, 2002) tracked at The focus is document-driven DSS. 

The goal is to identify important observable attributes, elements or
aspects that distinguish document-driven DSS from other DSS and from
other computerized information systems. Features are often associated
with end user functionality, but these attributes may or may not
confer a specific user benefit. Features help us understand the form
and structure of a document-driven DSS. Case examples help
demonstrate the complexity of how the features can be assembled to
create specific document-driven DSS. One question a systems designer
should ask is: "What features do you want in the proposed decision
support system?" DSS design is similar to purchasing a new car with
many customizable features. First, get the intended users to decide
on their needs, then find a basic type or model that is a good
fit, and finally get the users to identify and evaluate "must have"
and "desirable" features so that a cost trade-off can occur. Finally,
we build and customize the specific system.

Document-driven DSS often use the same document source storage system
as document creators use in their transaction work flow. This means
the DSS designer is building a subsystem and must work with all of
the constraints associated with the broader document management or
Enterprise Content Management (ECM) environment.

Vannevar Bush's (1945) article in the Atlantic Monthly created a
challenging vision for managing documents and augmenting people's
memory. Bush wrote "Consider a future device for individual use,
which is a sort of mechanized private file and library. It needs a
name, and, to coin one at random, "memex" will do. A memex is a
device in which an individual stores all his books, records, and
communications, and which is mechanized so that it may be consulted
with exceeding speed and flexibility. It is an enlarged intimate
supplement to his memory." Bush's memex is a much broader vision
than today’s document-driven DSS.

A recent IDC study sponsored by storage provider EMC forecasts 988
billion gigabytes of digital information created in 2010. The study
notes, "over 95% of the digital universe is unstructured data. In
organizations, unstructured data accounts for more than 80% of all
information." Content management is a major issue in organizations
(Kelly, 2005). Document-driven DSS are intended to help people use
digitized, unstructured content in decision making. 

A document-driven DSS is a computerized support system that integrates
a variety of storage and processing technologies to provide document
retrieval and analysis. The system or subsystem is intended to assist
in decision making. The Web provides access to large document
databases, including databases of hypertext documents, images, sounds
and video. Examples of documents that might be accessed by a
document-driven DSS are policies and procedures, product
specifications, catalogs, and corporate historical documents,
including minutes of meetings, corporate records, and important
correspondence. A search engine is a powerful decision-aiding tool
commonly associated with a document-driven DSS. The features
available for designing a document-driven DSS are becoming more
numerous and more sophisticated.

Case study examples of document-driven DSS at
include University of Alberta, Washington County, Iowa and BFGoodrich
Aerospace. The University of Alberta increases timely access to
policies and procedures and also manages the work flow for creating
and managing new policies (Stellent, 2004). Washington County, Iowa
has a Web-based Spatial DSS with data and document-driven decision
support subsystems. The system let's authorized users retrieve maps,
plans and drawings of county infrastructure like roads and buildings
(Tully 2006). BFGoodrich Aerospace improved its efficiency in
performing non-routine aircraft maintenance (Documentum, 2003) with a
workflow system, document management and document-driven decision

In autumn 2005, AIIM ( defined Enterprise Content
Management (ECM) as "the technologies used to capture, manage, store,
preserve, and deliver content and documents related to organizational
processes. ECM tools and strategies allow the management of an
organization's unstructured information, wherever that information
exists." ECM support collaboration of content providers, organizers
and administrators. According to Wikipedia, a content management
system (CMS) is a computer software system used to assist its users
in the process of content management. CMS facilitates the
organization, control, and publication of a large set of documents
and other content, such as images and multimedia resources. A CMS
often facilitates the collaborative creation of documents. Some CMS
systems also include workflow software support. Many of the ECM/CMS
supported tasks are transaction processing rather than decision
support, but the software often includes capabilities/features that
can be used for building a document-driven decision support
subsystem. Part of the design of a document-driven DSS is indexing
and organization of documents. ECM/CMS can also facilitate a decision
process workflow, with creation of reminders, deadlines, delegation of
subtasks, and other decision process administration functions. A
document-driven DSS can assist in monitoring decision process status,
routing of decision relevant information, and recording of decisions
and supporting document links. 

Bush's vision of memex identified some key features and abilities the
system would provide users:

1) retrieving records using indexes
2) linking together stored documents
3) associating documents
4) adding marginal notes and comments about documents
5) saving annotations and comments for future reference or to share
with colleagues
6) searching for and retrieving documents
7) browsing documents, especially rapid scanning

Bush's list is a good starting point for developing a features list
for document-driven DSS. The following are major features from a
user's perspective:

1) Ad hoc search and retrieval. Users can enter their own search
terms, use stored queries and the system often has an easy to user
search interface for applying logical operators. Systems usually
support common Boolean operators, such as AND, OR, and NOT and some
support operators like NEAR and LIKE. A user specifies a query which
initiates a search for documents that are likely to be relevant.

2) Alerts and triggers. Some systems help users establish rules for
email notification and for other predefined actions. Users may set
alerts for when a document change occurs or completion of a decision
processing task.

3) Append notes to a document. Notes and comments may be for future
reference or to share with colleagues.

4) Browsing and document navigation. Browsing is an interactive
capability that lets a user explore the document collection. The
system may provide for rapid scanning of a document.

5) Document translation/multilingual interface. The system translates
documents to/from languages.

6) Document management. Users have limited "working storage" for
comments, links and evaluative information, users can sometimes
publish comments, ratings, etc. Some systems have document
check-in/check-out. Users for decision support need to know if a
document is or can be modified.

7) Hyperlinks. The system includes links in documents to specific
information within that document or to another document.

8) Indexes, both human and machine generated. The index may include
an alphabetical listing of key words, names and/or topics. The index
or indexes are a guide to the contents of document collection. 

9) Metadata retrieval. Some systems provide data about when documents
were created and stored, version numbers, document creator, and other
history. Users should be able to easily retrieve such data.

10) Relevancy ranking. The search engine assigns a relevance score
and search results are displayed in a relevance order. The order is
determined by an algorithm that measures factors like number of
occurrences of the search term(s) in the documents and the density of
the term(s).

11) Record search history, save searches, publish searches for other
users. Some systems allow users to easily resume "temporarily
abandoned searches". Also, a mechanism may exist for tracking the
history of a user session or of a collection of user sessions. 

12) Show decision process flowchart. Systems with workflow software
may allow users to monitor the progress of specific decisions and the
associated documents.

13) Summarization. The system provides extracts text using
statistical cues to form summaries.

14) Text mining and text analysis. Some standalone software attempts
to extract patterns from natural language text. The system may have a
capability for comparing multiple versions of a document for

15) User action recording. In some systems users are expected to
indicate approval of documents, enter ratings or make other
evaluations as part of the decision process. Some systems record user
interaction and user decisions.

Document retrieval is a key capability that focuses on how people can
find needed documents and how much time is spent looking for them. We
also need to explore what technology options are available. In most
situations, the cost of retrieval for decision relevant documents can
be reduced with a well-designed document-driven DSS.

Please note: Decisions made using document-driven DSS can be
adversely affected by people misunderstanding the documents and by
system design features like how the document is displayed and what
tools are available for document retrieval.

The prospects and benefits for managing knowledge and supporting
decision making using document-driven DSS is evolving. The Web has made
document databases easier to access. Managers can perform their own
searches and have more timely unstructured information. Managers need
to carefully read and interpret the documents retrieved from the
system, but new tools are being developed to help in text mining and
analysis. To build sophisticated document-driven DSS, designers need
to organize documents and preplan indexes, create a user interface
with desired features, institute effective document governance and
management. As with any DSS, we should start a development effort by
identifying the decisions that we want to support and the decision
support capabilities and features managers need and want.

As always your comments, questions and suggestions are welcomed.


Angehrn, A.A. and T. Jelassi, "DSS Research and Practice in
Perspective", Decision Support Systems, 12 (4-5), November 1994,

Bush, V., "As We May Think", The Atlantic Monthly, July 1945, Vol.
176, No. 1; pp. 101-108,

Documentum Staff, "Optimizing Aircraft Maintenance Operations using a
Document-driven DSS", Documentum, Inc., 2001, posted at
DSSResources.COM May 17, 2003, 

Fedorowicz, J., "A technology infrastructure for document-based
decision support systems," Decision support systems (3rd ed.):
putting theory into practice, Prentice-Hall, Inc., Upper Saddle
River, NJ, 1993

Holsapple, C.W. and A. B. Whinston. Decision Support Systems: A
Knowledge-based Approach, Minneapolis, MN: West Publishing Co., 1996.

Hearst, M., "What Is Text Mining?" October 17, 2003, URL

IDC, "Groundbreaking study forecasts a 988 billion gigabytes of
digital information created in 2010,", 03/06/2007,

Kelly, D. A., "Tame your content: How enterprises of all sizes are
managing unstructured data," Oracle Magazine, March/April 2005, pp.

Power, D., How does a document management system differ from a
document-driven DSS? DSS News, Vol. 3, No. 15, July 21, 2002.

Stellent Staff, "University of Alberta increases timely access to
policies and procedures", posted at DSSResources.COM September 17,

Sullivan, Dan. Document Warehousing and Text Mining. New York: Wiley
Computer Publishing, 2001. 

Swanson, E. B. and M.J. Culnan, "Document-Based Systems for
Management Planning and Control: A Classification, Survey, and
Assessment." MIS Quarterly, December 1978, pp. 31-46. 

Sprague, R.H., M.J. Culnan, J. Fedorowicz, and D. Narisimhalu, Task
force on document-based decision support systems System Sciences,
1988. Vol.IV. Applications Track., Proceedings of the Twenty-First
Annual Hawaii International Conference on Publication Date: 5-8 Jan
1988, Volume: 4, pp. 262-264

Tully, M., "E-Docs Asset GIS: Washington County, Iowa", March 6,

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