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Abstract

It is important for managers and Information Technology professionals to understand data-driven decision support systems and how such systems can provide business intelligence and performance monitoring. Data-driven DSS is one of five major types of computerized decision support systems and the features of such systems vary across specific implementations. Different development packages also impact the capabilities of data-driven DSS and hence criteria for evaluating data-driven DSS development software are important to understand. Overall, this article builds on an historic foundation of prior decision support systems theory.
149
Information Systems Management, 25: 149–154
Copyright © Taylor & Francis Group, LLC
ISSN: 1058-0530 print/1934-8703 online
DOI: 10.1080/10580530801941124
UISM
Understanding Data-Driven Decision Support Systems
Understanding Data-Driven Decision Support Systems Daniel J. Power
University of Northern Iowa, Iowa, USA
Abstract It is important for managers and Information Technology professionals to understand
data-driven decision support systems and how such systems can provide business intelligence and performance
monitoring. Data-driven DSS is one of five major types of computerized decision support systems and the
features of such systems vary across specific implementations. Different development packages also
impact the capabilities of data-driven DSS and hence criteria for evaluating data-driven DSS development
software are important to understand. Overall, this article builds on an historic foundation of prior decision
support systems theory.
Keywords decision support, DSS framework, systems features
For almost 50 years, data-driven decision support has
been used for a wide variety of purposes. The U. S. Semi-
Automatic Ground Environment (SAGE) air-defense com-
mand and control system became operational in 1963
and it provided real-time decision support for more than
20 years. Operators accessed the SAGE system through
cathode ray tube displays and used a light pen to select
“tracks” of potential incoming hostile aircraft and man-
age their status (cf., Power, 2006, 2007b). One of the first
business-oriented, data-driven DSS was built using an
APL-based software package called AAIMS, for An Analytical
Information Management System. The system was devel-
oped from 1970–1974 by Richard Klaas and Charles Weiss
at American Airlines (cf., Alter, 1980).
As technology has evolved, data-driven decision sup-
port systems have become more sophisticated. According
to Watson, Wixom, Hoffer, Anderson-Lehman, and Rey-
nolds (2006), “Data management for decision support has
moved through three generations, with the latest being
real-time data warehousing”. Today data-driven decision
support is used for a variety of purposes including opera-
tional and strategic business intelligence queries, static
and real-time performance monitoring, and customer
relationship management.
Most of us have a general understanding of data-
driven decision support and recognize how important it
is in organizations. The purpose of this article is to clarify
the basic concepts and stimulate further research and
innovation related to this type of computerized support.
The next section examines key terminology especially
the terms data-driven DSS and business intelligence, then
the major features of data-driven DSS are summarized,
section 4 suggests some criteria for evaluating software
for developing data-driven DSS, the concluding section
draws some conclusions about the current state-of-the-art
related to data-driven decision support.
Business Intelligence and Data-Driven DSS
Managers can use systems that access current and histor-
ical data to support many decision tasks. When the tasks
are performed regularly then a computerized decision
support system can potentially increase access to the
data and help managers gain insights into organization
processes, customer activities, employee performance
and organization-wide performance metrics.
DSSResources.com defines a decision support system
(DSS) as “an interactive computer-based system or sub-
system intended to help decision makers use communi-
cations technologies, data, documents, knowledge, and/or
models to identify and solve problems, complete decision
process tasks, and make decisions. Decision Support Sys-
tem is a general term for any computer application that
enhances a person or group’s ability to make decisions.
In addition, Decision Support Systems refers to an aca-
demic field of research that involves designing and study-
ing Decision Support Systems in their context of use. In
general, Decision Support Systems are a class of comput-
erized information system that supports decision-making
Address correspondence to Daniel J. Power, University of North-
ern Iowa, Cedar Falls, IA 50614–0125, USA. E-mail: power@
DSSResources.com
150 Power
activities. Five more specific Decision Support System types
include: communications-driven DSS, data-driven DSS,
document-driven DSS, knowledge-driven DSS, and model-
driven DSS” (cf., Power, 2002a).
Business Intelligence, often referred to as BI, is a popu-
larized, umbrella term introduced by Howard Dresner of
the Gartner Group in 1989 to describe a set of concepts
and methods to improve business decision making by using
fact-based computerized support systems (cf., Nylund,
1999). The term is sometimes used interchangeably with
briefing books and executive information systems.
A business intelligence system is a data-driven DSS that
primarily supports querying of an historical database and
production of periodic summary reports. Data-driven DSS
have been called various names over the years including
data-oriented DSS (Alter, 1980), retrieval-only DSS by Bon-
czek, Holsapple and Whinston (1981), Executive Informa-
tion Systems, OLAP systems and Business Intelligence
systems. My preference is to use the term data-driven DSS
or data-driven decision support. Business intelligence
refers to a specific purpose for some data-driven DSS.
Data-driven DSS refers to a category or type of Decision
Support System that emphasizes access to and manipula-
tion of a time-series of internal company data and some-
times external 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 by computerized tools tailored to a
specific task and setting or by more general tools and
operators provide additional functionality. Data-driven
DSS with On-line Analytical Processing (OLAP) provide
the highest level of functionality and decision support
that is linked to analysis of large collections of historical
data (cf., Power, 2002a).
Emphasizing the broad purpose of providing data-
driven decision support may reduce the current confusion
surrounding the term business intelligence. Some think of
Artificial Intelligence as the primary source of tools for
business intelligence and focus on special studies using
data mining. The results of data mining can be used for
decision automation and if data is changing quickly some
data mining tools can be incorporated in data-driven DSS,
for example, in fraud detection or monitoring stock prices
(cf., Hormozi & Giles, 2004). Other researchers emphasize
performance monitoring and business reporting as the
focus of business intelligence. Dhar and Stein in their
1997 book “Seven Methods for Transforming Corporate
Data into Business Intelligence” discuss data warehousing
and OLAP in Chapter 4 titled “Data-Driven Decision Sup-
port.” Their perspective has certainly influenced the
expanded framework for identifying various types of DSS.
The most common data-driven computerized decision
support system is built using a data warehouse product
and a report and query product. Overall, this software
application category involves billions of U.S. dollars in
revenues each year. Beginning with Bill Inmon’s (1991)
book “Database Machines and Decision Support Systems,”
managers have sought to deploy such systems. Inmon’s
book provided the conceptual foundation for data-driven
DSS. Both Bill Inmon and Ralph Kimball (called “Dr.
DSS”) have had a major impact on what Information Tech-
nology practitioners think of when the term decision
support system is used (cf., Power, 2007b). Referring to
this broad category of information systems as data-driven
decision support systems is both understandable and use-
ful. This terminology also provides research continuity
extending back to Gorry and Scott-Morton’s (1971) article
that introduced the concept of a decision support system.
Various companies sell software that can be used to
build data-driven DSS including Teradata, Business
Objects, Cognos, Hyperion, and MicroStrategy. Teradata
software is primarily for the processing and development
of the backend data warehouse. The other vendors focus
on tools for creating a web-based user interface for a data-
driven DSS. Based upon recent DM Review magazine
reader surveys the major companies serving the business
intelligence community are IBM, Oracle, Microsoft, Busi-
ness Objects, and Teradata. Most of these large vendors
focus on the database component of a data-driven decision
support system.
Features of a Data-Driven DSS
Research on Executive Information Systems (Watson,
Rainer, & Koh, 1991) expanded the features managers
expect from data-driven DSS. In addition, a major advance
in technical capabilities for 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” Codd (Codd, Codd, & Salley, 1993).
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:
1. multidimensional conceptual view;
2. link to a variety of data sources;
3. easy for users to access and understand;
4. multi-user support;
5. intuitive data manipulation;
6. flexible reporting; and
7. analytical capabilities.
Powers (2007a) Data-Driven DSS Major Features
The following is a detailed summary of major features of
data-driven DSS presented from a user’s perspective from
Power (2007a).
Understanding Data-Driven Decision Support Systems 151
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 sum-
marized to the most detailed (drill-down).
Alerts and Triggers
Some systems help users establish rules for email notifi-
cation and for other predefined actions.
Create Data Displays
Users can usually choose among displays like scatter dia-
grams, 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 play-
back historical data in a time sequence.
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.
Data Summarization
Users can view or create pivot tables and cross tabula-
tions. Users can create custom aggregations and calculate
computed fields, totals and subtotals. A pivot table sum-
marizes selected fields and rows of data in a table format.
In a pivot table, a user can view data from different per-
spectives 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.
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.”
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 under-
stand the data in a system. Some metadata is used to
label screen displays and create report heading.
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 cre-
ated 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.
Statistical Analysis
Users can calculate descriptive statistics to summarize or
describe data, create trend lines and “mine” the data for
relationships.
View predefined data displays
Data-driven DSS often have displays created by the DSS
designer. A system for operational performance monitor-
ing often includes a dashboard display. The term is a met-
aphorical reference to an automobile’s dashboard. The
display integrates information from multiple sources/
metrics into gauges and dials that resembles the dash-
board of an automobile. A system for more long-term stra-
tegic performance monitoring may include a scorecard. A
scorecard is a table displaying performance metrics and
it may include indicators like arrows or a stoplight dis-
play. Bar and pie charts, scatter plots and two and three
dimensional maps may be used in predefined data dis-
plays.
View Production Reports
DSS designers may create and store predefined, periodic
reports as part of a data-driven DSS for users to easily
access.
Specific data-driven decision support systems will have
some subset of the above features depending upon the
needs of users, the purpose of the system, the development
environment selected, and the resources expended build-
ing the system. Performance monitoring may emphasize
predefined data displays and production reports, while a
system focused on ad hoc queries for historical data analy-
sis would emphasize data filtering and retrieval, as well as
data summarization and possibly statistical analysis.
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.
152 Power
A well-designed data-driven DSS emphasizes the design
of data displays and helps ensure that appropriate data is
retrieved.
Overall, with a data-driven DSS managers can more
easily 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 effec-
tive data governance and insure consistent data gathering.
In general, we should start a development effort by focusing
on the decision support capabilities and the features we
need and want in a new data-driven DSS.
Criteria for Evaluating Data-Driven DSS
Development Software
When asked how to evaluate competing software products,
some people are quick to list criteria like ease of use, cost
of the package, capabilities, vendor reputation and ease
of installation. Although such factors are important and
need to be considered in most situations, this evaluation
question should be framed more broadly and some other
issues should be addressed before specific criteria are dis-
cussed (cf., Power, 2002b). Software selection is a sequential
decision process. Begin the process by specifying require-
ments and ask, “What functions and tasks will managers
perform with the data-driven DSS? When and how will it
be used?”
Then the first major issue is determining whether
buying a vertical market package or assembling and cus-
tomizing software for a data-driven DSS project is a more
appropriate response to meeting the identified need. It is
often hard to know where the dividing line is between
“buying off-the-shelf” and “building” because once the
customization for the data-driven DSS becomes signifi-
cant, and then buying a vertical market package has
been transformed into a development project (even
though that may not have been intended).
Second, if the decision is to buy “off-the-shelf,” then
one must determine what products might meet the need.
It is important to recognize that one must identify com-
parable software packages. “Off-the-shelf” is often appro-
priate for task specific or vertical market data-driven DSS
software like web-based reporting software.
Third, once comparable products are identified then
one can ask, “which one is best in this particular situa-
tion?" At this point, criteria can be specified and prod-
ucts can be compared. Evaluators need to recognize that
dominant alternatives and dominating criteria exist in
situations. Sometimes one criterion is so important in
making a choice that all other criteria take on a second-
ary role. For example, the cost of the package may be so
important that relatively high cost packages have no
chance of being selected. In the same vein, some software
packages may be so appropriate and be such a “good fit”
with the perceived need that other packages receive little
consideration. For example, a manager developing a
small-scale, model-driven DSS may almost—without
explicit evaluation—“choose” to develop the application
in Microsoft Excel. Prescreen the list of possible DSS prod-
ucts to eliminate those that do not meet constraints like
the need to “fit” with other software or with existing pro-
cesses, or the need to meet special regulatory or legal
requirements. Also, eliminate products that do not meet
technical constraints in terms of operating systems or
infrastructure.
Fourth, if a dominant alternative does not exist, and
if no one criterion dominates all others, then six major
criteria should be identified and weighted for evaluat-
ing the comparable DSS packages. Criteria should be
generally independent of each other. Based on Power
(2002b), some criteria that should be considered include
the following:
1. Capabilities—examine the functions that a DSS
product can perform and how important they are to
the decision support need of targeted users. Deter-
mine if the package can be customized and in what
ways. Does it meet the need? Does it provide the
desired support?
2. Cost of the Package—examine the total cost of own-
ership including acquisition costs, implementation
and training costs, maintenance costs, and any
annual software license costs.
3. Ease-of-use—the ease of learning and using the capa-
bilities of a product to accomplish tasks. Ease of use
is in the mind of the user so ask users to assess this
criterion.
4. Ease of installation and operation—how easy is it to
configure, deploy and control use of a product. Is it
easy to transfer information to and/or from other
company information systems? Are there potential
technical implementation problems?
5. Performance—what is the speed or capacity of the
product when performing its functions. In addi-
tion, part of the performance criterion should be
software reliability.
6. Vendor reputation and reliability—the vendor mat-
ters, but in emerging product areas this criterion
can be difficult to assess. What kind of vendor and
technical support is needed and is available?
In the DSS literature, the debate has often been about
using rapid prototyping or a more structured systems
development life cycle (SDLC) method. With either
approach it is important to start with pre-design descrip-
tion and diagnosis of the actual decision-making process
Understanding Data-Driven Decision Support Systems 153
(Stabell, 1983). This step creates a decision-oriented
approach to DSS development.
A related diagnostic activity is conducting a DSS Audit.
In general, it can be very useful to audit operational and
managerial decision processes. An audit can be a first
step in identifying opportunities to redesign business
processes and include new data-driven DSS in business
processes (cf., Power, 2002a). Rockart (1979) identified
an approach for defining decision-making data needs
that is appropriate for Data-Driven DSS and especially
Executive Information Systems. Rockart’s Critical Suc-
cess Factors (CSF) Design Method focuses on individual
managers and on each manager’s current hard and soft
information needs. Diagnosis of decision-making
should be followed by additional initiation and diagnos-
tic activities and preparation of a feasibility study of the
technical and economic prospects related to developing
a DSS. The feasibility study should include an evalua-
tion of competing software products. This study should
occur prior to actually committing resources to building
a proposed DSS.
Conclusions
Helping managers monitor operational performance or
gain “intelligence” from historical data is a worthwhile
purpose for data-driven DSS. Such systems will be espe-
cially important in global enterprises. These distrib-
uted organizations generate data in many operational
systems and the only way to gain a “single version” of
the truth is to create an integrated decision support
data store that is accessible to all decision makers no
matter where they happen to be physically located.
Small and medium sized enterprises can also benefit
from data-driven DSS, but the data store is unlikely to
be a large-scale data warehouse. A database on a web
accessible server may provide the appropriate enabling
technology.
What have we learned over the years about building
data-driven DSS? One general conclusion is to identify
what decisions will be supported and who might use the
proposed data-driven DSS. A powerful sponsor increases
the chances that an enterprise-wide DSS will be successfully
built and deployed. Further, it is generally advisable to hire
outside expert advice for the first project (cf., Solomon,
2005; Power, 2002a).
Companies have an increasing amount of data in his-
torical data stores and that is creating storage and
retrieval problems. Sadly, much of the historical data is
of poor quality and source systems often need to be
updated and improved as part of a data-driven DSS
project. In some cases, new data collection systems may
need to be designed and implemented prior to imple-
mentation of a data-driven DSS.
The technologies for building data-driven DSS are
improving. Vendors have enhanced web-based interfaces
with dashboards, visualization and animation. New tools
simplify query development with pull down menus and
other structured approaches. In coming years, many
mainframe-based data-driven DSS will need to be updated
or replaced by web-based or web-enabled systems. The
Web 2.0 technologies, open source and new hardware like
tablet PCs and smart phones are the frontier for data-
driven DSS.
Author Bios
Daniel J. Power is a Professor of Information Systems and
Management at the College of Business Administration
at the University of Northern Iowa, Cedar Falls, Iowa
and the editor of DSSResources.COM, the Web-based
knowledge repository about computerized systems
that support decision making. Dan writes the column
“Ask Dan!” in DSS News. He can be reached at
power@dssresources.com.
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Business decision-making increasingly requires that the information technology (IT) and marketing functions work together. Despite their distinct objectives, marketing managers need improved access to data to drive strategy. Based on an absorptive-capacity perspective, this study examines how marketing and IT business knowledge at the executive level affect decision-making. Results indicate that IT business knowledge has a direct, positive effect on data-driven decision-making, and marketing business knowledge does not. In addition, information quality and marketing/IT cooperation have differential moderating effects.
Conference Paper
The purpose of this study is to investigate how big data analytics technology affects decision-making in the media industry, with a focus on digital newspapers. To achieve this goal, we propose a decision-making model to identify the relationship between audiences and news topics using big data analysis and classification, intending to help news practitioners optimize their marketing strategies. To evaluate our model, we conducted a case study using a Swiss local newspaper. Preliminary results indicate that audience reading time and volume have been significantly increased after implementing the decision that based on the model’s analysis. The study also provides some guidelines for editors and journalists to target their digital news content.
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Editor, DSSResources.COM Data-driven DSS are the most common of the five types of decision support systems in the expanded DSS framework (Power, 2002). These systems provide operational and strategic business intelligence using internal company data and sometimes external data. 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).
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Data management for decision support has moved through three generations, with the latest being real-time data warehousing. This latest generation is significant because of its potential for affecting tactical decision making and business processes. Continental Airlines is a leader in real-time business intelligence, and much can be learned from how they have implemented it.
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Book
For MIS specialists and nonspecialists alike, teacher and consultant Dan Power provides a readable, comprehensive, understandable guide to the concepts and applications of decision support systems. Power defines DSS broadly: interactive computer-based systems and subsystems that help people use computer communications, data, documents, knowledge, and models to solve problems and make decisions. This book covers an expanded framework for categorizing Decision Support Systems (DSS), a general managerial and technical perspective on building DSS, details and examples of the general types of DSS, and tools and issues associated with assessing proposals for DSS projects. A glossary and DSS readiness audit questions give special, ongoing value to all readers. Free eBook at https://scholarworks.uni.edu/facbook/67/
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Some guidelines regarding the risks and some processes that can be followed to help ensure a successful data warehouse (DW) are discussed. The guidelines discussed are for a large corporate DW initiative that will require a new infrastructure environment or to warrant analysis to determine whether it can be integrated into a comfortable environment. It is important to train users well in advance because one can also use this experience to confirm or acquire additional use patterns that may not have been identified. It is also important to focus the DW project efforts on data quality, delivery and training as well as obtaining the most comprehensive set of usage patterns possible.