Expert answer:BBA3551 Columbia Southern Data Marts and Data Ware

Solved by verified expert:Write a one- to two-page (250-500 word) paper that discusses the differences between data warehouses and data marts.
Also, discuss how organizations can use data warehouses and data marts to acquire data. You must use the CSU Online
Library to locate at least two sources for your paper.
APA rules for formatting, quoting, paraphrasing, citing, and listing of sources are to be followed. I’ve attatched the two articles that are required and will cite then upon completion, please cite any additional sources used.
seven_key_interventions_for_data_warehouse_success.pdf

the_data_mart__a_new_approach_to_data_wareousing.pdf

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114
January 2006/Vol. 49, No. 1 COMMUNICATIONS OF THE ACM
Seven Key Interventions for
DATA WAREHOUSE SUCCESS
The success of data warehouses depends on the interaction of
technology and social context. We present new insights into the implementation
process and interventions that can lead to success.
NO DATA WAREHOUSE IMPLEMENTATION CAN
succeed on its own. The trick is knowing when
and how to intervene. Data warehouses have
tremendous potential to present information.
They provide the foundation for effective business
intelligence solutions for companies seeking competitive advantage. While there have been notable
successes [2], there have also been significant failures [4, 7]. What accounts for such conflicting
results? In the 1990s, adaptive structuration theory
(AST) was developed to understand conflicting
results with group decision support systems [5].
This theory analyzes the technological and contextual aspects of the application of a technology,
focusing on their interactions. Using AST, we
examine the interaction of context and technology,
and pinpoint seven key interventions specific to
that interaction for data warehouse success.
We studied a large organization, in which the
data warehouse implementation was successful in
some units and unsuccessful in others. From interviews with users in multiple business units, both
successful and unsuccessful, we derive several
insights that are at odds with previous research.
First, users can champion a technology just as successfully as management. Second, a wide range of
data can be accessed more successfully than a narrow range (as provided
in a data mart). Third,
the scope and flexibility
of tools offered to users
should not always follow
the dictum “simpler is
better.” While restrictive
tools make obtaining information easier for many
users, some users will deem the warehouse successful only if they have the more intricate unrestrictive
tools required for ad hoc queries and reports. Details
of these insights will be provided later.
In the organization studied the same data ware-
By Tim Chenow e t h, K a r e n Cor r a l ,
and Hal uk D e mi r ka n
COMMUNICATIONS OF THE ACM January 2006/Vol. 49, No. 1
115
house was found to be
zational culture can be
START
both a success and a failure
described as very conserby different units within
vative and resistant to
NO
convince intervention
the company. (For the
change. This conser1
2
YES
convince intervention
Does top
Do users
Do
users
purposes of this study, sucvatism extends to the
management
support the data
support the data
YES
NO
support the data
warehouse?
warehouse?
warehouse?
cess was determined only
company’s philosophy
by whether the warehouse
concerning technology.
YES
was used.) This provides
While the company
NO
an opportunity for underbelieves that technology
GO FOR
STOP
DEVELOPMENT
standing data warehouses
is important to its corthat is free of some of the
porate success, its strong
confounding aspects compreference is for mature,
Figure 1. Intervention points proven technologies. The company also has a strong
monly encountered in
during the project’s
field studies. Because we
initiation phase. belief in the uniqueness of its business model, resultstudied the same technoling in a strong bias for the in-house development of
ogy in the same company,
as
opposed
to
results
from
systems.
tools or
solutions
are purchased, they
Figure 1. Intervention points during
theIfproject
initiation
phase.
different systems and different companies, the must be flexible enough to adapt to the way the
observed differences are not due to differences in company conducts business and not dictate business
technology or corporate culture. We analyze these dif- processes.
ferences using the theoretical framework supplied by
In 1995, the company began an in-house developAST: context (social systems); technological innova- ment
START effort to design and implement an enterprisetion (in this case, the data warehouse); and the inter- wide data warehouse. They chose to model the
NO results indicate
action of context and technology.
Our
warehouse using Inmon’s paradigm [3], which
convince intervention
2
YES
the interaction of the context with the technology is 1espouses a centralized
database,
referred to as the
convince intervention
Do top warehouse,” that is used to integrate and store
the key to understanding data
warehouse
success.
“data
Do users
Do users
management
the data
the data
YES inter- support
Based on the nature of thesesupport
interactions,
seven
datathe data
extractedNOfrom support
multiple
warehouse? operational systems.
warehouse?
warehouse?
ventions are identified.
This data warehouse, in turn, feeds area-specific data
marts.
Over the years the data warehouse has grown
YES
in size and complexity as more operational
systems
COMPANY OVERVIEW
NO
The company studied is a large, global financial have been integrated into its environment. From the
GO
effort was very much an
institution based in the U.S.
TheFORcompany is highly beginning, this development
STOP
DEVELOPMENT
successful and enjoys an excellent reputation both IT-driven initiative. The business justification for the
within its industry and among its clients. Its organi- warehouse was never fully developed, and the poten-
Chenoweth fig 1 (1/06)
If there is no management champion
for the warehouse, strongly supportive
users of the technology can convince
management of the technology’s value.
Both management and end users must
be convinced of the technology’s value
if the project is to go forward.
116
January 2006/Vol. 49, No. 1 COMMUNICATIONS OF THE ACM
tial business uses of the warehouse were not carefully proceeded in a top-down fashion, there was a notable
enumerated. The prevailing assumption of the devel- success in which the push was bottom-up (intervenFigure 2. Intervention points during the design phase.
opment team was that if the warehouse was built and tion point 2 in Figure 1). The users championed the
filled with data, then business units would find a use warehouse because they recognized the task fit and
value to the organization. They convinced their unit
for it.
During the fall of 2001, the company sponsored a leader, who initially had been unsupportive of the
study to evaluate its enterprise data warehouse, data warehouse, of the warehouse’s value and thereby
including how the warehouse was being used. Repre- converted the leader into a supporter. This result is
sentatives from the business units affected by the surprising in that it is counter to most prescriptions
3
warehouse were interviewed to determine the nature for technology adoption. It shows that not having a
o
champion is not necesof those impacts and the
want
to a
sarily a death sentence for
degree
to
which
each
unit
YES
ange of
YES
a technology. If there is
had
been
able
to
use
the
a?
3
no management chamwarehouse. We use the
Do
users want
pion for the warehouse,
AST framework to anaGO FOR
access to a
YES
DEVELOPMENT
broad range of
strongly supportive users
lyze the results of those
data?
of the technology can
interviews.
NO
convince management of
BUILD A POINTS
the technology’s value.
INTERVENTION
SINGLE
Both management and
ASTREPOSITORY
recognizes
that both
BUILD
BUILD A
end users must be conthe features of a technolDATA
SINGLE
MARTS
REPOSITORY
vinced of the technoloogy and the context in
gy’s value if the project is
which it is implemented
to go forward.
affect
the
use
of
that
tech4
4
Do
Do
AST states that the
nology.
“No
matter
what
users want
users want
limited data access
NO
YES
ted data access features are
YESdesigned into
more
structured the use
and analysis
and analysis
tools?
of
a
technology
is, the
a
system,
users
mediate
tools?
easier it will be to develop
technological
effects,
a consensus among the
adapting systems to their
PROVIDE
PROVIDE
UNRESTRICTIVE
RESTRICTIVE
users about how the
needs, resisting them, or
TOOLS
TOOLS
innovation should be
refusingPROVIDE
to use them at
RESTRICTIVE
used and the appropriateall” [5].
Because
there
is
a
TOOLS
Do users
ness of that use. Ambiguparticularly high degree
understand
the task fit?
ity about use of the
of interaction between
technology erodes the
the technological dimenDo users
users’ comfort with the
sions
and
the
contextual
understand
Figure 2. Intervention points technology, which in turn erodes their respect for it.
the task fit?
features of a data wareduring the design phase.
The third intervention point (see Figure 2) occurs
house, AST is an ideal
with the design of the warehouse. Data warehouses
lens for this study.
Important contextual aspects are the “rules and are generally designed around one of two plans. Some
resources actors use to generate and sustain this sys- data warehouses are designed with a set of data marts
tem” [5]. One of the frequently cited necessary con- that partition the data warehouse into smaller, focused
textual conditions for the successful implementation databases tailored to the information needs of a subset
Chenoweth
fig 2 (1/06)
of almost
any technology
is a champion [1]. It is no of users. Other data warehouses provide a single
different for data warehouses. The attitude of a unit’s repository that gives the users a very wide range of
leader affects all the factors leading to warehouse data.
In the company studied, business units that
acceptance. At the company studied, when a leader
was strongly supportive (intervention point 1 in Fig- accessed their data through data marts were usually
ure 1), users were willing to pursue continuing knowl- more successful than units that accessed it through a
edge of the technology even after introductory single repository. That is, most units either requested
training. If users do not want to use the data ware- a data mart, or simply did not use the single reposihouse, then it is necessary to provide additional train- tory. Data marts help reduce the inherent complexity
and ambiguity associated with data warehouses by
ing or motivation to change their attitudes.
While acceptance of the data warehouse generally providing a slice of the data that is tailored to meet the
COMMUNICATIONS OF THE ACM January 2006/Vol. 49, No. 1
117
PROVIDE
UNRESTRICTIVE
TOOLS
NO
PROVIDE
RESTRICTIVE
TOOLS
5
Do users
understand
the task fit?
PROVIDE
TRAINING ON
BUSINESS
APPLICABILITY
YES
NO
Do users
perceive IT as
supportive?
6
CREATE
COOPERATIVE
ENVIRONMENT
BETWEEN
USERS AND IT
YES
NO
NO
Does
the unit
have one or
more power
user?
NO
7
CREATE
POWER
USER ROLE
YES
IMPLEMENTATION
AND
MAINTENANCE
STOP
Figure 3. Intervention
points during the
training and support
phase.
specific requirements of a business unit. However, one of the
most successful uses of this data
warehouse was through a single
repository. That unit had a strong need for a very
broad range of data as well as contextual characteristics that distinguished it from the units that used data
weth fig 3 (1/06)
marts. In particular, people in that unit were proactive
in trying technology. As a result, they were not intimidated by technology and were generally technically
knowledgeable.
The fourth intervention point (see Figure 2) is in
selecting the tools that will be available to the users.
Tools range from the highly restrictive, which limit
the users’ choices and thereby reduce ambiguity and
complexity, to the highly unrestrictive, which require
the user to have more expertise. Most units that were
successful with data marts preferred restrictive tools
118
January 2006/Vol. 49, No. 1 COMMUNICATIONS OF THE ACM
for accessing the data, but the unit that was successful with the single repository rejected restrictive
tools. Those users wanted the greater flexibility
offered by the unrestrictive tools, and were willing to
expend additional effort for more capability.
The relevance of the task to the organization, the
degree to which a technology supports a task, and
the degree to which users understand the task fit can
influence the acceptance of the technology. This is
the fifth intervention point (see Figure 3). In this
study, those business units that successfully used the
warehouse clearly understood the relationship
between the warehouse and the business issues relevant to the unit. In other words, they saw the applicability of the data warehouse to their tasks. This was
especially true of units that received data marts,
which not only reduce the ambiguity of the technology, but if properly focused, make the applicability
to the unit’s task more obvious. Giving a unit a single repository (instead of the more narrowly focused
data mart) can overload the users with information.
Beyond the design of the technology, users can be
supported by training on the business applicability
of the warehouse. This helps them see how the warehouse can assist them in performing their jobs.
A lack of knowledge of a technology can lead to
difficulties using the technology, or even abandonment of the technology; this leads to the sixth intervention point (see Figure 3). In this study, the
business units that successfully used the warehouse
typically had two mechanisms to acquire knowledge.
First, they had a good working relationship with the
warehouse development team. Because of this relationship, members were comfortable going to the
warehouse development team for help with problems
concerning their data warehouse applications. Conversely, those business units that were experiencing difficulty utilizing the warehouse did not draw on this
resource, and generally characterized the development
team as unresponsive and “difficult to deal with.” The
perceived availability of the data warehouse development team as a support group is an important intervention point in the success of the warehouse. If users
lack the perception of support, an intervention will be
needed to create a spirit of cooperation. Without that
perceived support, users will lack an understanding of
the purpose of the warehouse, as well as having difficulty learning how to use it.
The second mechanism successful business units
had for acquiring knowledge was experts within the
unit. These super users understood the data warehouse technology itself and the business issues facing
the unit, at least to the degree necessary to extract relevant data from the warehouse. In other words, they
The relevance of the task to the
organization, the degree to which a
technology supports a task, and the degree
to which users understand the task fit
can influence the acceptance of the
technology.
understood both the business needs of the unit and
the potential of the data warehouse to meet those
needs. The super users were also highly skilled at using
the tools. These users were recognized as authorities
within the unit concerning matters related to the
warehouse and played a pivotal role in disseminating
knowledge. Providing the resources necessary to create
the role of super users is another intervention point
for management.
We have expressed the seven intervention points as
sequential; however, each of these points can be
addressed throughout the project.
CONCLUSION
Organizations are spending millions each year on
data warehouse development, but the majority of the
efforts fail [6], and little is understood about why
these failures occur or how to prevent them. Most
previous recommendations for data warehouse
applications have suggested one-dimensional
approaches. By considering the interaction of the
contextual and technical dimensions (the human
factors and the specifics of the technology design),
the previous results become less contradictory. For
example, conventional wisdom holds that having a
management champion with a tightly focused (data
mart) design and restrictive tools will lead to success.
In this case study, we observed that the reverse situation can be just as successful. If the users see the
potential of the data warehouse to deliver value to the
organization, they can be the champions and convince management to adopt the technology. Similarly,
because of its simplicity, the data mart approach is frequently recommended as the preferred approach. Providing what the users want and need is more
important. If users understand both the technology
and the organization’s business processes, a single data
repository may actually be more satisfying for them.
In the same way, too little flexibility in tools can be
just as harmful as too much. The level of tool flexibility that users require for success varies based on their
technical knowledge and their business needs. This
article not only explains those paradoxes, but identifies the key points at which interventions may have to
occur in order to achieve the level of leadership, focus,
and flexibility required for data warehouse success. c
References
1. Beath, C.M. Supporting the information technology champion. MIS Q.
15, 3 (Sept. 1991), 354–370.
2. Cooper, B.L., Watson, H.J., Wixom, B.H., and Goodhue, D.L. Data
warehousing supports corporate strategy at First American Corporation.
MIS Q. 24, 4 (Dec. 2000), 547–567.
3. Inmon, W.H., Imhoff, C., and Sousa, R. Corporate Information Factory.
John Wiley, New York, 1997.
4. Kelly, S. Data Warehousing in Action. John Wiley, New York, 1997.
5. Poole, M.S., and DeSanctis, G. Understanding the use of group decision
support systems: The theory of adaptive structuration. Reprinted in
Organizations and Communications Technology, J. Fulk and C. Steinfield,
Eds., Sage Publications, Newbury Park, CA, 1990.
6. Vatanasombut, B., and Gray, P. Factors for success in data warehousing:
What the literature tells us. J. Data Warehousing 4, 3 (Fall 1999), 25–33.
7. Watson, H.J., Gerard, J.G., Gonzalez, L.E., Haywood, M.E., and Fenton, D. Data warehousing failures: Case studies and findings. J. Data
Warehousing 4, 1 (Spring 1999), 44–55.
Tim Chenoweth (timchenoweth@boisestate.edu) is an assistant
professor of networks, operations and information systems at Boise
State University.
Karen Corral (Karen.Corral@asu.edu) is an assistant professor of
information systems in the W.P. Carey School of Business at Arizona
State University.
Haluk Demirkan (Haluk.Demirkan@asu.edu) is an assistant
professor of information systems in the W.P. Carey School of Business
at Arizona State University.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for
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lists, requires prior specific permission and/or a fee.
© 2006 ACM 0001-0782/06/0100 $5.00
COMMUNICATIONS OF THE ACM January 2006/Vol. 49, No. 1
119
INTERNATIONAL REVIEW OF L AW C OMPUTERS
& TECHNOLOGY , VOLUME 11, N UMBER 2, P AGES 251–261, 1997
The Data Mart: A New Approach to Data
Warehousing
PAM ELA PIPE
Introduction
Vendors have recently begun to deliver low-cost and integrated data warehouse packages
intended for the rapid development of departmental data warehouses, or so-called data
marts. The availability of these packages requires organizations to consider the role of a
data mart in a data warehousing system, and whether a data mart should be built before,
after, or in parallel with a corporate enterprise data warehouse. In some situations a set of
distributed data marts may even eliminate the need for an enterprise-le vel data warehouse
solution. This paper discusses the role of data marts, reviews the pros and cons of the
different approaches to building a data warehousing system involving data marts, and also
looks at data mart product requireme nts. Throughout the paper, the SmartM art package
from Information Builders Inc. is used to describe the characteristics of an integrated data
mart package.
Types of Data W arehouse
·
Data warehouses come in all shapes and …
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