Data Warehousing:
A Vital Role in the Pursuit of Competitive Advantage
Kimberly Merritt
Dr. Richard Braley
Cameron University
Abstract
Despite vast quantities of data collected in organizations today, many managers have
difficulty obtaining the information they need for decision-making. This problem has given rise
to the development of data warehouses. Data warehouses support managers’ complex decision
making through applications such as the analysis of trends, target marketing, competitive
analysis, and many others. Data warehousing has evolved to meet the needs of decision-makers
without disturbing existing operational processing. In the past, an organization focused upon
information concerning customers, products, inventories, sales and other superficial profile
information bites that were readily available. Now, by utilizing data warehousing and data
mining technologies, an organization is able to focus upon “affinities” of customers,
relationships between marketed products, minimal inventory-storage windows that maximize
movement from the inventory storage area to the shelf and sales. Data about sales, and what is
done with data about sales, has changed the face of retail and wholesale marketing. This paper
will investigate the strategic importance of data warehouses through an investigation of the
applicable technology, growth of the market, and the factors spurring that growth. In addition,
future trends in this rapidly expanding segment of information systems will be explored and the
resulting strategic implications for modern retail businesses will be addressed.
Introduction
Corporations are increasingly dependent on information technology as a means by which
to achieve and retain competitive advantage. In many cases, information technology is no longer
seen as a cost of doing business, but rather is being exploited to ensure the success and survival of
the firm. In the increasingly complex business world, massive amounts of relevant internal and
external data exist that, if captured, warehoused, mined and analyzed, will provide valuable
guidance in the increasingly complex realm of managerial decision-making. And yet, modern
organizations are said to be drowning in data but starving for information.
Organizations are anxious to take advantage of vast stores of historical operational data,
especially information about many aspects of their current business activities. In the past, an
organization focused upon information concerning customers, products, inventories, sales and
other superficial profile information bites that were readily available. Now, with data
warehousing and data mining, an organization focuses upon “affinities” of customers,
relationships between marketed products, minimal inventory-storage windows that maximize
movement from the inventory storage area to the shelf and sales. Data about sales, and what is
done with data about sales, has changed the face of retail and wholesale marketing. Using data
warehousing and data mining, Wal-Mart Corporation maintains a record of every cash register
receipt, from every customer transaction, twenty-four hours every day, every day of the year,
from every Wal-Mart store. Wal-Mart’s motto concerning data mining is this: Every store will be
treated as if it were the only store owned.
This paper will investigate the strategic importance of data warehouses through an
investigation of the applicable technology, growth of the market, and the factors spurring that
growth. In addition, future trends in this rapidly expanding segment of information systems will
be explored and the resulting strategic implications for modern businesses will be addressed.
Description of Data Warehousing
Until recently, companies did not have enough computing power at their disposal to be
able to take advantage of stored data. Now, however, the emergence of massive parallel
processing is enabling organizations to analyze enormous amounts of historical data through the
development of data warehouses. Parallel processing has emerged as the supercomputing system
of choice for most massive data analysis systems. Older, vector-driven supercomputing has been
superceded because of the reliability of parallel processing using hard disk technology called
Redundant Array of Individual Disks (or RAID). Where the older systems required large amounts
of time for searching and correlating data bases, massive parallel processing (MPP) holds the data
at multiple hard disk locations with data mined at all locations simultaneously and then compared
with data warehoused and mined throughout the system. MPP systems utilize hundreds or
thousands of hard drives for storage. Multiple processing units (CPUs) break down a query into
smaller parts and work on it simultaneously. Coordination of these multiple processors requires
additional system capability. The work is assigned to the CPUs by one specialized processing
unit called a parsing engine. The CPUs perform their assigned duties and return the result of the
analysis back to the parsing engine.
From the answers to the queries decisions are made. For example, toothpicks might be
shelved next to paper plates because data analysis showed that 99% of the customers who
purchased paper plates also had a box of toothpicks in their basket. Hand cream might move to
the gardening area during a single month out of a year because warehoused and mined data
showed that 88% of customers who purchased gardening shovels during the month of April also
purchased a bottle of hand cream. Those relationships are called “affinities.” The objective of an
“affinity” analysis is to make it as easy as possible for the customer to arrive in the retail outlet,
move to their purchase arena and find all of the items they want to purchase. Helping the
customer to easily make purchases is the goal of Wal-Mart Corporation, the largest retailer in the
world.
This recent, dramatic increase in processing power has empowered corporations to
consider information processing opportunities that were previously unavailable. Data
warehousing has been called the center of the architecture for information systems of the future
(Inmon, 1995). McFadden, Hoffer and Prescott (1999) define a data warehouse as, “An integrated
decision support database whose content is derived from the various operational databases” (p.
602). Inmon (1995) defines a data warehouse as a subject-oriented, integrated, time-variant,
nonvolatile collection of data used in support of managerial decision-making processes.
These are the fundamental data warehousing concepts:
Subject-Oriented: A data warehouse is organized around the key subjects (or
high-level entities) of the enterprise. Major subjects may include customers,
patients, students, and products.
Integrated: The data housed in the data warehouse are defined using consistent
naming conventions, formats, encoding structures, and related characteristics.
Time-variant: Data in the data warehouse contain a time dimension so that they
may be used as a historical record of the business.
Nonvolatile: Data in the data warehouse are loaded and refreshed from
operational systems, but cannot be updated by end users (Inmon & Hackathorn,
1994).
The process of obtaining information from the warehouse and exploiting it for the good
of the firm is data mining (Barquin, 1996; Gardner, 1998). The construction and use of a data
warehouse affords a number of potential advantages to the firm, including: better inventory
management, more effectively targeted sales and marketing efforts, and improved customer
satisfaction (Whiting, 1999). However, mining the data often forces the manager to resolve
interpretation conflicts. Minute relationships emerge that harbor both compelling and irrelevant
information. Potential information advantages surface amid a sea of potentially useless
information.
Events Spurring the Increased Use of Data Warehousing
The concept of a data warehouse is a fairly new one. According to McFadden, Hoffer,
and Prescott (1999), Devlin and Murphy published the first article describing the architecture of a
data warehouse in 1988 (Devlin & Murphy, 1988); Inmon published the first book on data
warehousing in 1992 (Inmon, 1992). Hackathorn (1993) described the rise of data warehousing
and the proliferation of its use as the result of many separate advances in information technology
occurring over many years. In addition to the evolution from vector-driven computing to RAID
technology discussed above, these factors are:
1.
Improvements in database technology, particularly the development of
the relational data model and relational database management systems
(DBMS)
2.
The emergence of end-user computing, facilitated by powerful, intuitive
computer interfaces and tools
3.
Advances in middleware products that enable enterprise database
connectivity across heterogeneous platforms
Classifications of Data Warehouses
The primary business drive behind the creation of data warehousing is the recognition
that there is a distinct difference in operational data and decision support data. Operational data is
used to support the day-to-day operations of the firm and is focused on the functional processing
requirements of a business user (Gardner, 1998). On the other hand, informational data is
generally subject oriented, with an enterprise view, providing information required for sound,
strategic business decisions (Gardner, 1998). Informational data may contain historical data as
well as operational data from a number of sources, both internal and external.
When firms relied on operational data, they built operational databases. These traditional
databases were designed based on relational database design principles. Data was represented as
tables, and the goal for the database was to provide short update and query transaction time
(Benander & Benander, 2000). The physical structure of the operational database was designed to
ensure efficiency and integrity over small sets of clearly related data.
In contrast, the data warehouse is designed to support data analysis (Benander &
Benander, 2000). The environment is one of ad hoc queries and unstructured data. There are three
main types of data warehouses: operational data stores, enterprise data warehouses, and data
marts.
Operational data stores (ODS) are the latest development in data warehousing (Benander
& Benander, 2000). An ODS is used to support customer information files and contains all
information pertaining to current, prospective, and past customers. The ODS is also used to track
all recent customer activity and marketing interactions. This data warehouse application is
especially valuable in a customer-oriented organizational structure.
An enterprise data warehouse (EDW) is larger in scope than an ODS. This design also
contains product, accounting, and organizational information as well as customer information
(Benander & Benander, 2000). An EDW may contain up to five years of data. Because of the
size and complexity of an EDW, most are not accessed by end-users, but are rather used to
populate data marts.
A data mart is a subject-oriented or department-oriented data warehouse whose scope is
much smaller and more focused than an EDW (McFadden, Hoffer, & Prescott, 1999, Benander &
Benander, 2000). The smaller scale of the data mart allows end-users to access data pertinent to
the area in which they work without the size and complexity associated with an EDW.
Applicable Technology
As previously mentioned, corporations frequently have tremendous amounts of data but
little information. There are many causes for this inconsistency, but two major ones are disparate
legacy systems and the practice of developing systems with a focus on operational data. The
resulting information gap leaves managers with no support for decision-making. The construction
of a data warehouse bridges this information gap by pulling together data from disparate systems
and presenting usable information to decision makers. There is, however, a risk in that massive
data correlations amplify insignificant relationships making them appear to be fundamental when
they are actually farcical.
Multiple systems architectures exist for the development of a data warehouse. A two-tier
architecture involves the operational source systems and the data warehouse. Users query the data
warehouse directly (McFadden, Hoffer, & Prescott, 1999). A three-tier architecture exists when
the operational source systems load a data warehouse which in turn loads the data marts
(McFadden, Hoffer, & Prescott, 1999). A four-tier model results from server-based information
access tools that let Internet users interactively query a database and view the results as HTML
pages. The four tiers are the web browser, the web server, application servers, and the data
warehouse (“Trends,” 1999).
The major steps in the process of building and exploiting a data warehouse depend on the
nature of the architecture employed. However, in general terms, Gallegos (1999) defines the steps
as data acquisition, data storage, and data access.
Data Acquisition
Data acquisition includes accumulating, cleansing, and standardizing the data (Gallegos,
1999) obtained from operational source systems, also called legacy systems (Kimball, 1998).
McFadden, Hoffer, and Prescott (1999) refer to this process as data reconciliation and utilize the
terms capture, scrub, and transform. Regardless of the particular term used, the process involves
extracting the relevant data from the source files; removing errors, duplicates, empty fields, and
generally improving the quality of the data; and preparing or converting the data into the format
desired for the data warehouse through various functions such as selection and joining. Some
authors suggest summarization of the data (Gallegos, 1999), while other suggest maintaining
detail-level data (Gardner, 1998). Regardless the method employed, the focus remains upon
keeping “relevant” data relevant and minimizing the appearance previously mentioned impurities.
Therefore, the value of a data warehouse is dependent on (1) the validity of the data it
contains, and (2) the relevancy of the interpretation of that data. In a recent survey, more than
80% of corporate executives reported that improving customer data quality was a top priority
(Faden, 2000). Data cleansing was once an obscure, specialized technology. However, with the
increasing focus on the accuracy of data, cleansing has become a core requirement for data
warehousing. The technology used for data cleansing was originally designed for use with
address lists. Increasingly, the software is also used to help identify other relationships in a
company’s data and for adding information from other sources during the cleansing process.
Following acquisition and cleansing, the acquired data is moved to a data staging area, a
storage area and set of processes that prepare the source data for use in the data warehouse
(Kimball, 1998). The data staging area may be spread over a number of machines.
Data Storage
The acquired data then populates the actual data warehouse on presentation servers. This
is the target physical machine on which the data warehouse data is organized and stored for direct
querying by end users, report writers, and other applications (Kimball, 1998). Indexes are also
created (McFadden, Hoffer, & Prescott, 1999). If a three-tier architecture is employed, the data
marts will be populated from the data warehouse.
The most commonly used data model for data storage is the star schema (McFadden,
Hoffer, & Prescott, 1999; Benander & Benander, 2000), also known as the dimensional model
(Kimball, 1998). Another possible storage configuration is a snowflake schema. The strength of
either storage schema is its ability to identify affinities by associating pieces of data in unique and
multiple ways.
Benander and Benander (2000) describe a star schema to include a central “fact” table
that contains a large number of rows that correspond to observed business events or facts (factual
or quantitative data about the business). Surrounding the “fact” table are multiple “dimension”
tables that contain classification and aggregation information about the central fact rows
(descriptive data). The dimension tables have a one-to-many relationship with rows in the central
fact table. This design provides extremely fast query response time, simplicity, and ease of
maintenance for read-only database structures.
Data Access
The existence of a data warehouse does not guarantee a company a strategic advantage.
Once the data warehouse is constructed and populated, the challenge becomes access and
interpretation of the data and transformation into useful information and knowledge (Chen &
Sakaguchi, 2000). Data access refers to the methods used by end users to access data in the data
warehouse.
Data access can result from traditional query tools, on-line analytical processing (OLAP)
tools, data-mining tools, and data-visualization tools (McFadden, Hoffer, & Prescott, 1999;
Kimball, 1998), each of which is discussed in the following paragraphs.
Traditional query tools include reporting tools, spreadsheets, and personal computer
databases populated from the data warehouse. On-line analytical processing (OLAP) tools
involve the use of a set of graphical tools that provides users with multidimensional views of
data, and allows users to analyze data based on windowing techniques (McFadden, Hoffer, &
Prescott, 1999). Users can slice data to produce a simple two-dimensional table or view, or can
drill the data down to a finer level of detail (Gallegos, 1999).
Data mining refers to the discovery of knowledge from the data contained in the
warehouse (McFadden, Hoffer, & Prescott, 1999). Some authors consider all data access to be
data mining, while others refer to a more limited definition of data mining. The tools associated
with data mining can be generally divided into two categories, statistical models and machine
learning (Chen & Sakaguchi, 2000, McFadden, Hoffer, & Prescott, 1999). Statistical models
contribute to evaluating hypotheses and results and applying those results. Machine learning is a
branch of leading-edge artificial intelligence programming. It suggests using a training set of
data from which the data mining system learns and finds the parameters for models. According
to Chen and Sakaguchi (2000), this inductive reasoning approach can be used for a number of
systems, including: neural networks, decision trees, and genetic algorithms.
Finally, data access can be facilitated through the use of data visualization tools. These
tools are graphical and multimedia software products that provide for visual representation of
data (McFadden, Hoffer, & Prescott, 1999). Trends and exceptions are often easier to identify
when the data is presented graphically.
Growth of the Market
Although the concept of the data warehouse is barely ten years old, the evolution of the
technology has been rapid. With the emergence of cost-effective MPPs using commercial offthe-shelf (COTS) disk drives with RAID III Technology, vector systems diminished in value
when compared to parallel processing manufacture, installation and start-up. Data warehousing is
now one of the hottest topics in information systems. The market segment for this technology is
estimated at over $15 billion (Chen & Frolick, 2000). Currently, over 90 percent of larger
companies either have a data warehouse or are starting one (McFadden, Hoffer & Prescott, 1999).
Burwen (1997) reported that 62 data warehousing projects showed an average return on
investment of 321 percent, with an average payback of 2.73 years. Users also reported that
expenditures on data warehousing technology were expected to reach nearly $200 billion by
2001.
Emerging Technologies and Future Trends
In only ten years, U.S. businesses have rapidly adopted data warehousing technology in
an effort to exploit the strategic advantage offered by sophisticated decision support systems.
However, there are many challenges inherent in the current technology. According to Chen and
Frolick (2000), the drawbacks of the current approach to data warehousing include: the expense
of client/server infrastructure, the growth of a mobile workforce in which employees will no
longer have continuous access to high-speed connections, and system compatibility. The future
trends in this technology will address these current limitations.
Data Warehousing via Internet/Intranet
Web technology is increasing access to data warehouses. Data warehousing liberates
information, and the Internet makes it easy and less costly to access information from anywhere
at anytime (Chen & Frolick, 2000). It is estimated that the marriage of these two technologies
will make the Internet the primary decision support delivery platform by 2001 (Chen & Frolick,
2000, Chen & Sakaguchi, 2000). Web-based access to the data warehouse has significant
advantages: capacity to deliver access to more users; simplicity of use and platform
independence; and lower establishment and management costs (Booker, 1999, Chen & Frolick,
2000). Not long ago, the concept of a data warehouse was known to only the most sophisticated
user who accessed the warehouse through expensive, complex, client-side business intelligence
applications (Booker, 1999). Almost covert in nature, these business intelligence operations
persist as massive parallel processing extends beyond terascale computing to petascale
computing. However, the web and related technology provide a method of granting access to
these databases to hundreds or thousands of employees, regardless of the computing scale.
Company intranets are being employed internally to raise visibility and maximize use of
a data warehouse while the Internet is being exploited externally to extend the benefits of a data
warehouse beyond the company to customers, vendors and other partners (“Trends,” 1999,
Booker, 1999). This use of web technology has greatly affected the market for information access
tools and will contribute to substantial growth of this market in the future. This has increased the
need for competent, trained programmers for warehousing systems. Additionally, end users now
are less systems engineers and more inquisitive entrepreneurs.
The emergence of browser- and Java-based analysis tools reduced the complexity of data
analysis and allows users to focus on data interpretation rather than on learning how to use the
technology and language the technology supports. The new web-based access tools are similar in
function to the fat client/server systems, but are much simpler to use (Booker, 1999).
There are challenges to implementing web-based data warehousing. The first is user
scalability (Chen & Frolick, 2000). It may be difficult to estimate the number of users who will
be accessing the data warehouse concurrently. This has significant implications for the software
and hardware chosen to support the data warehouse.
The second challenge to the development of web-based data warehousing is speed (Chen
& Frolick, 2000). The speed of storage-access-query-response cycles is substantial. The
complexity of user queries and the large amount of data involved further complicate the oftenslow transfer speed associated with the Internet. The only viable solution with today’s technology
is to eliminate multiplexing and packet-switching, along with their inherent data checks, and
initiate data streams with prioritized data checks of specific data blocks, thus dramatically
reducing transfer speeds. The integrity of the systems and their stored data must be relied upon,
rather than relying on a process of checking and re-checking the data during transfer and storage.
Selective sampling of data during transfer and selective sampling of stored data will result in
faster transfer and storage speeds.
Finally, another challenge to web-based access to data warehouses is security (Chen &
Frolick, 2000). Having access to data is not enough. The data must be reliable and therefore
capable of generating quality information (McNee, et al., 1998). As corporations consider
granting external entities access to the data warehouse, the data must be near perfect (Davis,
1999). Currently, as many as 80 percent of U.S. corporations have suffered at least one type of
data loss. Data integrity is threatened by unauthorized attempts to access private data, computer
viruses that can corrupt data, and simple, electronic malfunctions. In order to make good
decisions, modern managers need access to complete information, and thus a data warehouse may
need to provide access to external data as well as internal data (“Ten Mistakes,” 1999).
Chen and Frolich (2000) suggest that security of web-based data warehouses is a matter
of balancing the rights of authorized users while preventing the theft or corruption of the
information. Add to that the need for trusted recovery after natural disasters, the need to
continuously up-date warehoused data, and the inherent vulnerability of web-based customers
who purchase warehouse time, and security costs sky-rocket for warehouse vendors.
In the future, access to the data warehouse will be granted to more and more internal
users. Some users will require sophisticated access to allow for complex data analysis, while
others will be satisfied with more simple access and predefined queries. Therefore, a combination
of client applications is also a new possibility for firms. Those users who require sophisticated
on-line analytical capabilities may need to retain a fat client/server application while those users
who require a more simple form of data acquisition and analysis can access the data warehouse
through a web-based solution. This concept of dual routes of access is going to increase in
importance as firms consider granting broader access to internal users (Booker, 1999).
With that access, internal security loosens and vulnerability increases. External
accessibility will also increase because of the potential benefit that can be gained by allowing
supply chain partners and customers into the data warehouse. Companies that leveraged the
power of the web to provide access to the data warehouse internally are finding that sharing the
same information externally can provide tremendous benefits as well (Davis, 1999). Customers
allowed access to the data warehouse are empowered, while granting access to supply chain
partners can result in a tighter, leaner, more responsive supply chain.
The Importance of Distributed Processing
Although the size of data warehouses is predicted to grow in the future, it is possible that
source data will be obtained from input sources that are ever decreasing in size. The expanding
market of microprocessor-equipped devices with small data bases will make it possible to gather
data at sources never before considered (Whiting & Caldwell, 1999). Until recently, the lack of
small database systems has hindered the widespread use of application-specific portable
computing devices for collecting data and sending it back to a central data store for analysis.
However, database vendors are developing and marketing “lite” versions of database products.
Sybase, for example, has a database product that is only 50 Kbytes, or 1/20 the size of its full-size
big brother. Other companies have released similar products (Whiting & Caldwell, 1999).
IBM is concentrating resources on the pervasive computing market. Company officials
see three markets developing in this area: hardware and software components for device
manufacturers; consulting and development work to support business customers; and systems for
service providers building security, billing, and management infrastructures. IBM is also
developing transcoding technology based on XML that will enable the synchronization of
portable and embedded databases with company databases (Whiting & Caldwell, 1999).
The most exciting potential from the trend toward distributed processing lies in the ability
to link it with the trend toward increasing the size of data warehouses. These two trends present
the possibility of gathering data from the mobile and embedded devices, then linking that data to
a centralized database for analysis and mining: automated, continuous, massive data replacement
from remote sources. Federal Express, already engaged in this practice, has already realized
results (Whiting & Caldwell, 1999). Company officials have indicated that they no longer engage
in mass marketing, but rather target the customers specifically. Other firms that have invested in
this technology are also reaping benefits. Data warehouses will continue to evolve in the future
and will be loaded with near real-time data (McNee, et al., 1998) to give decision makers access
to up-to-the-minute information. Past business entities had to automate, immigrate or evaporate.
Future business entities must make massive data marketing commonplace, or perish.
Growth in the Size of Data Warehouses
Data warehouses are very large databases, but they are predicted to get even bigger
(McNee, et al., 1998). By 2001, the size of data warehouses is predicted to grow by 36 times,
while the number of users is predicted to grow by 70 times (Gallegos, 1999).
Application Development
The state of technology in the data warehouse market has now reached a level of maturity
sufficient to allow vendors to begin to develop packaged applications (“Trends,”1999). Packaged
applications may save time and money, but they may lack the flexibility of custom development.
There is also a trend in the Enterprise Resource Planning (ERP) market to include data
warehousing solutions as part of the ERP systems (“Trends,” 1999). These ERP solutions will be
attractive to businesses because the vendor has already addressed many issues surrounding data
integration, transformation, movement, and modeling.
The transaction processing systems of the past focused on maintaining internal recordoriented data that was relatively structured in nature. Modern executives rely on data that is often
unstructured in nature (“Ten Mistakes,” 1999). For example, it may be necessary to store video,
audio, graphics, etc. Future data warehouse systems will incorporate ever-increasing amounts of
unstructured data in ever-increasing varieties of formats (McNee, et al., 1998).
Conclusion
The potential benefits from sharing a data warehouse are enormous. However, a very
efficient, reliable system must be in place before it is opened to customers and suppliers. A data
warehouse has no value if the users cannot access the data. Effective information access tools
allow users to intuitively navigate through the database without the need to memorize methods or
commands.
Despite vast quantities of data collected in organizations today, many managers have
difficulty obtaining the information they need for decision-making. This problem has given rise
to the development of data warehouses. Data warehouses support managers’ complex decision
making through applications such as the analysis of trends, target marketing, competitive
analysis, and many others. Data warehousing has evolved to meet the needs of decision-makers
without disturbing existing operational processing.
The process of developing a quality data warehouse is long and complicated; however,
the potential returns to the firm are enormous. The future of business is uncertain; however it is
certain that to remain competitive, firms must invest in and commit to a form of data
warehousing.
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