FREE ESSAY ON THE USE OF DATA WAREHOUSE IN DECISION MAKING PROCESS

THE USE OF DATA WAREHOUSE IN DECISION MAKING PROCESS

INTRODUCTION
It is obvious that there is no organization running without data. The data can be viewed
as tangible assets of an organization just as any physical asset. So, they need to be
stored and made available to those who need them when they need them. However, the data
by themselves are useless. So, they must be put together to produce useful information.
In turn, information becomes the basis for relational decision making. To facilitate the
decision-making process, a new development of database systems was developed called "data
warehouse".
The data warehouse can be generally described as a decision-support tool that collects
its data from operational databases and various external sources, transforms them into
information and making that information available to decision-makers (top managers) in a
consolidated and consistent manner. (2:64)(4:82)
BACKGROUND
The data warehouse is not more than a database but separated from other databases like
the operational database distributed database and text database. When did management
start to utilize this powerful tool and why they seek to use it.
The data warehouse has been developed at the beginning of 1980s. However, it was optimize
to transform non-organized and lightly summarized data from the operational database into
analytical tool that supports intelligent decision-making. (6:19)
The term DSS (Decision support system) database is used interchangeably with the data
warehouse. On the other hand, other names for the operational database are transactional
database and production database.
WHAT IS A DATA WAREHOUSE?
The data warehouse can be very simply defined as an integrated, subject-oriented, time
variant and non-volatile database that provides support for decision-making (5:39)
(6:19). The following four sections will explain what this definition means.
Integration 
The data warehouse is a centralized database that integrates data from different sources
(6:19) with diverse formats. This integration of the data provides a unified view of the
overall organizational situation. Data integration enhances decision-making and helps the
manager to better understand the operations of the organization (6:19).
Subject-Oriented
The data in DSS database are organized to provide answers to questions coming from
different areas within the organization. They are arranged by topic such as sales,
marketing, finance and so on. The DSS database contains specific subject for each topic
like customer, product, region and so on. This form of data organization is different
that of more process-oriented of the operational database system. (5:39,43)
Time Variant
The data warehouse contains historical data over a long time. Those data reflect what
happened last week, last month, the past five years and the like. (6:19)
Non-Volatile
Once the data enter the data warehouse, they are never removed or changed. Because the
data warehouse represents the entire history of the organization, the data from
operational database are always added to it. Since DSS data are never deleted and new
data are periodically added, the data warehouse is always growing. That's why the data
warehouse must be able to have hardware that supports gigabytes and even terabytes size
of databases. (5:43) 
THE DIFFERENCE BETWEEN OPERATIONAL DATABASE AND THE DATA WAREHOUSE
The operational database and the DSS database differ in the roles the do as well as the
data characteristics for each one.
Main Role 
The transactional database is optimized to support transactions that represent daily
operations (2:67). For example, during the registration period at KFUPM, each time a
student adds, drops courses, or changes sections, he must be accounted for by the
operational database system of the university. So, student data and course data are in
frequent update mode.
On the other hand, the data warehouse is optimized to support data analysis and
decision-making (2:64). Basically, it takes the summarized data from the operational
database, filters them for analysis and decision making processes (2:64). For instance,
the manager of the admission and registration department may ask for the number of
students at KFUPM taking ENGL-214 last summer. The data warehouse answers this query for
him. Then, he would take decision whether to increase number of sections of this
particular course or not.
Operational Data Vs. Warehoused Data
Transactional data and DSS data are different in the summarization level, transaction
type, query activities and dimensionality.
Summarization level
The degree to which DSS data are summarized is very high when contrasted with the
operational data (5:39). For example, rather than storing thousands of sales transactions
for a given store on a given day, the data warehouse might simply store the total number
of units sold and the total price during that day. Then, the store manager may decide
whether to continue or discontinue selling or producing such products.
Transaction type
The operational database and the data warehouse are different in terms of transaction
type. Whereas production data are characterized by update transactions, DSS data are
mainly characterized by query (read only) transactions (2:67). The DSS data also require
periodic update (2:67) to load new data that are summarized from the transactional
database as well as other external sources. Therefore, the warehoused data are historic
(2:64) while the operational data represent transactions as they happen.
Query activity
It is difficult, if not impossible, to optimize a single database for both processing
purposes as well as for decision-making needs. For that reason, the data warehouse is
optimized for ad hoc (on demand or as needed) complex queries needed by decision-makers
(2:67).
The production database, on the other hand, is optimized to allow more processing for the
repetitive update transactions (2:67). So, it is difficult to get ad hoc queries from
that operational database because of the continuously updated transactions.
Dimensionality
Dimensionality is the most distinguishing characteristic of the DSS data. The data
warehouse is set to provide the larger picture (2:64). In other words, it includes many
data dimensions. For instance, a sale manager may ask how many units of product X were
sold to customer Y during the last T months (2:66). So, he or she can view the data from
three dimensions: product, customer and time. In fact, (s)he could view the data from
many dimensions. This multidimensional view of the data is different from the single view
of the operational database.
HOW DOES DATA WAREHOUSE ASSIST IN DECISION-MAKING
In order to gain the assistance of the data warehouse in the analysis and decision-making
process, four main stages are required: storing the data, data extraction & filtering,
query tools and presentation tools.
Data Store
The data store is a repository where the meta data (information about the data) are kept
to describe the characteristics of the data in the DSS database. It is also linked with
the transactional database so that any modification of the transactional data will be
updated in the data warehouse as well. (2:65)
Data Extraction & Filtering
The data warehouse contains two main types of data: data extracted from the production
database and data from external sources (3:32) like stock price indicators. The data
extraction & filtering tools are used to extract and validate the data taken from the
transactional database and external data sources (3:32). The warehoused data are not the
copy of the operational data. Instead, they are summarized and organized for analysis and
query speed. Also, using data from external sources means having to solve data formatting
conflicts (1:74). For example, when comparing the GPA (Grade Point Average) of two
different universities say KFUPM and KFU (King Faisal University), many inconsistencies
must be solved. First, the GPA of KFUPM students are calculated out of four while they
are calculated out of five for KFU students. Second, KFUPM uses the standard English date
whereas KFU uses the standard Arabic date. And there are many other conflicts that DSS
database filtering tools are able to solve them so that data are stored in standard
format.
Query Tool
The data warehouse contains very huge data. However, the manager of any organization may
only need specific portion of those data. As a result, the query tools are used in the
DSS database in order to retrieve the appropriate and relevant data (6:20). The manager
may then, analyze those data so that he or she comes up with the right decisions that
serve the organization (6:20).
Presentation Tool
The query tools provide some ad hoc queries and repots. Unfortunately, to use the query
tools, the manager has to know the details of the query tools such as SQL (Structured
Query Language), QBE (Query By Example) and many others. The presentation tools help the
manager to select the most appropriate presentation format like bar graph, maps and
summary reports. Of course, one picture is better than one thousand words. (5:43) 
CONCLUSION 
The data warehouse is read only database optimized for data analysis, query processing
and decision-making process. Not surprisingly, it has become the main data source for
modern decision support system during the past few years. Therefore, all organizations
should exploit the power of this tool so that their top management could carry out the
decision-making process with more confidence to achieve the desired goals. 
REFERENCES
1. Ballou, Donald P., and Giri Kumar Tayi. "Enhancing Data 
Quality in Data Warehouse Environments." Communications of the ACM Jan.1999: 73-78.
2. Gould, Lawrence. "What You Need to Know About Data 
Warehousing." Automotive Manufacturing & Production Jun. 1998: 64-67.
3. Scheuerman, Michael. "Planing to Build A Data 
Warehouse." Credit Union Magazine Dec. 1998: 32-33.
4. Stephenson, Miles, and Michael McCathren. "Digital 
Decisions." Restaurant Hospitality Feb. 1999: 82-84.
5. Taylor, Rick. "Knowledge Is Power." Credit Union 
Management Jan. 1999: 39, 43. 
6. Teresko, John. "Information Rich, Knowledge Poor."
Industry Week 1 Feb,1999: 19-24.
Bibliography
1. Ballou, Donald P., and Giri Kumar Tayi. "Enhancing Data 
Quality in Data Warehouse Environments." Communications of the ACM Jan.1999: 73-78.
2. Gould, Lawrence. "What You Need to Know About Data 
Warehousing." Automotive Manufacturing & Production Jun. 1998: 64-67.
3. Scheuerman, Michael. "Planing to Build A Data 
Warehouse." Credit Union Magazine Dec. 1998: 32-33.
4. Stephenson, Miles, and Michael McCathren. "Digital 
Decisions." Restaurant Hospitality Feb. 1999: 82-84.
5. Taylor, Rick. "Knowledge Is Power." Credit Union 
Management Jan. 1999: 39, 43. 
6. Teresko, John. "Information Rich, Knowledge Poor."
Industry Week 1 Feb,1999: 19-24.