Page 166 - DMGT505_MANAGEMENT_INFORMATION_SYSTEM

Unit 8: Databases and Data Warehouses

8.5.2 What is a Data Warehouse? Notes

Data warehouse provides architectures and tools for business executives to systematically
organise, understand, and use their data to make strategic decisions. In the last several years,
many firms have spent millions of dollars in building enterprise-wide data warehouses as it is
assumed a way to keep customers by learning more about their needs.

In simple terms, a data warehouse refers to a database that is maintained separately from an
organisation’s operational databases. Data warehouse systems allow for the integration of a
variety of application systems. They support information processing by providing a solid platform
of consolidated, historical data for analysis.
According to W.H. Inman, a leading architect in the construction of data warehouse systems, “a
data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of
data in support of management’s decision making process.” The four keywords, subject-oriented,
integrated, time-variant, and non-volatile, distinguish data warehouses from other data
repository systems, such as relational database systems, transaction processing systems, and file
systems. Let us understand the four key words in more detail as follows:
1. Subject-oriented: A data warehouse focuses on the modeling and analysis of data for
decision makers. Therefore, data warehouses typically provide a simple and concise view
around particular subject issues by excluding data that are not useful in the decision
support process. For example, a typical data warehouse is organised around major subjects,
such as customer, vendor, product, and sales rather than concentrating on the day-to-day
operations and transaction processing of an organisation.
2. Integrated: As the data warehouse is usually constructed by integrating multiple
heterogeneous sources, such as relational databases, flat files, and on-line transaction
records, the data cleaning and data integration techniques need to be applied to ensure
consistency in naming conventions, encoding structures, attribute measures, and so on.
3. Time-variant: Data are stored to provide information from a historical perspective (e.g.,
the past 5-10 years). Every key structure in the data warehouse contains, either implicitly
or explicitly, an element of time.
4. Non-volatile: A data warehouse is always a physically separate store of data transformed
from the application data found in the operational environment. Due to this separation, a
data warehouse does not require transaction processing, recovery, and concurrency control
mechanisms. It usually requires only two operations in data accessing: initial loading of
data and access of data.

8.5.3 Use of Data Warehouses in Organizations

Many organisations are creating data warehouse to support business decision-making activities
for the following reasons:

1. To increasing customer focus, which includes the analysis of customer buying patterns
(such as buying preference, buying time, budget cycles, and appetites for spending),
2. To reposition products and managing product portfolios by comparing the performance
of sales by quarter, by year, and by geographic regions, in order to fine-tune production
strategies,

3. To analysing operations and looking for sources of profit, and
4. To managing the customer relationships, making environmental corrections, and managing
the cost of corporate assets,

LOVELY PROFESSIONAL UNIVERSITY 161