“A subject-oriented, integrated, time-variant, and non-volatile collection of data in support of management’s decision-making process.” (Inmon, 1993). Discuss what this statement is saying about the data in a data warehouse and contrast the purpose of such systems with OLTP systems.

Question

What will be an ideal response?

Answer 1

Subject-oriented Data
The warehouse is organized around the major subjects of the enterprise (e.g. customers, products, and
sales) rather than the major application areas (e.g. customer invoicing, stock control, and product sales).
This is reflected in the need to store decision-support data rather than application-oriented data.
Integrated Data
The data warehouse integrates corporate application-oriented data from different source systems, which
often includes data that is inconsistent. The integrated data source must be made consistent to present a
unified view of the data to the users.
Time-variant Data
Data in the warehouse is only accurate and valid at some point in time or over some time interval.
Time-variance is also shown in the extended time that the data is held, the implicit or explicit
association of time with all data, and the fact that the data represents a series of snapshots.
Non-volatile Data
Data in the warehouse is not normally updated in real-time (RT) but is refreshed from operational
systems on a regular basis. (However, emerging trend is towards RT or NRT DWs). New data is
always added as a supplement to the database, rather than a replacement.
A DBMS built for Online Transaction Processing (OLTP) is generally regarded as unsuitable for data
warehousing because each system is designed with a differing set of requirements in mind. For
example, OLTP systems are designed to maximize the transaction processing capacity, while data
warehouses are designed to support ad hoc query processing. An organization will normally have a
number of different OLTP systems for business processes such as inventory control, customer
invoicing, and point-of-sale. These systems generate operational data that is detailed, current, and
subject to change. The OLTP systems are optimized for a high number of transactions that are
predictable, repetitive, and update intensive. The OLTP data is organized according to the requirements
of the transactions associated with the business applications and supports the day-to-day decisions of a
large number of concurrent operational users. In contrast, an organization will normally have a single
data warehouse, which holds data that is historic, detailed, and summarized to various levels and rarely
subject to change (other than being supplemented with new data). The data warehouse is designed to
support relatively lower numbers of transactions that are unpredictable in nature and require answers to
queries that are ad hoc, unstructured, and heuristic. The warehouse data is organized according to the
requirements of potential queries and supports the long term strategic decisions of a relatively low
number of managerial users.