Data Warehousing Essay Research Paper Abstract

Data Warehousing Essay, Research Paper

Abstract

Data warehousing is the technology trend most often associated with enterprise

computing in today’s business environment. The data warehouse, in fact, is a

culmination of new developments in database technology, including entity-relationship

modeling, heuristic searches, mass data storage, neural networks, multiprocessing, and

natural-language interfaces. The data warehouse is a centralized, integrated repository of

information, one which can provide a vital competitive edge for corporate decision-

making and product development. Types of data warehouses include the operational data

store (ODS), the data mart, which is of great value in analyzing sales information, and the

enterprise data warehouse, which can take either a centralized or distributed approach,

PC Week, Feb 8, 1999.

Data Warehouses

The type of data warehouse an organization adopts should depend on the way the

business operates and the types of decision support it needs. One of the simplest types of

data warehouse, an operational data store (ODS) is a replicated production database that

has been adjusted for errors. An ODS is used primarily to generate standard operations

reports and to provide transaction detail for summary-level analysis. Since an ODS

replicates an OLTP system, some experts do not consider it a true data warehouse type.

However, because ODS fit the broad definition and many data warehouses contain

them. Depending on an organization’s reporting needs, an ODS may be updated

monthly, weekly, or more frequently, sometimes almost in real time (PC Week, Feb 8

1999). The main advantage is that it enhances production system performance, since

reporting and query functions are off-loaded from the OLTP system to the ODS (PC

Week, Feb 8 1999).

Another type of data warehouse is the data mart. Data marts are limited in scope,

usually taking their information from a single department or business process. Data marts

may be used for analyzing sales information in a specific region or for a particular

product line, for example. Data marts usually contain only summary data but they can be

linked to operational data stores for drilling down to transaction details if necessary. Data

marts can be managed by IT departments, but only as often as they are managed directly

by users in a department or work group (PC Week, Feb 8 1999).

While many OLAP applications can be performed on data marts, cross-

departmental analysis, executive information systems, and data-mining applications need

information gathered from the entire enterprise to be most effective. The enterprise data

warehouse is used for this type of extensive data collection and analysis. Because of its

scope and complexity, the enterprise data warehouse is usually managed by the central IT

group.

As its name implies, an enterprise data warehouse contains information taken

from throughout an organization. This is the most complex type of warehouse to build

and maintain, since data must be merged from multiple systems into common subject

areas (PC Week, Feb 8 1999).

Data-mining tools work with various statistical techniques for modeling data and

for estimating and predicting outcomes based on what they have learned. Data-mining

work best with large data sets (PC Week, Feb 8 1999).

Data Warehouse Components

Although a data warehouse sounds like a single entity, it is really a multi-tiered,

multi-application conglomerate that comprises several components. Each component may

be handled by one or more pieces of hardware or software. No vendor has a complete

data warehouse package, (PC Week, Feb 8 1999).

Functionally, a data warehouse extracts data from operational systems and loads

it into a holding area where it is “scrubbed”, which means it is made to conform with

warehouse standards. Then the data will be merged, time-stamped and dated in the right

order, and loaded into databases for use by data access tools. Since the data goes through

a number of transformations and it is ultimately placed in data structures different from

the ones it came from, those changes are mapped in catalogs or dictionaries. Such

catalogs are managed with metadata tools. Data that defines or describes data in the

warehouse is called metadata. There are typically two kinds of metadata. Information that

users need to know, such as table and column names and definitions, are called frontend

metadata. Everything else, such as how a particular data element maps to its original

database, is backend metadata.

Security.

Security considerations for data warehouses are different from those for OLTP

systems. For a data warehouse to pay for itself, lots of users have to be able to benefit

from it, and therefore more users will need access to data than are traditionally authorized

by OLTP security (Computerworld, Feb 15,1999).

Conclusion

Today, many corporations come to appreciate that the information they gather

each day is an asset, they will rely more and more on data warehousing. But while a data

warehouse can provide managers with the means to ask questions of their data and get

back meaningful answers, it can not automatically make a company more profitable.

“Good technology can not substitute for good management.”

References

1. Computerworld, Feb 15, 1999 p14, “Human side key to data warehousing.”

By: Stewart Deck.

2. PC Week, Feb 8 1999 v16 i6 p56, “Data Warehouses in Need of Renovation.”

By: John Taschek.

3. PC Week, Feb 8, 1999 v16 i6 p71, “100 Top Data Warehousing Leaders Step Up.” By: Jeff Moad.