Why Data Warehousing?
The concept of data warehousing has evolved out of the need for easy access to
a structured store of quality data that can be used for decision making. It is
globally accepted that information is a very powerful asset that can provide
significant benefits to any organization and a competitive advantage in the
business world. Organizations have vast amounts of data but have found it
increasingly difficult to access it and make use of it. This is because it is in
many different formats, exists on many different platforms, and resides in many
different file and database structures developed by different vendors. Thus
organizations have had to write and maintain perhaps hundreds of programs
that are used to extract, prepare, and consolidate data for use by many different
applications for analysis and reporting. Also, decision makers often want to dig
deeper into the data once initial findings are made. This would typically require
modification of the extract programs or development of new ones. This process
is costly, inefficient, and very time consuming. Data warehousing offers a better
approach.
Data warehousing implements the process to access heterogeneous data
sources; clean, filter, and transform the data; and store the data in a structure
that is easy to access, understand, and use. The data is then used for query,
reporting, and data analysis. As such, the access, use, technology, and
performance requirements are completely different from those in a
transaction-oriented operational environment. The volume of data in data
warehousing can be very high, particularly when considering the requirements
for historical data analysis. Data analysis programs are often required to scan
vast amounts of that data, which could result in a negative impact on operational
applications, which are more performance sensitive. Therefore, there is a
requirement to separate the two environments to minimize conflicts and
degradation of performance in the operational environment.