Data data and are useful for place where

warehouse is a collection of data that helps in decision making. It is one of
the components of business intelligence. It is a type of database which is generally
used in processing queries and analysis and contains details of previous
transaction or processes. To ensure quality of data it usually goes through
various steps of data cleaning. Relational database is used for online
transactions in which data is stored in previously defined categories which includes
insertion, update and deletion.

            Data warehouse is used for analytical processing where as
relational database is used for transactional processing. When it comes to
query analysis, data warehouse shows high performance and relational database
has a low performance.  In data warehouse,
online Analytical processing (OLAP) is used to which is used to improve
response time and helps in analyzing better, which is done by denormalization
of the data. Where in online data transactional processing (OLTP) the data is
highly normalized which helps in quick response time and provides a lot of

            Both operational data and decision support data have different
functions from each other. Operational data is used to store the data in RDBMS
and supports the transactions which takes place in the business. Operational
data is more real time whereas decision support data shows processes or transaction
which have already occurred.  Operational
data is usually used in places to keep up with the simple operations such as keeping
track of every item sold in a store. Decision support data require denormalization
of data and are useful for place where data there are not a lot of data changes
done on daily basis. Since decision support data requires high speed it does
not include all the details of every transaction but at the same time it contains
huge amount of data. It is highly likely that it contains many duplications and
the data is in denormalization form.  Operational
data usually has a short time span whereas decision support has a longer time
span. In decision support data, data can be analyzed at different levels
staring from an overall summary to details of each transaction whereas operational
data tends to focus on individual transaction.


example where database could be used to support decision making could be company
such as amazon which requires large database for its inventory and for its various
products. They also must keep up with the large amount of their customer’s
information which includes billing details, shipping date, payment method etc. Another
example could be it can also be useful in companies where decision making is required
to take further actions or future decisions for the company or using queries to
analyze the data. This can be based on several details. Last example of this
could be that, it is helpful for organizations who need to keep track of their
things which include keeping track of assets, liabilities, sales and profits.

            Data mining is a subset of business intelligence used by
various companies these days. Data warehouses and data mining these days are
used by companies along with sophisticated software to predict the market. A
good example could be data mining used by large organization such as a credit company
to predict as to what type of advertisement a client would be interested in or
which areas to focus more from where we can a potential client. Another example
is that is also helpful in keeping up with the market and latest trends which
help companies to outgrow positively and plan for future. Last example of data
mining could be that it is used by companies to analyze the change whether
there is an overall increase or decline in their market value or a product.  


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