explanation of how data warehousing, online transactional databases, and data mi

Question

explanation of how data warehousing, online transactional databases, and data mining can solve or reduce these difficulties. Be specific. Data mgmt difficulties:The amount of data increases exponentially Data is scattered all across the organization Only small portions of the data are relevant for specific decisions There is an ever-growing need for external data in making decisions Raw data may be stored in different formats There are legal issues involved in transferring the data across country borders Data security, quality, and integrity

Explanation / Answer

Data Warehouse is a repository of enterprise or business databases which provides a clear picture of current and historical operations of organizations. Since it provides a coherent picture of the business conditions at a particular point of time, it is used for the efficient decision making process. it involves the development of system that helps the extraction of data in flexible ways.
A business’s data is usually stored across a number of Databases. However, to be able to analyze the broadest range of data, each of these databases needs to be connected in some way. This means that the data within them need a way of being related to other relevant data and that the physical databases themselves have a connection so their data can be looked at together for reporting purposes..
A great example of data warehousing is what Facebook does. Facebook gathers al your data such as your friends, your likes, your groups etc. All these data are stored into one central repository. Although Facebook is storing all these information into separate databases, they store the most relevant and significant information into one central aggregated database.
Data Mining is a set of methods used for data analysis, created with the aim to find out specific dependence, relations and rules related to data and making them out in the new higher level quality information . Data Mining gives results that show the interdependence and relations of data. These dependences are mainly based on various mathematical and statistical relations .
It is necessary to choose adequate Data Mining algorithms for making Data Warehouse more useful.
Example of Data Mining: Fraud detection of credit card usage Credit card companies will alert you   when they think your credit card is fraudulently used by someone other than you. Companies will have a history of the customer’s purchases and know geographically where the purchases have been made. If a purchase is made in a city far away from where you live, the companies will put an alert to possible fraud since their Data Mining shows that you don’t normally make purchases in that city. Companies can either disable the card for that transaction or put a flag for suspicious activity.
On line transaction processing, or OLTP, is a class of information systems that facilitate and manage transaction-oriented applications, typically for data entry and retrieval transaction processing.
Globalization and Internet have transformed many aspects of our life. Now all we need is a proper computer and an Internet Connection and literally there are not a lot of things you can’t do. And this includes the online financial transaction culture brought about by e-commerce website. Anyways, it is not limited to selling or buying goods but has reached a whole new level (you can even hire someone to work for you – online). This degree of comfort, this level of confidence and security are all brought about by the online transaction processing systems. These systems handle several transactions other than financial, say, surveys. Nowadays small to enterprise level web based and desktop applications are completely based on these online processing systems for their customers.

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