Data Warehousing

Data Warehousing
Data warehouse is a repository of an organizations electronically stored data. Data warehouses are designed to facilitate reporting and analysis[1].
This definition of the data warehouse focuses on data storage. However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata.
In contrast to data warehouses are operational databases that support day-to-day transaction processing.
In the past, it was very difficult for managers or executives to get {draw:frame} information about their company as a whole. This is still challenging today for companies that dont use data {draw:frame} warehouses. When a company uses a number of different systems, the {draw:frame} information they retrieve can be inconsistent. Data {draw:frame} warehouses are useful because they collect data and remodel it. The {draw:frame} information is placed in a single unit, and the company can get a clear picture of how their company is performing. Most importantly, they will be able to make decisions with a great deal of confidence. Data will be stored in the warehouse from multiple sources. Once the data is stored, it must be cleaned and transformed.
Figure: Data Warehousing Environment
Understanding The Challenges of Using Data Warehouses
While data {draw:frame} warehouses can be greatly beneficial to the companies that use them, there are many challenges that a company will face in their implementation and utilization. Some experts have even said that data warehouses are one of most overrated tools in the computer industry.
It is also important for {draw:frame} companies to realize that data {draw:frame} warehouses are not core business tools. What this means is that a {draw:frame} data warehouse is much more vulernable to the politics that may occur within {draw:frame} a company or organization. If the {draw:frame} data warehouse does not have the support of the employees, it will fail. Many employees have a hard time using data {draw:frame} warehouses because of their complexity, and the {draw:frame} companies they work for will often make the situation worse by failing to educate them. It is also challenging for {draw:frame} companies to keep their data {draw:frame} warehouses in tune with their production units. To make matters worse, many of their developers are not trained in calibrating them.
Thus instead of being scared, {draw:frame} a company must be aware of the pitfalls involved with using a {draw:frame} data warehouse.
Benefits of data warehousing
Some of the benefits that a data warehouse provides are as follows: [7][8]
A data warehouse provides a common data model for all data of interest regardless of the datas source. This makes it easier to report and analyze information than it would be if multiple data models were used to retrieve information such as sales invoices, order receipts, general ledger charges, etc.
Prior to loading data into the data warehouse, inconsistencies are identified and resolved. This greatly simplifies reporting and analysis.
Information in the data warehouse is under the control of data warehouse users so that, even if the source system data is purged over time, the information in the warehouse can be stored safely for extended periods of time.
Because they are separate from operational systems, data warehouses provide retrieval of data without slowing down operational systems.
Data warehouses can work in conjunction with and, hence, enhance the value of operational business applications, notably customer relationship management (CRM) systems.
Data warehouses facilitate decision support system applications such as trend reports (e.g., the items with the most sales in a particular area within the last two years), exception reports, and reports that show actual performance versus goals.
{text:bookmark-start} {text:bookmark-end} Disadvantages of data warehouses
There are also disadvantages to using a data warehouse. Some of them are:
Data warehouses are not the optimal environment for unstructured data.
Because data must be extracted, transformed and loaded into the warehouse, there is an element of latency) in data warehouse data.
Over their life, data warehouses can have high costs. The data warehouse is usually not static. Maintenance costs are high.
Data warehouses can get outdated relatively quickly. There is a cost of delivering suboptimal information to the organization.
There is often a fine line between data warehouses and operational systems. Duplicate, expensive functionality may be developed. Or, functionality may be developed in the data warehouse that, in retrospect, should have been developed in the operational systems and vice versa.
Evolution in organization use of data warehouses
Organizations generally start off with relatively simple use of data warehousing. Over time, more sophisticated use of data warehousing evolves. The following general stages of use of the data warehouse can be distinguished:
Off line Operational Database
Data warehouses in this initial stage are developed by simply copying the data off an operational system to another server where the processing load of reporting against the copied data does not impact the operational systems performance.
Off line Data Warehouse
Data warehouses at this stage are updated from data in the operational systems on a regular basis and the data warehouse data is stored in a data structure designed to facilitate reporting.
Real Time Data Warehouse
Data warehouses at this stage are updated every time an operational system performs a transaction (e.g. an order or a delivery or a booking.)
Integrated Data Warehouse
Data warehouses at this stage are updated every time an operational system performs a transaction. The data warehouses then generate transactions that are passed back into the operational systems.
Sample Applications
Some of the applications data warehousing can be used for are:
Credit card churn analysis
Insurance fraud analysis
Call record analysis
Logistics management.
{text:bookmark-start} {text:bookmark-end} The future of data warehousing
Data warehousing, like any technology niche, has a history of innovations that did not receive market acceptance.[9]
A 2009 Gartner Group paper predicted these developments in business intelligence/data warehousing market .[10]
Because of lack of information, processes, and tools, through 2012, more than 35 percent of the top 5,000 global companies will regularly fail to make insightful decisions about significant changes in their business and markets.
By 2012, business units will control at least 40 percent of the total budget for business intelligence.
By 2010, 20 percent of organizations will have an industry-specific analytic application delivered via software as a service as a standard component of their business intelligence portfolio.
In 2009, collaborative decision making will emerge as a new product category that combines social software with business intelligence platform capabilities.
By 2012, one-third of analytic applications applied to business processes will be delivered through coarse-grained application mashups).
=> (Advantages )

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