Quite promptly, data warehousing has grown into a distinctive, well-liked and prevalent business application class. Data warehouses built by the early builders are already serving as key elements for their IT policy and architecture. There have been a number of data warehouses established and employed for all sizes and types of businesses, and all of them have been greatly successful. Dealers of hardware and software have efficiently created products and services that particularly focus on the market of data warehousing.
A data warehouse consists of data that is managed and “situated after and outside the operational systems.” (Gupta, n. d. )During the historical course of systems development, the operational systems and the data processed by them had been given essential importance. A number of systems took birth, such as legacy systems – which were the most vital source of data for analysis systems; decision support and executive information systems – which were targeted towards lower to mid-level managers and high level executives respectively; operational systems – which dealt with amassing, cleaning and incorporating new data with related already gathered data; and some others.
The history of data warehousing can be traced back when Murphy and Devlin, two IBM researchers, in late 1980s, formulated the “business data warehouse”. Principally, the very concept was aimed towards providing an architectural model. The model was intended for data flow from operational systems to decision support setting. The data warehousing concept made an effort to tackle with a number of problems related with this flow, primarily its high costs.When there was no data warehousing architecture present, redundancy of information was needed, to a huge extent, to strengthen the “multiple decision support” settings to function individualistically.
Each setting or environment assisted different users, however usually needed almost the same data. Data warehousing defined Data warehouse is like a vessel, a storage place for an organization’s data that is stored electronically. They are created to enable and assist “reporting and analysis.” (Inmon, 1995)Here the definition largely implies the storage of data. However, some other vital components of data warehousing system also include the retrieval and analysis of data, the extraction, the transformation and loading of data, and the management of that data. As Mailvaganam (2007) puts it, data warehouses simply “store an aggregation of a company’s data.
” A very broad definition of a data warehouse has been given by Gupta (n. d. ).According to him: “A data warehouse is a structured extensible environment designed for the analysis of non-volatile data, logically and physically transformed from multiple source applications to align with business structure, updated and maintained for a long time period, expressed in simple business terms, and summarized for quick analysis. ” Evolution of Data Warehousing Discipline A number of factors have had an impact on the rapid evolution and growth of the data warehousing field.
A group of factors have been most momentous in this case, most significant being the accelerated drive in the hardware and software technologies. As for computer hardware, with its sharp decrease in prices and increase in power, along with computer software, with its easy-to use capability, quick and easy evaluation of loads of gigabytes of data and information, and business knowledge has been possible. Furthermore, the upsurge of internet/intranet and web based applications have very significant implications for data warehousing applications.Basically, it is the steep rising of hardware and software power, as well as the convenience of reasonable and simple-to-use analysis and reporting instruments that have served a very vital role in the data warehouse evolution. For the evolution of data warehousing science, another very important impact is by the underlying changes in the business organizations and structures.
The rise of a global economy pulsating with energy has overwhelmingly brought a great change in the information demands made by businesses and corporations throughout the world.Furthermore, phenomena like “business process reengineering” and “downsizing”, economic factors, the emergence of vendors with accepted and well-liked business application suites, and an increased awareness of investment in technology, have all played a very vital function in the growth of data warehousing. (Gupta, n. d. ) Separation of data warehousing and operational systems The basic conception of data warehousing is that the information and data put in storage for business analysis can easily be retrieved, and it can most efficiently be done by taking it apart from the information and data present in the operational systems.Over the years, a number of grounds for this separation have come into light.
It has been seen how development and innovations in technology and variations in the business’ nature have made numerous processes of business analysis very much complicated and high-tech. Data warehousing systems, besides producing standard reports, also enable sophisticated online analysis as well as multi-dimensional analysis. The data present in data warehouses is de-normalized via a dimension-based model. Furthermore, optimizing of data warehouses is done for speedy data retrieval.
For this purpose data in data warehouse is stored multiple times. On the other hand, optimizing of operational systems is done for the protection of data integrity and the pace of documenting business transactions by means of an entity-relationship model and database normalization. Usually the data normalization rules put forward by Codd, are followed by operational system designers for ensuring data integrity. The data in a data warehouse is collected using the operational systems. It is then kept in the data warehouse even after it has been washed out from the operational systems.
Use of Data Warehouse in BusinessA comprehensive assessment of the type of business or activity backed up by a data warehouse is critical to studying data warehousing systems. There exists a very stimulating and fascinating trend that is perceived with a number of data warehousing strategies and plans. The data warehouse users, particularly who are new, merely want to acquire the data and information that they were previously capable of obtaining by means of old ways and techniques. They basically want to repeat their inquiries, accounts and statements with the data warehouse in order to be certain about the correspondence of all numbers.This is due to the users’ mixed feelings of apprehension and excitement about the new tools.
It is only after a period of time that the new data warehouse is used and the users start exploring new competencies that are accessible to them. Soon after, significant input is given in the data warehouse enhancement process. A critical purpose of data warehouse is to create it accommodating and accessible to the most possible extent, and not setting high entry prices for its usage, so that it can be accessed by many potential end-user tools and platforms.In most data warehousing projects, the selection of a “preferred data warehouse access tool” for effective users is required. The performance of the data warehouse can be adjusted to the necessities of the instrument essential and suitable for these active and effective users. (Gupta, n.
d. ) Many data warehouse users require accessing a set of standard reports and queries. These reports required by different users are periodically automatically produced. When any particular report is needed by the users, it can easily be viewed as it would have been already run by the data warehouse system, and users would not have to run it by them.Such a facility can specifically be helpful for reports and documents that require a longer time to run.
A significant characteristic of data warehouses is the automatically produced and predefined summary analysis. When people in a corporation wish to look into product sales figures, they do not require summarizing it every time they need it, and in fact, can simply view the summary through data warehouse. These summary views are an object of a large bulk of examination and investigation in a data warehouse.For instance, the product summary view, in a conventional data warehouse, may answer for the numerous queries where different products are selected by different users and other queries related to the time periods for product sales and profit margin.
Very easy to build, these queries offer quick response. Moreover, the data warehouse system is prone to be served as interface with those applications that make use of it as the basis of operational system data. Data and information is provided to other data warehouses, also smaller ones (known as data marts) by data warehouses.Often, the interfaces of operational system and the data warehouse become increasingly even and steady as well as powerful.
As a reliable source of data, the data warehouse is said to be a much better consistent and dependable source for numerous types of data as compared with the operational systems. Yet, it is critical to note that a great deal of the operational state information is not passed over to the data warehouse. Therefore, data warehouse should not be thought of as a source of all operation system interfaces.
