Data warehousing
Name
Institution
Quintessentially, Data warehousing has been regarded as combining data from multiple with the usual varied sources into a comprehensive and an easy manipulated database (Rainardi, 2007). The most common systems of data warehousing consist of queries, reporting and analysis. For many years, data warehousing has been commonly used by many companies in analyzing business trends over time. There are foundations concepts required by business professionals while choosing data warehousing. One of the concepts includes database management.
According to Rainardi (2007), the database management approach involves effects of processing data and storage. Rainardi argues that, the data required by various applications are merged in addition, integrated into the common database as a replacement of being stored in many independent data files. In addition, database management approaches emphasizes on maintaining, updating the common database, having users application programs to share the data within the database and granting a reporting with inquiries response capability so that the final users can at ease acquire reports with swift responses in requests for information. Additionally, the basic concept includes storing data effectively in order to use it efficiently. With this in mind, it is worth noting that, ability of moving data from one place to another should be emphasized. In this regard, data requires to be moved from the place of destination, to a database and then to the final user, and this process requires time in making the logistics.
Operational Processing and informational processing
The most important assets for any organization are information. The assets are always kept by operational processing. Operation processing has been outlined as the process through which data is kept in. Operational processing is usually designed to sustain high volume of transactions processing and minimal back-end reporting. They are generally process oriented which implies that it mainly focuses on business processes or else tasks. Some of these tasks include registration, billing among others.
Studies have also shown that, the processing is mostly concerns with current data that occur on daily basis (Prabhu, 2004). As a result, data within operational processing are updated occasionally based on the requirements. In addition, they are optimized to perform moderately small volumes of data with fast inserts.
On the contrary, informational processing is regarded as the change processing of information in which is detectable by the observer. In this regard, it entails changing the form of presentation of the text file from a digital computer system. Information processing consists of four basic parts. These include input, storage, processor and output. These steps are required in that, when dealing with information, it requires a process of acquiring, retaining in addition, and using information as the concept referred as informational processing. As the name suggests, it stores information in long-term memory. The benefit of durable memory is that, there is no need of constantly rehearsing information to store. Additionally, there is no restricting information stored because it is permanent in its storage nature. This implies that, they are generally concerned with historical data. They also often optimized to perform relatively large volumes of data. Therefore, they are best designed to support high volume of analytical processing with subsequent elaborated report generation. In addition, informational processing is subject oriented. Such subjects are populated with each data in storing information.
Why so many organizations are seeking to exploit data warehouses for competitive advantage.
The competitive advantage is a fundamental firm performance currently. Incidentally, so many organizations are seeking to use data warehouses for competitive advantage. Reasons as to why many firms do these differ. For instance, Prabhu (2004) argues that, when comparing traditional methods for data analysis with data warehousing, it is with no doubt that, many companies have discovered the potential of using data warehousing as a form of competitive advantage. Business information acquired from data analysis is a fundamental success for any firm wishing to maximize competitive benefit. Traditional approaches of data analysis do not scale handling voluminous data sets. The explosive growth in data with database has produced the need for having new techniques with tools, which can automatically transform the processed data into helpful information as well as knowledge.
Consequently, the intensive competitive global market place has led many firms to seek out competitive advantage through eliminating inefficiencies of data processing, maximizing relationships with the firm stakeholders and optimizing internal operations. To assist in this, many organizations are installing data warehousing to enhance their quality decision making in business since data warehousing is suitable in creating reports and analyzing business data’s.
The rate of innovation is rising in business environment. As a result, it has made the firms realize that knowledge is their fundamental factor. The concept of creating, storing, coding, exchanging and using knowledge has become the competitive advantage for many firms.
Data warehousing architecture is required in accommodating dynamic firm requirements, which cannot be anticipated. Lack of it has caused religious wars in that, warehousing has become famous. Data base architecture is defined, as design used in defining the target state with the subsequent required in achieving the target state. Generally, it describes the data structure of how it is processed, stored along with been utilized in given system. When calculating the populating of data warehousing various techniques are required. First, the data is captured into data warehouse with data marts. Secondly, the data is captured from the external and operational systems. Thirdly, it is transformed in usable format of data warehouse and fourthly, it is loaded into the data warehouse or else the data mart (Rainardi, 2007).
References
Prabhu, C.S.R. (2004). Data warehousing concepts, techniques, products and applications. New York: PHI Learning Pvt. Ltd
Rainardi, V. (2007). Building a Data Warehouse: With Examples in SQL Server. California: apress publishing
Use the order calculator below and get started! Contact our live support team for any assistance or inquiry.
[order_calculator]