Data Warehousing for Business Intelligence - Coursework Example

Data Warehousing for Business Intelligence Data Warehousing Essay Table of Contents Introduction 3 Use of the Literature to Chart the Historical Changes in the Field of Data Warehousing 4 Explanations of the Two Data Warehousing Methodologies 5 Conclusion and Recommendation 10 References 11 Introduction The growing trend of the business environment has made the Business Intelligence (BI) system a crucial aspect for the success of the business organisation. The use of data warehouses and business intelligence for the purpose of decision making assists in saving money and earning profits. BI system assists in converting raw materials into important usable information. Data warehousing is the main source of storing historical data of an organisation and acts as a corporate memory. In other words, it can be explicated to be a portion of the ‘Architected Data Environment’ which functions as an integrated source of data for the purpose of processing information. It acts as a source of information which is collected from numerous sources both historical and current data. It helps to resolve the queries related to the business with the use of the stored data. Data warehouse stores information in a steady, searchable arrangement so that the users of the business can easily browse information and implement it. Business intelligence acts as a component of data warehousing which supports the process of decision making. It also helps to create awareness regarding the operations of the business and behaviour of the customers (Caserta Concepts, 2012). The Business Intelligence/Data Warehousing (BIDW) are essential constituents to meet the competitive pressure, gather intelligent information to upsurge the pace of the business and earn profit which is the foremost objective of any for-profit organisation. It can be simply quoted that the BIDW collects data and forms important information in a systematic manner on a timely basis in order to earn profitability (Sun Microsystems Inc, 2005). Use of the Literature to Chart the Historical Changes in the Field of Data Warehousing Data warehousing was materialised for various reasons due to the advances in the field of information technology. The key reason for the development of data warehousing was due to the important differences between operational and information system. Operational system provides information on the real time basis whereas the information systems are used to support decision constructed on the past point in time data. This difference in the systems gave rise to the establishment of data warehousing. The operational system focussed on point in time approach and the information system on period of time. This was the primary reason for the development of the data warehousing. Several other aspects contributed for the improvement of data warehousing in the organisations, such as need for the improvement in database technology. Correspondingly, in terms of improvement, there has been an emergence of the two key business models i.e. relational data model and relational database management system. To make the interface easier as well as convenient and to advance the middleware products, the need for data warehousing was created (Slide Share, 2011; Sun Microsystems Inc, 2005). Data warehousing has grown rapidly since its commencement. The 40 years of the evolvement of data warehousing is evaluated from the 1970s to the present scenario. In the initial years i.e. 1970s the organisations used the operational systems for data processing. The system was not being able to incorporate large data and was also unable to meet the data analysis request frequently. Besides, in this era, the databases were static and were stored in the mainframe. To get information regarding specific queries and for the purpose of data gathering, a request was managed from the use of the recorded tapes. This was inconsistent as well as time consuming which was creating a need among the people for a better storage system of the data. In the next decade i.e. in the 1980s, the real time computer applications were found to be decentralised. The era saw an invention and the use of the relational model along with the database management system for fast processing in the organisations. It was becoming a wave for the companies to use these models to make the business process easier than earlier. The problem which still existed with the use of the operational database was the inefficiency in the retrieval of the data because of the storage of enormous data. Subsequently, in the year 1990, a feasible solution to the issues faced by the people in the earlier years was established in the form of data warehousing. It was formed to augment and manipulate the data to make perfect decisions concerning the business. With the advancement of the technology, the need for secure and reliable data storage was formed by enhancing the data warehousing system. It can thus be stated that the evolution of the data warehousing acted as a boon for the business organisations as it helped to enhance the performance of the people (Slide Share, 2011; Sun Microsystems Inc, 2005). Explanations of the Two Data Warehousing Methodologies Aligning the facet of data warehousing and the notion of business intelligence is the most important criterion for the success of an organisations. The evaluation of the methodology for the data warehouse is significant to gain in-depth knowledge about the processing of the business and to make corrective decisions. The methodology of a business needs to be focussed on the need of the organisation and to derive a positive output. Data warehousing as evaluated is an important and a useful technology for the storage of the data in the business organisations. There are several methodologies of data warehousing which are based on certain frameworks. The frameworks of the methodologies implemented by the companies include data driven, goal driven and user driven (List & et. al., 2002). It is observed that data warehousing requires different methodologies than other IT systems. The key features of data warehousing methodologies include usage of data which is exploratory and with low predictability. The focus of the methodology is to present the data and the loading of the data (List & et. al., 2002). It is ascertained that there are various data warehousing methodologies which have core competencies to act as a benefit for the business organisation and to improve its effectiveness. The sources of these various methodologies are classified into categories such as the core ‘technology vendor’, ‘infrastructure vendor’ and ‘information modelling’. The methodologies which are most commonly used by the companies include SAP methodology, Oracle and the Microsoft SQL Server methodology among others. The SAP methodology is used by several business organisations that have Enterprise Resource Planning (ERP) as its core competency. The ERP is software which is used to improve the core competencies of the business by managing the sales, finances and the warehouse (Sen & Sinha, 2005). The ERP is used in the SAP methodology to enhance the speed, increase the efficiency of the operations and to have competitive edge over the others. The biggest competitions of the SAP’s ERP are Oracle and Microsoft. SAP has its own data warehouse which is known as the business warehouse which acts as a decision supporting tool. The prudent technologies used by SAP include ERP and CRM technology which are important to increase the competencies of the business organisation. ERP as a business driven framework has several benefits as it supports the global operations of the business and accommodates with the quick altering environment. It also helps in understanding the business and meeting the customer demands by analysing proper data without taking much time. As the data are stored in a sequential manner in the data warehousing SAP methodology makes it easier for the usage by the manager with easy accessibility. ERP technology entails wide data information of the company (Khan, 2005). The SAP technology is used to meet the requirement of large data at a speedy rate and it also analyses the multi structured set of data. The technologies used by SAP are various. The ‘SAP Business Objects BI’ portfolio empowers the businesses to advance valued visions through the usage of the big data technology. The tools of SAP such as the SAP Predictive Analysis and Visual Intelligence are implemented by the business individuals in order to manipulate the data at a rapid pace (Olofson & Vesset, 2012). Also, the enterprise data is gathered centrally in the enterprise data warehouse of SAP NetWeaver BI. It is observed that the data are extracted from numerous sources and then get loaded into SAP NetWeaver BI. This in turns enables to maintain every data which are relational and multi-dimensional. After the maintenance, the technical clean-up procedures are followed and the rules of the business are implemented to evaluate the data gathered. After the data is collected using the SAP system, it needs to be transformed to be effectively used by the people in the business. These data stored in the warehouse are of great significance for the business as it stores important financial information. The transformation process is significant as it performs the technical clean up and helps in amalgamating the data for the business purpose. With regard to transformation, the data in SAP NetWeaver BI system is transformed from the source format to the target format in data warehousing. The data is consolidated in a synchronised manner following the transformation rules. The SAP NetWeaver BI offers various features for data warehousing to be efficient and effective for the business as a type of application. The application type which is SAP NetWeaver BI acts as an operational data for the analysis purpose (Learn SAP, n.d.). The architecture of SAP NetWeaver BI is multilayer which assists to integrate data from varied sources, transform, clean up and store the data to make the interpretation of the data efficiently. Figure 1 represents the various stages involved in data warehousing of SAP NetWeaver BI. Figure 1: (Learn SAP, n.d.). The data from many sources get stored unchanged in the Persistent Staging Area (PSA). It acts as a backup and can provide information at other time in case there are some errors. The data are transferred from the PSA to the next layer to guarantee quality and also to integrate the data. The data warehouse is the base and plays the role of central database for the analysis of the information with a secured and reliable architecture for the business user (Learn SAP, n.d.). The other prominent data warehousing methodology is Oracle methodology which is one of the competitors of SAP. The core competency of Oracle is based on the use of the database management system (DBMS). The DBMS is used to collect the data for the purpose of analysing it for the Oracle used by the business users. Moreover, the DBMS_STATS are used to gather the data in the table form in the partition level and the sub-partition level in the database which makes it easier for the usage by the people in the business. The DBMS also uses the hash sub-partition and acts as an optimiser which can derive the statistics from other levels. Oracle DBMS has the ability to store the data which can be accessed rapidly, easily and in a secured way. The management tool assists in analysing the data as it makes the procedure of evaluation easier for the business when they require past data for taking strategic decision. It also provides security to the data storage by implementing control lists which store encrypted evidence each year. The “Oracle Data Vault” is used in the Oracle DBMS with the auditing features which makes the analysis easier and convenient. Oracle DBMS is also used in data warehousing to get information regarding the mostly used data by the business user (Oracle, 2010). The design of the Oracle facilitates to manage data effectively. The data in Oracle is acquired from various sources and huge data in an advanced manner can be analysed with this feature. The data collected which are huge in number includes the use of applications such the Cloudera’s distribution comprising Apache Hadoop, Cloudera Manager, Oracle Linux and Oracle HotSpot Java Virtual Machine. The data collected are analysed and thus help in accessing faster service to the business users. It is observed that Oracle collects statistics at global level only if the table changes above 10% but will collect the partition level statistics every time. The data are transformed using the Oracle database management system and with the use of Java language. The application type used by Oracle is the RDBMS server. The server helps in developing two and three tier architecture. The two tier is used to support the decision making process through the analysis of the data stored in data warehousing. The application helps in accessing the source data which are gathered from various sources and aids in the decision making process using the Decision Support System (DSS). Correspondingly, this approach facilitates in providing easily assessed fast and flexible information for the business user (Shahzad, 2000; Russom, 2012). Conclusion and Recommendation Data warehousing is a fundamental notion in the modern day business periphery for the users as it aids in getting detailed information from the past data when required. The data are stored in a systematic manner with various tools and technologies in use which makes the purpose of data warehousing safe, secure and reliable. There are several methodologies used by data warehousing and the best for the business users is SAP methodology. SAP methodology is suggested to be used as it collects data from numerous sources, transforms it into detailed information and also creates a backup in case of any error in the technology. It is faster and convenient to be used by the business users. References Caserta Concepts, 2012. Data Warehouse Concepts. Resources. [Online] Available at: http://www.casertaconcepts.com/resources/data-warehouse-concepts/ [Accessed February 05, 2014]. Khan, A., 2005. SAP and BW Data Warehousing: How to Plan and Implement. I Universe. List, B. & et. al., 2002. A Comparison of Data Warehouse Development Methodologies Case Study of the Process Warehouse. Institute of Software Technology and Interactive Systems, pp. 1-13. Learn SAP, No Date. SAP Business Intelligence. Your SAP Training Partner, pp. 4-267. Olofson, C.W. & Vesset, D., 2012. Big Data: Trends, Strategies and SAP Technology. White Paper, pp. 1-16. Oracle, 2010. Best Practices for a Data Warehouse on Oracle Database 11g. An Oracle White Paper, pp. 1-31. Russom, P., 2012. High-Performance Data Warehousing. Tdwi Best Practices Report, pp. 3-31. Sun Microsystems Inc., 2005. Business Intelligence and Data Warehousing (BIDW). Transform Raw Data Into Business Results, pp. 4-20. Slide Share, 2011. Data Warehouse Concepts. Data Warehouse. [Online] Available at: http://www.slideshare.net/obieefans/data-warehouse-concepts-8164443 [Accessed February 05, 2014] Sen, A. & Sinha, A.P., 2005. A Comparison of Data Warehousing Methodologies. Communications of the ACM, Vol. 48, No. 3, pp. 79-84. Shahzad, M.A., 2000. Data Warehousing With Oracle. White Paper, pp. 1-18. Read More