Data Warehousing: SAP Enterprise Resource Planning System


The modern business environment has dictated that organizational data should be stored in such a manner that it is easily retrieved for query and analysis, mostly referred to as data warehousing. Data warehousing helps organizations identify specific sources of revenue generation, by allowing the storage of cumulative information. Additionally, data warehousing provides an opportunity to focus on the overall nature of the business, thus helping managers to develop a clear picture of what is needed for progress. SAP business information helps to extract data from the source system, generate operational data store objects, generate data cubes, and access information that has been gathered.

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SAP R/3 ERP system is one of the software that has helped organizations like Wal-Mart increase efficiency in its chain of operations.


Business management activities have intensified in the last two decades, with the emergence of new information technologies that define the business development process. Many observers believe that any business that has survived for the last 20 years must have developed a good analysis, planned its operations, and promptly reacted to the continuous changes in the business environment (Singh, 1998, p.7).

Information technology has revolutionized organizational activities throughout the globe, creating an unprecedented way of information acquisition and transfer. Sadly, many organizations have not adapted to the changes despite the availability of powerful computers coupled with quick and easy internet access, indicating how a good number of executives, as well as decision-makers, have no access to the critically important information in the organization (Ward & Dafoulas, 2006. Furthermore, a good percentage of this data set is just locked up in computer systems in an unorganized manner, thus making it difficult to access or make adequate it adequately (Davenport & Harris, 2007).

What is Data Warehousing?

Data warehousing is the electronic process of storing information in a methodological manner, thus allowing the users to construct, use, and manage organizational data. According to Reed (2010), the definition also involves how to construct, use, manage, and maintain hardware and software. The most comprehensive definition of a data warehouse is that of Bill Inmon, who is popularly known as the father of data warehouses. Inmon (1995) described a data warehouse as “a subject-oriented, integrated, time-variant and non-volatile collection of data support of management’s decision-making process”. The subject-oriented aspect of data warehousing is its ability to offer information in regard to a specific subject rather than the company’s operational activities. The integration part is based on the basis of data that has been gathered from various sources and subsequently grouped together into one whole unit for ease of manipulation (Missbach & Hoffman, 2000; Henry & Biao, 2002). The time variant means that the whole set of data stored in the warehouse is defined by a specific duration of time in the management process. Lastly, the non-volatile describes the data warehouse stability, that is, the ability of a data warehouse to continue receiving more data without removal of the older ones, subsequently helping the management to develop consistency in the process of business operations (Henry & Biao, 2002).

The simplified version of this definition was offered by Ralph Kimball. Kimball (2002, p.310) defines a data warehouse as “a copy of transaction data specifically structured for query and analysis”. In other words, data warehousing is the act of creating a data warehouse and how to make use of it. Data warehousing is thus based on the need to develop reliability in the consolidation of a unique approach to the data reporting process, and analyze it at various levels of organizational levels in a consolidated system.

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The benefits Data Warehousing

The late 20th century and beyond has seen many changes happen in the business environment, with various organizations finding it difficult to survive without embracing the change brought about by new technological innovations.

Data warehousing allows for the identification of opportunities to help a firm develop other revenue sources, hence grow businesses. The emergence of data warehousing techniques helps organizations store cumulative data, which can be easily retrieved to help make decisions for future progress. It also has the ability to provide better information, commonly known as ‘business intelligence’ (Ward & Dafoulas, 2006). In this aspect, the types of queries mean that the authorized users are given a personalized extract of the requested data so as to allow them to do further analysis and query the packaged information that the data warehouse has provided.

The other important benefit of data warehousing is its ability to focus on the entire nature of the business, through cross-sectional unit areas, something that cannot be achieved easily by the normal operational systems (Mohanty, 2005). For instance, through data warehousing analysis, the business is able to understand who is the top ten at-risk customers are (customers who are about to stop trading with it for one reason or the other); and help identify what should be done to avoid such eventualities. In order to successfully solve this query, the data warehouse will offer information from “customer service application, sales application, the order management application, the credit application, and the quality application” (Ward & Dafoulas, 2006, p.291).

With data warehousing, various tools are available to help access the required information with ease. These tools have various features that are critical in the management of data sets as well as the provision of providing prompt business intelligence (Kimball, 2002).

SAP’s Data Warehousing Solution

SAP’s data warehousing solution has been in use since 1997. Currently, it is available in many versions, with the latest being the SAP R/3 ERP system. In an environment where SAP is used, the business information warehousing is accorded to the business intelligence solution (Missbach & Hoffman, 2000). The business intelligence concept is meant to guide the generation of knowledge as regards the present business situation, the ability of the business, and that of its competitors (Missbach & Hoffman, 2000). The information extracted is sometimes large and unstructured, thus requires entering, processing, and analysis to get the true picture of internal, external, and inter-organizational structures. It is therefore noted that SAP business information warehousing is able to support:

  • The process of extracting data from the source system;
  • The initiative to generate Operational Data Store objects;
  • The generation of data cubes; and
  • The process of accessing data and information gathered (Buxmann, et al. 2004).

The SAP R/3 ERP system was traditionally an online transaction processing system, where it is used to extend the entire planning process (Yang, 1998; Ganczarski, 2009). This is popularly referred to as Material Requirement Planning (MRP). Some on the other hand call it Distribution Requirement Planning (Bhansali, 2009). With the increased use of, all the business information is centralized in terms of functionality, ushering in a new approach where SAP is able to help in the pricing considering the roles of each unit and business scenarios (Westerman, 2000).

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Strategic Data Warehousing at Raymond James Financial

Raymond James financial has applied the use of SAP data warehousing solution to implement the corporate strategy in the financial market (Bhansali, 2009). After realizing that the level of awareness of their corporate strategy-making process was low, they looked for a better solution as this situation was not healthy to their strategic corporate alignment of data warehousing. They therefore resorted to the building of SAP business information warehouse interface to help the organizational management structure understand the joint responsibility between data warehouse managers and business managers (Bhansali, 2009).

Wal-Mart Model Data Warehouse

An enterprise data warehouse was introduced at Wal-Mart in 1989. An enterprise data warehouse is built on the basis of collecting information from the entire enterprise and centralizing the information to the corporate enterprise of the larger company. In practice, it is noted that enterprise data warehouse grows with the company and thus offers timely information whenever needed, as symbolized by Wal-Mart (Westerman, 2000). The two essential features of the enterprise data process that has helped Wal-Mart a great deal are the ability to centralize information about the entire organization; and that every newly created data is fed back into the operational system.

From these features, Wal-Mart’s logistics departments are able to use the data in the warehouse to determine any specific item and their demands throughout the year (Westerman, 2000). They are also able to use the historical data to identify points of negotiation with prospective suppliers as well as locate the sales trends of items in each store. Significantly, the world’s leading retailer has continued to add more data about its operations, price, suppliers, and sales tend from the time they adopted the SAP R/3 ERP system. At present, it is estimated that 99% of Wal-Mart’s data elements are stored and continuously maintained at its enterprise data warehouse. It is, therefore, possible to state that a data warehouse helps in the integration of data from various disintegrated sources and changes them to a usable multidimensional product (Vassiliadis, et al. n.d).


Bhansali, N. (2009), Strategic Data Warehousing: Achieving Alignment with Business. New York. CRC Press.

Buxmann, P., Konig, W., Fricke, M., & Hollich, F. (2004), Inter-Organizational Cooperation with SAP Solutions. London. Springer.

Davenport, H. & Harris, G. (2007), Competing on Analytics: The New Science of Winning Harvard Business School Press.

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Ganczarski, Joe. (2009) Data Warehouse Implementations: Critical Implementation Factors Study. New Jersey. VDM Verlag.

Henry, F., & Biao F. (2002), SAP BW: A step-by-step Guide. Washington DC. Addison Wesley.

Inmon, W.H. (1995), Tech Topic: What is a Data Warehouse? Prism Solutions. Volume 1.

Kimball, R. (2002), The Data Warehousing Toolkit: The Complete Guide to Dimensional Modelling, 2nd Edition. New York. John Wiley & Sons.

Missbach, M., & Hoffman, U. (2000), SAP Hardware Solutions: Servers, Storage, and Networks for New York. Prentice Hall.

Mohanty, P. (2005), Introduction to Data Warehousing. Chicago. McGraw Hill Publishers

Reed, M. (2010), A definition of Data Warehousing. Intranet Journal. Web.

Singh, H. (1998), Data Warehousing: Concepts, Technologies, Implementations, and Management. California. Prentice Hall.

Vassiliadis, P., Quix, C., Vassiliou, Y., & Jarke, M. (n.d), Data Warehouse Process Management. Athens. National Technical University of Athens. Web.

Ward, P., & Dafoulas, G. (2006), Data Warehousing: The Route to Mass Customization. Boston. Wiley & Sons.

Westerman, P. (2000), Data Warehousing: Using the Wal-Mart Model. New York. Morgan Kaufman.

Yang, J. (1998), WareHouse Information Prototype at Stanford (WHIPS). Stanford. Stanford University.

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