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The White Paper by Ken Orr titled Data Warehousing Technology is an interesting piece of scholarship that touches upon all stages of Data Warehouse implementation. Identifying that Data Warehousing is crucial in the age of information explosion, the authors assert that only a fraction of all past data is available to decision makers in an organization. This can be a handicap, as access to a comprehensive historical dataset can offer great advantages over competitors. Data Warehouses particularly find application in creating and maintaining Informational Systems for a business enterprise thereby providing strategic advantage for the organization.

The DWA represents the “overall structure of data, communication, processing and presentation that exists for end-user computing within the enterprise”. (Orr, 1996) There are eight distinct layers comprising the Data Warehouse Architecture (DWA). These are: “1.Operational Database/External Database Layer, 2. Information Access Layer, 3. Data Access Layer, 4.Data Directory (Metadata) Layer, 5.Process Management Layer, 6.Application Messaging Layer, 7.Data Warehouse Layer, 8.Data Staging Layer”. (Orr, 1996) And when it comes to developing data warehouses, some of the key dimensions to be considered are: Scope of data warehouse, data redundancy and type of end-user. Within Data Redundancy there are three levels or options. These are: ‘1. “Virtual” or “Point-to-Point” Data Warehouses, 2. Central Data Warehouses, 3.Distributed Data Warehouses”. (Orr, 1996)

Having considered the various options available, adopting a robust Data Warehouse Strategy is the next key step. The author emphasizes the point that developing a balanced data warehousing strategy is critical for its success. Again, there are various strategies that could be adopted. First is a Virtual Data Warehouse environment. The second strategy is “to simply build a copy of the operational data from a single operational system and enable the data warehouse from a series of information access tools.” (Orr, 1996)

Writing the White Paper in 1996, the author was presenting a relatively nascent concept of Data Warehousing then. But in the following 15 years, much new conceptual knowledge and technological fine-tuning has happened in this area. This is perhaps why it appears that the author has given a superficial coverage to the subject matter. Being a White Paper and not a typical research paper, the article is not rigorous in its qualitative and quantitative analysis. Instead of carrying out original research as conventional research papers and in-depth studies would do, this White Paper on Data Warehousing Technology succinctly presents the need for such a technology, its scope of application and the implementation options available to top managers. Insofar as it serves as an authoritative introduction to the subject of Data Warehousing, there is no deliberation or discussion included. Moreover, as White Papers are perused by policy-makers who are generally not technology savvy, the lack of depth in the article is justified. The emphasis is on easy comprehension by a lay reader and the paper amply satisfies this need. Perhaps the author could have attached a bibliographic list at the end of the article for further exploration of the subject.
Overall, the White Paper in question meets high professional standards. If there is an area of criticism, then it pertains to the abstract nature of the presentation without case examples. The lack of such concrete examples also hinders the end-user of the article in that they would have difficulty in adapting/applying the concepts to their organization/institution.

Reference:

Ken Orr, Data Warehousing Technology: A White Paper, Copyright 1996 by The Ken Orr Institute; revised edition, 2000, retrieved from < http://kenorr.com/pg%2033%20d.w.%20whitepaper.htm> on 25th August, 2011

The article titled Rating Your Dimensional Data Warehouse is a concise piece of scholarship that outlines the key metrics in evaluating a Data Warehouse. Data Warehouse as a data storage concept and decision making aid has evolved a lot over the past two decades. Yet, no robust set of metrics were developed to analyse and evaluate the dimensions of a Data Warehouse. Author Ralph Kimball sets out to do the same, as he proposes a list of 20 criteria for what makes a system ‘dimensional’. Each criteria can be assigned a value of ‘0’ (bad) or ‘1’ (good) and then added up to arrive at the final rating. While a sum total score of 0 would indicate a system completely un-supportive of a dimensional approach, a score of 20 would indicate a system that is fully supportive of a dimensional approach.

The author outlays 12 of the 20 criteria in the article. Some of the criteria that pertain to the Architecture of the Data Warehouse are: Explicit Declaration, Conformed .

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