Difference between revisions of "Data warehouse"

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Revision as of 15:50, 28 June 2009

McFadden et al define Data Warehousing as a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management decision-making. The meanings of the key terms are as defined below: 1. Subject-Oriented: Organization of data in a warehouse is around the key subjects (or high-level entities) of the enterprise. For instance, patients, students, and products. 2. Integrated: The data is assumed to be using consistent naming conventions, formats, encoding structures, and related characteristics for sharing and usability. 3. Time—variant: Data contain a time dimension so that they can be used for historical purposes. 4. Nonvolatile: Data are refreshed from operational data, and cannot be updated by users. Considering the above key terms data warehousing could be defined as the process by which an organization extract meaningful information from historical data.

For many years, several companies have been dropping data everywhere, yet there was no knowledge on how to mine them to find anything meaningful. During this time, businesses have been backing up and archiving those data without knowing what to do with them. Those data were merely kept there for historical purposes. Lately though, business intelligent (BI) tools like OLAP from Business Object, Oracle, SAS and Microsoft among others are helping to transform those raw data into smart information. However, healthcare industries have not been taking the advantage of the benefits of the BI tools. McFadden et al (p. 531) noted that data warehousing came to light as a result of advancement in information systems technology over several decades. Some of these advances are as illustrated below:  Improvement in database management technology, especially in relational database management systems (RDBMS) and data modeling.  Improvement in computer hardware, especially with respect to mass storage and parallel computer architectures  The emergence of intuitive computer interfaces and tools  Advancement in middleware products that ensure database connectivity across various platforms. The discovery that led to the development of data warehousing was, understanding the difference between operational (or production) systems and informational (or decision support) systems.

Why does an organization need Data Warehousing? McFadden et al explain the two major factors driving the need for data warehousing in most organizations today as: 1. A business requirement that needs to integrate company-wide of high quality information. 2. Separation of informational (historical) from operational data The two factors mentioned above will be subsequently considered.

For any clinical organization today, it is essential to separate operational data from informational data by creating a data warehouse. According to McFadden Fred R. et al, this principle is supported by the following factors: 1. A data warehouse centralizes data (at least logically) that are scattered throughout disparate operational systems and makes them readily available for decision support applications. 2. A properly designed data warehouse adds value to data by improving their data quality and consistency. 3. A separate data warehouse eliminates much of the contention for resources that results when informational application are confounded with operational processes.

A few decades ago, physicians knew pretty much about all there is to know about medicine; most doctors could recollect the names of their patients. However, today, no doctor can keep up with the explosion of medical and health information. While health care organizations have recognized the use of computer in other industries, its application in healthcare have not been encouraging. This is because, among other factors, it takes too long to get information in many cases, there are no easy accessibility to data, and no uniform standard among various vendors. However, according to McGee, some healthcare organizations like Parners Health, which is a conglomerate of several Boston-area hospitals (Massachusetts General and Brigham and Women’s, etc.) have used iLog decision support and EMC Documentum content-management software to share clinical best practices for some time.

Although BI technology tools in use today still have limits, its future applications and the resulting breakthroughs in medicine are forgone conclusions

Submitted by: Gbenga Abimbola


  1. Himmelsbach, Vawn, “How business Intelligence is making healthcare smatter” http://www.connectingforhealth.nhs.uk/worldview/protti10/
  2. McFadden, Fred R. et al: Modern Database Management: Basic Concepts of Data Warehousing, Addison-Wesley, New York: 1993
  3. McGee, Marianne Kolbasuk: A pill, A Scapel, A Database. Information Week 2006, 1,076: 39-45
  4. “Data Warehouse” http://pharmacy.auburn.edu/pcs/review/datawh.htm
  5. McGee, Marianne Kolbasuk: A pill, A Scapel, A Database. Information Week 2006, 1,076: 39-45
  6. Whitten, Jeffrey L et al: System Analysis And Design Methods: Database Design,导热油炉

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