Information systems depend on the way data is stored and retrieved for use. Without a system where information is saved and collected, a user cannot track the history of changes or utilize data that was once present in a database. To perform these actions, the concept of data warehousing was introduced. The purpose of this paper is to examine the origin of data warehouses, present their current use, and consider the attitudes of experts and users towards the concept. Furthermore, the essay aims to assess the benefits and potential drawbacks of using data warehouses, as well as the perspectives that these environments may have.
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Origin and History
It is clear that the development of data warehouses was closely connected to the increased use of databases by businesses. Databases were utilized to collect and update data about transactions, workers, business operations, and other active processes in the corporate world (Devlin and Murphy 62). However, this type of data storing meant that some information, once updated, got lost and could not be retrieved without creating a specific system for its storage. Thus, while databases allowed organizations to store contemporary information, they provided limited opportunities for long-term research and analysis.
As an outcome, the concept of data warehousing was introduced. In the 1980s, the idea of an architectural model that would store data from different sources and time periods was developed by Devlin and Murphy (62). Some scholars previously considered ideas similar to those presented in the paper by Devlin and Murphy. Nevertheless, this article led to the growth of data warehouse research and for-profit organizations offering their own systems for database management (Kimball and Ross 26). Thus, data warehouses entered the business sphere, and new research opportunities were open to analysts.
Later, several data warehousing alternatives were offered, driving innovation in the field further. For example, Kimball devised a toolkit for creating and installing a data warehouse for a business (Kimball and Ross 32). Inmon developed a “hub-and-spoke Corporate Information Factory (CIF) approach” that focused on normalized data (Kimball and Ross 56). While the models were in competition with each other, their creators also looked at integrative solutions where several ideas were implemented to create complex warehouses.
At the present moment, the concepts offered by Kimball, Inmon, and other researchers are still implemented in various business fields. One of the most notable examples is the healthcare industry, which is heavily dependent on data. Healthcare providers collect significant amounts of information every day – patients’ identification, health records, prescriptions, diagnoses, nurses’ and doctors’ duties, schedules, pay, and many other types of data are essential for organizations’ operation. The digitization of the field meant that most hospitals and facilities began using databases instead of paper-based records (Ghani et al. 417).
Later, such systems as the Electronic Health Record (EHR) and Clinical Decision Support (CDS) were introduced – their functionality is closely tied to retrieving current data (Khan and Hoque 3). However, these solutions are unsuitable for medical research because they only store the latest information and have to be updated with each patient visit.
Thus, healthcare organizations started implementing data warehousing into their structure to have a system for unobstructed access to old data. The first example of such use is demonstrated in the research by Turley et al., who look at the Clinical Data Warehouse (CDW) that was integrated by Health Sciences South Carolina (HSSC) in 2013 (1245). In this case, the purpose of the warehouse was to store data for Learning Healthcare Systems (LHS) that had access to patient and hospital information from all regions in the United States. With the help of the CDW, HSSC was able to increase data pools for vital clinical research and improve healthcare quality in several states (Turley et al. 1245). The ability to work in a system with broad access to old data played a crucial role in medical innovation.
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The second example of data warehousing in healthcare deals with cost standardization. Visscher et al. proposed and implemented a data warehouse for an infrastructure provided by the National Institutes of Research (396). This system collected data about healthcare cost measurements based on patient health, treatment, and other EHR information. As an outcome, Visscher et al. analyzed the costs for procedures, activities, and services and standardized them with a costing algorithm for future use (396). A similar solution can be offered to all for-profit facilities to maintain their economic growth.
Common and Personal Attitudes
The use of data warehouses is a crucial part of data analysis in business and healthcare. The implementation of warehouses seems to go with the digitization of processes because data has to be stored for access as well as research (Moscoso-Zea et al. 64661).
Therefore, the common opinions find both sides of data systems – databases and data warehouses – necessary in creating a system for immediate and long-term objectives’ completion (North et al. 19). My attitude is similar to the views described above. Data warehouses are invaluable in using large amounts of information that are stored over time to find patterns, identify weaknesses in performance, and note what changes are happening to a company or its clients.
Fundamental Strengths and Weaknesses
As discussed above, the main benefit of a data warehouse is its ability (and core purpose) of storing large amounts of data that would otherwise disappear. Thus, warehouses maintain data history – changes in the source and their evolution (North et al. 21). Moreover, data warehouses have the ability to integrate information from several databases, acting as a single source of knowledge for an analyst. As data warehouses operate separately from transactional databases, their use for research does not impede the primary business operations, helping analysts to work, while the business continues to update its databases (Moscoso-Zea et al. 64668).
If one were to use a database that is used for everyday operations, it would possibly interfere with company performance. Data in warehouses can also be restructured to be more understandable for users, delivering answers more suitable for research than for simple representation (Visscher et al. 396). Finally, data warehouses are optimized for handling large queries, and their functionality allows one to complete predictive and prescriptive analyses (Kimball and Ross 93). Overall, the list of benefits sets data warehouses apart from databases and other systems for data storage.
Nonetheless, data warehouses have some weak sides as well that businesses should consider before implementation. First, data warehouses may be rather expensive to set up and scale (North et al. 20). Second, as research is a priority in these systems, the speed of operation may be slow. Data warehouses require significant power and have a substation impact on the computers’ CPU (central processing unit), which implies that hardware’s characteristics should be sufficient for the warehouse to operate. Finally, data warehouses, as all data stored on physical carriers, are not safe from external influence (disasters, power outbreaks, and other issues).
Data warehouses continue to be a topic of discussion in the sphere of process digitization. Arora and Gupta, for instance, believe that data warehouses will be an addition to the future system of e-governance (28). The scholars argue that data warehousing will be integrated by more organizations and government entities to store citizen data. Ghani et al. suggest the implementation of data warehousing into other spheres of healthcare, including telemedicine (415). Here, the architecture of such systems is predicted to deliver better care to patients and provide them with more detailed consultations.
Khan and Hoque also highlight the innovative potential in health data technology. They suggest that countries with low quality of healthcare delivery can improve their performance and develop a system for more insight into the needs of people (Khan and Hoque 2). It should be noted that all mentioned experts predict that data warehousing will evolve with the integration of big data and cloud computing. Overall, the field of healthcare seems to be one of the most interested in data warehousing technologies.
The history of data warehouses shows that this concept has evolved with other aspects of data handling and analysis. Such systems offer organizations a chance to look at information without disturbing the main transactions. Furthermore, they store data that would be lost otherwise, providing a perspective for discovering trends and fluctuations. Data warehouses require sufficient financial and technological resources, but they perform activities that a simple database cannot do. Their current and future implementation projects are centered in the field of healthcare.
Arora, Rakesh K., and Manoj K. Gupta. “e-Governance Using Data Warehousing and Data Mining.” International Journal of Computer Applications, vol. 169, no. 8, 2017, p. 28-31.
Devlin, Barry A., and Paul T. Murphy. “An Architecture for a Business and Information System.” IBM Systems Journal, vol. 27, no. 1, 1988, pp. 60-80.
Ghani, Mohd Khanapi Abd, et al. “Telemedicine Supported by Data Warehouse Architecture.” ARPN Journal of Engineering Applied Sciences, vol. 10, no. 2, 2015, pp. 415-417.
Khan, Shahidul Islam, and Abu Sayed Md Latiful Hoque. “Towards Development of Health Data Warehouse: Bangladesh Perspective.” 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT). IEEE, 2015, pp. 1-6.
Kimball, Ralph, and Margy Ross. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. 3rd ed., John Wiley & Sons, 2013.
Moscoso-Zea, Oswaldo, et al. “A holistic View of Data Warehousing in Education.” IEEE Access, vol. 6, 2018, pp. 64659-64673.
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North, Max M., et al. “Data Warehousing: A Practical Managerial Approach.” Computer Science and Information Technology, vol. 5, no. 1, 2017, pp. 18-26.
Turley, Christine B., et al. “Leveraging a Statewide Clinical Data Warehouse to Expand Boundaries of the Learning Health System.” eGEMs, vol. 4, no. 1, 2016, p. 1245.
Visscher, Sue L., et al. “Developing a Standardized Healthcare Cost Data Warehouse.” BMC Health Services Research, vol. 17, no. 1, 2017, p. 396.