A data warehouse is a system that integrates data from a wide range of sources, which helps in the running of business intelligence. Data warehouses are often preloaded with large amounts of data, which allows queries and analyses that guide businesses on how to invest. In simpler terms, data warehousing gathers information from various sources to produce more accurate results that could profit businesses. For instance, the data from cash registers in a company and its website ratings occur in different locations. Using data warehousing could combine the data and assess the general customer service of the business.
Benefits
Data warehousing could also be used to create better conditions for employees. Using data warehousing to combine their clocking in and out information, demographic data, and salary statements could inform a company how to improve its employee treatment. For organizations that have grown from mergers, data warehousing is essential because data sourcing from different areas allows the organization a holistic view of its running (Krishnan, 2019). Data warehousing is particularly preferred in data mining because it allows analysts to find patterns that could increase a business's profit.
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Data warehousing accommodates a hefty workload since it supports data analysis and unorganized queries. It also automatically updates regularly and can store data for a long time. It also scans data fast because it can handle up to one million rows of records (Ballard et al., 2018). Data warehousing uses the same model for its data, regardless of the model from the data source, giving analysts an easy time interpreting it. Further, it arranges information simply and understandably so that concerned parties who have little or no knowledge in data analysis and interpret it too.
Data warehousing can arrange information with regard to a particular subject (Ballard et al., 2018). For instance, sourcing data from the cash register could give different results, such as the number of sales for a particular item and the standard means of payment for most customers. With data warehousing, a company may focus only on the sales while disregarding the other results or channeling them to a different project. This topic arrangement makes it easier for analysts to interpret and present their findings (Elhebir et al., 2017). It also considers time by analyzing the difference in data from different periods. Data warehousing is also advantageous because data does not change once it is in a warehouse. This factor reduces the risk of intentional or unintentional data alteration.
Role in Data Analytics
Although its design is expensive and challenging to build, data warehousing gives companies an upper hand in marketing and customer satisfaction. AT&T is one of the companies that have greatly benefited from data warehousing. The company sources data from their customer's interaction with their website to reduce system failures. If a customer has trouble connecting to the website, the company is made aware long before making a complaint. Data analytics can improve a company's services to its customers by assessing the preferences and satisfaction. Data warehousing is a key figure in this solution as it acquires data from different areas and determines patterns (Krishnan, 2019). For instance, AT&T offers video subscription membership to its customers. To improve this service, it could use data warehousing to determine how many customers are satisfied with particular videos, then use that information for advertisement.
Conclusion
Data warehousing is in itself efficient and revolutionary, but it is also a technological stepping stone to better systems for analyzing data. Comparing its features from its inception to today is enough proof that, like all technologies, data warehousing is subject to improvement over time. As such, large companies that would benefit from its service must adopt data warehousing. In doing so, these companies will not only improve their profits and administration but also pave the way for the development of warehousing.
References
Ballard, C., Herreman, D., Schau, D., Bell, R., Kim, E., & Valencic, A. (2018). Data modeling
techniques for data warehousing (p. 25). IBM Corporation International Technical Support Organization.
Krishnan, K. (2019). Data warehousing in the age of big data . Newnes.
Sen, A., & Sinha, A. P. (2017). A comparison of data warehousing
methodologies. Communications of the ACM , 48 (3), 79-84.
