Data Warehousing

Data Warehouse
How Businesses use Data Warehousing
Introduction
Data warehousing is a technological way for businesses to align data with performance benchmarks so that organizations can obtain a long-range view of aggregated data and engage in complex analytics. These analytics typically give the organization a better understanding of what its stockpile of information means, what data trends over time reveal, and what the data indicates is in store for the business in the future. This paper will provide a description of data warehousing, examples of how it is used in a business, challenges that an organization might face when utilizing a data warehouse (i.e., how it can be implemented and what type of training is required to run it), how data warehousing may change in the next five years, and what organizational leaders can do to be prepared.
What is a Data Warehouse?
A data warehouse is a digital storage facility that integrates data from numerous sources within a business. As most businesses have multiple divisions and departments, each of these can act as a data source or stream that flows into the organization’s data warehouse. A firm’s sales department, finance department, marketing department and so on would each send their data to the data warehouse. Once there, the data can then be accessed and analyzed by stakeholders in the firm, who require analytical reports for planning or evaluation purposes. The data warehouse can be used to store information for e-mails, a company web server, shipping information, sales info, marketing data, financial systems, supply chain information, customer data, transactions, payrolls and more (Bhat & Bose, 2018).
The data warehouse also serves as a backup for data from the source system that provides it—which means that if the source system is ever corrupted or compromised, data of that system is not necessarily lost, as it can still be retrieved from the data warehouse. The data warehouse can be arranged in diverse ways, depending on the type of architecture used to set it up; it offers the possibility for data integration, a variety of tool and software applications for different users’ needs; and the processing of Big Data ore metadata on a routine basis (Rainer & Cegielski, 2012).
Examples of How a Data Warehouse is Applied in Business
There are a variety of designs that can be used when applying the data warehouse in a business setting. The bottom-up design is the most basic example: it allows a business to produce reports and analyses that can be created in data marts, which are like smaller compartments that can be combined to create a data warehouse. The data marts communicate with one another using a specific mode of information sharing that they each share in common.
Then there is the top-down example, which is the inverse of the bottom-up: in this example, the data warehouse is conceived with the most minute data terms possible stored within it. When a business requires a specific analysis, the data marts are established within the data warehouse, whereas in the bottom-up approach, the data marts are created first based on specific business functions that are required.
In practical terms, the data warehouse could be used by a business to track customers or to track employees. For instance, if a business wants to track what its clients and consumers are doing in terms of products browsed, products purchased, promotions utilized, and so on, it can track all of this data by incorporating customer data from any data source that the business operates—whether that is the cash register (the point of sale), the company’s website, the company’s call center, the company’s mailing list, and so on. A business can collect, process and analyze information about how a consumer shops online, what the consumer looks at, how many minutes the consumer spends on any one webpage, where the consumer goes from there, where the consumer comes from to get to the page, etc. (Debortoli, Müller & vom Brocke, 2014).
This is what online companies like Amazon or Best Buy do; it is what Google does with its analytics; it is what Facebook and other social media sites do (and it is actually part of their business model: they collect this data in their data warehouse which they then use to show advertisers that they can target specific individuals with tailor-made ads, so to speak). Mesa, for example, is a type of data warehouse used for the advertising system run by Google (Gupta et al., 2016). For Google, Mesa “ingests data generated by upstream services, aggregates and persists the data internally, and serves the data via user queries” (Gupta et al., 2016, p. 117). Mesa is integrated with other data warehouses used by Google, and thus is able to leverage the data services of Google’s Colossus and MapReduce as well (Gupta et al., 2016). The more data the business has, the more interlocking systems can become and more leverage over data analytics a company can maintain.
Challenges
One of the biggest challenges related to data warehousing is the challenge “to consolidate data to create a single point of truth for all customer info” (Chen, Schutz, Kazman & Matthes, 2016, p. 5103). In other words, just because the data is collected and stored does not mean it is accessible, organized and capable of being analyzed in any meaningful way. For that to occur, the data warehouse has to be managed, which is where information technology architects and code writers come into play. They write the code and design the programs that collect the appropriate data, feed it through the loops, and distill the information that is desired. Architects have to work, however, with upper management, who need to have an idea of what type of information they could use to make their business more efficient, effective, and productive. Information is useless if it is not being consumed for a reason. Understanding the power of data and how it can be used to generate meaning is the main challenge associated with data warehousing.
How a Data Warehouse can be Implemented in an Organization
Data warehouses can be implemented in any organization that has caught on to the practice of using Big Data as a source of information to augment, accentuate and enhance their business processes. Data warehouse and Big Data are the 21st century tools of the business world: they are essential elements to have in the Digital Era, and a company that is not using Big Data and data warehouses to better understand its clients, how its employees are managed, how the market is shaping up, and how its web traffic correlates with sales and marketing is a company that going blind into its respective sector.
Type of Training Required
In order to create the data warehouse architects are needed with plenty of IT and IS experience. The architects and the IT code writers will require training in various software languages, such as Java, SQL, and other code languages that will be used to create the world in which the data is harvested, stored and analyzed thanks to algorithms created and implemented in the IT department. IT degrees are helpful, but typically this kind of training is acquired on the job.
How Data Warehousing May Change 5 Years from Now
As Chen et al. (2016) note, “in big data system implementation, due to constant rapid technology changes, new and old systems need to be integrated continuously” (p. 5103). This means that over the next five years, data warehousing technology is going to change from what it is today and be enhance by a variety of services, whether they are virtual warehouses, cloud computing, block chain technology or some other innovation that comes along. A company’s data warehouse must always be based on a fluid concept that can be updated or augmented so as to allow for integration into new technologies and systems.
What Organizational Leaders Can Do to be Prepared
In order to be prepared, organizational leaders have to maintain an IT department or firm that can oversee and guide their data warehousing needs. Information is power and in order to be competitive, organizational leaders must know how their competitors are wielding that power. As innovative breakthroughs arrive, the ways in which data will be gathered and analyzed will change—and leaders have to be ready to embrace those changes.
Conclusion
Data warehousing is the present and the future when it comes to business in the 21st century. Thanks to the rise of the Internet, today’s businesses must be in position to benefit from the Big Data that is available to those who are set up to collect it, organize it and analyze it. If effective analysis is conducted, companies stand to become more efficient and productive. Data warehousing is, therefore, a proper tool for the future of business in the Digital Era.

References
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