How Businesses Use Data Warehousing: A Complete Guide

Introduction

Data warehousing is a technological approach that allows 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 about the business's future. This paper provides 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 — including how it can be implemented and what type of training is required — how data warehousing may change over 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. Because 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 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 related to emails, a company web server, shipping information, sales data, marketing data, financial systems, supply chain information, customer data, transactions, payroll, 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, that data 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 or metadata on a routine basis (Rainer & Cegielski, 2012).

Examples of Data Warehousing 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 have in common.

Then there is the top-down design, which is the inverse of the bottom-up approach. In this design, the data warehouse is conceived with the most granular 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, and promotions utilized, it can do so by incorporating customer data from any data source the business operates — whether that is the point of sale, the company's website, the company's call center, or the company's mailing list. 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, and where the consumer comes from to reach the page (Debortoli, Müller, & vom Brocke, 2014).

This is what online companies like Amazon and Best Buy do; it is what Google does with its analytics; and it is what Facebook and other social media sites do — indeed, collecting and warehousing this data is a core part of their business model. They use the data warehouse to demonstrate to advertisers that they can target specific individuals with tailored ads. 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 is therefore able to leverage the data services of Google's Colossus and MapReduce as well (Gupta et al., 2016). The more data a business has, the more interlocking its systems can become, and the greater leverage over data analytics the company can maintain.

Challenges of Data Warehousing

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 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 must 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 necessary processes, and distill the desired information. Architects must work, however, with upper management, who need to have a clear sense of what type of information could make the business more efficient, effective, and productive. Information is useless if it is not consumed for a purpose. Understanding the power of data and how it can be used to generate meaningful insight is the central challenge associated with data warehousing.

Conclusion

Data warehousing is both the present and the future of business in the 21st century. Thanks to the rise of the Internet, today's businesses must be positioned to benefit from the Big Data that is available to those who are set up to collect, organize, and analyze it. If effective analysis is conducted, companies stand to become more efficient and productive. Data warehousing is, therefore, an essential tool for the future of business in the Digital Era.

References

Bhat, P., & Bose, A. (2018). Application of information system in Amazon: Issue and perspectives. International Journal, 6(1), 23–29.

Chen, H. M., Schütz, R., Kazman, R., & Matthes, F. (2016). Amazon in the air: Innovating with big data at Lufthansa. In System Sciences (HICSS), 2016 49th Hawaii International Conference on (pp. 5096–5105). IEEE.

Debortoli, S., Müller, O., & vom Brocke, J. (2014). Comparing business intelligence and big data skills. Business & Information Systems Engineering, 6(5), 289–300.

Gupta, A., Yang, F., Govig, J., Kirsch, A., Chan, K., Lai, K., … & Bhansali, S. (2016). Mesa: A geo-replicated online data warehouse for Google's advertising system. Communications of the ACM, 59(7), 117–125.

Rainer, R., & Cegielski, C. (2012). Introduction to information systems: Enabling and transforming business (4th ed.). Wiley.