Quantitative data is a powerful tool for just about any entity willing to use it, but perhaps its most useful application is within the world of business. Businesses are responsible for examining their own finances via audits and policies with a careful eye time after time, and make alterations to their own business model frequently. Each of these decisions and actions requires the meticulous use of quantitative data, as each decision a company makes must be a profitable one, or at least have a probable chance of being profitable. Data is both the means and the end, in a way, because a business requires money, yet also, oftentimes, must spend money to acquire this money, and both of those require great amounts of quantitative data to achieve. However, utilizing quantitative data is a double-edged sword, as there are also a number of pitfalls and drawbacks that must be addressed in order to determine the overall usefulness of it.
Perhaps one of the greatest advantages of quantitative data usage is that it acts as a reduction or simplification of experience, as one article puts it (Bernard, 2012). Essentially, this means that quantitative data almost always represents something else in a condensed and easier-to-understand way. For instance, quantitative data used to measure the increase in sales during the holidays helps to simplify the issue by reducing these people and their purchasing experiences and habits into mere numbers (Bernard, 2012). This is crucial because numbers are the lifeblood of any good business, and managing these numbers should be a top priority for any business. Another advantage of quantitative data is that it is extremely universal. In fact, virtually anything can be broken down into the universal language of numbers, even decidedly subjective criteria, such as emotions. This is because the data is observed by humans and then processed into quantitative data (Bernard, 2012). This means that surveyors and other number-crunchers can examine virtually anything and find some way to convert it into quantitative data, which is a boon to businesses, which frequently require it to measure any number of things.
This brings up one of the largest pitfalls of quantitative data: it can, at times, be almost too objective. Quantitative data utilizes what many people refer to as cold, hard facts, and oftentimes there are aspects of humanity or business, or anything else, that simply cannot be communicated effectively enough through numbers. For this reason, oftentimes quantitative data is communicated side-by-side with qualitative data, which relays information in terms of qualities or more subjective categorizations (Sandelowski, 2000). However, this pitfall is rendered irrelevant for businesses, since they rely more on objective data, and tarnishing that data with subjective perceptions would only muddy the data and keep the business from making the most economically sound decision possible (Sandelowski, 2000). There are a number of considerations to be made when deciding what data should be used to run an organization. It is difficult to list specifics, but one the utmost priority to a business must be profit, so any business should utilize whatever form or quantity of quantitative data would allow them to be as efficient and profitable as possible. From there, the primary data that should be used is data collected to ensure the long-term survival of a company, such as customer demographics, profitable areas, monetary strengths of the company, and the risk factors of potential threats or opportunities for the organization. For this purpose, the cost-benefit analysis is crucial, as it balances the potential gains of an action (or inaction) with the possibility of profit. This allows businesses to gauge the viability of certain threats or actions. The cost-benefit analysis is also used for the gathering of information related to the competitors of a business, as knowing the inherent strengths and weaknesses, from a quantitative standpoint, will help the business to capitalize on these weaknesses as much as possible and maximize profits for the business.
The organization being studied for this assignment is Wal-Mart. Wal-Mart has a number of goals, both short-term and long-term. Wal-Mart’s ultimate goals are profit, but also expansion, although it could be argued that expansion is simply another means to achieve profit (Stalk et al., 1992). The fact remains that Wal-Mart has invested heavily in expansion efforts so that it may saturate even medium or small-sized cities and towns (Stalk et al., 1992). Wal-Mart is actually structured into three primary divisions, Wal-Mart stores in the U.S., Wal-Mart International, and Sam’s Club, which is a wholesale vendor that operates primarily based on membership fees from customers (Stalk et al., 1992). This gives Wal-Mart a great deal of flexibility in a number of different markets, to the point where virtually anyone, within reason, is within access to a Wal-Mart, both geographically and financially. One of Wal-Mart’s key structuring concepts is the use of mass-distribution (Stalk et al., 1992). Essentially, this means that Wal-Mart is able to sell at extremely low prices because it corresponds directly with wholesalers, buying in bulk and negotiating to get the prices, among other tactics, in order to ensure that Wal-Mart can purchase as cheaply as possible, which, in turn, allows it to turn around and sell these products for cheaply as well (Stalk et al., 1992). Wal-Mart utilizes a fair basis hierarchical structure in order to manage the numerous chains and stores (Stalk et al., 1992). As with most businesses, Wal-Mart has a great number of upper management executives, along with a CEO and owner, along with, under them, a slew of marketers and upper-managers who are responsible for some of the most important decisions behind Wal-Mart as a franchise. Below them are the regional and store managers, who are responsible for more of the practical side of the business; managing supplies, inventory, and the general well-being of each store and region individually (Stalk et al., 1992). For this reason, Wal-Mart likes to select individuals who are knowledgeable about each geographic area before selecting them to manage a particular store or location, so that they know the ins and outs of the area and its demographics (Stalk et al., 1992).
Because Wal-Mart relies so heavily on efficiency and expansion in order to survive, it is a good idea to use quantitative data to be constantly improving the performance of Wal-Mart. The first way that this may be achieved is through the use of statistics. These statistics would have to be indicative of every facet that governs Wal-Mart’s survival (Savage, 2012). For example, statistics concerning the popularity of a particular new product must be gathered and examined in order to determine the long-term viability of this product. On a more local level, each Wal-Mart would be wise to take demographic analyses of its own unique customers to acquire a basic sense of who they are selling to. This demographic information would include facets such as age, gender, race, income level, living situation, and other demographic information, gathered via in-store surveys. One of the most important facets of statistics is that they are essentially an objective way to look at probability (Savage, 2012). This means that gathering statistics data, for any purpose, should be used to formulate strategies that relate to taking risks; risks that would have otherwise been uninformed and ill-advised without these statistics (Savage, 2012).
Another important facet of quantitative data collection for businesses is the concept of benchmarking, which is the practice of comparing one’s business to another, especially where finances are concerned (Watson, 1993). In Wal-Mart’s case, there is not a great deal of specific competition for them on a local level. However, on a national level, there are other chains that fill a similar niche as Wal-Mart, such as Target. Wal-Mart would be wise to benchmark not just its finances against Target’s but also benchmark more subjective criteria such as consumer satisfaction. For example, if many of Target’s consumers report satisfaction with the cleanliness and tidiness of Target, it would be wise for Wal-Mart to institute similar measures for its stores (Watson, 1993). These benchmarking practices allow businesses to indirectly exchange quantitative data with one another simply by observing the data generated by other businesses. This way, businesses are able to keep up with one another, relatively speaking, while maintaining minimal contact with one another, if that is something they are trying to avoid. Lastly, Wal-Mart must also utilize quantitative data to predict specific trends within their own field. This is known as trend analysis, and it is a crucial aspect of quantitative data (Savage, 2012). Trend analysis would allow Wal-Mart to make educated predictions about the future by examining the way things are headed in the present. For example, Wal-Mart might examine the growth of a specific toy and predict that this toy might be the next multi-billion dollar franchise. While this rarely turns out to be the case, the concept is still sound. By looking at current data, Wal-Mart is able to extrapolate this data into the future and essentially perform a very objective form of fortune-telling (Savage, 2012). Trend analysis would also allow Wal-Mart to cut off its competitors at the pass, so to speak, and use quantitative data to take advantage of specific business opportunities before its competitors are even aware of it.
These uses of quantitative data are mere examples. There are, in the world of business, nearly countless ways that quantitative data may be used to effectively manage an organization. For example, quantitative studies can show the relationship between transformational leadership and employee motivation. What the utilization of quantitative data comes down to, as explained earlier, is prediction. This is crucial in not just applications such as trend analysis, but most aspects of business, including things like demographics. They are used extensively when analyzing risks, for example, and predicting whether or not they should take a particular action. Essentially, quantitative data allows businesses to take difficult concepts and simplify them into a form they can utilize effectively, and for this reason, it is essential for the successful management of any successful business.
Bernard, H. R. (2012). Social research methods: Qualitative and quantitative approaches. Thousand Oaks, CA: Sage.
Sandelowski, M. (2000). Focus on research methods combining qualitative and quantitative sampling, data collection, and analysis techniques. Research in Nursing & Health, 23, 246-255.
Savage, L. J. (2012). The foundations of statistics. New York: Dover Publications.
Stalk, G., Evans, P., & Shulman, L. E. (1992). Competing on capabilities: The new rules of corporate strategy (Vol. 63). Harvard Business Review 70(2), 3-7.
Watson, G. H. (1993). Strategic benchmarking. New York: John Wiley & Sons.