Data Analysis Exercise and Proposed Data Analysis Topic Selection
The purpose of this assignment is to assess various types of data and the relevance of that data for use in data analysis. Students are encouraged to find data sources for topics, industries, or companies in which they have interest. There is also data available within the SAS® Visual Analytics student interface that may be used. By looking at the available data, students should start to formulate the types of questions that they can ask and answer through data analysis.
Select an organization of interest from the list of organizations that you identified in your Week 1 assignment or an organization from some other source. You are looking for an organization that has:
Applied data analysis in a positive way.
Readily-available organization and industry data (e.g. user or customer data).
Data in a suitable format for input into SAS® Visual Analytics (preferably Microsoft Excel (XLSX) or comma-separated values (CSV) format) that can be used in future assignments.
Send a private message to your instructor identifying the organization and the source of data that you plan to use. Your instructor will either approve it or suggest an alternative.
Evaluate this organization as a candidate for data analysis in a maximum of 1,050 words, and complete the following:
Identify the purpose of the data analysis (i.e. how it will be applied to a specific business need).
Summarize the source and type of data being collected and analyzed.
Analyze a sample description of one or two records in the database, describing the available data fields collected.
Describe the anticipated results and what they mean for the organization.
Cite a minimum of one peer-reviewed reference from the University Library.
Format your assignment consistent with APA guidelines.
Week 2 Most Challenging Concepts
Creating a SAS® Table from a CSV File
Watch the “Creating a SAS Table from a CSV File” video.
Consider the following as you watch:
Why is it important to convert a Microsoft® Excel® document to a CSV file?
Using the Import Data Utility in SAS® Studio
Watch the “Using the Import Data Utility in SAS Studio” video.
Consider the following as you watch:
How might the Import Data Utility be used to bring in data from your source for analysis? What types of preparation steps may be necessary?
Week 2 Electronic Reserve Readings
Data Science Foundations: Fundamentals
Search for this video using the Lynda.com® Video Access link above.
Type the title “Data Science Foundations: Fundamentals” in the Search Bar to find the video.
Watch the following tutorial from Section 3 of the “Data Science Foundations: Fundamentals” video by Barton Poulson:
Consider the following as you watch:
How can you be certain that you are using the data you access in a way that was intended? Which ethical issues should you be aware of?
Evaluation of Starbucks Company as a Candidate for Data Analysis and Data Analytics
Part A: Evaluation of Starbucks as a Candidate. Word count: 950
Starbucks is a large multinational company. It is a coffeehouse chain with over 30,000 branches worldwide. It has branches in Africa, Asia, USA, South America, North America, and Oceania. Starbucks was started back in 1971 and has been in business for over 45 years.
Starbucks offers a variety of products. Although there are standard products, in some cases, the products could depend on location (Thompson & Arsel, 2004). Some of the products offered include hot and cold drinks. It has coffee and tea products. Snacks are also a standard product not to forget the fresh juices. Some other products include sandwiches and muffins. Their products are not just limited to foods; drinkware is also one of the products sold at Starbucks. In select stores, other products such as beer and wine can be is sold. Each of the products mentioned above is just but a category of some kind. Coffee, for example, comes in quite a variety and way of serving. It could be cold or hot. Additionally, it could be of some flavor, for example, espresso, café latte, Frappuccino among many.
Besides, Starbucks keeps records of its stores count in every city and region of the world where they operate. This would be helpful to determine whether the business is growing or not. In keeping the statistics, Starbucks keeps records of the city, region, a quarter of the yea, and the stores count.
How the analysis would happen depends on some aspects. When analyzing the Starbucks data, some of the most important information to capture a place, time (which includes season and time of day), type of product, and then the product itself. Some of the aspects measured would be the amount of product sold on the average of day, month, season, and year for the particular outlet (this will capture country as well). The company would, however, be more interested in revenue. Thus the amount of income for the item would be necessary. Hence sales of the particular item averaged over the day, season, or year would be of help in analyzing customer behavior.
What makes Starbucks the company of choice for this exercise then? This is the underlying question that makes up this paper. It would not be worthwhile to choose a company that does not capture the information. Starbucks publishes its data in annual reports for each of its stores. It would, therefore, be quicker to find this information to use for practice.
There is some other information that makes Starbucks a company of choice. Starbucks has one of the companies that has heavily applied data analysis and data analytics in growing its business (Ramadan, Kasuma, Yacob, Shahrinaz & Rahman, 2017). Additionally, Starbucks analytics results can even be found online implying that as a company that appreciates analytics, it will be easy to compare what has already been done against what is being done in terms of analytics. An example of former analytics is as shown in figure1 below. Although it would be hard to disseminate the data to the public, many of their analyses would be readily available. Loyalty cards are another source of data that Starbucks uses to collect data. As a result, therefore, the data collection information can be used to understand that Starbucks has much information in its data centers and that their data would be reliable.
Figure 1: Chart of regional growth of Starbucks since 1994. Adapted from ‘Starbucks’ Growth: A 20-Year Review’ by Vivek Bhardwaj, 2014
Whereas the above information would be of great admiration to experiment, it is quite a massive information to be provided. Additionally, it could be sensitive information to competitors. Sources describe how it is used by Starbucks itself. For purposes of experimentation in these analytics exercises, Starbucks provides information on stores count. The most readily available data is stored count for given countries. Change in the number of store in a given place is construed as a response to market behavior. This metric will be used for analytics exercise. The data is collected every quarter.
As a matter of conclusion, Starbucks is a suitable choice on account of the availability of data and analyses. Most companies would not readily disclose data, and Starbucks is no exception. However, analyses of their customer data can be readily found. As an international company, Starbucks has records of stores count in every region. The particular data can be used to track how Starbucks has been expanding over time. Besides the general expansion, regional and seasonal expansion records can also be found.
This data is appropriately presented in excel, thus working with the Starbucks data would be easier compared to having it presented in such forms as pdf as seen in other companies. The categorical presentation of the data also makes it easier to work on it. What is more is that the data is readily accessible from their website, thus making the data collection process from Starbucks an easy task.
Although Starbucks has not disclosed its customer data to the public, it is worth noting that some of its customer analysis results are provided. It, therefore, follows that for the given analysis, comparing the results against the available analyses would provide more insights into understanding the aim of the analysis. For example, if a particular product in a region increased in sales and was followed by the subsequent opening of new stores in the region, it could be easily implied, such as product impact on the regional expansion of the business. Hence a more straightforward analysis utilization of available data. This is, in fact, one of the things that choose Starbucks a wiser endeavor as far as this exercise is concerned: availability of data and availability of other analyses.
Part B: Additional Information
Analyzing quarterly change in the number of stores per country or continent gives insights into the market expansion of Starbucks. The quarterly analysis is essential because, from it, it can be determined which season of the experiences great demands for the Starbucks products. Doing so by region and country also helps understand the continental or regional places where the brand fits into their culture, probably socio-economic culture. From this analysis, the company can understand which continents or countries to target the most. Additionally, it provides more insights into which season would be most effective. In conclusion, this analysis is of great help in making an informed decision when opening a new branch during market expansion.
Generally, the type of data being collected and analyzed by Starbucks is customer data through sales and surveys through the point of sale and mobile applications. However, in this exercise, the data available in the stores count per city/region. It is provided by the Starbucks annual report. The data is downloadable from their website for investors (https://investor.starbucks.com/financial-data/supplemental-financial-data/default.aspx). This data ranges from 2016 to 2019 every quarter. The source of the data is the company itself makes it more credible and thus reliable as exact data thus making the analysis results in an accurate representation of the Starbucks Company. The findings of the analysis could thus be extended to Starbucks Company.
This data has various aspects that could be analyzed. The fields include year, quarter, city or country, region, and store count. From this data, an analysis of the store count for regions is essential. From the trend of the store count, the senior management can determine whether a business is growing or not. It can also be determined the rate of expansion in accordance with the regional culture. From the settings thereof, the management can determine how to incorporate their understanding of the regional environment to create custom products.
The analysis of the regional distribution of business through stores is expected to find out how the means of stores change over time for particular regions. From an explanation by SA, Rahim, and Abughazaleh (2018), through understanding this change, the management can determine whether to set up a new business in the neighborhood or not. Additionally, understanding the differences in the mean distribution would help understand how to strategize on new research concerning expansion. For example, culture or socio-economic factors that would affect setting up a new one. For example, if stores count in an American city shows a significant increment, hen suggestions to set up a new store in the neighboring city would attract a worthy consideration without necessarily so much market research. That is to say, the set in the existing city business acts as the blueprint for the neighboring city. Similarly, if setting up a business in a region like Africa would result in the closure of some, then it would beat logic to set up a new one without further investigation in the region. Conclusively speaking, the analysis would help the management in making an informed decision when it comes to deciding which part of the world to expand the business.
Bhardwaj, V. (2019). Starbucks’ Growth: A 20-Year Review. Retrieved 6 August 2019, from https://seekingalpha.com/article/2153683-starbucks-growth-a-20-year-reviewRamadan, A., Kasuma, J., Yacob, Y., Shahrinaz, I., & Rahman, D. (2017). Loyalty Program, Store Satisfaction, and Starbuck’s Brand Loyalty Among the Millennial. Advanced Science Letters, 23(8), 7420-7423. Doi: 10.1166/asl.2017.9489
SA, M., Rahim, A., & Abughazaleh, Z. (2018). Big Data in Marketing Arena: Big Opportunity/Benefit, Big Challenge, and Research Trends: An Integrated View. International Journal
Of Economics & Management Sciences, 07(04). Doi: 10.4172/2162-6359.1000533
Thompson, C., & Arsel, Z. (2004). The Starbucks Brandscape and Consumers’ (Anticorporate) Experiences of Glocalization. Journal Of Consumer Research, 31(3), 631-642. Doi: 10.1086/425098