Week 1 Reponse 1 BUS624 and BUS625
kingstonman41
BUS625DataDecisionAnalyticsWeek1Response1.docxBUS625: Data & Decision Analytics Week 1 Response 1
Guided Response: Your initial response should be a minimum of 300 words in length. Respond to at least two of your classmates by commenting on their posts. Do you see any additional value in their data? Though two replies are the basic expectation for class discussions, for deeper engagement and learning you are encouraged to provide responses to any comments or questions others have given to you. Continuing to engage with peers and the instructor will further the conversation and provide you with opportunities to demonstrate your content expertise, critical thinking, and real-world experiences with the discussion topics.
Below there are two of my classmate’s discussion that needs I need to response to their names are Joann Newell and Farshad Farzad
Joann Newell Discussion
I currently work for JP Morgan Chase, and was surprised at how easy it was to fine financial information with a quick google search. Within the document for the 2018 annual report were many sets of data, but what I found important and condensed was a 10 year look of where we were and where we are now. The Annual Report was specifically addressed to the Shareholders, however the information could be useful to many.
|
TEN-YEAR |
RETROSPECTIVE |
|
|
|
($ IN BILLIONS) |
2008 |
2018 |
10-YEAR CAGR |
|
REVENUE |
$4.8 |
$9.1 |
7% |
|
NET INCOME |
$1.4 |
$4.2 |
11% |
|
AVERAGE LOANS |
$82 |
$206 |
10% |
|
AVERAGE DEPOSITS |
$103 |
$171 |
5% |
The data points of Revenue, Net Income, average loans and average deposits are Categorical, nominal variable, as they are the names of the categories (De Veax, Sharpe, & Velleman, 2019, p. 9) and the years of 2008 and 2018 are also categorical as they too are names of categories but are ordinal variable as they possess an order (De Veax, Sharpe, & Velleman, 2019, p. 15). The data within the chart, the financial change over the ten years, would be quantitative.
What are the important factors in the data set? How would you handle missing data?
The important factors in the data set provided are the growth over a ten-year time span and breaking that down further to show the percentage of that change. Missing data in this chart includes what makes up the revenue and net income, and if there were any factors like promotions to consider in the growth. In this case for the data that isn’t provided in the chart above there are correlating documents in the annual report with more depth, however in the case that the information wasn’t provided and I were an analyst within the company I would pull the information and create other charts or list of what exactly contributed to the increases.
Explain the value of the data who might find this useful and why?
The data is valuable as it shows a positive result in whatever was changed from 2008 to 2018 in practices be that promotions or other marketing, improved customer service, or simply a change in economics during that time in order to prepare to do better for the upcoming years. Many would find this useful like stockholders making decisions on how to best invest; marketing teams to generate “buzz” to obtain new customers or an increase in activity for existing customers; executive positions need the information to make accurate decisions on programs to approve, or spending in certain areas over others; those looking to invest would appreciate the data in anticipation of the next ten years looking similar or better.
Farshad Farzad Discussion
|
($ MIllions) |
4Q19 |
3Q19 |
4Q18 |
|
Net Revenue |
$14,040 |
$14,259 |
$13,695 |
|
Consumer and Business |
6,442 |
6,688 |
6,567 |
|
Home Lending |
1,250 |
1,465 |
1,322 |
|
Card, Merchant Services, Auto |
6,348 |
6,106 |
5,806 |
|
Noninterest Income |
7,233 |
7,290 |
7,065 |
|
Provision for Credit Losses |
1,207 |
1,311 |
1,348 |
|
Net Income |
$4,231 |
$4,273 |
$4,028 |
JP Morgan Chase and Company recently posted their fourth quarter earnings for fiscal year 2019. The data points under review are specific to Consumer and Community Banking section of JP Morgan. The specific data points are, Net Revenue, Noninterest Expense, and Provision for credit losses. This time-series analysis is reviewed through two lenses, quarter over quarter and year over year. According to Sharpe (2019) “[a] time series is an ordered sequence of values of a single quantitative variable measured at regular intervals…” (p. 10).
The data is reviewed by looking at third quarter 2019 results measured against fourth quarter 2019 results. Net Revenue is broken down by the different lines of businesses under the Consumer and Community. The lines of businesses are: Consumer and Business Banking, Home Lending, and Card, Merchant Services and Auto. Net revenue in the fourth quarter is slightly down compared to the third quarter. However, year of year growth shows an overall growth for the line of business. There is missing data that could be included such as, an itemized breakdown of both the Consumer and Business Banking to identify a weak spot in either line of business. In addition, Home Lending is shown as one category, it does not break it down between purchase and refinance businesses. Lastly, Card, Merchant Services and Auto, are listed as one, whereas if they were individualized on the results, one could identify which one of these lines of business are carrying the overall revenue. The data listed is represented in millions of dollars indicated by the ($ millions), which makes the data quantitative. As Sharpe (2019) explains “…quantitative variables, the units tell how each value has been measured” (p. 9).
When shareholders are making decisions on whether they would like to buy, retain, sell, their stocks, they would review this data to be able to make an informed decision. However, data does not tell the whole story, it is just a glimpse of what is going on in the business. Unless the data is verified by a third company, you should always ensure you are making decisions to buy, retain, or sell stock, based on many factors. Sharpe (2019) argues “[a]lways be skeptical. One reason to analyze data is to discover the truth” (p. 12).
