Data-Driven Decision Making Project

posted in: Research Paper | 0

Data-Driven Decision Making Project

Assignment Overview

Length: 15-20 slides + Excel file with all accompanying analyses

Background

Making data-driven decisions is a key step to achieving the Business Function/Processes and Strategic Planning MBA program learning outcomes as well as a very sought-after professional competency across all industries. The World Economic Forum lists Complex Problem Solving as the most important skill for thriving in the age of the Fourth Industrial Revolution we are going through, and Judgment and Decision Making as a top-ten skill (Gray, 2016).

Using data analytics to inform your decisions and solve problems gives you many advantages, such as the ability to explore and compare alternatives, to test your own assumptions and come up with an objective solution, and to gather convincing evidence in support of your proposals. This assignment provides you the opportunity to experiment with all data analyses covered in the course, demonstrate sophisticated comprehension of each method and ultimately – to celebrate your improved decision-making through data analytics.

Data-Driven Decision Making Project Summary

For this project you need to think of an opportunity or challenge your organization or department is currently facing and ask a management question to address it. You will also need to collect data that is relevant to your question ana analyze it; thus, it is important to focus on a topic you can investigate through available to you data. Examples of organizational opportunities include: increase market share, reach out to new customers, attract talent, expand product/service portfolio, serve customers more efficiently, etc. Examples of organizational challenges include: profit loss, customer loss, high turnover rate, non-attractive compensation and benefits package, etc. You can build a dataset for the assignment either by collecting/using data from your organization or by downloading an existing dataset (refer to the Resources Folder posted under week 1 for ready to use datasets and links to others).

Data-Driven Decision Making Project Step-by-step Directions

  1. Articulate Your Management Question (1-2 slides)

1.1. Identify one management opportunity for improving your organization (or department) or one challenge it is facing. Explain why it is important to address it.

1.2. State your opportunity/challenge as a management question.

  1. Collect Data (1 slide)

2.1. Find a dataset that is relevant to your management question. You can use data from a free dataset (refer to Week 1 Resources Folder for available data, links to free online data and tips for searching online for data). Introduce your dataset and describe it.

2.2. Choose 4 variables that you are interested in and copy-paste them in a new Excel file, which you will work on for the rest of the assignment.

2.3. Explain your choice of 4 variables; explicitly argue how each is related to your management question.

  1. Describe Your Data (2-5 slides)

3.1. Identify the type of each variable and describe how they are measured.

3.2. Describe each of the 4 variables in your dataset by finding their (1) mean, (2) median, (3) mode, (4) range (5) quartiles.

3.3. Explain thoroughly what the descriptive statistics tell you about each of the variables.

3.4. Decide which measure(s) of central tendency represent your variables most accurately and why. These will be the measures you will use for your final argumentation.

  1. Visualize Your Data (2-5 slides)

4.1. Choose a method to visualize your variables (e.g. pie chart, bar chart, segmented bar chart, stacked bar chart, histogram, frequency table, boxplot, time series plot)

4.2. Explain the motivation of your choice.

4.3. Visually present each of your 4 variables.

  1. Formulate a Hypothesis and Test It (1slide)

5.1. Hypothesize a relationship between two of the variables in your dataset. Build an argument about the association you foresee and support it with a minimum of 2 academic articles.

5.2. Run a hypothesis testing analysis.

5.3. Describe your results and elaborate on them: Do the results confirm your prediction? If yes, what conclusion can you make? Do they reject your prediction? If yes, how can you explain the result? Your argument should be founded in both analytics – population, sample, variables, procedures, and research – what we already know about the hypothesized relationship.

5.4. Draw a conclusion how the hypothesis tests informs your decision on your management question.

  1. Run a Linear and Multiple Regression Analyses (2 slides)

6.1. Run a linear regression analysis between two of your variables. Interpret the results.

6.2. Run a multiple regression analysis using all 4 of your variables. Explain which is the dependent variable in your analysis and why. Interpret the results.

6.3. Elaborate how the multiple regression improves your understanding of the data compared to your linear regression.

6.4. Draw conclusions about your management question based on your regressions.

  1. Build Your Argument and Propose Recommendations (1-2 slides)

7.1. Build a comprehensive argument about your management question referring to the conducted analyses.

7.2. Make two recommendations for further action in regards to your management question, e.g. emphasize the need for additional analysis or propose a specific action step.

  1. Attach all your data analyses in an Excel file, which includes:

8.1. 1 spreadsheet with the raw data of the four variables in your dataset

8.2. 1 spreadsheet with all five descriptive statistics for each of the four variables

8.3. 1 spreadsheet with the visualization of each of the four variables

8.4. 1 spreadsheet with the hypothesis test

8.5. 1 spreadsheet with the linear and multiple regression analyses

References:

Gray, A. (2016, January 19). The 10 skills you need to thrive in the Fourth Industrial Revolution. Retrieved from: https://www.weforum.org/agenda/2016/01/the-10-skills-you-need-to-thrive-in-the-fourth-industrial-revolution/

Data-Driven Decision Making Project Grading Checklist

  • Management Question (20 points)
  • Management opportunity/challenge is clearly identified (10 points)
  • Management opportunity/challenge is well-motivated (5 points)
  • Management question is explicitly stated (5 points):

Total for Management Question: /20

  • Data Presentation (20 points)
  • 4 variables are presented (8 points, 2 for each variable)
  • the choice for each variable as it relates to the management question is well explained (12 points, 3 for each variable)

Total for Data Presentation: /20

  • Data Description (40 points)
  • The type of each variable and the way they are measured is clearly described (10 points, 2.5 for each variable)
  • The (1) mean, (2) median, (3) mode, (4) range and (5) quartiles for each of the four variables are presented (20 points, 1 for each of the 20 measures)
  • The descriptive statistics results are well explained (10 points)
  • A decision on which measure(s) of central tendency represent the variables most accurately is well motivated (10 points)

Total for Data Description: /50

  • Data Visualization (30 points)
  • Each variable is visualized through at least one method (20 points, 5 for each variable)
  • The choice of visualization method/s is well-motivated (10 points)

Total for Data Visualization: /30

  • Hypothesis Testing (30 points)
  • A relationship between two variables is hypothesized (5 points)
  • The hypothesized relationship is supported with a minimum of 2 references to academic research (15 points)
  • The result of the analysis is comprehensively discussed and explained (10 points)
  • A conclusion is drawn on how the hypothesis test informs the decision on the management question (10 points)

Total for Hypothesis Testing: /40

  • Regression Analyses (40 points)
  • The results of the linear regression are well interpreted (10 points)
  • The results of the multiple regression are well interpreted (10 points)
  • The choice of dependent variable is well motivated (10 points)
  • A comparison of the results of the two regression analysis is made (10 points)
  • A conclusion is drawn on how the regression analyses inform the decision on the management question (10 points)

Total for Regression Analyses: /50

  • Argument & Recommendation (30 points)
  • A comprehensive argument about the management question is presented based on the conducted in the assignment analyses (20 points)
  • Two recommendations for further action in regards to the stated management question are made (20 points)

Total for Argument & Recommendation: /40

  • Excel Analyses (40 points)
  • All descriptive statistics for each of the four variables are correct (10 points, 0.5 for each)
  • All visual presentations of each of the four variables are correct (10 points, 2.5 for each)
  • The hypothesis test is correct (10 points)
  • The linear and multiple regression analyses are correct (10 points, 5 for each)

Total for Excel Analyses: /40

Final Grade: /290

MLB Data Set 9e

Team League Year Opened Team Salary Attendance Wins ERA BA HR Year Average Salary (millions)
Arizona National 1998 70.76 2036216 69 5.09 0.261 190 2000 1.99
Atlanta National 1996 87.62 2020914 68 4.51 0.255 122 2001 2.26
Baltimore American 1992 115.59 2172344 89 4.22 0.256 253 2002 2.38
Boston American 1912 182.16 2955434 93 4.00 0.282 208 2003 2.56
Chicago Cubs National 1914 116.65 3232420 103 3.15 0.256 199 2004 2.49
Chicago Sox American 1991 98.71 1746293 78 4.10 0.257 168 2005 2.63
Cincinnati National 2003 116.73 1894085 68 4.91 0.256 164 2006 2.87
Cleveland American 1994 86.34 1591667 94 3.84 0.262 185 2007 2.94
Colorado National 1995 98.26 2602524 75 4.91 0.275 204 2008 3.15
Detroit American 2000 172.28 2493859 86 4.24 0.267 211 2009 3.24
Houston American 2000 69.06 2306623 84 4.06 0.247 198 2010 3.30
Kansas City American 1973 112.91 2557712 81 4.21 0.261 147 2011 3.31
LA Angels American 1966 146.45 3016142 74 4.28 0.26 156 2012 3.44
LA Dodgers National 1962 223.35 3703312 91 3.70 0.249 189 2013 3.65
Miami National 2012 84.64 1712417 79 4.05 0.263 128 2014 3.95
Milwaukee National 2001 98.68 2314614 73 4.08 0.244 194 2015 4.25
Minnesota American 2010 108.26 1963912 59 5.08 0.251 200 2016 4.40
NY Mets National 2009 99.63 2789602 87 3.58 0.246 218
NY Yankees American 2009 213.47 3063405 84 4.16 0.252 183
Oakland American 1966 80.28 1521506 69 4.51 0.246 169
Philadelphia National 2004 133.05 1915144 71 4.63 0.24 161
Pittsburgh National 2001 85.89 2249021 78 4.21 0.257 153
San Diego National 2004 126.37 2351426 68 4.43 0.235 177
San Francisco National 2000 166.50 3365256 87 3.65 0.258 130
Seattle American 1999 122.71 2267928 86 4.00 0.259 223
St. Louis National 2006 120.30 3444490 86 4.08 0.255 225
Tampa Bay American 1990 73.65 1286163 68 4.20 0.243 216
Texas American 1994 144.31 2710402 95 4.37 0.262 215
Toronto American 1989 112.90 3392299 89 3.78 0.248 221
Washington National 2008 166.01 2481938 95 3.51 0.256 203

 

Data-Driven Decision Making Project

Data-Driven Organizations

Last Updated on June 21, 2020 by Essay Pro