Decisions Analytics Project Title

2-3 sentences that explain your business/research/industry question and some high-level findings and suggestions. This is like the abstract of your executive summary. Tell the reader a little bit about your question, why this is important, a high-level finding, and the future implications of your findings. This can include some statistics, but shouldn’t dive deep into specifics about your analysis. Make this short and captivating so that the user wants to continue reading about this topic and what you’ve done. You can use bullet points in this section to emphasize some key points or findings that are further explained in the:

• Challenge/problem section
• Solution section
• Suggestion section But make sure to surround the bullets with additional context for what the reader is about to encounter in your summary.

Industry/Civic/Academic Question

• What is your Industry/Civic/Academic question?
• Why is this important to you? To the reader?
• Provide some links for the reader to read more about this question and what potential issues this can solve in the future

Data Questions

• What kind of data was important to use to answer this question, how did you find it, and why did you use this information? Was there some data that you wish you had? If so, how would you change your approach?
• What metrics did you think were important to understanding a quantitative answer to your original question and why? Did you “translate” any of your data into numbers?
• Did you base your data analysis on another type of analysis or desired outcome?
• Use website links to your data sources, for example, if you used data from Baltimore City Open Data, add a link for the reader to learn more.

Data Answers

• What were the results of your data analysis and how did this contribute to your final solution?
• What type of visualizations can best demonstrate what you found and what you think is important to emphasize to your audience?
• Keep your data visualizations and tables in here for people to follow along with your analysis and explore how your data findings are relevant. For example, if you analyzed Baltimore Police Department Salary Data, add in a graph here to emphasize the trends in number of Police Officers and overtime earnings in fiscal years 2014-2018:

Industry/Civic/Academic Answer(s)

• What’s the answer to your original question? Were you able to come to any type of conclusive answer—why or why not?
• How would you build on your analysis if given more time?
• What do these answers mean for us in the real world?
• What do these answers mean for the audience in this industry?