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Chrisanthe Steininger ( She/Her )
()
13 May 2021
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In thinking about the challenges of data analytics projects, the following questions would help
to work through the difficulties:
1) Poorly defined problem:
What are the goals? - This one seems like an obvious first step. You cannot jump into a project
without knowing what you are trying to achieve.
What resources will I need to solve the problem? - It's critical to know if you have the right
resources to begin the work, and if not, you need to identify what you need and how you will
get it.
2) Incomplete or incorrect data:
Where did this data come from? - This might give more context to the data and would help to
understand the gaps.
Can we gather additional data? - The answer might be "no," but it's important to think about
whether or not you have access to supplemental data that would help to complete any gaps.
How can I transform this information? - This is part of the analysis, and would help to think
through how you will clean the data to make it more useful to complete the project.
3) Insufficient communication:
How can I best explain myself? - This is important to think about so you consider all the
options for communicating. Perhaps there are tools you can use, or there are preferred
communication methods across your team or audience.

5/29/22, 7:49 PM Topic: Module 5 Discussion Board Assignment
Who is the intended audience? - This is a really important piece of communication. The
audience dictates your communication style, whether in the work process itself, or in the
product of the work.
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Yingrui Shao
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14 May 2021
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Hello Chrisanthe,
Thanks for sharing your idea with us. I agree that it's very important to learn about the
intended audience. The audience determines how we will communicate the results of the
analytics. In addition, I think that we show work on how to best explain the results of
analytics. As for the quality of data, we should be aware of the data sources, such as
primary or secondary data sources. Sometimes, we need to confirm whether we have
selected the correct data for model building.
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Chrisanthe Steininger ( She/Her )
()
15 May 2021
Edited by Chrisanthe Steininger
()
on 15 May 2021
at 15:06
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Hi Yingrui,
Absolutely - I think that the data sources are very important to understand. This gives
more context to the data itself, and also can confirm the legitimacy or accuracy of the
data. Reputable sources allow for more confidence in accurate data, which allows you
to have confidence in your analysis. Thanks!