Data analytics mindset presentation | Information Systems homework help

Here is the context:  Imagine that this presentation is being delivered to organizational leaders.  You are trying to make the case for why the organization should have a data analytics mindset, why data projects are so important, and a process for how all data projects should be managed.  Your presentation should include 


· The importance of adopting a data analytics mindset

· A detailed process for working with stakeholders, both to gather requirements and to communicate results

· Why it is important that the data analyst understand the business implications of the data project

· The process of gathering resources and data to ensure that the project can be completed properly

· The use of a sandbox to build a model than can then be implemented to address the data problem or opportunity

· A method for operationalizing the solution

Each data project is different so these points may need to be addressed from a general perspective since you are not addressing a specific challenge or opportunity.  

Your Fawcett and Provost text might be a good place to get some of the information you will need but you can (and should) turn to other sources to help support your argument.  Please consult 3-5 external sources to help you make your case. You want to put forward a well-supported case for a new approach to data projects.

Your presentation should have at least 10 slides all of which need to be well-formatted and informative.  Each slide should have accompanying text that you can either put into the notes part of the slide or on a separate document.  The text needs to be complete and fully explanatory to help make your case.

Separate from the 10 slides should be a cover slide and one that shows the 3-5 references that you consulted to make your case and build your presentation. Those references should be listed in APA format.

Together, these should make a compelling argument for the adoption of an analytics mindset that is governed by a data analytics life cycle.

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