Identify a problem or an opportunity from your choice of four scenarios with accompanying data sets in Chapters 5, 8, 9, or 10 in your Data Mining for the
Masses resource. Then craft a data analytics project proposal that leverages data analytics, evaluates the current use of data, and highlights recommended tools
with the ultimate goal of improving business value. Remember your audience as you craft your proposal.
Specifically, the following critical elements must be addressed:
I. Introduction
A. Background: Describe the context and environment of the organization and analyze how the company is currently leveraging data analysis and
analytics tools to make decisions.
B. Data Sources: Evaluate the data sources the organization is currently using for their benefits and limitations in meeting the goals the data is
currently being used for. In other words, is the currently used data appropriate for its current usage? Why or why not?
C. Data Needs: Analyze the various sources of data available to the organization or the data the organization could potentially begin collecting that
could add business value. In other words, what data (existing or potential) could provide a benefit to the organization you chose to focus on, and
how?
D. Data Analytics Initiative: How can you exploit data analytics to add business value or uncover new opportunities? Identify the opportunity for a
data analysis initiative that could provide additional business value to the organization, and explain. (You do not necessarily have to solve a
problem or fill a gap within the organization. Instead, you could identify a new initiative that improves or adds valuable insight or information to
the organization for decision making.
II. Proposal
A. Goals: What are the goals of this initiative? How do they align with the organizational mission? And how do you plan to measure success? Be sure
to consider the progress and pathway for data analytics projects of the type you chose to propose.
B. Data Analytics Life Cycle: Apply the data analytics life cycle to your proposed initiative, and walk your audience (management) through the life
cycle as it applies to the initiative.
C. Value of life Cycle: Based on your application of the life cycle to the initiative, analyze how the life cycle will help you infer predictability,
performance, quality, and security of your initiative and its results.
D. Data: Evaluate the existing or desired data for its applicability to your proposed data analytics initiative. In other words, what are the benefits and
limitations of the current data for the use you have in mind, including potential collection and security implications?
E. Tool Applicability to Initiative: Assess the current data analytic tools for their applicability to your initiative. In other words, how well will the
existing tools and technology in place work with your initiative?
F. Tool Applicability to Data: Assess the applicability of the existing tools for the data you have or will have, based on your analysis of the
characteristics of that data. In other words, how fitting are the existing tools for the data, considering the various forms the data may take?
G. Tool Recommendations: This course covers many analytic tools and technologies, including their benefits and limitations for various uses and
data. Recommend two tools that are not already used and could reasonably be applied to your initiative. Assess the applicability and value of
these tools as they relate to your available and planned data and the goals you have established for the initiative.