Data Integrity and Scrubbing Portion

IT 675 Milestone Three Rubric: Data Integrity and Scrubbing Portion The final project for this course is a two-part project: an executive presentation and a technical proposal. The final project presents a detailed scenario regarding the merger of two insurance companies. For the project, the student is positioned as the chief information officer (CIO) and is asked to lead an initiative to merge the data infrastructures of both insurance companies into a single consolidated data warehouse. For this milestone (due in Module Six), you will submit your data integrity and scrubbing portion of the plan. Review the scenario for the final assessment. Using the scenario, develop this portion of the project plan. To meet requirements you will need to address the four aspects of this subsection of the proposal, which are as follows: 1) data integrity, 2) primary key(s), 3) customer data, and 4) duplicate data. The following critical elements will be addressed in this submission: Data Integration and Scrubbing:

a) Data Integrity: How will you combine date fields with various formats (i.e., MMDDYYYY vs. DDMMYYYY)? What other data issues will need to be addressed?

b) Primary Key(s): What will you use as a unique identifier to combine the records? What primary keys, foreign keys, and indexes will you need to create? c) Customer Data: Once the data is merged into the data warehouse, how will you be able to differentiate customers from Virtual World Insurance

Company and customers from Maxon Insurance Company? d) Duplicate Data: How will you eliminate duplicate records in the database to ensure data quality?

Requirements of Submission: Written components of projects must follow these formatting guidelines when applicable: double spacing, 12-point Times New Roman font, one-inch margins, and discipline-appropriate citations.

Critical Elements Exemplary (100%) Proficient (90%) Needs Improvement (75%) Not Evident (0%) Value

Data Integration and Scrubbing: Data

Integrity

Meets “Proficient” criteria and methods described are the best methods for ensuring data integrity for the given scenario and specific issue

Articulates the correct methods for combining data fields with various formats to ensure data is not lost or compromised

Articulates methods for combining data fields with various formats, but methods are not correct for ensuring data is not lost or compromised

Does not articulate methods for combining data fields with various formats

20

Data Integration and Scrubbing: Primary

Keys

Meets “Proficient” criteria and identified keys and indexes are the most appropriate for each of their designated purposes within the data warehouse

Articulates appropriate primary keys, foreign keys, and indexes for creation that will ensure a clear and accurate warehouse

Articulates primary keys, foreign keys, and indexes necessary, but not all will ensure a clear and accurate warehouse

Does not articulate primary keys, foreign keys, and indexes necessary

20

Data Integration and Scrubbing: Customer

Data

Meets “Proficient” criteria and articulated methods are the most appropriate given the accompanying explanation, accompanying scenario, and integration issues that have been identified in the proposal

Articulates plausible methods for differentiating customer data from each company after data is merged

Articulates methods for differentiating customer data from each company after data is merged, but not all methods are plausible, or necessary detail is left out of explanation

Does not articulate methods for differentiating between customer data from each company after data is merged

20

Data Integration and Scrubbing: Duplicate

Data

Meets “Proficient” criteria and identified strategies are the most appropriate given the accompanying explanation, accompanying scenario, and integration issues that have been identified in the proposal

Articulates valid, plausible strategies for eliminating duplicate records and ensuring data quality and accuracy

Articulates strategies for eliminating duplicate records and ensuring data quality and accuracy, but not all strategies are valid or plausible

Does not articulate strategies for eliminating duplicate records to ensure data quality and accuracy

20

Articulation of Response

Submission is free of errors related to citations, grammar, spelling, syntax, and organization, and is presented in a professional and easy-to- read format

Submission has no major errors related to citations, grammar, spelling, syntax, or organization

Submission has major errors related to citations, grammar, spelling, syntax, or organization that negatively impact readability and articulation of main ideas

Submission has critical errors related to citations, grammar, spelling, syntax, or organization that prevent understanding of ideas

20

Earned Total 100%