The project sponsors of the U.S. Student Aid Data project want you to participate in the project debrief meeting. You are to provide information around the methodology and practices you used to develo

Eric Case

DAT 390

May 27, 2019

U.S. Student Aid Data Warehouse Evaluation

The Quality of Data New Data Warehouse

The straightforward meaning of data quality involves quality characteristics or guidelines which can act as significant processes to resolve if data is complete, logical, pertinent, reliable, accurate, and valid. Some of the crucial characteristics for the new data warehouse include:

1. Completeness: It talks about the anticipated accessibility of the data. It is likely that data does not exist, nonetheless it could be still acknowledged to be complete, since it meets all the sponsors’ expectations. All data prerequisite has ‘compulsory’ and ‘optional’ characteristics. Case in point, if the client’s city is not a compulsory column in the operational system and if we bring this data to DW, business analysis based on customer’s city won’t be accurate.

1. Consistency: This implies that data within the enterprise ought to be working with each other and must not deliver contradictory information. Case in point a credit card is revoked, and not active, however the card billing status displays ‘ due ’. This type of unpredictable data across data sets should not triumph.

1. Validity: This means the correctness as well as rationality of data. Case in point, a bank account number should fall within a specific range, numeric data should be all digits, dates should have a valid month, day and year and the spelling of names should be proper.

1. Integrity: Data integrity verifies that data has remained unaltered in transit from creation to reception. Appropriate relationship linkages among records are very important else it might introduce unnecessary duplication throughout the system.

1. Conformity: This dimension verifies whether data is expected to adhere to certain standards and how well it’s represented in an expected format. For example, date may be represented either as ‘dd/mm/yyyy’ format or as ‘mm/dd/yyyy’ format. So, conformance to specific data format is essential.

1. Accuracy: Checks for the accurate representation of the real-world values. For example, the bank balance in the customer’s account is the real value that the customer deserves from the Bank. Any inaccuracy in the existing data can have a worse impact on the operational and analytical applications.