Develop a set of factors that might influence the quality of the data, for which you will devise tests.

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Data Quality Evaluation Plan

Assume that you are a newly hired enterprise data architect . The company has recently been engaged in a project to develop master data to support enterprise-level planning and reporting.

The executives of this organization have hired a consultant to get them started, and your position was the result of their recommendations for moving the project forward. As part of their assessment and recommendations, the organization was provided with a comprehensive inventory of all existing data that is being collected and stored throughout the organization. They have also provided a preliminary plan that recommends which of this existing data is to be considered master data.

Your first task is to establish, from the variety of systems and data covered in the inventory, where the duplication of effort exists, how well the duplicate data from multiple sources are synchronized with one another, and finally, the overall quality of the data. These factors will help to inform the decision as to which systems are most appropriate to use as the source system for each of the master data elements involved.

For this assignment:

Formulate a high-level plan for how you will proceed with the task of determining the quality of the redundant data elements that will become the master data.
Develop a set of factors that might influence the quality of the data, for which you will devise tests. For example, when dealing with multiple systems, each containing customer data, the challenge might be to match data for a single unique customer across those systems.
Develop a high-level plan for resolving differences in data between systems. Among those differences, there will some data that has data synchronization errors. For example, you are successful in integrating unique client data across systems but discover that the address listed for some of these clients is not the same across all systems.
Develop a high-level plan for how you will address missing data. For example, some of the systems that contain customer data do not require input, so those fields have been left blank in those systems.