Southern New Hampshire UniversityDADAT- 325 DAT 325 Project One
Purpose Statement:
Data quality is how well the data meets the company’s expectations or requirements. If a company has
low quality data the conclusions made based on that data can have disastrous consequences. Some
examples are shipping products to the wrong customer address, making sales predictions based on
incorrect in
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Southern New Hampshire UniversityDADAT- 325 DAT 325 Project One
Purpose Statement:
Data quality is how well the data meets the company’s expectations or requirements. If a company has
low quality data the conclusions made based on that data can have disastrous consequences. Some
examples are shipping products to the wrong customer address, making sales predictions based on
incorrect information, or inaccurate compliance reporting causing fines against the company.
Improved data quality leads to better decision making throughout an organization. When a business has
high-quality data it can be more confident in the conclusions made based on that data. High quality data
will better meet the expectations of the data users. Thus the importance of quality data can’t be
stressed enough.
Understanding the requirements of a project or task is the first step to ensuring the quality of the data
that is obtained. This also helps decrease the chance of having to acquire data more than once due to
misunderstanding the original request. If the data is higher quality it will also need less cleaning. Saving
the organization additional time and cost.
By using data quality requirements an organization can define expectations that will ensure the data is
suited for particular purposes. Some examples are Thomas Redman’s Quality Functional Deployment
(QFD), David Loshin’s Use Case Model, and the Data Quality Assessment Framework (DQAF). Using any
of these methods will create additional significance into the requirements and sustain high-quality data
(Sebastian-Coleman, 2013).
Organizational Goals:
The three goals that need to be accomplished with respect to the data are:
Quality assessment of data obtained from Wayne Enterprises.
o This can be accomplished by reviewing the data and ensuring it meets the data quality
dimensions of completeness, timeliness, validity, accuracy and consistency (defined in
detail in the next section).
The data is cleaned/transformed.
o The workflow sequence can be used to clean the data. This is a four-step process
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