Running Head: DATA QUALITY e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e
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Communicating With Nontechnical Audiences
DAT-300
SNHU
DATA QUALITY e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e
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In the evolving business landscape, businesses require high-quality data on which they
base their decisions and strategic actions. Each and every business department needs a certain
kind of data in order to carry out its respective functions and roles. In order to make effective
decisions, organizations need to have access to quality data. Good quality data is one that
has distinctive features in terms of completeness, validity, and accuracy (Ghasemaghaei &
Calic, 2019). In the competitive business setting, good quality data can be used as an asset
that can help teams and business units to function effectively.
The ultimate purpose of data quality is to aid in the decision-making process of an
organization. Firms need to take diverse kinds of decisions that have the potential to lead to
numerous implications. Data quality serves as the catalyst that helps to make good and timely
decisions that have a low level of risk. When decisions are based on quality data and
information, their effectiveness increases. It empowers teams and individuals to understand
how it would impact them at a micro level and shape their performance (Ghasemaghaei &
Calic, 2019). On the other hand, when poor quality data is used in the decision-making
process, the productivity and performance of teams and individuals suffer. It could also give
rise to serious ramifications for the entire organization.
It is necessary for other departments to be aware of data quality so that they can
optimally use it to contribute towards the business goals and objectives (Ghasemaghaei &
Calic, 2019). The insight can boost the confidence of teams and units and help them to have
a better ability to manage their duties and responsibilities.
Some actions that other department should take to help preserve data quality include
controlling incoming data, avoid data duplication, and proper collection of data requirements.
DATA QUALITY e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e
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The departments must not adopt a lenient approach while enforcing data integrity as it could
compromise the quality aspect. e
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References
Ghasemaghaei, M., & Calic, G. (2019). Can big data improve firm decision quality? The role
of data quality and data diagnosticity. Decision Support Systems, 120, 38-49.
