bad data case study
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School
Western Carolina University *
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Course
300
Subject
Information Systems
Date
Dec 6, 2023
Type
docx
Pages
1
Uploaded by ProfFangRook40
What should managers and companies due to minimize their use of bad data?
Bad data is best defined as any data that lacks structures and is therefore suffering from quality
related issues that may include inaccuracies and inconsistencies. The most simple way to insure
minimal bad data is to have someone whos sole responsibility is to make sure data is current and
input correctly. However, in the case of Amazon, There should be verification factors in place for
third party vendors. One such method could be an internal review department that weighs factors
such as account purchases, account creation date, and how many other reviews has this account
left.
How might intuition, the analytical decision style, and the conceptual decision style help to work
against problems arising from using bad data?
While all very different, all three decision styles can blend together to make decisions. In a literal
sense the term intuition means to understand without the need for reason, as almost an instinct.
Analytical style is more comfortable with ambiguity and wiggle room when it comes to
uncertainty in the decision process. But this makes the decision maker more cautious when
adapting to new information in the decision making process. Those with a conceptual style tend
to be broad in their decision making and consider alternatives. With those facts in place the
decision process could potentially work with the intuition decision style picking the the
trajectory of what data sources are reliable and efficient, analytical would study these data
findings and the conceptual decision style can look at the project as a whole to verify data from
almost a top down perspective to watch all data coming into the project and find new avenue of
obtaining better or more applicable data.
What does this case illustrate about big data and analytics?
Big data as a term is defined as huge and complex sets of data. This case perfectly illustrates that
bigger is not always better. Analytics are typically computer driven through programming. This
case highlights a very negative correlation between the two where it seems that to much reliance
was placed upon analytics to create accurate data sets with incorrect or outdated information.
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