Need discussion response written. Please use Grammarly to make sure it flows and makes sense, not all fill words and run-on sentences. Please use proper punctuation. It needs to be in topic 275 words not including sources/questions.
Discussion Post 4: Data Redundancy-Dobbs
In a post “The Costs of Data Redundancy and Data Inconsistency”,
contributor Kirill Kovalenko defines what data redundancy is and what
its detrimental effects can be on a database. Additionally, Kovalenko
discusses inconsistency in data entry and storage in a database. Data
redundancy is when the same data is entered across more than one
location and data inconsistency is a situation where multiple areas of
the database store the same data from different inputs by end users
(Kovalenko, 2019). To minimize redundancy, Kovalenko recommends having a
thoughtful approach to database design and advocates for a normalized
data schema at the outset of the project; for inconsistency, rules for
storing data and establishing a single truth for a given entity or
attribute can be implemented (Kovalenko, 2019).
The
distinction between data redundancy and inconsistency is an important
one and it is refreshing to see a discussion on this difference. Many
databases suffer from both problems due to hasty development and poorly
implemented rules and documentation for what information should be
entered into the system and stored in the underlying database. For
example, if a system seeks to enter information about a customer as well
as customer details such as payment information, shipping addresses,
orders, and other business related data, some databases may store these
details in text fields in a single table designated for the customer,
and then store that information again in another table for the orders
placed. This results in information such as payment and shipping details
being duplicated across tables and having to be reconciled to ensure
that they are the same. Although commonplace, it can have deleterious
effects on the organization – if a customer provides an updated shipping
address, which table should store that information?
A
similar, yet different, problem exists with inconsistency. If one area
of an organization is responsible for customer data, such as a sales
division, while another area – perhaps the fulfillment or logistics
department – also maintains information about the customer and the order
that needs to be completed, which area of the organization should keep
the information about the customer and their shipping address up to
date? And in which table in the database is this information maintained?
As Kovalenko states, a single source of truth for entity attributes
should always been built into the design of a database to avoid these
types of issues.
Kovalenko
discusses the emergence of Artificial Intelligence and Machine Learning
to help combat data redundancy and inconsistency, but these measures
are often not necessary. A highly normalized database does not suffer
from problems of bad and duplicate data entry, and should there be a
redundancy or inconsistency identified, new tables or columns with
primary and foreign keys to linked tables can be added to resolve the
issue. Artificial Intelligence would have a difficult time solving a
scenario about the shipping address for a customer and where that
information should be maintained. AI is more applicable in generating
statistics and establishing trends for where orders originate or what
products are more frequently purchased. Application developers should
collaborate with database administrators to identify how data flows in
the organization and devise an Entity Relationship Diagram which best
serves that data flow. As carpenters say “measure twice and cut once”
and that sentiment certainly applies to database design and the entry of
data into a system!
References
Kovalenko, K. (2019, May 14). The Costs of Data Redundancy and Data Inconsistency. BizData. https://www.bizdata.com.au/blogpost.php?p=costs-of-data-redundancy-and-data-inconsistency
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