1. What are the business costs or risks of poor data quality? Support your discussion with at least 3 references. Data area utilized in most of the activities of corporations and represent the premise for choices on operational and strategic levels. Poor quality information will, therefore, have considerably negative impacts on the potency of a company, whereas good quality information is typically crucial to a company's success. The development of information technology throughout the last decades has enabled organizations to gather and store huge amounts of data. However, because the data volumes increase, thus will the complexity of managing them. Since larger and additional complicated info resources are being collected and managed in organizations nowadays, this implies that the chance of poor data quality increases.Poor data quality might have significant negative economic and social impacts on an organization.The implications of poor data quality carry negative effects to business users through: less client satisfaction, increase in running prices, inefficient decision-making processes, lower performance and low employee job satisfaction. References: 1. Haug, A., Zachariassen, F., & van Liempd, D. (2011). The cost of poor data quality. Journal of Industrial Engineering and Management, 4(2), 168-193 2. https://www.edq.com/blog/the-consequences-of-poor-data-quality-for-a-business/ 3. Knowledge Engineering and management by the masses. 17th International Conference,EKAW 2010,Lisbon,Portugal,October 11-15,2010 Proceedings 2. Data Mining: Data Mining is an analytic method designed to explore knowledge (usually massive amounts of data - generally business or market connected - conjointly called "big data") in search of consistent patterns and/or systematic relationships between variables, and then validate the findings by applying the detected patterns to new subsets of data. The ultimate goal of data mining is prediction - and predictive data mining is that the most typical sort of data processing and one that has the foremost direct business applications.The process of data mining consists of three stages: (1) the initial exploration, (2) model building or pattern identification with validation/verification and (3) deployment. Reference: 1. Three perspectives of data mining Zhi-Hua Zhou. 2. http://www.statsoft.com/Textbook/Data-Mining-Techniques 3. https://paginas.fe.up.pt/~ec/files_0506/slides/04_AssociationRules.pdf 3. Text Mining: Text mining and text analytics area broad umbrella terms describing a variety of technologies for analyzing and processing semi-structured and unstructured text data. The unifying theme behind every of those technologies is that the ought to “turn text into numbers” thus powerful algorithms will be applied to giant document databases.Converting text into a structured, numerical format and applying analytical algorithms require knowing how to both use and combine techniq ...

1. 1. What are the business costs or risks of poor data quality?
Support your discussion with at least 3 references.
Data area utilized in most of the activities of corporations and
represent the premise for choices on operational and strategic
levels. Poor quality information will, therefore, have
considerably negative impacts on the potency of a company,
whereas good quality information is typically crucial to a
company's success. The development of information technology
throughout the last decades has enabled organizations to gather
and store huge amounts of data. However, because the data
volumes increase, thus will the complexity of managing them.
Since larger and additional complicated info resources are being
collected and managed in organizations nowadays, this implies
that the chance of poor data quality increases.Poor data quality
might have significant negative economic and social impacts on
an organization.The implications of poor data quality carry
negative effects to business users through: less client
satisfaction, increase in running prices, inefficient decision-
making processes, lower performance and low employee job
satisfaction.
References:
1. Haug, A., Zachariassen, F., & van Liempd, D. (2011). The
cost of poor data quality. Journal of Industrial Engineering and
Management, 4(2), 168-193

2. 2. https://www.edq.com/blog/the-consequences-of-poor-data-
quality-for-a-business/
3. Knowledge Engineering and management by the masses. 17th
International Conference,EKAW 2010,Lisbon,Portugal,October
11-15,2010 Proceedings
2. Data Mining: Data Mining is an analytic method designed to
explore knowledge (usually massive amounts of data - generally
business or market connected - conjointly called "big data") in
search of consistent patterns and/or systematic relationships
between variables, and then validate the findings by applying
the detected patterns to new subsets of data. The ultimate goal
of data mining is prediction - and predictive data mining is that
the most typical sort of data processing and one that has the
foremost direct business applications.The process of data
mining consists of three stages: (1) the initial exploration, (2)
model building or pattern identification with
validation/verification and (3) deployment.
Reference:
1. Three perspectives of data mining Zhi-Hua Zhou.

3. 2. http://www.statsoft.com/Textbook/Data-Mining-Techniques
3.
https://paginas.fe.up.pt/~ec/files_0506/slides/04_AssociationRu
les.pdf
3. Text Mining: Text mining and text analytics area broad
umbrella terms describing a variety of technologies for
analyzing
and processing semi-structured and unstructured text data. The
unifying theme behind every of those technologies is that the
ought to “turn text into numbers” thus powerful algorithms will
be applied to giant document databases.Converting text into a
structured, numerical format and applying analytical algorithms
require knowing how to both use and combine techniques for
handling text, starting from individual words to documents to
entire document databases.
References:
1. Research trends on Big Data in Marketing: A text mining
and topic modeling based literature analysis.

4. 2.
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal
.pcbi.0040020
3.
https://www.elderresearch.com/hubfs/Whitepaper_The_Seven_P
ractice_Areas_of_Text_Analytics_Chapter_2_Excerpt.pdf
Running head: SHORTENED TITLE
The Title of the Paper
First name Last name
PHI 208 Ethics and Moral Reasoning
Prof. Phil Osipher
November 5, 1955
SHORTENED TITLE
Put the opening question here
Introduction:
Provide a brief introduction to the topic here

5. Position Statement:
Provide a position statement here
Supporting Reason:
Identify and explain a supporting reason here
Opposing Reason:
Identify and explain an opposing reason here
SHORTENED TITLE 3
References:
Include any references here
1. Examine Business cost or dangers because of poor data
quality:
Issue with giving us a data quality is a constant issue that
torments different associations , and on the off chance that IT
pioneers don't make sense of how to improve the accuracy of
their data, that could be dead genuine results. There are
different ways that associations submit mistakes with get-
together and providing a client data. Human goof is a
significant one. For instance, when a client is altering a
business' site, he or she may present a rash bungle, for example,

6. incorrect spelling a word, giving an outdated address or giving
the wrong telephone number. Once these goofs are added to the
system, they can be hard to changes. They can also induce
entire deals. Associations depend upon correct data to help their
displaying, and client advantage tries.
2. Data Mining:
Data mining is a technique used to expel usable information
from a more prominent arrangement of any undefined data. It
reasons separating from data in far reaching cluster of data
utilizing and not less than of one programming.
2) Datamining has applications in different fields, similar to
science and research. As a use of data mining, associations can
take in extra about their clients and build up all the systems,
identified with different business limits and also use assets in
prefect and savy way. This urges associations are nearer to
their goal and settle on better choices. Data mining intense data
collecting and warehousing and also PC arranging. For this,
data and reviewing the future occasions, data mining uses
advanced numerical counts. Data mining is called Knowledge
Discovery in Data.
3. Text Mining:

7. Text mining is sort of records mining, without that records
mining devices are after treat with based data long with
databases, it prints content also execute painting along
unstructured then semi-dependent data units text messages,
files and HTML reports and consequently forth. Here, final
results are text mining is a miles higher. Text mining is the
conduct in regards to organizing put to printed setting
(regularly parsing, aggregately with the summation over a
temperate derived text mining then it removes on others, yet
consequent application among a database), determining designs
inside the organized data, through out the evaluation then
money concerning the yield.
References
1) Hakkinen,l., & Hilmola,o-p.(2008). ERP evaluation During
the shakedown Phase: Lessons from an after-sales
division.Information System journal, 18(1), 73-100.
2) U.M Fayyad and R. Uthurusamy (eds.). Proc. !st Int. Conf.
Knowledge Discovery and Data Mining (KDD'95), AAAI
Press,Aug.1995.
3) Redman, T.C Data Quality for the Information Age. Artech
House,Bostaon, MA,1996