Poor prediction of customer behavior and evaluation of

Poor data quality

Data
is said to be of poor quality if it has errors or is incomplete. Poor data
cannot be beneficial since business needs complete and accurate data to make an
informed decision on a daily basis. Poor data quality can result from wrong
data collection and entry, data manipulation in the transfer, or system error
(Wang & Strong, 2011). In other words, quality of the data can occur
because of many reasons.

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 Poor data quality leads decisions makers to
make poor or no decision (Haug et al., 2011). Again, poor data results in lost
sales, misallocation of resources, faulty strategies, and incorrect inventory
levels thus frustrating and driving customers away (Berry & Linoff, 2010).
These costs affect all business functions since they are interdepended. Furthermore,
business incurs additional costs since resources must be allocated for
detection and correction of errors. 

In
summary, data quality entails the degree of correctness, standardization,
completeness, and structure of the data. Business should ensure quality data is
collected and it should maintain its quality throughout the processing stage.
This entails ensuring proper collection and handling techniques. Quality data
helps business to grow and succeed since it facilitates better decision-making,
improves strategy implementation and boosts sales of the business.

Data mining

Data
mining is a process of sorting or extracting actionable and strategic
information from large data sets to establish relationships and patterns for
problem-solving through analysis. The extracted data helps the business to
achieve efficiency and can be used in prediction of future trends (Rouse,
2017). It can also be used in prediction of customer behavior and evaluation of
business success.

Data
mining is important to different functions of the business, For instance, sales
and marketing division can mine consumer data to improve on marketing
strategies. The department can use historical sales data to establish a pattern
that would help the business to produce goods and deliver services that meet
customer needs (Mosley et al., 2010). Finance department uses data mining tools
to predict the future financial performance of the business. In contrast, data
mining tools help to manufacture industry to improve quality and safety of the product
in addition to managing supply chain operations (Han et al., 2011).

Overall,
data mining is the extraction of valuable data from a larger data set for
analysis. The concept of data mining continues to as information economy grows
whereby a lot of information is available in social media. Data mining can be
used to analyze business success since results achieved depends on the ability
of business to extract strategic information from different data resources.

Text mining

Text
mining denotes the process of retrieving information through analysis of
textual material to obtain the key concepts and reveals
the hidden trends and relationships without obliging you to know the exact
words used by the author (Aggarwal & Zhai, 2012). This process helps the business
to retrieve valuable information from text-based content like social media,
emails, and so on. The idea here is to extract and manage quality content, and
relationships within the information.

In
text mining, the text analytics application can be applied to transfer text and
phrases that are in unstructured form into arithmetical or numerical values so
that it can be connected with the structured data in the database for analysis using
customary data mining methods (Feldman & Sanger, 2007). An iterative
approach can help the organization to use text analytics to understand specific
values of the content such as emotion, significance, sentiment, and intensity
(Berry & Castellanos, 2008).

In
summary, text mining is an emerging concept that entails obtaining valuable
information by filtering a lot of research and extract the relevant information
needed. It identifies and maps trends and patterns across million research
articles that would help the researcher to come up with valuable research.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References

Aggarwal, C. C., & Zhai, C. (Eds.). (2012). Mining text
data. Springer Science & Business Media.

Berry, M. J., & Linoff, G. (2010). Data mining
techniques: for marketing, sales, and customer support. John Wiley &
Sons, Inc.

Berry, M. W., & Castellanos, M. (2008). Survey of text
mining II (Vol. 6). New York: Springer.

Feldman, R., & Sanger, J. (2007). The text mining
handbook: advanced approaches in analyzing unstructured data. Cambridge
university press.

Han, J., Pei, J., & Kamber, M. (2011). Data mining:
concepts and techniques. Elsevier.

Haug, A., Zachariassen, F., & Van Liempd, D. (2011). The costs
of poor data quality. Journal of Industrial Engineering and Management, 4(2),
168-193.

Rouse, M. (2017, March). Data mining. Retrieved from http://searchsqlserver.techtarget.com/definition/data-mining
 

Mosley, M., Brackett, M. H., Earley, S., & Henderson, D.
(2010). DAMA guide to the data management body of knowledge.
Technics Publications.

Wang, R. Y., & Strong, D. M. (2011). Beyond accuracy: What data
quality means to data consumers. Journal of management information
systems, 12(4), 5-33.

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