Data businesses in making certain critical decisions that

Data Quality in BusinessIntroductionBusiness
organizations run on the quality of data available. Business data
assists businesses in making certain critical decisions that impact the
company in the long run (Berry & Linoff,  2017). It means therefore
that the quality of data in the organization significantly influence the
nature and quality of decision-making process and hence the quality of
the decision. Poor quality of data means poor judgment and policies.
Good quality data, on the other hand, means big decisions and strategies
for business. It would be important for companies to note that poor
data quality can bring several costs to the firm which can eventually
adversely affect the business to closure. Such costs may include wrong
business decisions leading to poor business performance. Eventually, the
industry might fall or make huge losses following a market loss and
other managerial costs (Berry & Linoff,  2017).Data
mining is the process by which businesses discover and establish the
sequence in sets of astronomical data that involve procedures at the
intersection of learning machine, database systems, and statistics
(Berry & Linoff,  2017). It can also be defined as a crucial process
where methods which are perceived to be intelligent are used by
business organizations to extract data. Text mining, on the other hand,
is the process that is used to deliver information of high quality from
the text (Tan, 2013). This process usually structuring input text,
pattern delivery through the data that is usually structured and
eventually output evaluation and interpretation.Cost of Poor Data QualityPoor
quality data is costly to handle (Tan, 2013). The results of using poor
data in making decisions in business lead to the business making wrong
decisions. Decisions made for business dictates the course of action for
the business.This means that business organizations using poor data
quality to generate information for further consideration in making
decisions are likely to run high costs of maintenance, huge losses from
sales, loss of the market, poor quality of products and services among
others (Tan, 2013). Eventually, the business may be forced out of the
industry.Companies need to proactively filter every data that they
receive for business use to from running such costs and risks.Data MiningThe
process of data mining by businesses is usually aimed at extracting
information from a set of data and transform it into a structure that
easily comprehended for further usage in the company. It is useful in
business as it is a step for analysis in the process of knowledge
discovery. It is a process that business organizations use to gather and
filter data for use in business processes (Tan, 2013).Text MiningPrimarily,
text mining is a process of input text structuring which usually
involve parsing together with other additional linguistic features
delivered and removing others and subsequent database insertion
(English, 2014). It usually included categorization of texts, clustering
of texts and extraction of texts. ConclusionBusiness
requires data that is processed into information which is eventually
used in the formulation of business policies and decision making
(English, 2014). This translates that businesses need to be cautious of
any data to ensure its quality before using the data in the business. As
noted above poor quality data leads to poor decision making hence huge
costs and loses upon usage. Data mining is the process by which business
organizations analyze knowledge and filter data in business. Text
mining is text input process which is used by businesses parse input
text structure with other traditional features to realize quality data
for business use (Berry & Linoff,  2017).ReferenceBerry,
M. J., & Linoff, G. (2017). Data mining techniques: for marketing,
sales, and customer support. John Wiley & Sons, Inc.English,
L. P. (2014). Improving data warehouse and business information
quality: methods for reducing costs and increasing profits. J. Wiley
& Sons.Tan, A. H. (2013, April). Text mining: The
state of the art and the challenges. In Proceedings of the PAKDD 1999
Workshop on Knowledge Discovery from Advanced Databases (Vol. 8, pp.
65-70). sn.