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).

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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.