Poor prediction of customer behavior and evaluation of

Poor data quality Datais said to be of poor quality if it has errors or is incomplete. Poor datacannot be beneficial since business needs complete and accurate data to make aninformed decision on a daily basis. Poor data quality can result from wrongdata collection and entry, data manipulation in the transfer, or system error(Wang & Strong, 2011). In other words, quality of the data can occurbecause of many reasons.  Poor data quality leads decisions makers tomake poor or no decision (Haug et al., 2011). Again, poor data results in lostsales, misallocation of resources, faulty strategies, and incorrect inventorylevels 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 fordetection and correction of errors.  Insummary, data quality entails the degree of correctness, standardization, completeness, and structure of the data.

Business should ensure quality data iscollected and it should maintain its quality throughout the processing stage. This entails ensuring proper collection and handling techniques. Quality datahelps business to grow and succeed since it facilitates better decision-making, improves strategy implementation and boosts sales of the business. Data mining Datamining is a process of sorting or extracting actionable and strategicinformation from large data sets to establish relationships and patterns forproblem-solving through analysis. The extracted data helps the business toachieve efficiency and can be used in prediction of future trends (Rouse, 2017). It can also be used in prediction of customer behavior and evaluation ofbusiness success. Datamining is important to different functions of the business, For instance, salesand marketing division can mine consumer data to improve on marketingstrategies.

The department can use historical sales data to establish a patternthat would help the business to produce goods and deliver services that meetcustomer needs (Mosley et al., 2010). Finance department uses data mining toolsto predict the future financial performance of the business.

In contrast, datamining tools help to manufacture industry to improve quality and safety of the productin addition to managing supply chain operations (Han et al., 2011). Overall, data mining is the extraction of valuable data from a larger data set foranalysis.

The concept of data mining continues to as information economy growswhereby a lot of information is available in social media. Data mining can beused to analyze business success since results achieved depends on the abilityof business to extract strategic information from different data resources. Text mining Textmining denotes the process of retrieving information through analysis oftextual material to obtain the key concepts and revealsthe hidden trends and relationships without obliging you to know the exactwords used by the author (Aggarwal & Zhai, 2012). This process helps the businessto retrieve valuable information from text-based content like social media, emails, and so on. The idea here is to extract and manage quality content, andrelationships within the information. Intext mining, the text analytics application can be applied to transfer text andphrases that are in unstructured form into arithmetical or numerical values sothat it can be connected with the structured data in the database for analysis usingcustomary data mining methods (Feldman & Sanger, 2007). An iterativeapproach can help the organization to use text analytics to understand specificvalues of the content such as emotion, significance, sentiment, and intensity(Berry & Castellanos, 2008).

Insummary, text mining is an emerging concept that entails obtaining valuableinformation by filtering a lot of research and extract the relevant informationneeded. It identifies and maps trends and patterns across million researcharticles that would help the researcher to come up with valuable research.               ReferencesAggarwal, C. C.

, & Zhai, C. (Eds.). (2012). Mining textdata. Springer Science & Business Media.

Berry, M. J., & Linoff, G. (2010). Data miningtechniques: for marketing, sales, and customer support.

John Wiley &Sons, Inc. Berry, M. W., & Castellanos, M. (2008). Survey of textmining II (Vol.

6). New York: Springer. Feldman, R., & Sanger, J. (2007). The text mininghandbook: advanced approaches in analyzing unstructured data. Cambridgeuniversity press.

Han, J., Pei, J., & Kamber, M. (2011).

Data mining: concepts and techniques. Elsevier. Haug, A., Zachariassen, F., & Van Liempd, D. (2011). The costsof 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 dataquality means to data consumers. Journal of management informationsystems, 12(4), 5-33.