MIS D3

Discussion-3

by  Vijay Manohar  - Tuesday, May 19, 2020, 8:08 PM

 

What are the business costs and risks of poor data quality?

Every company, Analytics should be executed on Standard data and it should be made mandatory. Poor Data Quality will have an adverse effect on the performance, ideologies, and also master plan of the company. In the Performace factor, Poor Data Quality end up in price rise, workers at the end of the day won't feel happy with their job. As a direct proportion, this would end up in customers not being happy with our product. They order a product and it ended up in delivering to a different address. The main disadvantage is we end up losing in all ways and correcting poor data quality involves a lot of dollars and hours getting wasted.(Redman, T. C., 1998)(Celko, J., 1995)(Davenport, T.H., 1997)

What is Data Mining?

Data Mining is a technology used in order to filter the data and pull out the knowledge from the dataset. The mechanism involved in order to pull out meaningful information which is mixed along with raw data present in unlike datasets across, unlike databases the company owns. There are different other terminologies coined about it as Data Archeology, Data Dredging, and so on. This is one of the primary focussed areas adopted by Machine Learning and a lot of money is invested by every company in researching their own data which is going to predict the future of the company and might result in more profit to the company. It also has various other oppositions in loading the data from unlike datasets as it brings a Scalability issue. Also, the information retrieved through data mining can't be validated and also there are some other factors such as Safeguarding the information and  Securing the Organizations data.(Chen, M. S., Han, J., & Yu, P. S.,1996) (R. Agarwal, C. Faloutsos, and A. Swami.,1993) (R. Agarwal, T.Imielinski, and A. Swami.,1993).

What is Text Mining?

Text Mining is divided into multiple kinds and data mining is one suck kind. It is used in order to pull out knowledge and particular structural patterns from the dataset. They are mainly in the form of Textual format or data is stored in text. Text mining is an important and famous kind of data mining because the data type used is Text which is very easy for data retrieval and processing. There are multiple steps involved in text mining. They are Purification of data and converting into Information . The next steps are the extraction of information, where a particular data pattern is applied and from the information, the data is extracted.(Tan, A. H., 1999)( Simoudis, E. 1996)(Hearst, M.A. 1997)

References:

       Redman, T. C. (1998). The impact of poor data quality on the typical enterprise. Communications of the ACM, 41(2), 79-82. 

       Celko, J.Don't warehouse dirty data.Datamation (Oct. 15, 1995),42-52.

       Davenport, T.H. Information Ecology. Oxford University Press,New York, 1997.

    Chen, M. S., Han, J., & Yu, P. S. (1996). Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and data Engineering, 8(6), 866-883. 4

R. Agarwal, C. Faloutsos, and A. Swami. Efficient Similarity Search in Sequence Databases.Proceedings of the 4th Intl. conf. on Foundations of Data Organization and Algorithms, October,1993.

    R. Agarwal,T.Imielinski, and A. Swami. Database Mining:A Performance Perspective.IEEE Transactions on Knowledge and Data Engineering, pages 914-925, December 1993

Tan, A. H. (1999, 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.

       Simoudis, E. (1996), “Reality check for data mining”, IEEE Expert, 11(5).                  Hearst, M.A. (1997), “Text data mining: Issues, techniques, and the relationship to information access”, Presentation notes for UW/MS workshop on data mining, July 1997