Data Management


Discussion post to below 3 questions with atleast 300 words

 1.  What are the business costs or risks of poof data quality? Support your discussion with at least 3 references.

2.  What is data mining? Support your discussion with at least 3 references.

3.  What is text mining? Support your discussion with at least 3 references.

Response to below 2 posts – atleast 150 words

1.Business costs or risks of poor data quality: Poor data quality may lead decision makers to not be able to make poor decisions or not be able to make decisions at all. Poor data may lead to lost sales and other opportunities, mistakes in the allocation of resources, flawed strategies, orders may be wrong, inventory levels may be incorrect and customers may become irritated and driven away. The cost of poor quality data spreads throughout the company affecting systems from shipping and receiving to accounting and customer services. Additional costs are incurred when employees must take time to hunt down and correct data errors.

Data Mining : Data mining is the practice of automatically searching largest storage data to discover patterns and trends that change from time to time for simple analysis of data. Data mining uses experienced mathematical algorithms to divide the data evaluate the probability of the future occurrences. Data mining is also known as knowledge Discovery in Data. The important properties of data mining are

·        Automatic discovery of patterns

·        Prediction of likely outcomes

·        Creation of actionable information

·        Focus on large data sets and data bases.

Data mining is practiced by building models. These models uses algorithms to mine the set of data. The notion of automatic discovery refers to the execution of data mining models. Data mining models are used to mine the data on which they are built, but most of these techniques are used on the new data. The process of applying a data mining to the new model is known as scoring.

Text mining: Text mining involves arranging the text document or resources to get a valuable structured information. This requires knowledgeable analytical tools that process text in order to get specific keywords or key data points from relatively raw or unstructured data. In text mining , engineered systems use things like taxonomies and lexical analysis to determine the parts of a text document are valuable as mined data.

Text mining is also known as text data mining or text analytics,  text mining can be easily defined as the process of deriving high quality information from the text. Compared to data mining, the processes of structured information and extracting the useful information from data sets to transform them for further use can be easily done by the text mining. The unstructured data can be easily  searched through text mining when compared to other data mining techniques.


 What are the business costs or risks of poof data quality?

Poor quality data can imply a multitude of negative consequences in a company. To start with, poor quality data that is not identified and corrected can have significant negative economic and social impacts on an organization. The implications of poor-quality data carry negative effects to business users through less customer satisfaction, increased running costs, inefficient decision-making processes, lower performance and lowered employee job satisfaction. Poor data quality also increases operational costs since time and other resources are spent detecting and correcting errors. Poor quality data cannot be trusted and may result in the inability to make intelligent business decisions. Since data are created and used in all daily operations, data are critical inputs to almost all decisions and data implicitly define common terms in an enterprise, data constitute a significant contributor to organizational culture. Thus, poor data quality can have negative effects on the organizational culture. Poor data quality also means that it becomes difficult to build trust in the company data, which may imply a lack of user acceptance of any initiatives based on such data. 

 What is data mining?

Data Mining can define as the discovery of knowledge from structured data. Today most available business data is unstructured information; even though it may also contain numbers, dates, and facts in structured fields, unstructured information is typically text. The presence of unstructured information makes it more difficult to effectively perform knowledge management activities using traditional business intelligence tools.

Data mining system can be categorized according to various criteria, as follows:

  • Classification according to the kinds of databases mined
  • Classification according to the kinds of knowledge mined
  • Classification according to the kinds of techniques utilized
  • Classification according to the application adapted 

 What is text mining?

Text mining is the process of exploring and analyzing large amounts of unstructured text data aided by software that can identify concepts, patterns, topics, keywords and other attributes in the data. It’s also known as text analytics, although some people draw a distinction between the two terms; in that view, text analytics is an application enabled using text mining techniques to sort through data sets.

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