1.Reply 1 
Data Management
According to (Koltay, 2016) Data Management is all about how we bring value to an organization but also concerned with management of data life-cycle from creation to deletion/archival in the organization. We need to invest a lot of money to set many security controls for effectively managing data as data carrying organizations need to be aligned with several regulations at country level and at state level. 
Data Mining deals with transformation of data into value, looking for hidden patterns at a group level and discovers unknown relationships in the data. 
Like Data mining, text mining is bringing value from text looking for hidden patterns at a group level and discovers unknown relationships in the text. 
Bringing value to the organization. 
User interface and experience are important attributes for a success of an organization and the success is dependent on how organization handles data, process it and brings value from data. Organizations need to invest time and money to create a policy for maintaining data quality standards. 
Now a days, Data is a fuel for all the organizations and if we don’t maintain quality in data. We might incur costs in various ways starting from lawsuits (Data life-cycle in alignment to data regulations based on regions) to losing customers (because of data handling requirements). 
Every organization generates a lot of data from all application endpoints and how we manage data and analyze data is where value lies, and this can be accomplished using data and text mining.
Conclusion
To summarize, data management should be an important investment and it should be a main requirement starting from analyzing data and very important attribute data to collect from the inception (collecting data) into work or massage data for accuracy for implementation (processing data) to execution (archival/disposition of data). 

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Reply 2:
What are the business costs or risks of poor data quality? Support your discussion with at least 3 references.
Ans. 
I do agree that poor data quality increases business costs down the lane. The reason being a lack of structure or organization while gathering data. Poor data leads to inaccurate information Unstructured data leads to two different consequences:
a.       The existing structure must be fixed so that the data is more streamlined and organized.  This improves data mapping and accuracy.
b.       The existing data must be remapped and reorganized to have it streamlined.
Data validation must be done periodically. certain processes or tools must be implemented to achieve that.
The problem needs to be recognized as early as early as possible. The time taken to identify the problem is in direct co-relation (Linear or even worse, exponential) with the amount of time it takes to fix the problem. This not just takes a lot of human hours rather it might have a bigger impact on the organization, financially speaking. 
2.       What is data mining? Support your discussion with at least 3 references.
Ans. 
      Data mining can be described as a process of retrieving and analyzing a specific data set (a field, a table or an entire set) from the data heap. The data that’s mined will be processed and delivered in the later stages. This process is done to generate new data. Several tools/scripting languages like Statistica, SPSS, SAS, R and Python are well known and widely used in the IT industry for analyzing data. The entire process can be split up into four different stages:
·       Collecting data
·       Processing data
·       Recommendations
·       Creating new data
3.       What is text mining? Support your discussion with at least 3 references.
Ans. 
Text mining can sometimes be similar to Data mining but in a different way. Data mining is done when the data is structured whereas text mining is done on unstructured data. In Python terminology, it may be also called as scraping. Text mining can be hard and complex at times. Even with developments and advancements in technology, data inaccuracies can still be found. With the upcoming developments in Artificial intelligence, data predictions could be a booming technology.

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