Discussion and replies
1. What are the business costs or risks of poof data quality? Support your discussion with at least 3 references.What is data mining? Support your discussion with at least 3 references.What is text mining? Support your discussion with at least 3 references.
Read and respond to at two (2) of your classmates .In your response to your classmates, consider comparing your articles to those of your classmates. Below are additional suggestions on how to respond to your classmatesâ€™ discussions:
Â· Ask a probing question, substantiated with additional background information, evidence or research.
Â· Share an insight from having read your colleaguesâ€™ postings, synthesizing the information to provide new perspectives.
Â· Offer and support an alternative perspective using readings from the classroom or from your own research.
Â· Validate an idea with your own experience and additional research.
Â· Make a suggestion based on additional evidence drawn from readings or after synthesizing multiple postings.
Â· Expand on your colleaguesâ€™ postings by providing additional insights or contrasting perspectives based on readings and evidence.
Reply to the 1st topic
What are the business costs or risks of poof data quality?
Now a dayâ€™s organizations are facing low quality of data which has serious negative impacts on both social and economic. Since data became very important in managing operations of an organization it must be protected and maintained very well. Otherwise this is going to affect decision making capacity of organization. When large amounts of data are collected, companies are going to face problems in managing them which will lead to low quality of data. When this data is used in making decisions it will affect organization greatly. So organization has to take measures in managing the data.
Poor quality of data can be caused due to human errors or when certain guidelines are not followed while collecting the data. Another reason is that data is not updated or old data will become unusable. Due to this poor quality of data organizations are going to face low production, customer satisfaction will be low, additional costs in detecting errors and time wastage. Further employees performance will be affected and their trust in organization will be decreased. It will take lot of time to earn back trust of employees and customers (Harding, 2016).
What is data mining?
Data mining is a method which is used for recognising correlations, anomalies and patterns present in huge amounts of unstructured data for predicting new trends. There is about 90% of unstructured data present in this world and using it for better purposes depends on us. By using this technique, we can make accurate decisions, decrease risks and improve profits and relationship between customers etc. This is used in research, marketing and genetics for analyzing and predicting outcomes. By discovering unhidden patterns from data, where these predictions will have huge impact on businesses (sas.com, 2017).
What is text mining?
Text mining processes unstructured texts in order to find hidden patterns so that organization can make decisions based on this information. For text mining to be more useful and effective, natural language processing (NLP) can be used. This NLP and text mining combined together forms artificial technologies which is widely used for gaining insights from documents of texts. While using text mining with NLP, it will easily understand concepts even it is defined in various types. We can say that data and text mining are similar but text mining is used for large amounts of unstructured texts (expertsystem.com, 2016).
Reply to 2nd topic
Data is an important tool in any organizationâ€™s success. Data is used for nearly all decision-making operations and strategic developments. The quality of data is therefore very important. According to Newell et al. (2002), data management is the collection of a record of signs and observations that are collected from various sources for the purpose of adding value. Therefore, data quality can be defined as the fitness of data for the use of value-addition purposes. Poor data quality can, therefore, be very risky and costly to a business (Batini, et al. 2009).
The business costs to an organization include the economic and social impacts. Economically, poor data quality affects the operational costs of a company. Time is a resource in operations that will be spent on identifying and correcting poor data. Additionally, socially the cost of poor data quality will be low customer satisfaction, lower employee performance and low employee job satisfaction (Ballou et al. 2004). Data is made or created and used on a daily basis and is, therefore, a crucial part of the organization’s input for decision-making purposes. Poor data quality will affect the decision-making processes of an organization and in-turn lead to poor outcomes.
According to Clifton (2018), data mining is the process by which meaningful and interesting patterns in data are identified and their relationship to large volumes of data. Data mining uses advanced computer analytics to analyze large collections of data in data warehouses where there are large-scale data storage capacities. Data mining is useful to many sectors in the economy that includes retail, banking, insurance, and politics. For example, the retail sector uses data mining to find out how it can sell more items to many people based on their purchasing patterns.
According to SAS (2018), data mining not only includes the identification of patterns in large volumes of data but also includes finding the anomalies that are present in the data. This information is important for increasing revenue, cutting down costs in an organization, reducing risk and improving the customer experience of a company. The most significant importance of data mining is enabling the quick decision-making process. Data mining also results in increased revenue because one can deliver more value to the customers since one knows more about them (Galetto, 2018). This allows the company to gain greater insight into customer patterns.
According to Mehl (2006), text analysis is the linguistic, machine intelligence statistical techniques of collecting, analyzing textual context and sources. This process leads to the establishment of high-quality information from texts. The text input has to be structured then the structure analyzed for patterns and then the data is evaluated and interpreted. The process of text mining also involves converting appearances such as color, texture and other physical characteristics into numbers. This allows for the numerical analysis for the measurement of data (Prato, 2013). Text mining is a branch of data mining and is used collaboratively.
Text analysis does not produce an end in itself. Text analysis only leads to further analysis by an expert who helps to add more features, experience, and knowledge to the picture in order to complete the bigger picture. Text mining can help the organization in several ways that include risk analysis, improving customer interactions, making better-informed business decisions (Bennett, 2017). The most important part of any text analysis exercise is to begin by identifying the problem that they are trying to solve.