Case Study Chapter 6 Foundations of Business Intelligence: Databases and Information Management

Chapter 6
Case I 
Interactive Session : Technology
WHAT CAN BUSINESSES LEARN FROM TEXT MINING

1. What challenges does the increase in unstructured data present for businesses?
Text mining enables many companies to respond to their customers satisfaction surveys, and web mining enables many web search engines to facilitate collecting data that people need to be more profitable. Now, a huge amount of unstructured data is distributed by these systems. A manager is able to use this system and make an accurate decision for unprecedented cases. information Business intelligence tools deal primarily with data that have been structured in databases and files. However, unstructured data, mostly the kind

of data we generate in e-mails, phone conversations,

blog postings, online customer surveys, and tweets are all valuable for finding patterns and trends that will help employees make better business decisions. 

Text mining tools are now available to help businesses analyze these data. These tools are able to extract key elements from large unstructured data sets, discover patterns and relationships, and summarize the information. Businesses might turn to text mining to analyze transcripts of calls to customer service centers to identify major service and repair issues.

2. How does text-mining improve decision-making?

Text mining system enables airlines to rapidly extract customer sentiments, preferences, and requests for example, when the airlines suffered from unprecedented levels of customer discontent in the wake of a February ice storm in 2007. Managers were concerned about their reputation degrading but there had been no means to glean their responses without text mining. Fortunately, they could make decisions and figure out a lot of measures to respond to customers’ discontent.  The reason is that text mining facilitates gleaning from many unstructured text data and compiles them. This data wouldn’t be analyzed by decision making systems like MIS and DSS because text mining is not structured data. Text mining is indispensable for decision making of unstructured data. text mining improve in decition making by Offering unique insights into customer behaviour and attitudes.

3. What kinds of companies are most likely to benefit from text mining software? Explain your answer.

In the past, only government and large companies tend to use text mining system but now, text mining system can be geared towards small businesses. Restaurants, hotels, supermarkets etc. are applying the system and able to make a decision as well as earn profits. Every company is able to use both structured data and unstructured data. Above all, internet search engines like Google and Yahoo are doing good business because they used AdWord and AdSence which efficient advertising system is kind of web mining.

4. In what ways could text mining potentially lead to the erosion of personal information privacy? Explain.

Nowadays, companies tend to use and manage personal information for their business. Mobile phone companies manages huge amount of privacy data as structured data. However sometimes hacker invade this data and abuse it. 

According to text mining, some companies use personal information as unstructured data which is gathered from survey or questionnaires. This case is different from structured data because unstructured and mining data is not provided by customer. There is a risk to occur some unprecedented accidents.

Case II

Interactive Session : Organizations

CREDIT BUREAU ERRORS – BIG PEOPLE PROBLEMS

1. Assess the business impact of credit bureaus’ data quality problems for the credit bureaus, for lenders, for individuals.

The business impact of credit bureaus' data quality problems for the credit bureaus, for lenders, for individuals is that businesses lease and promote people based on the credit bureaus' data. It said that one of the three entrepreneur look at the credit bureaus' data when leasing and promoting workers. This is the business impact of credit bureaus' data quality.

2. Are any ethical issues raised by credit bureaus’ data quality problems? Explain your answer.

Yes, there are ethical issues raised by credit bureaus' data quality problems, because some people fill out their applications wrong on purpose so other people get their bad credit. That is ethically wrong to do that. More and more people are receiving bad credit that they don't even deserve. These are the ethical issues raised by credit bureaus' data quality problems.

3. Analyze the management, organization, and technology factors responsible for credit bureaus’ data quality problems.

The management factor responsible for credit bureaus is data quality problem is that they need to manage the credit for people better than they have even though it said they can't do it accurately for 3.5 billion people. The organization factor is that they need organize the data better and keep it updated so that people aren't receiving bad credit undeservingly. The technology factor is that the technology needs to keep all the data updated and make sure the technology is up to date as well.

4. What can be done to solve these problems?

The steps to solve the problem is to make sure the information of lenders are up to date and correct. There will be some mistakes, but if you keep a closer eye on the credit data, you can minimize the problem. Another possible solution is to be more strict on the requirements to take out a loan, so lenders actually make sure they have good credit before taking out a loan. This will minimize the problem of people with bad credit, which in cause will minimize the problem of bad credit going to the wrong people.

Group 4 : 

• Riza Riyanti (C1L011020)
• Findi Verliana Utami (C1L011029)
• Mira Nur Fajar S (C1L011030)
• Ista Oktina (C1L011031)