Course Code: CBA OM-BA
Course Description: Business Analytics
Case No.:/ Case Title: Module 2: Understanding Organization and its Data
Team No.: Team No. 2
Problem Statement: (Define your Problem Statement here) How can the group relay the value and benefit of data mining to organizations in Cebu Objectives: To design and present a relational database To communicate the use of denormalization in a data warehouse through designing and presenting a data warehouse table To show how data can aid in a data mining activity and the privacy concerns that come with data mining
Facts	Ideas	Learning Goals/ Objectives	Action Plan
What we know (Based on your previous knowledge, and learning and by reading and observing the content of the case problem) List them below	What we dont know (What are the questions in your mind when you think about the case problem?) List them below.	What we need to find out (What are the specific areas which we think are important to examine, conduct research and investigate to help us solve the problem?)	Responsible/ Resources Needed (Assign team member responsible to examine, do research and investigate the issue identified in column 3 in order solve the problem?)
1. Data mining is an automatic or semi-automatic technical process used by companies to extract usable data from a larger set of any raw data and turn it into useful information.	Data mining can be both beneficial and harmful to companies	How to address the limitations of Data Mining.
2. A database is an organized collection of information stored in a way that makes logical sense and that facilitates easier search, retrieval, manipulation, and analysis of data. It is an organized collection of data stored as multiple datasets.	Which system is better to use: organizational or operational.	How to address the ethical issues faced in Data Mining
3. Data warehousing is the electronic storage of a large amount of information by a business or organization. A data warehouse is designed to run query and analysis on historical data derived from transactional sources for business intelligence and data mining purposes. Data warehousing is used to provide greater insight into the performance of a company by comparing data consolidated from multiple heterogeneous sources.	When is it good to use normalization?	Remedies for Out-Of-Synch Data
4. Datasets are a group of data files--usually numeric or encoded--along with the documentation files (such as a codebook, technical or methodology report, data dictionary) which explain their production or use. Generally a dataset is un-usable for sound analysis by a second party unless it is well documented.
4. Limitations on data mining: Noisy and Incomplete Data Distributed Data Complex Data Affects Performance Incorporation of background knowledge Data visualization Data privacy and security
5. Operational Database or OLTP is a database management system where data is stored and processed in real-time.
Operational Data Systems, consisting largely of transactional data, are built for quicker updates.
Operational Data Systems are highly secured as they offer built-in support for encryption, auditing, and protection from cyber.
Operational Data Systems usually have a learning curve where personnel are required to give relevant training to manage such databases and that increases the overheads expenses.
Organizational data is used to make business decisions, as opposed to recording the data from actual operational business processes.
Data analytics can breach customer privacy as information such as online transactions, purchases, or subscriptions, can be viewed by the parent companies.
6. Ethical issues in data mining: Personal Data Transparency Governance Privacy threatened by web-data mining Individuality
7. Out-of-sync data refers to stale data where changes to the dataset did not reflect because no update was introduced. This results in inconsistency between databases due to inaccuracies and errors making the data low quality and unreliable.
8. Normalization is defined as the process of organizing data in a database. This includes creating tables and establishing relationships between those tables according to rules designed both to protect the data and to make the database more flexible by eliminating redundancy and inconsistent dependency.
9. Normalization is good for OLTP systems because it gets updated frequently and must take special care of data integrity. Thus, there is a need to remove redundant data from the database and store non-redundant and consistent data into the system. The main focus of OLTP system is to record the current Update, Insertion and Deletion while transaction. The OLTP queries are simpler and short and hence require less time in processing, and also requires less space.
10. Normalization is not so good in OLAP systems because it can lead to issues with performance. Data aggregations become much more difficult to write. The transaction in OLAP are long and hence take comparatively more time for processing and requires large space. The transactions in OLAP are less frequent as compared to OLTP. Even the tables in OLAP database may not be normalized.
Note: 1. This is the product of collaboration and brainstorming with your team members after reading the case problem. Every member must contribute based on Online collaboration and readings and posted in Discussion at CANVAS. Information must be complete before the Due date. 2. This is due at 11:59 PM a day before the Synchronous meeting. 3. The generated list in each column can be as many depending on the case problem information.