MIS 3000- Ex 7 Ch 6 Data BI PDF

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MIS 3000- Ex 7 Ch 6 Data BI...

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MIS 3000 Fall 20, Ex 7 (due Sunday, 10/11/2020 via Moodle)

Name: Alexandra Popa

Instructions: This is a take-home, individual exercise. (10 points; 19 blanks). Please fill in the most appropriate answer, based on the textbook or class PPTs, for each of the following statements. Chapter 6 1. Data rich, Information poor refers to the scenario where managers have so much data and information that they cannot make a decision.

2. A data warehouse is a(n) logical collection of information, gathered from many different operational databases, that supports business analysis activities and decision-making tasks.

3. Dirty data refer(s) to erroneous or flawed data.

4. An information cube is used represent multidimensional information.

5. The different information levels include individual, department, enterprise.

6. Information Timeliness implies that information is current with respect to the business requirement.

7. Data governance refer(s) to the overall management of the availability, usability, integrity, and security of company data.

8. Data Dictionary compile(s) all of the metadata about the data elements in the data model.

9. Extraction, transformation, and loading (ETL) is a process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse.

10. Dynamic information includes data that changes based on user actions.

11. A(n) Data-driven websites website is an interactive website kept constantly updated and relevant to the needs of its customers using a database.

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MIS 3000 Fall 20, Ex 7 (due Sunday, 10/11/2020 via Moodle)

Name: Alexandra Popa

12. A(n) Data map is a technique for establishing a match, or balance, between the source data and the target data warehouse.

13. Big data is a collection of large, complex data sets, including structured and unstructured data, which cannot be analyzed using traditional database methods and tools.

14. Discovery in data mining refer(s) to the process of identifying new patterns, trends, and insights.

15. Data profiling in data mining refer(s) to the process of collecting statistics and information about data in an existing source.

16. Anomaly detection in data analytics refer(s) to the process of identifying rare or unexpected items or events in a data set that do not conform to other items in the data set.

17. Data visualization describes technologies that allow users to see data to transform into a business perspective.

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