Deck 10: Data Quality and Integration

Question 1

One simple task of a data quality audit is to:

A) interview all users.
B) statistically profile all files.
C) load all data into a data warehouse.
D) establish quality metrics.

Answer 1

B

Question 2

Including data capture controls (i.e., dropdown lists) helps reduce ________ deteriorated data problems.

A) external data source
B) inconsistent metadata
C) data entry
D) lack of organizational commitment

Answer 2

C

Question 3

Data that are accurate, consistent, and available in a timely fashion are considered:

A) Oracle-based.
B) Microsoft-based.
C) high-quality.
D) low-quality.

Answer 3

C

Question 4

The methods to ensure the quality of data across various subject areas are called:

A) Variable Data Management.
B) Master Data Management.
C) Joint Data Management.
D) Managed Data Management.

Question 5

Quality data can be defined as being:

A) unique.
B) inaccurate.
C) historical.
D) precise.

Question 6

All of the following are popular architectures for Master Data Management EXCEPT:

A) Identity Registry.
B) Integration Hub.
C) Persistent.
D) Normalization.

Question 7

Datatype conflicts is an example of a(n) ________ reason for deteriorated data quality.

A) external data source.
B) inconsistent metadata
C) data entry problem
D) lack of organizational commitment

Question 8

Data governance can be defined as:

A) a means to slow down the speed of data.
B) high-level organizational groups and processes that oversee data stewardship.
C) a government task force for defining data quality.
D) a means to increase the speed of data.

Question 9

Getting poor data from a supplier is a(n) ________ reason for deteriorated data quality.

A) external data source.
B) inconsistent metadata
C) data entry problem
D) lack of organizational commitment

Question 10

One characteristic of quality data which pertains to the expectation for the time between when data are expected and when they are available for use is:

A) currency.
B) consistency.
C) referential integrity.
D) timeliness.

Question 11

The best place to improve data entry across all applications is:

A) in the users.
B) in the level of organizational commitment.
C) in the database definitions.
D) in the data entry operators.

Question 12

In the ________ approach, one consolidated record is maintained, and all applications draw on that one actual "golden" record.

A) persistent
B) identity registry
C) federated
D) integration hub

Question 13

One way to improve the data capture process is to:

A) allow all data to be entered manually.
B) provide little or no training to data entry operators.
C) check entered data immediately for quality against data in the database.
D) not use any automatic data entry routines.

Question 14

Which of the following are key steps in a data quality program?

A) Avoid a data quality audit.
B) Apply TQM principles and practices.
C) Do not allow outside data.
D) Keep all data on one server.

Question 15

Data quality is important for all of the following reasons EXCEPT:

A) it minimizes project delay.
B) it aids in making timely business decisions.
C) it provides a stream of profit.
D) it helps to expand the customer base.

Question 16

Conformance means that:

A) data have been transformed.
B) data are stored, exchanged or presented in a format that is specified by its metadata.
C) data are stored in a way to expedite retrieval.
D) data is a harbinger.

Question 17

Data quality ROI stands for:

A) return on installation.
B) risk of incarceration.
C) rough outline inclusion.
D) rate of installation.

Question 18

External data sources present problems for data quality because:

A) data are not always available.
B) there is a lack of control over data quality.
C) there are poor data capture controls.
D) data are unformatted.

Question 19

TQM stands for:

A) Thomas Quinn Mann, a famous data quality innovator.
B) Total Quality Manipulation.
C) Transforming Quality Management.
D) Total Quality Management.

Question 20

Data quality problems can cascade when:

A) data are not deleted properly.
B) data are copied from legacy systems.
C) there is redundant data storage and inconsistent metadata.
D) there are data entry problems.

Question 21

Converting data from the format of its source to the format of its destination is called:

A) data transformation.
B) data loading.
C) data scrubbing.
D) data storage.

Question 22

In the ________ approach, one consolidated record is maintained from which all applications draw data.

A) persnickity
B) cautious
C) persistent
D) data-oriented

Question 23

Data may be loaded from the staging area into the warehouse by following:

A) SQL Commands (Insert/Update).
B) SQL purge.
C) custom-written letters.
D) virus checking.

Question 24

Which type of index is commonly used in data warehousing environments?

A) Joint index
B) Bit-mapped index
C) Secondary index
D) Tri-dex

Question 25

A technique using artificial intelligence to upgrade the quality of raw data is called:

A) dumping.
B) data reconciliation.
C) completion backwards updates.
D) data scrubbing.

Question 26

The process of transforming data from a detailed to a summary level is called:

A) extracting.
B) updating.
C) joining.
D) aggregating.

Question 27

The process of combining data from various sources into a single table or view is called:

A) extracting.
B) updating.
C) selecting.
D) joining.

Question 28

Informational and operational data differ in all of the following ways EXCEPT:

A) level of detail.
B) normalization level.
C) scope of data.
D) data quality.

Question 29

User interaction integration is achieved by creating fewer ________ that feed different systems.

A) clients
B) networks
C) computers
D) user interfaces

Question 30

A method of capturing only the changes that have occurred in the source data since the last capture is called ________ extract.

A) static
B) incremental
C) partial
D) update-driven

Question 31

A technique using pattern recognition to upgrade the quality of raw data is called:

A) data scrounging.
B) data scrubbing.
C) data gouging.
D) data analysis.

Question 32

A characteristic of reconciled data that means the data reflect an enterprise-wide view is:

A) detailed.
B) historical.
C) normalized.
D) comprehensive.

Question 33

________ duplicates data across databases.

A) Data propagation
B) Data duplication
C) Redundant replication
D) A replication server

Question 34

An approach to filling a data warehouse that employs bulk rewriting of the target data periodically is called:

A) dump mode.
B) overwrite mode.
C) refresh mode.
D) update mode.

Question 35

Loading data into a data warehouse does NOT involve:

A) appending new rows to the tables in the warehouse.
B) updating existing rows with new data.
C) purging data that have become obsolete or were incorrectly loaded.
D) formatting the hard drive.

Question 36

The major advantage of data propagation is:

A) real-time cascading of data changes throughout the organization.
B) duplication of non-redundant data.
C) the ability to have trickle-feeds.
D) virus elimination.

Question 37

All of the following are ways to consolidate data EXCEPT:

A) application integration.
B) data rollup and integration.
C) business process integration.
D) user interaction integration.

Question 38

All of the following are tasks of data cleansing EXCEPT:

A) decoding data to make them understandable for data warehousing applications.
B) adding time stamps to distinguish values for the same attribute over time.
C) generating primary keys for each row of a table.
D) creating foreign keys.

Question 39

Data federation is a technique which:

A) creates an integrated database from several separate databases.
B) creates a distributed database.
C) provides a virtual view of integrated data without actually creating one centralized database.
D) provides a real-time update of shared data.

Question 40

Event-driven propagation:

A) provides a means to duplicate data for events.
B) pushes data to duplicate sites as an event occurs.
C) pulls duplicate data from redundant sites.
D) triggers a virus.

Question 41

Dirty data can cause delays and extra work on information systems projects.

Question 42

Retention refers to the amount of data that is not purged periodically from tables.

Question 43

Data quality is essential for SOX and Basel II compliance.

Question 44

Quality data are not essential for well-run organizations.

Question 45

Quality data does not have to be unique.

Question 46

The uncontrolled proliferation of spreadsheets, databases and repositories leads to data quality problems.

Question 47

A data quality audit helps an organization understand the extent and nature of data quality problems.

Question 48

Dirty data saves work for information systems projects.

Question 49

Lack of organizational commitment is a potential reason for an organizations deteriorated data quality.

Question 50

A data governance committee is always made up of high-ranking government officials.

Question 51

Completeness means that all data that must have a value does not have a value.

Question 52

A data expeditor is a person assigned the responsibility of ensuring that organizational applications properly support the organization's enterprise goals of data quality.

Question 53

Improving data capture process is a fundamental step in data quality improvement.

Question 54

Which of the following is a basic method for single field transformation?

A) Table lookup
B) Cross-linking entities
C) Cross-linking attributes
D) Field-to-field communication

Question 55

Completeness means that all data that are needed are present.

Question 56

A review will thoroughly review all process controls on data entry and maintenance.

Question 57

Data which arrive via XML and B2B channels is always guaranteed to be accurate.

Question 58

Conformance refers to whether the data is stored, exchanged or presented in a format that is as specified by its metadata.

Question 59

Generally, records in a customer file never become obsolete.

Question 60

A data steward is a person assigned the responsibility of ensuring the organizational applications properly support the organization's enterprise goals for data quality.

Question 61

Data federation consolidates all data into one database.

Question 62

Static extract is a method of capturing only the changes that have occurred in the source data since the last capture.

Question 63

One of the biggest challenges of the extraction process is managing changes in the source system.

Question 64

Refresh mode is an approach to filling the data warehouse that employs bulk rewriting of the target data at periodic intervals.

Question 65

A data stewardship program does not help to involve the organization in data quality.

Question 66

Master data management is the disciplines, technologies and methods to ensure the currency, meaning and quality of data within one subject area.

Question 67

After the extract, transform, and load is done on data, the data warehouse is never fully normalized.

Question 68

ETL is short for Extract, Transform, Load.

Question 69

Application integration is achieved by coordinating the flow of event information between business applications.

Question 70

Data are moved to the staging area before extraction takes place.

Question 71

Data scrubbing is a technique using pattern recognition and other artificial intelligence techniques to upgrade the quality of raw data before transforming and moving the data to the data warehouse.

Question 72

There are six major steps to ETL.

Question 73

Data nationalization provides a virtual view of integrated data without actually bringing the data into one physical database.

Question 74

The data reconciliation process is responsible for transforming operational data to reconciled data.

Question 75

The major advantage of the data propagation approach to data integration is the near real-time cascading of data changes throughout the organization.

Question 76

Data propagation duplicates data across databases, usually with some real-time delay.

Question 77

Sound data modeling is a central ingredient of a data quality program.

Question 78

Total quality management (TQM) focuses on defect correction rather than defect prevention.

Question 79

User interaction integration is achieved by creating fewer user interfaces.

Question 80

Data reconciliation occurs in two stages, an initial load and subsequent updates.