Data Quality

Question 1

What is the DMBoK definitiion of data quality management?

Answer 1

The planning, implementation, and control of activities that apply quality management techniques to data, in order to assure it is fit for consumption and meets the needs of data consumers.

Question 2

What are the 4 business drivers for establishing a formal Data Quality Management program?

Answer 2

• Increasing the value of organizational data and the opportunities to use it
• Reducing risks and costs associated with poor quality data
• Improving organizational efficiency and productivity
• Protecting and enhancing the organization’s reputation

Question 3

7 direct costs are associated with poor quality data. Name 4

Answer 3

• Inability to invoice correctly
• Increased customer service calls and decreased ability to resolve them
• Revenue loss due to missed business opportunities
• Delay of integration during mergers and acquisitions
• Increased exposure to fraud
• Loss due to bad business decisions driven by bad data
• Loss of business due to lack of good credit standing

Question 4

What are the 4 goals Data Quality programs focus on?

Answer 4

• Developing a governed approach to make data fit for purpose based on data consumers’ requirements
• Defining standards and specifications for data quality controls as part of the data lifecycle
• Defining and implementing processes to measure, monitor, and report on data quality levels
• Identifying and advocating for opportunities to improve the quality of data, through changes to processes and systems and engaging in activities that measurably improve the quality of data based on data consumer requirements

Question 5

Data Quality programs should be guided by these 10 principles

Answer 5

• Criticality:
• Lifecycle management
• Prevention
• Root cause remediation
• Governance
• Standards-driven
• Objective measurement and transparency
• Embedded in business processes
• Systematically enforced
• Connected to service levels

Question 6

Which principle of Data Quality Management is to focus improvement efforts on data that is most important to the organization and its customers?

Answer 6

Criticality or Critical Data

Question 7

What are the six core dimensions of data quality?

Answer 7

• Completeness: The proportion of data stored against the potential for 100%.
• Uniqueness: No entity instance (thing) will be recorded more than once based upon how that thing is identified.
• Timeliness: The degree to which data represent reality from the required point in time.
• Validity: Data is valid if it conforms to the syntax (format, type, range) of its definition.
• Accuracy: The degree to which data correctly describes the ‘real world’ object or event being described.
• Consistency: The absence of difference, when comparing two or more representations of a thing
  against a definition.

Question 8

The _________ cycle is a problem-solving model known as “plan-do-check-act’.

Answer 8

Shewhart / Deming

Question 9

In the ________ stage of the DQ Improvement Life Cycle, the Data Quality team assesses the scope, impact, and priority of known issues, and evaluates alternatives to address them.

Answer 9

Plan

Question 10

In the ________ stage of the DQ Improvement Life Cycle, the DQ team leads efforts to address the root causes of issues and plan for ongoing monitoring of data.

Answer 10

Do

Question 11

In the ________ stage of the DQ Improvement Life Cycle, the team actively monitors the quality of data as measured against requirements. As long as data meets defined thresholds for quality, additional actions are not required.

Answer 11

Check

Question 12

In the ________ stage of the DQ Improvement Life Cycle, activities occur to address and resolve emerging data quality issues.

Answer 12

Act

Question 13

What framework focuses on data consumers’ perceptions of data. It describes 15 dimensions across four general categories of data quality:

Answer 13

Strong-Wang Framework

Question 14

What 4 general categories are described in the Strong-Wang framework?

Answer 14

• Intrinsic DQ
• Contextual DQ
• Representational DQ
• Accessibility DQ

Question 15

In the Strong-Wang framework, What 4 dimensions are there in Intrinsic Data Quality?

Answer 15

o Accuracy
o Objectivity
o Believability
o Reputation

Question 16

In the Strong-Wang framework, Which of these dimensions is not part of Contextual Data Quality?
o Value-added
o Interpretability
o Timeliness
o Completeness
o Appropriate amount of data

Answer 16

Interpretability. Should be relevancy

Question 17

As part of the Strong-Wang Framework, which data quality category do these dimension belong?
o Interpretability
o Ease of understanding
o Representational consistency
o Concise representation

Answer 17

Representational DQ

Question 18

In the Strong-Wang Framework Accessibility DQ category there are two dimensions, what are they?

Answer 18

o Accessibility
o Access security

Question 19

There are 8 DQ issues caused by Poor System Design, name 6 of them.

Answer 19

• Failure to enforce referential integrity
• Failure to enforce uniqueness constraints
• Coding inaccuracies and gaps
• Data model inaccuracies
• Field overloading: Re-use of fields over time for different purposes,
• Temporal data mismatches: In the absence of a consolidated data dictionary, multiple systems could implement disparate date formats or timings, which in turn lead to data mismatch and data loss when
  data synchronization takes place between different source systems.
• Weak Master Data Management
• Data duplicatiom: Single Source / Multiple Local Instances
  o Multiple Sources / Single Instance

Question 20

________________ is a form of data analysis used to inspect data and assess quality. It uses statistical techniques to discover the true structure, content, and quality of a collection of data.

Answer 20

Data Profiling

Question 21

Name the 2 activities prevalent in Data Quality Management

Answer 21

Maturity Assessment and Profiling

Question 22

What are the 5 statistical techniques used to inspect data and assess quality in data profiling?

Answer 22

• Counts of nulls
• Max/Min value
• Max/Min length
• Frequency distribution of values for individual columns
• Data type and format

Question 23

Profiling also includes __________ analysis, which can identify overlapping or duplicate columns and expose embedded value dependencies.

Answer 23

cross-column

Question 24

_____________ analysis explores overlapping values sets and helps identify foreign key relationships.

Answer 24

Inter-table

Question 25

______________ is the process of adding attributes to a data set to increase its quality and usability

Answer 25

Data enhancement or enrichment

Question 26

There are 8 type of data enrichment or enhancements listed in DMBok. Name 6 of them.

Answer 26

• Time/Date stamps
• Audit data
• Reference vocabularies
• Contextual information
• Geographic information
• Demographic information
• Psychographic information
• Valuation information

Question 27

Which data enrichment or enhancement type adds information such as location, environment, or access methods and tagging data for review and analysis.

Answer 27

Contextual Information

Question 28

Which data enrichment or enhancement type is used to segment the target populations by specific behaviors, habits, or preferences, such as product and brand preferences, organization memberships, leisure activities, commuting transportation style, shopping time preferences, etc.?

Answer 28

Psychographic information

Question 29

Which data enrichment or enhancement type would include data lineage?

Answer 29

Audit data

Question 30

Which data enrichment or enhancement type uses business specific terminology, ontologies, and glossaries to enhance understanding and control while bringing customized business context.

Answer 30

Reference vocabularies

Question 31

Which data enrichment or enhancement is used for asset valuation, inventory, and sale?

Answer 31

Valuation Information

Question 32

_____________ is the process of analyzing data using pre-determined rules to define its content or value. This process enables the data analyst to define sets of patterns that feed into a rule engine used to distinguish between valid and invalid data values. Matching specific pattern(s) triggers actions.

Answer 32

Data Parsing

Question 33

In Data Quality, having proven that the improvement process can work, the next goal is to apply it strategically. Doing so requires _______ and ______ potential improvements.

Answer 33

identifying and prioritizing

Question 34

Provide continuous monitoring by incorporating ________ and __________ processes into the information processing flow.

Answer 34

control and measurement

Question 35

Data quality incident tracking requires staff be trained on how issues should be _______, _________, and _________.

Answer 35

logged, classified, and tracked

Question 36

Data quality reporting should focus on these 7 areas.

Answer 36

• Data quality scorecard, which provides a high-level view of the scores associated with various metrics, reported to different levels of the organization within established thresholds
• Data quality trends, which show over time how the quality of data is measured, and whether trending is up or down
• SLA Metrics, such as whether operational data quality staff diagnose and respond to data quality incidents in a timely manner
• Data quality issue management, which monitors the status of issues and resolutions
• Conformance of the Data Quality team to governance policies
• Conformance of IT and business teams to Data Quality policies
• Positive effects of improvement projects

Question 37

Name 6 ways to prevent poor quality data from entering an organization.

Answer 37

• Establish data entry controls
• Train data producers
• Define and enforce rules: Create a ‘data firewall,’ which has a table with all the business data quality rules used to check if the quality of data is good, before being used in an application such a data
  warehouse.
• Demand high quality data from data suppliers
• Implement Data Governance and Stewardship: Ensure roles and responsibilities are defined that describe and enforce rules of engagement, decision rights, and accountabilities for effective management of data and information assets (McGilvray, 2008). Work with data stewards to revise theprocess of, and mechanisms for, generating, sending, and receiving data.
• Institute formal change control: Ensure all changes to stored data are defined and tested before being implemented. Prevent changes directly to data outside of normal processing by establishing gating processes.

Question 38

____________ actions are implemented after a problem has occurred and been detected.

Answer 38

Corrective

Question 39

What are 3 corrective actions that can be taken against poor data?

Answer 39

• Automated correction
• Manually-directed correction
• Manual correction

Question 40

Which corrective action uses automated tools to remediate and correct data but requires manual review before committing the corrections to persistent storage.

Answer 40

Manually-directed correction

Question 41

What are the 4 goals of a data quality program?

Answer 41

• Developing a governed approach to make data fit for purpose based on data consumers’ requirements
• Defining standards and specifications for data quality controls as part of the data lifecycle
• Defining and implementing processes to measure, monitor, and report on data quality levels
• Identifying and advocating for opportunities to improve the quality of data, through changes to processes and systems and engaging in activities that measurably improve the quality of data based on data consumer requirements