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A. Data reduction

B. Profiling

C. All of the above

D. Clustering

E. Application reduction

F. Association

A. FALSE

B. TRUE

A. FALSE

B. TRUE

A. FALSE

B. TRUE

A. Sustainable, realistic, embedded

B. Sustainable, embedded, measured

C. Measured, objective, embedded

D. Optimism, objective, sustainable

E. Embedded, systematic, sustainable

A. Service of Record

B. System of Record

C. Service over Record

D. System on Record

A. Why change fails

B. The triggers for effective change

C. How people experience changes

D. Data security

E. Data architecture

F. The barriers to change

A. Difficulties in unique dimensions

B. Difficulties in unique identification

C. The number of data sources and the differences between them

D. Reference data anomaly detection

A. The best way to keep up with the evolution is to survey communities, customers and employees and what they consider to be appropriate use of the information

B. An organization must develop and sustain a culture of ethical data handling to detect potential ethical risks. On identifying ethical risks, the onus is on the organization to modifying the processing to achieve acceptable data handling

C. A healthy internal governance, risk and compliance function, including internal data audits will identify potential ethical issues

D. Constant attention to ethical legislation and legal proceedings and international ethical bodies will enable the organization to recognize potential compliancy issues that can be addressed

E. An active data quality and privacy assessment program can be used to detect ethical risks. Once identified, we can reduce the associated risks according to the organizations ethical values

A. Capture and document all metrics and store in a central repository

B. Undertake an audit of current process and produce a report

C. Perform a one off review of data quality

D. Create a new data store for regulator required metrics

E. Provide the framework and guidance to enable a business led ongoing Data Governance process

A. Subject Area model

B. Conceptual model

C. Physical model

D. Logical model

A. Data warehousing

B. Data modelling

C. Data security

D. Data architecture

E. Data quality

A. Define Big Data data strategy & Business Need(s)

B. Develop Data Science Hypotheses and Mehods

C. Choose Data Sources

D. Acquire & Ingest Data source(s)

A. Formalization

B. Scope definition

C. Knowledge retention/documentation

D. Normalization

A. Automated correction algorithms capable of detecting and correcting errors

B. Train data producers

C. Institute formal data change control

D. Establish data entry controls

E. Implement data governance and stewardship