AIDA 181: ASSIGNMENT 2 EXAM
QUESTIONS AND ANSWERS GRADED A+
2025/2026
Data governance - ANS set of rules and decisions for managing data. It provides the
definitions, standards, and procedures for how data should be used and by whom.
Data preparation and capture - ANS This includes internal data entry processes to capture
accounting transactions, customer data, other operational information, and outside data
sources such as research material or financial statistics.
Data access - ANS knowing where the data is and how it can be retrieved.
Data quality - ANS involves processes to make sure data is accurate and usable for its
intended purpose.
Data integration- give an example around accounting - ANS financial statements being
dynamically updated each time an accounting transaction occurs.
Data Management Benefits - ANS Increased overall efficiency, Enhanced on-demand access
to data, Sounder decision-making
What are some examples of the data governance committee (DGC) responsibilities? -
ANS Establish and maintain data interactions across organizational functions
Monitor internal data projects for alignment with corporate strategy
1 @COPYRIGHT 2026 ALLRIGHTS RESERVED.
, Minimize conflicts, redundancies, and inefficiencies
Respond to problematic data issues with the necessary resources, recommendations, and
approvals for remediation
Data Governance Tools - ANS Internal policies and procedures for handling, coding,
processing, and analyzing data that the organization creates, acquires, and uses
External policies and procedures, such as standards and guidelines, from other organizations or
compliance sources
Enterprise data models, which describe data relationships and interdependencies—that is, a
view of data across the organization
Collaborative tools such as agile project management programs that can capture discussions
and documents, facilitate workflows, log communications, maintain calendars, and share the
status of processes with stakeholders in real time
Data Quality Principles: Appropriateness - ANS Is the data appropriate for the intended
objective? In some cases, current and historical data may be relevant. For example, a previous
business loan offering may provide some pertinent historical information for the bank. Sales
history of similar products in the UK may help pinpoint geographic targets.
Data Quality Principles: Reasonableness - ANS Data is deemed to be reasonable if it has
already been checked, verified, or audited. The materiality or relevance of data.
Data Quality Principles: Comprehensiveness - ANS Data is comprehensive to the extent that
each dataset contains all elements necessary for business needs. Does the software company
have the demographics for each target city? Are the resources in place to ensure delivery and
support of the applications?
Data Quality Principles: Material limitations and alternatives - ANS If a geographic target has
limited prospects (based on analysis of prospect size and need), the software company may
need to find an alternative city or defined territory.
Data Quality Principles: Sampling Methods - ANS Sampling may uncover invalid or inaccurate
data and facilitate prevention or necessary adjustments.
2 @COPYRIGHT 2026 ALLRIGHTS RESERVED.
QUESTIONS AND ANSWERS GRADED A+
2025/2026
Data governance - ANS set of rules and decisions for managing data. It provides the
definitions, standards, and procedures for how data should be used and by whom.
Data preparation and capture - ANS This includes internal data entry processes to capture
accounting transactions, customer data, other operational information, and outside data
sources such as research material or financial statistics.
Data access - ANS knowing where the data is and how it can be retrieved.
Data quality - ANS involves processes to make sure data is accurate and usable for its
intended purpose.
Data integration- give an example around accounting - ANS financial statements being
dynamically updated each time an accounting transaction occurs.
Data Management Benefits - ANS Increased overall efficiency, Enhanced on-demand access
to data, Sounder decision-making
What are some examples of the data governance committee (DGC) responsibilities? -
ANS Establish and maintain data interactions across organizational functions
Monitor internal data projects for alignment with corporate strategy
1 @COPYRIGHT 2026 ALLRIGHTS RESERVED.
, Minimize conflicts, redundancies, and inefficiencies
Respond to problematic data issues with the necessary resources, recommendations, and
approvals for remediation
Data Governance Tools - ANS Internal policies and procedures for handling, coding,
processing, and analyzing data that the organization creates, acquires, and uses
External policies and procedures, such as standards and guidelines, from other organizations or
compliance sources
Enterprise data models, which describe data relationships and interdependencies—that is, a
view of data across the organization
Collaborative tools such as agile project management programs that can capture discussions
and documents, facilitate workflows, log communications, maintain calendars, and share the
status of processes with stakeholders in real time
Data Quality Principles: Appropriateness - ANS Is the data appropriate for the intended
objective? In some cases, current and historical data may be relevant. For example, a previous
business loan offering may provide some pertinent historical information for the bank. Sales
history of similar products in the UK may help pinpoint geographic targets.
Data Quality Principles: Reasonableness - ANS Data is deemed to be reasonable if it has
already been checked, verified, or audited. The materiality or relevance of data.
Data Quality Principles: Comprehensiveness - ANS Data is comprehensive to the extent that
each dataset contains all elements necessary for business needs. Does the software company
have the demographics for each target city? Are the resources in place to ensure delivery and
support of the applications?
Data Quality Principles: Material limitations and alternatives - ANS If a geographic target has
limited prospects (based on analysis of prospect size and need), the software company may
need to find an alternative city or defined territory.
Data Quality Principles: Sampling Methods - ANS Sampling may uncover invalid or inaccurate
data and facilitate prevention or necessary adjustments.
2 @COPYRIGHT 2026 ALLRIGHTS RESERVED.
