Data Governance Systems Flashcards

Question 1

What is data governance, and why do organizations need it?

Accepted Answer

Data governance is the set of policies, roles, and processes an organization uses to manage its data as a valued asset. It defines who may act on data, under which rules, and who is accountable. Its goal is data that stays accurate, consistent, secure, and trustworthy for decisions.

Question 2

Why is data considered a business asset that needs management?

Accepted Answer

Data supports pricing, planning, customer service, and regulatory reporting, so its value depends on how well it is kept. Like equipment or money, data can lose value through errors, duplication, or misuse. Managing it deliberately protects that value and reduces the costs and risks that poor data creates.

Question 3

What are the main goals of a data governance program?

Accepted Answer

The main goals are improving data quality, clarifying ownership and responsibility, protecting sensitive information, and supporting consistent decision making across the organization. Programs also aim to reduce duplicated effort, help people find trustworthy data, and show compliance with internal policies and external rules. Each goal connects back to trust in the data.

Question 4

What is the difference between data governance and data management?

Accepted Answer

Data governance decides what should happen to data, including the rules, owners, and standards that apply. Data management carries out those decisions through the technical and operational work of storing, integrating, cleaning, and moving data. Governance sets direction and accountability, while management does the hands-on execution.

Question 5

What is a data owner, and what does that role typically do?

Accepted Answer

A data owner is a senior person accountable for a specific data domain, such as customer or product data. The owner approves who may use the data, sets its business rules, and makes decisions about its quality and retention. Owners are accountable for outcomes even when others perform the daily work.

Question 6

What is a data steward, and how does it differ from a data owner?

Accepted Answer

A data steward is responsible for the day-to-day care of data, including definitions, quality checks, and issue resolution. Unlike an owner, who holds accountability and decision rights, a steward carries out the rules and keeps the data in good condition. Stewards often work closely with owners, IT teams, and data users.

Question 7

What is a data governance council, and why do many programs have one?

Accepted Answer

A data governance council is a cross-functional group that sets priorities, approves policies, and resolves disputes about data across departments. Many programs create one because data often crosses business units, and no single team can settle conflicting definitions or competing needs alone. The council gives decisions a clear forum and visible sponsorship.

Question 8

Why does executive sponsorship matter for a governance program?

Accepted Answer

Executive sponsorship gives a governance program the authority to set rules, allocate budget, and resolve conflicts that lower-level staff cannot settle. Without visible support from senior leaders, policies are often ignored, and owners may not treat data responsibilities as real work. Sponsors also help explain the purpose of governance to the wider organization.

Question 9

What does data quality mean in a governance context?

Accepted Answer

Data quality describes how well data fits its intended use. High-quality data is accurate, complete, consistent, timely, valid, and unique. Quality is judged against business needs, so the same data may be good enough for one purpose and unsuitable for another. Governance sets the standards and measures used to judge it.

Question 10

What are the most common dimensions used to measure data quality?

Accepted Answer

Common dimensions include accuracy, completeness, consistency, timeliness, validity, and uniqueness. Accuracy asks whether values reflect the real world, completeness asks whether required values are present, and consistency checks whether the same fact agrees across systems. Timeliness asks whether data is current enough, while validity checks format and allowed values.

Question 11

What is a data quality rule, and how is one applied?

Accepted Answer

A data quality rule is a testable condition that a value or record must meet, such as a postal code containing exactly five digits. Rules are applied through automated checks at data entry or on existing datasets. Records that fail are flagged and routed to a steward for correction.

Question 12

What is metadata, and why does it matter for governance?

Accepted Answer

Metadata is data about data, such as a field's name, its definition, its source, its owner, and when it was last updated. It matters for governance because it makes data understandable and traceable, so people can tell what a field means and whether it is safe to use. Good metadata reduces misinterpretation.

Question 13

What is a business glossary, and how does it support consistent definitions?

Accepted Answer

A business glossary is a shared list of agreed terms and definitions that an organization uses in its daily work, such as what counts as an active customer. It supports consistency by giving departments one approved meaning for each term, so reports from different teams can be compared. Each entry usually names an owner.

Question 14

What is a data lineage record, and what questions does it answer?

Accepted Answer

A data lineage record traces where a data element originated, how it moved, and what transformations were applied along the way. It answers questions such as which source system produced a figure, which calculations changed it, and which reports depend on it. Lineage supports troubleshooting, impact analysis, and audit responses.

Question 15

How does a data catalog help people find and understand data?

Accepted Answer

A data catalog is a searchable inventory of an organization's data assets, enriched with metadata such as descriptions, owners, sensitivity labels, and usage notes. It helps people find relevant datasets quickly, judge whether they fit a purpose, and request access through the proper channel. Catalogs work best when stewards keep entries current.

Question 16

Why is a single source of truth often a governance goal?

Accepted Answer

A single source of truth is one authoritative location or version for a given data element, so everyone works from the same value. Governance pursues it to avoid conflicting copies that produce different answers. Because one master record is not always practical, programs often designate authoritative sources for the most important data.

Question 17

What is data classification, and why do organizations use it?

Accepted Answer

Data classification is the practice of labeling data by sensitivity level, such as public, internal, confidential, or restricted. Organizations use it so that protective controls match the risk of each data type. Clear labels guide decisions about storage, sharing, encryption, and retention, and they help staff know how to handle information correctly.

Question 18

What is the principle of least privilege in data access?

Accepted Answer

The principle of least privilege means giving each user or system only the access needed to perform an assigned task, and no more. It limits the damage that a mistake, compromised account, or misuse could cause. Access is reviewed regularly, and permissions are removed when roles change.

Question 19

What is role-based access control, and how does it simplify permissions?

Accepted Answer

Role-based access control grants permissions to defined job roles rather than to each individual person. Users receive the roles their work requires, so access changes when someone changes jobs, instead of requiring many separate adjustments. This approach makes permissions easier to audit and reduces the chance of forgotten access.

Question 20

What is personal data, and why does governance treat it with extra care?

Accepted Answer

Personal data is information that relates to an identified or identifiable individual, such as a name, contact details, or account number. Governance treats it with extra care because its misuse can harm people, expose organizations to legal liability, and erode customer trust. Policies therefore govern collection, purpose, retention, and sharing.

Question 21

What does data minimization require from an organization handling personal information?

Accepted Answer

Data minimization requires collecting and keeping only the personal data that is needed for a stated purpose. Organizations should ask whether each field is necessary before gathering it, and they should delete or anonymize data once the purpose has ended. Less data held means less exposure if a breach occurs.

Question 22

What is the difference between encryption at rest and encryption in transit?

Accepted Answer

Encryption at rest protects stored data on disks, databases, and backup media, so it stays unreadable if those storage locations are accessed without authorization. Encryption in transit protects data moving across networks, such as between a browser and a server or between two systems. Governance usually requires both for sensitive data.

Question 23

What is an audit trail, and what should it record?

Accepted Answer

An audit trail is a chronological log of actions taken on data, such as who accessed a record, what changed, and when the change occurred. It should record enough detail to reconstruct events, while protecting the log itself from tampering. Audit trails support investigations, compliance reviews, and accountability for data changes.

Question 24

Why do data governance and information security need to work together?

Accepted Answer

Governance defines what data is sensitive, who should use it, and under which rules, while security teams implement the technical safeguards that enforce those decisions. Without governance, security controls may protect the wrong data or lack clear owners to approve exceptions. Without security, governance policies remain promises that nothing enforces.

Question 25

What is a data governance policy, and what should it contain?

Accepted Answer

A data governance policy is a formal statement of principles and rules that guide how data is created, used, protected, and retired. It should state its purpose and scope, define key roles, describe the required standards, and explain how compliance is monitored and how exceptions are handled. Policies are approved by senior leadership.

Question 26

How do data standards differ from data policies in a governance program?

Accepted Answer

Policies state what must be achieved and why, while standards specify the concrete, measurable requirements that support those goals. For example, a policy might require accurate customer records, and a standard might define the required fields, formats, and validation rules. Standards give teams something specific to build and check against.

Question 27

What is a data retention schedule, and why is it important?

Accepted Answer

A data retention schedule lists each category of records, how long it must be kept, and what should happen at the end of that period. It is important because keeping data too long increases cost and risk, while deleting it too early can break legal, financial, or operational obligations.

Question 28

What does regulatory compliance mean for a data governance program?

Accepted Answer

Regulatory compliance means meeting the legal and industry rules that apply to the organization's data, such as privacy, financial reporting, or health information requirements. A governance program translates those obligations into internal policies, controls, and evidence that can be shown to auditors or regulators. Compliance is a continuing responsibility, not a one-time project.

Question 29

Why should a governance program keep a record of policy exceptions?

Accepted Answer

Recording exceptions shows where policies cannot be followed and why, so leaders can judge whether the risk is acceptable. Each exception should name an approver, a business reason, a time limit, and any compensating controls. Tracking them over time reveals patterns that may signal a policy needs revision.

Question 30

What is a data sharing agreement, and what should it cover?

Accepted Answer

A data sharing agreement is a written contract that sets the terms under which data is exchanged between two or more parties. It should cover the purpose of sharing, the data elements included, permitted uses, security obligations, and how the agreement ends. Clear terms prevent misunderstandings about who may do what with shared data.

Question 31

What is a data protection impact assessment, and when is it used?

Accepted Answer

A data protection impact assessment is a structured review of how a planned project or system will handle personal data and what risks it creates for individuals. It identifies risks early, guides how to reduce them, and records the decisions made. It is usually done before launching a new system that handles sensitive information.

Question 32

How does an organization prepare for an external data audit?

Accepted Answer

Preparation starts with current policies, up-to-date inventories, and clear records showing who approved what and when. Teams should confirm that controls operate as documented, gather evidence such as access reviews and training logs, and identify known gaps before auditors do. A designated contact should coordinate requests so answers stay consistent.

Question 33

What are the typical stages of starting a data governance program?

Accepted Answer

A typical program begins with assessing the current state of data and its pain points, then defining goals and scope. Next come assigning roles, drafting policies and standards, and selecting tools. Pilots in one high-value area usually come before wider rollout, and measurement continues throughout.

Question 34

How should an organization choose which data to govern first?

Accepted Answer

Organizations should start with data that is most critical to key decisions, regulatory duties, or customer experience. A useful approach is to rank candidate datasets by business impact, risk, and current quality problems. Starting small with a well-defined domain produces visible results that build support for governance across the organization.

Question 35

How do you measure whether a data governance program is working?

Accepted Answer

Measure outcomes that matter to the business, such as the share of critical records that pass quality rules, the time needed to resolve data issues, and the number of access requests handled on time. Track adoption too, such as steward participation in issue resolution. Metrics should be reviewed and tied to specific improvement actions.

Question 36

What is a data issue management process, and what steps does it include?

Accepted Answer

A data issue management process is a repeatable way to report, prioritize, resolve, and learn from problems with data. Typical steps include logging the issue, assessing its impact, assigning an owner, correcting the source and affected records, and confirming the fix. Root cause analysis then helps prevent the same problem from returning.

Question 37

Why is change management important when new governance rules are introduced?

Accepted Answer

Change management helps people understand why rules are changing, what they must do differently, and how to get help. Without it, even well-designed policies may be ignored, worked around, or applied inconsistently. Training, clear communication, and feedback channels make adoption more likely and reduce resistance.

Question 38

How can governance be built into everyday workflows instead of being an extra task?

Accepted Answer

Governance works best when checks and responsibilities are embedded in the tools people already use, such as data entry forms, approval steps, and onboarding processes. For example, a required field with a validation rule prevents bad data at the point of entry. Embedding controls this way reduces manual effort and makes compliance the default.

Question 39

What common mistakes cause data governance programs to stall or fail?

Accepted Answer

Frequent problems include launching with too much scope, buying tools before agreeing on policies and roles, and treating governance as an IT project alone. Programs also stall when owners lack time or authority, and when leaders stop visibly supporting the work. Early, realistic planning and shared ownership help avoid these traps.

Question 40

How should a governance program evolve once its initial rollout is complete?

Accepted Answer

A mature program moves from launching policies to continuously improving them, using metrics, feedback, and changing business needs as guides. It expands coverage to additional data domains, automates more controls, and refines roles as people gain experience. Regular reviews keep policies current with technology, regulation, and organizational priorities.

Data Governance Systems Flashcards

1Foundations of Data Governance

2Data Quality and Metadata

3Access, Privacy, and Security

4Policies, Standards, and Compliance

5Implementing Governance in Practice

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