Data Governance Exam 1 Review Ques ons and answers
Newest RATED A+ 2025/2026
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Difference Between Data and Informa on
:
• Raw facts with no context.
▪ E.g., Name: John Smith
Age: 20
DOB: 3/13/2000
Major: Finance
Salary: $20,000
:
• Processed data arranged in a meaningful way to facilitate decision making.
▪ E.g. "At age 20, John was earning $1,000 a month while studying finance."
:
• Data cons tutes the building blocks of informa on.
• Informa on is produced by processing data
• Informa on is used to reveal the meaning
• Accurate and mely informa on is essen al to good decision-making.
What is a Database?
:
• A database is a shared, integrated so<ware structure that houses a collec on of:
▪ Data (raw facts of interest)
▪ Metadata (Data about data or informa on about data's structure and meaning).
:
• Database Management System (DBMS) is a collec on of programs that manage the database
and control its access, making it possible to share the data with applica ons and users.
Typical DBMS Capabili es
• Data dic onary
• Query and repor ng
,• Data presenta on for different users
• Backup and recovery
• Data integrity management
Rela onal DBMS
• Represent data as two-dimensional tables.
• Each table contains data on en es and aAributes.
• Table: Grid of Columns and Rows
▪ Rows aka records
▪ Fields (aka columns) - represent aAributes and store values.
▪ A primary key uniquely iden fies each record.
▪ A foreign key is a primary key from one table used as a lookup value to iden fy records in
another table.
Problems with Tradi onal File Structure
• Files maintained separately by different departments.
• Data redundancy - unnecessarily duplicated data.
• Inconsistent data
▪ E.g., John Smith's age vs his DOB
• Lack of flexibility and data sharing.
• Poor security.
Data Modeling
:
• The basic building blocks of all data models are en es, aAributes, and rela onships.
• En ty - person, place, thing, or event for which data.
• AAribute - characteris c of an en ty.
, • Rela onships - describe the associa on among en es:
▪ - - ( : ):
- A painter paints many different pain ngs, but each one is painted by only one painter.
▪ - - ( : ):
- Employee learns many job skills, each job skill might be learned by many employees.
▪ - - ( : ):
- A Verizon manager oversees one store.
Logical vs Physical Model
Big Data
• Massive sets of structured and unstructured data from web traffic, social media, sensors, etc.
• Volumes of data are too great for a typical DBMS
• Can reveal more paAerns and rela onships.
• At its core, big data is about predic ons.
• "Datafy" an event - capture an event in a quan fied format so it can be tabulated/analyzed.
Data Warehouse
:
• A home for high value data or data assets that originates in other corporate applica ons.
• :
▪ A coordinated and periodic copying of data from various sources both inside and outside the
company, into an environment op mized for analy cal and informa onal processing.
• Stores current and historical data
• Provides analysis and repor ng tools
• Relies exclusively on data obtained from other applica ons.
Data Management
Newest RATED A+ 2025/2026
5.0 (2 reviews)
Difference Between Data and Informa on
:
• Raw facts with no context.
▪ E.g., Name: John Smith
Age: 20
DOB: 3/13/2000
Major: Finance
Salary: $20,000
:
• Processed data arranged in a meaningful way to facilitate decision making.
▪ E.g. "At age 20, John was earning $1,000 a month while studying finance."
:
• Data cons tutes the building blocks of informa on.
• Informa on is produced by processing data
• Informa on is used to reveal the meaning
• Accurate and mely informa on is essen al to good decision-making.
What is a Database?
:
• A database is a shared, integrated so<ware structure that houses a collec on of:
▪ Data (raw facts of interest)
▪ Metadata (Data about data or informa on about data's structure and meaning).
:
• Database Management System (DBMS) is a collec on of programs that manage the database
and control its access, making it possible to share the data with applica ons and users.
Typical DBMS Capabili es
• Data dic onary
• Query and repor ng
,• Data presenta on for different users
• Backup and recovery
• Data integrity management
Rela onal DBMS
• Represent data as two-dimensional tables.
• Each table contains data on en es and aAributes.
• Table: Grid of Columns and Rows
▪ Rows aka records
▪ Fields (aka columns) - represent aAributes and store values.
▪ A primary key uniquely iden fies each record.
▪ A foreign key is a primary key from one table used as a lookup value to iden fy records in
another table.
Problems with Tradi onal File Structure
• Files maintained separately by different departments.
• Data redundancy - unnecessarily duplicated data.
• Inconsistent data
▪ E.g., John Smith's age vs his DOB
• Lack of flexibility and data sharing.
• Poor security.
Data Modeling
:
• The basic building blocks of all data models are en es, aAributes, and rela onships.
• En ty - person, place, thing, or event for which data.
• AAribute - characteris c of an en ty.
, • Rela onships - describe the associa on among en es:
▪ - - ( : ):
- A painter paints many different pain ngs, but each one is painted by only one painter.
▪ - - ( : ):
- Employee learns many job skills, each job skill might be learned by many employees.
▪ - - ( : ):
- A Verizon manager oversees one store.
Logical vs Physical Model
Big Data
• Massive sets of structured and unstructured data from web traffic, social media, sensors, etc.
• Volumes of data are too great for a typical DBMS
• Can reveal more paAerns and rela onships.
• At its core, big data is about predic ons.
• "Datafy" an event - capture an event in a quan fied format so it can be tabulated/analyzed.
Data Warehouse
:
• A home for high value data or data assets that originates in other corporate applica ons.
• :
▪ A coordinated and periodic copying of data from various sources both inside and outside the
company, into an environment op mized for analy cal and informa onal processing.
• Stores current and historical data
• Provides analysis and repor ng tools
• Relies exclusively on data obtained from other applica ons.
Data Management
