Data Management Flashcards by Deleted Deleted

What is data management?

The process of collecting, storing, organizing, protecting, and maintaining data so it can be used effectively.

Data management is crucial for effective data utilization.

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Why is data management important?

It ensures data accuracy, security, availability, and supports better decision-making.

Effective data management leads to improved organizational performance.

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What are the main components of data management?

Data collection
Storage
Organization
Security
Backup
Maintenance

Each component plays a vital role in the overall management of data.

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What is data governance?

A set of rules and policies that ensure proper data usage, quality, and security.

Data governance is essential for maintaining data integrity.

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What is a database?

An organized collection of data that can be easily accessed, managed, and updated.

Databases are fundamental to data management.

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What types of data storage are used in data management?

Primary storage
Secondary storage
Cloud storage

Different types of storage serve various data management needs.

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What is data redundancy?

The duplication of data in multiple locations.

Redundancy can lead to inefficiencies and data management challenges.

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What is data normalization?

The process of organizing data to reduce redundancy and improve consistency.

Normalization is crucial for maintaining data integrity.

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Difference between structured and unstructured data?

Structured data: Organized in tables
Unstructured data: No fixed format (e.g., emails, images)

Understanding the difference is key for data management strategies.

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What is metadata?

Data that describes other data (e.g., file size, author, date created).

Metadata provides context and helps in data management.

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What is data quality?

Data accuracy, completeness, consistency, and reliability.

High data quality is essential for effective decision-making.

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What is data cleansing?

The process of correcting or removing inaccurate, incomplete, or duplicate data.

Data cleansing is vital for maintaining data quality.

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What are common data quality issues?

Missing values
Duplicate data
Incorrect formats
Inconsistencies

Identifying these issues is the first step in improving data quality.

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How do you ensure data accuracy?

By validation rules, data checks, and regular audits.

Regular audits help maintain high standards of data accuracy.

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What is data validation?

Ensures that data meets defined rules and standards.

Data validation is a critical step in data management.

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Why is data security important?

To protect data from unauthorized access, breaches, and loss.

Data security is essential for maintaining trust and compliance.

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What methods are used to secure data?

Passwords
Encryption
Access control
Firewalls

These methods help safeguard sensitive information.

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What is encryption?

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Converts data into unreadable form to prevent unauthorized access.

Encryption is a key technique in data security.

What is access control?

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Limits who can view or modify data.

Access control is crucial for protecting sensitive information.

What is data privacy?

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Ensures personal data is handled according to laws and policies.

Data privacy is essential for compliance with regulations.

What is data backup?

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A copy of data stored for recovery in case of failure.

Regular backups are vital for data recovery strategies.

What is disaster recovery?

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The process of restoring data after system failure or disaster.

Disaster recovery plans are essential for business continuity.

How do you ensure data availability?

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Using backups, redundancy, and reliable storage systems.

Ensuring availability is critical for operational efficiency.

What is data integrity?

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Ensures data remains accurate and unchanged unless authorized.

Maintaining data integrity is crucial for trust in data.

What is **version control**?

Manages changes to data or files over time. ## Footnote Version control is important for tracking data changes.

How do you manage **large amounts of data**?

Using databases, cloud storage, and data management tools. ## Footnote Effective management strategies are essential for handling large datasets.

What challenges are faced in **data management**?

* Data security * Data quality issues * Storage limitations * Compliance ## Footnote Addressing these challenges is crucial for effective data management.

How do you handle **duplicate data**?

By data cleansing, normalization, and unique identifiers. ## Footnote Proper handling of duplicates is essential for data quality.

What tools are used in **data management**?

* DBMS * Excel * SQL * Cloud platforms * Data analytics tools ## Footnote Various tools facilitate effective data management.

What improvements would you suggest for **data management systems**?

* Better security * Automation * Regular data audits ## Footnote Continuous improvement is key to effective data management.

What are the **two main types** of databases?

* Relational database * Non-relational database ## Footnote Both types have their own pros and cons.

Who developed the concept of **relational databases**?

Edgar Codd ## Footnote He developed this concept in 1969 while working for IBM.

What does **SQL** stand for?

Structured Query Language ## Footnote SQL is used to send commands to relational databases.

In a relational database, what is a **primary key**?

A unique ID that identifies each record or row ## Footnote It remains unchanged even if other attributes of the record change.

What is a **foreign key** in a relational database?

A field that refers to the primary key of a record in another table ## Footnote It establishes a relationship between two tables.

What is a key advantage of using **relational databases**?

Allows defining connections between records across tables ## Footnote This helps in storing and updating data efficiently.

What is the official name of the UK that can be updated in a relational database?

United Kingdom of Great Britain and Northern Ireland ## Footnote Changes made in one location reflect throughout the database.

What are some examples of **non-relational databases**?

* Document-oriented databases * Graph stores * Time-series databases * Key-value stores * Geospatial databases * Wide-column databases ## Footnote Non-relational databases emerged to handle high rates of operations.

What is a characteristic of **non-relational databases** regarding data schema?

No defined data schema ## Footnote Records can belong to more than one entity and have different fields.

In non-relational databases, how are records often structured?

As documents with nested records ## Footnote This allows for flexibility in the data structure.

What is a **collection** in the context of non-relational databases?

A way of grouping similar documents ## Footnote Each document can have a different set of fields.

True or false: **Relational databases** are always easier to maintain than non-relational databases.

FALSE ## Footnote Relational databases can be complicated and hard to maintain.

What ultimately determines whether to use a **relational or non-relational database**?

The type of data and how it will be used ## Footnote Both types are useful for different reasons.

Data Management Flashcards

Basic Interview Questions. (43 cards)