Big Data Basics
The 5 V's
IoT and Data Mining
Data Quality & Governance
Dashboards & Data Policies
100

What is the difference between Big Data and Small Data?

Small = simple, manageable. Big = huge, fast, complex.


100

Which V refers to the amount of data?

Volume

100

What does IoT stand for?

Internet of Things

100

What are the 3 C’s of Data Quality?

Consistency, Completeness, Correctness

100

Name a dashboard tool.

Tableau, Power BI, Looker, Qlik, Domo

200

Give one reason Big Data is challenging to process.

Storage, speed, privacy, security.


200

Which V deals with the speed of data generation?

Velocity 

200

Name one IoT challenge.

Security, privacy, compatibility, huge data volumes.

200

Why does poor-quality data cause problems?

Leads to bad decisions, errors, misleading results.

200

What is the purpose of a dashboard?

Communicate insights clearly + support decisions.

300

Name two industries heavily using Big Data.

Healthcare, retail, banking, marketing, tech, etc.

300

Which V refers to data formats like videos, text, images?

Variety 

300

What is Data Mining? 

The process of discovering patterns, correlations, trends, or predictions in large datasets using statistics and machine-learning methods.

300

What is Data Governance?

Rules for managing data ethically and properly.

300

One challenge of dashboards?

Bad data, too many KPIs, slow updates.

400

What makes Big Data “big”?

Size, speed, variety, and complexity.

400

Define Veracity.

Data accuracy + trustworthiness.

400

Name one real-world example of data mining.

Fraud detection, product recommendations, hospital risk predictions.

400

Name two components of a Data Governance framework.

Standards, roles, security, privacy, metadata, auditing.

400

What is Data Retention?

Rules for how long data is kept + when it’s deleted.

500

What is integration?

challenge that involves making sure data from multiple sources matches and works together.

500

What are the 5 V's of Big Data?

Volume, Velocity, Variety, Veracity, and Value 

500

Name and describe one data mining technique.

Classification, clustering, regression.

500

How does strong data governance prevent misleading insights?

Ensures clean, accurate data and proper analysis.

500

What must be included in a Data Retention Policy?

Data types, timeframes, storage, legal rules, deletion procedures.

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