What Is Data? A 2025 Guide to Types, Lifecycle & FAQs

1. Data ≠ Information

• Data → unprocessed facts (e.g., 42, 2025-06-23, “Like”).

• Information → data that’s been organized so humans can understand context (e.g., “42 mm rainfall on 23 June 2025”).

• Knowledge → insights and decisions drawn from information (“Carry an umbrella tomorrow—monsoon has begun”).

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2. Core Types of Data

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3. The Five V’s of Big Data

1. Volume – zettabytes created each year (147 ZB estimated for 2024) soax.com

2. Velocity – streaming in near-real-time.

3. Variety – text, images, logs, IoT signals.

4. Veracity – trustworthiness & bias.

5. Value – insights that justify the cost. databasetown.comdatatas.com

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4. The Data-Lifecycle Blueprint

Generate → Collect → Store → Process → Analyze → Share → Archive/Dispose pg-p.ctme.caltech.edu

1. Generate – sensors, apps, transactions.

2. Collect – batching or streaming into landing zones.

3. Store – warehouses, data lakes, lakehouses.

4. Process – ETL/ELT, cleaning, transformation.

5. Analyze – BI dashboards, ML models.

6. Share – APIs, reports, data products.

7. Archive/Dispose – retention rules, secure deletion.

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5. Data-Quality Dimensions

A practical checklist:

• Accuracy – correct values

• Completeness – no unjustified nulls

• Consistency – same across sources

• Timeliness – fresh enough for purpose

• Validity – follows business rules

• Uniqueness – no duplicates icedq.comdqops.com

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6. Data Governance & Compliance (2025 snapshot)

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7. Security & Privacy Essentials

1. Classify data by sensitivity

2. Encrypt in transit & at rest

3. Zero-trust & least-privilege access

4. Backup & immutable snapshots

5. Monitor leaks—use 2FA & password managers geeksforgeeks.orgthescottishsun.co.uk

Bonus trend: Nations are racing toward quantum-safe networks (e.g., ISRO & DRDO projects in India) to protect future data flows. timesofindia.indiatimes.com

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8. Step-by-Step Guide for Working With Data

1. Frame a question – What do we need to know?

2. Identify sources – internal logs, open datasets, surveys.

3. Ingest & store – choose schemas wisely.

4. Clean & transform – handle nulls, standardize units.

5. Explore & visualize – look for patterns/anomalies.

6. Model & test – statistics, ML, AB tests.

7. Communicate insights – narrative + visuals.

8. Operationalize – automate pipelines; monitor drift.

9. Iterate – treat analytics as a product.

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9. Frequently Asked Questions (FAQ)