Understand the characteristics big data it is important to design effective analytical architectures, policies, and processes. This article describes in a systematic way 10 characteristics (often called 10V), its implications for technology, examples of industry application, as well as practical recommendations for organizations to extract value from large-scale data. The discussion is structured for professional readers: data practitioners, IT architects, and decision makers.
What are the "characteristics of big data"?
Characteristics of big data is a set of attributes that distinguish large — scale data from traditional data-beyond the mere “multiplicity” of data. These attributes include technical aspects (eg. processing speed), quality (accuracy and relevance), security (privacy & compliance), to the business side (value generated). Establishing clear characteristics helps choose the right technology, process pipeline, and governance policies.
Evolution of the concept from 3V to 10V
Initially the concept of big data is popular with 3V: Volume, Variety, and Velocity. Seiring praktik dan kebutuhan bisnis berkembang, model tersebut diperluas—meliputi Veracity, Value, Visualization, Validity, Volatility, Variability, dan Vulnerability—sehingga sering disebut 10V. This expansion reflects that big data solutions must address not just the amount and format of data, but also its quality, security, temporal relevance, and value-extracting capabilities.
10 characteristics of Big Data and examples
Below each V is given a brief definition, technical/operational implications, and practical examples.
1. Volume
Definition: The size or amount of data that must be stored and processed.
Implications: Memerlukan storage terdistribusi (data lake, object storage), strategi kompresi, dan arsitektur yang mendukung scale-out.
Examples: Data transaksi, clickstream, dan log yang mencapai terabyte/petabyte per hari.
2. Velocity
Definition: The rate at which data is generated, sent, and needs to be processed (real-time vs. batch).
Implications: Memicu kebutuhan stream processing, low-latency ingestion (mis. Kafka, Flink), dan desain pipeline yang mendukung both streaming & batch.
Examples: IoT and clickstream sensor Data that must be analyzed on the spot.
3. Variety
Definition: Diversity of data formats: structured (table), semi-structured (JSON/XML), and unstructured (text, images, audio, video).
Implications: Need tools that support multiple formats (NoSQL, object stores) and flexible ETL/ELT processes.
Examples: Combined transactions, server logs, customer reviews, and product images.
4. Value
Definition: Business value that can be extracted from data through analytics and models.
Implications: Focus on clear use-cases and ROI metrics; not all data should be stored aimlessly.
Examples: Model rekomendasi yang meningkatkan conversion rate dan lifetime value pelanggan.
5. Veracity
Definition: Reliability, accuracy, and noise/bias levels in the data.
Implications: Diperlukan data quality frameworks, pembersihan data, verifikasi sumber, dan metadata yang kuat.
Examples: Sensor Data that has outliers or medical records with incomplete entries.
6. Validity
Definition: Data conformance to analytic definitions and needs (whether the data is valid for use).
Implications: Schema validation, aturan business logic, serta testing model untuk memastikan data relevan.
Examples: Demographic Data that must meet the format and scope of definitions defined for campaign analytics.
7. Variability
Definition: Contextual changes and semantic variations in data (value/format inconsistency between times).
Implications: Pipeline must be tolerant to scheme changes; monitoring to catch drift / inconsistency.
Examples: Third-party API structure changes affecting field names / formats.
8. Volatility
Definition: Seberapa cepat data kehilangan relevansinya (retention period dan decay).
Implications: Define retention, tiered storage, and aggregation policies for legacy data.
Examples: High-value realtime log Data is only a few days-after which it is aggregated or archived.
9. Visualization
Definition: The need to represent insights from large datasets so that they can be understood by business users.
Implications: Investasi pada dashboarding dan visualisasi yang skalabel serta storytelling data.
Examples: Dashboard operasi real-time untuk monitoring SLA atau performa kampanye marketing.
10. Vulnerability
Definition: Security, privacy and compliance risks inherent to data (leaks, unauthorized access).
Implications: Encryption, Role-Based Access Management, Data masking, audit trails, and regulatory compliance (eg. GDPR, PDPL).
Examples: Patient medical records and financial data that require extra protection. Multimedia Nusantara University.
Example of a simple case study of the application of 10V
- E-commerce: Volume transaction & clickstream, Velocity real-time recommendations, Value increased through personalization.
- Health: Veracity (quality of medical records) and Vulnerability patient privacy is a priority.
- Telecom & IoT: Velocity, Variability, and Volatility on sensor data and logs.
- Finance: Validity and Vulnerability essential for fraud detection and compliance.
Implications for Architecture & Technology
10V characteristics demand holistic architecture: data lake to unify volume/variety; batch & stream processing untuk velocity/variability; metadata & data governance untuk veracity/validity; serta enkripsi, access control, dan audit untuk vulnerability. Technology selection should be adjusted priority V on the use-case (eg. real-time analytics prioritize latency and throughput).
Common challenges and practical recommendations for implementing Big Data
Challenges: data quality, infrastructure costs, heterogeneous source integration, skilled HR shortage, and regulatory compliance.
Brief recommendations:
- Start from a clear use-case (Value first).
- Prioritize the most impactful V for your business.
- Bangun pipeline modular (ingest → storage → processing → serving).
- Terapkan data governance, monitoring, dan testing otomatis.
- Measure the ROI of each data Initiative.
Closing
Mapping the characteristics of big data (10V) provides a practical framework for defining the technologies, processes, and policies needed. By understanding each V - from Volume and Velocity to Vulnerability—organizations can design pipelines that are efficient, secure, and focused on Business Value.
Want to see how the platform can help manage these aspects? Audithink's Comprehensive Features provides integrated solutions for data, governance, and analytics pipelines designed to meet 10V challenges.
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