What is Big Data?
Discover what Big Data means in IoT, and learn how the world’s most creative technical innovators are using Big Data to transform their business.
Big Data at a Glance
- Massive datasets, smarter insights: Big Data refers to extremely large and complex data sets that, when analyzed, reveal patterns, trends, and predictions beyond the reach of traditional tools.
- Essential for IoT success: Connected devices generate huge volumes of information, and Big Data analytics transforms raw IoT data into actionable business intelligence.
- AI-powered future: With tools like Soracom Flux and MCP Server, businesses can harness Big Data and AI together for real-time analysis, automation, and predictive decision-making.
What is Big Data?
Big Data describes datasets so large, fast-moving, and varied that they cannot be handled by conventional databases or analytics systems. Instead, advanced technologies like distributed computing, cloud analytics, and machine learning are used to process and extract value.
Big Data is typically defined by the “Three Vs”:
- Volume: Massive amounts of data (terabytes to exabytes).
- Velocity: The rapid pace of data creation and processing.
- Variety: Multiple formats – from structured databases to unstructured media like video, audio, and IoT sensor streams.
For IoT, Big Data is the bridge between raw device output and business insight, enabling predictive maintenance, smarter resource allocation, and real-time optimization.
Benefits of Big Data
- Improved decision-making: Real-time analytics supports faster, more confident business actions.
- Predictive capabilities: Identifies patterns that help forecast outcomes and manage risks.
- Operational efficiency: Streamlines processes, cuts costs, and optimizes system performance.
- Personalization at scale: Powers tailored products, services, and marketing campaigns.
- Innovation driver: Fuels breakthroughs in AI, automation, and product development.
Challenges with Big Data
Despite its potential, Big Data brings challenges:
- Scalable infrastructure required: Large and fast-moving datasets need distributed storage and compute resources.
- Data quality issues: Insights depend on consistent, clean, and well-integrated information.
- Privacy and security risks: Sensitive data must be safeguarded across networks and storage.
- Skills gap: Extracting value may require expertise in data science, AI, and machine learning.
Big Data and IoT
The growth of the IoT has made Big Data essential. Every connected device – whether a sensor, wearable, or vehicle – generates continuous streams of information. Without Big Data, this information remains noise. With it, organizations can detect patterns, forecast outcomes, and automate responses.
Examples include:
- Smart cities: Barcelona processes sensor data from parking, transit, and environmental monitoring to improve urban services.
- Transportation: UPS analyzes IoT fleet data to optimize routes, cut fuel consumption, and reduce emissions.
- Telecommunications: Carriers use Big Data to handle billions of interactions daily, improving network quality and customer experience.
👉 IoT creates the data; Big Data makes it meaningful.
Example Use Cases for Big Data in IoT
- Smart grids: Monitor demand, detect outages, and integrate renewables.
- Healthcare: Enable predictive diagnostics and remote monitoring.
- Industrial IoT: Predictive maintenance, quality assurance, and supply chain optimization.
- Retail: Customer behavior analytics to optimize inventory and promotions.
- Agriculture: Analyze soil, weather, and crop data to increase yields and efficiency.
Big Data vs. Traditional Data Analysis
| Feature | Traditional Data | Big Data |
| Volume handled | Megabytes to gigabytes | Terabytes to exabytes |
| Speed (velocity) | Batch processing | Real-time streaming analytics |
| Data formats | Mostly structured | Structured + semi-structured + unstructured |
| Tools required | Relational databases, spreadsheets | Distributed computing (Hadoop, Spark), AI/ML, cloud analytics |
| Insights enabled | Historical reporting | Predictive and prescriptive analytics |
How Soracom Enhances Big Data for IoT
Big Data analytics is powerful, but IoT developers often face the challenge of collecting, transporting, and processing huge amounts of device data. Soracom simplifies this with connectivity plus AI-powered services that accelerate the Big Data pipeline:
- Soracom Flux (AI-driven data pipeline): A low-code platform for building IoT applications that can filter, transform, and analyze data streams at the network level. Flux makes it easier to extract insight without building complex backend systems.
- MCP Server (Massive Concurrent Processing): Purpose-built to handle large-scale, high-frequency IoT data bursts, applying AI-driven processing for reliable, efficient delivery.
- Soracom Harvest: Securely stores IoT device data for rapid prototyping and lightweight analytics.
- Soracom Funnel: Routes IoT data directly into Big Data platforms and cloud providers like AWS, Azure, or Google Cloud.
- Soracom Lagoon: Creates real-time dashboards and visualizations to make Big Data more accessible across teams.
- Secure global connectivity: With Soracom SIMs/eSIMs and Virtual Private Gateways (VPG), IoT devices stay connected securely across borders while protecting sensitive data.
👉 With Soracom, businesses don’t just collect Big Data — they turn it into real-time intelligence that fuels smarter decisions, predictive insights, and automated operations.
Big Data in IoT: Industry Use Cases at a Glance
| Industry | How IoT Generates Data | How Big Data + AI Delivers Value |
| Smart Cities | Traffic, energy, and environmental sensors | Reduce congestion, optimize utilities, improve sustainability |
| Healthcare | Wearables and remote monitoring devices | Predict risks, improve treatment outcomes, reduce readmissions |
| Transportation | Fleet telematics and route sensors | Lower fuel costs, improve delivery times, enhance safety |
| Retail | In-store sensors, beacons, POS data | Personalize offers, optimize inventory, improve CX |
| Agriculture | Soil, drone, and weather station data | Boost yields, conserve resources, predict harvest timing |
| Manufacturing | Machine health and production line monitoring | Enable predictive maintenance, improve quality, reduce downtime |