For modern organizations, the ability to quickly analyze and act on new information opens up new possibilities. Using real-time data is becoming essential for maintaining a competitive edge. Before we dive into the use cases, let’s start with the basics.
What Are Real-time Data?
Real-time data refers to information that is generated, processed, and delivered immediately or with minimal delay, allowing for rapid response. This approach contrasts with the traditional method of gathering and analyzing data at scheduled intervals.
Real-time data processing is often confused with stream processing. However, while stream processing focuses on continuous data flow, real-time data processing emphasizes speed, ensuring data are acted on as quickly as possible.
Real-time Data: Sources and Use Cases
Real-time data has applications across various sectors, including marketing, finance, logistics, and HR. It enhances process monitoring, enabling quick reactions to changes and optimizing resource use in ways previously unattainable.
Real-time data can be generated by various software systems. Common sources include:
- Internet of Things (IoT): Devices like smart meters, motion sensors, cameras, and HVAC systems.
- ERP and CRM systems: Customer, order, and resource management data.
- Mobile apps: Insights into user traffic, locations, and activity.
- Social media: Metrics on user reactions, post reach, and comment counts.
In practice, any device equipped with sensors and data-sharing capabilities, as well as most software systems, can contribute to real-time data flow.
Why Use Real-time Data?
Processing data in real time offers several advantages, influencing competitive positioning, operational efficiency, and responsiveness to changing conditions. Key benefits include:
Enhanced Personalization
Marketing and sales activities can be tailored to current customer needs, improving satisfaction and engagement.
Faster Decision-making
Companies can make informed decisions quickly, focusing on the most relevant and timely data.
Improved Security
Anomalies are detected more rapidly, reducing the risk of outages, errors, fraud, or cyberattacks.
Optimized Resource Utilization
Organizations can adjust operations to current demands, making more effective use of resources like energy, vehicles, and warehouse space.
How Can You Process Data in Real Time?
There are three main methods for working with real-time data:
Batch Processing
In batch processing, data are collected and processed in chunks or batches. This can happen automatically at set intervals or on demand. While it may seem contrary to real-time processing, batch processing can still be effective for non-urgent tasks. For example, a company using external advertising systems might import data on ad performance once daily rather than continuously.
Event-driven Architecture
This approach triggers data processing in response to specific events. Common in monitoring, analytics, and e-commerce, event-driven processing initiates workflows only when certain actions or conditions occur. For instance, when a user adds an item to their shopping cart or submits a form, the system immediately processes relevant data. Here, data are processed in real time “on demand,” with the system always on standby to respond to specific triggers.
Stream Processing
In stream processing, data flows continuously without clear start or end points. Information bundles are usually small, and data are sent in short intervals. This method is ideal for real-time analysis of large data volumes, such as IoT or network traffic data. Data are processed continuously, providing near-instantaneous results that enable quick responses. Storing information in an accessible way is essential for both current and historical data retrieval.
Real-time Data Processing Stages
All the methods mentioned above involve two key stages:
Data Collection: The process begins as information enters the system.
Data Processing: Data are then filtered, aggregated, enriched, and transformed.
After processing, the system can initiate various operations. For example, it might store information in a database for future reference or automatically transfer it to other systems for further use.
Some data may be discarded, which might seem counterintuitive. However, given the vast amount of information processed in real time, not all data is valuable enough to retain.
Choosing the Right Approach to Real-time Data Processing
The best real-time data processing method depends on your business needs and goals.
For example, let’s consider a shipping company that uses IoT sensors with GPS to monitor container locations in real time. The company could use:
- Stream processing to monitor freight locations continuously.
- Event processing to receive alerts when shipments arrive at specific destinations.
Choosing an approach also requires evaluating the characteristics and limitations of the data source. Not all data sources can create events, which are essential for event-driven architecture. In such cases, stream or batch processing may be necessary.
Real-time Data Processing: Challenges and Risks
Real-time data processing comes with unique challenges that must be managed to harness its full potential. Key considerations include:
- Scalability: Systems need to handle large and variable data volumes efficiently.
- Outages: Real-time systems are vulnerable if data flow is interrupted or processing capabilities are insufficient.
- Data Management: Managing large amounts of information is crucial to avoid quality issues.
- Cost: Continuous data processing can be expensive due to data volume and processing requirements.
- Monitoring: Robust monitoring is essential to oversee system operations and quickly detect any failures.
Real-time Data Processing: Best Practices
Following best practices for real-time data processing helps minimize common risks and maximize efficiency.
- Select the right architecture. Choose a scalable, flexible platform with advanced automation capabilities. This will help you easily address future challenges and adapt to changing demands.
- Implement automation. Given the massive volumes of real-time data, automation is essential. Don’t rely solely on manual tasks for processing or decision-making. Instead, automate workflows, alerts, notifications, and ongoing analyses.
- For real-time data processing, manual oversight should focus only on the most critical areas.
Thoughtfully planning and structuring operations for real-time data processing is crucial. Without a clear strategy, real-time data can generate unnecessary costs with limited benefits. To explore more, check out our article, A Guide to Mastering Data Integration in 10 Steps.
How Does FME Fit into Real-time Processing?
FME is a versatile data integration platform that supports over 450 data sources, along with custom integrations via APIs, R, and Python. It combines no-code and low-code functionalities, allowing team members and decision-makers to work with an intuitive, graphical interface.
With FME, both basic and complex data processes are easily handled. You can even refine data through external tools to enhance information quality and align it with business requirements.
FME also enables advanced analysis and data visualization, incorporating Location Intelligence for mapping geolocation data. Automations within FME allow the system to generate tasks and notifications, send emails, and initiate scripts in external applications—all without manual intervention.
These features make FME an excellent solution for real-time data processing.
If you’re interested in implementing real-time data processing at your company or learning more about FME’s capabilities, feel free to reach out. Our experts can help you find the best solution tailored to your business needs.
