Many modern applications must push processing and analysis closer to where data is generated, bypassing traditional centralized models that are often hampered by latency issues.
As data-driven technologies proliferate, businesses and industries are finding themselves overwhelmed by the sheer volume and speed at which data is generated. This surge in data generation has brought about new challenges, especially in sectors like autonomous cars, intelligent manufacturing, financial services, and eCommerce. Increasingly, latency is the defining issue in such use cases.
Why? The crux of the challenge is not just the ability to collect vast amounts of data but the necessity to analyze and act on it in real time. The problem is that traditional approaches rely on models where data is backhauled to central databases for analysis. That approach incurs great delays as large volumes of data must be transferred from the source of generation to the central processing facility.
The delay introduces great latency that cannot be tolerated for many modern real-time applications. For example, consider the addition of a few seconds to ship video data from forward-looking cameras on an autonomous vehicle for analysis. That delay makes the analysis worthless as the vehicle has likely driven past (through?) the object the analysis detects.
What’s needed is a solution that pushes the processing and analysis closer to where data is generated, bypassing traditional centralized models that are often hampered by latency issues.
See also: Report: Latency Issues Hamper Digital Business Advances
The Explosion of Use Cases Where Latency Matters
Analysis latency has become a much more important issue in recent years due to the proliferation of use cases that need real-time decisions based on data generated outside an organization’s normal infrastructure.
Today, data is generated everywhere. Examples include data from sensors in a factory, autonomous vehicles, smartphones, and financial transactions. In many cases, decisions must be made at the source of the generated data.
Specifically, the analysis must be made at the edge in real time. Some common examples of this scenario include:
Autonomous cars: Self-driving cars are perhaps the most prominent example of where latency is not acceptable and where decisions must be made at the source of data generation. These vehicles are equipped with an array of sensors that generate massive amounts of data in real time. Decisions like braking, obstacle avoidance, and route planning must be made instantaneously. Relying on a centralized cloud infrastructure to analyze this data could mean life or death; milliseconds of delay can lead to accidents.
Intelligent manufacturing: Modern manufacturing relies on automation, robotics, and interconnected devices. These systems generate vast amounts of data, from machine performance metrics to environmental conditions in the factory. If a machine is about to malfunction or if an operational inefficiency arises, it must be addressed immediately to avoid downtime. Such an operation cannot tolerate delays in transmitting data to distant data centers.
Fraud prevention in financial services: The financial industry processes millions of transactions every day, and the need for real-time fraud detection has never been more critical. Traditional methods often involve sending transaction data to central servers for analysis, but this introduces delays that can allow fraudulent transactions to slip through. Organizations must bring fraud detection closer to where transactions occur—whether that’s at ATMs, point-of-sale systems, or mobile banking platforms. Doing so allows financial institutions to instantly detect anomalies and prevent fraud before it happens.
Personalization in eCommerce: In the competitive world of eCommerce, personalization is key to engaging customers and driving sales. As shoppers browse websites or use mobile apps, their preferences and behaviors generate valuable data. This data can be used to deliver personalized product recommendations, discounts, or other offers. However, sending this data to centralized servers for analysis can create delays, leading to missed opportunities.
Why Traditional Approaches Fall Short
Historically, businesses have relied on centralized cloud infrastructures to process and analyze data. In this model, data is collected at the edge, transmitted to distant data centers for processing, and then sent back to the edge with the resulting insights. While this approach has its advantages, it suffers from one key limitation: latency. In industries where milliseconds matter, the time it takes to send data back and forth can lead to missed opportunities or even catastrophic failures.
For example, in the case of autonomous cars, sending data to a centralized cloud to analyze road conditions or obstacles could result in dangerous delays. Similarly, a factory that waits for data to be processed in the cloud may experience equipment failures or operational inefficiencies before it can act on the insights. In financial services, even slight delays in fraud detection can lead to significant financial losses.
The issue of latency becomes even more pronounced as the volume of data increases. Centralized systems can become bottlenecks, struggling to process data in a timely manner. This is where edge computing offers a compelling alternative by processing data closer to where it is generated, enabling real-time decision-making.
What’s Needed to Address the Latency Issue?
To reduce decision latency, organizations must have solutions in place that can derive insights from data at its source (at the edge) in real time. Such solutions must have a core set of functionality, features, and capabilities to manage the immense volume and velocity of data generated at the edge and then perform real-time analysis of that data.
Some of the most desirable attributes to such a solution include:
High-throughput and low-latency processing: The solution must be optimized for handling massive amounts of data with near-zero latency. This is critical for edge applications where real-time decisions are required, such as in autonomous vehicles or machinery on a factory floor. It enables immediate data processing and analytics at the point of data generation.
Event-driven architecture: A solution must be designed to handle event-driven data streams, which are essential for IoT applications like autonomous vehicles and industrial operations. It can ingest, process, and respond to data in milliseconds, allowing real-time decision-making based on the latest information.
Real-time analytics and decisioning: Any solution for this scenario must be capable of analyzing and acting on streaming data in real time, supporting AI and machine learning models that require instant insights and decision-making. For example, autonomous cars can process sensor data to make split-second decisions on navigation or hazard avoidance.
Transactional consistency at scale: Unlike many streaming data platforms, an ideal solution for real-time edge decisions must support strong transactional consistency, ensuring that decisions and data processing are accurate, even under the extreme demands of real-time edge environments.
In summary, an optimal solution must have the ability to combine high-speed data processing, real-time analytics, and more, addressing the need for instant insights and decision-making from edge-generated data.
Additional Resources
Real-time Data Processing at Scale for Mission-critical Applications (Blog Post)
What Does Real-time Mean in Today’s World? (Interview)
Intelligent Manufacturing with Real-Time Decisions (Analyst Report)
How Volt Meets Mission-Critical Application Requirements: The Volt Active Data Architecture
