The real-time revolution is here, but not evenly distributed.

The real-time revolution is here, but not evenly distributed.

real-time technology

If you’ve been keeping up with the latest trends in technology and attending conferences or reading analyst reports, you might get the impression that real time is now an integral part of every organization. It seems like everyone is able to sense and respond to events in milliseconds. However, the reality is quite different. Real time is still a work in progress, but there are good reasons why there is a hunger for it.

Real-time technology is the backbone of many exciting innovations such as artificial intelligence, predictive analytics, embedded systems, streaming apps, real-time location monitoring, and alert systems. These technologies rely on real-time capabilities to function effectively. However, industry surveys show that real-time capabilities are still a dream rather than a reality for many organizations.

For example, in supply chain management, executives actively seek real-time shipment visibility, but only a quarter of them currently have access to it. Similarly, a survey by Unisphere Research and ChaosSearch reveals that only 23% of enterprises have information available to end-users in real time. So, why is real time still a work in progress?

According to Nick Amabile, CEO of DAS42, not every business requires real-time data. It depends on whether the requirement is operational or analytical. Operational systems, such as information security monitoring, logistics, marketing personalization, and fraud detection, benefit from real-time data. However, analytical use cases can have some degree of latency. While user-facing reporting may need to be real-time, executive reporting can be hours old.

Making the move to real time can be a complex and expensive endeavor. Tyson Trautmann, VP of engineering for Fauna, explains that larger organizations and technology-centric industries often have the infrastructure to handle real-time data. However, this infrastructure is often built on top of legacy systems that were not designed for real-time capabilities, creating a high operational burden. Therefore, organizations need to weigh the benefits of moving from batch processing to true real-time.

In addition to infrastructure challenges, data quality and trust are crucial when dealing with real-time data. With less time to clean and prepare the data, decisions can be based on incomplete or inaccurate information, leading to poor outcomes. Sam Pierson, senior vice president at Qlik, emphasizes the importance of a strong data strategy and infrastructure to ensure that valid and trusted data is used in real time.

Despite the challenges, there are tools and platforms available that make real time more accessible. Cloud service providers like Amazon Web Services, Google Cloud, and Microsoft Azure offer managed services tailored for real-time processing. Distributed, in-memory, and time-series databases have also emerged to address the need for efficient real-time data workloads. Open-source offerings such as Apache Kafka, Apache Flink, and Apache Storm have enriched the real-time data processing ecosystem.

Furthermore, advancements in edge computing and the potential of 5G technology for lower latency and higher data handling capabilities have opened new frontiers for real-time applications. However, the adoption of real-time data infrastructure is still unevenly distributed across different industries, and complexities arise from the multitude of data management technologies and cloud providers, as well as governance and privacy regulations.

In conclusion, while real time is still a work in progress for many organizations, there is a growing demand for its capabilities. Businesses need to carefully evaluate their operational and analytical needs before investing in real-time systems. With the availability of tools and platforms, even smaller or medium-sized organizations with limited budgets can take advantage of real-time capabilities. Real time may not be necessary for every use case, but when implemented effectively, it can provide valuable insights and improve decision-making processes.