“Real-time” doesn’t mean today, this hour, this minute, or even this second — the real thing is instantaneous — and it’s poised to transform the global economy.
Social media updates, the news cycle, mobile phone alerts — a lot of technologies purport to, and may in fact seem to deliver information in real time. But 2-hour or even 5-minute push notifications do not qualify as such.
The term came into use in the ‘60s just as Moore’s Law was coming into effect. But only in the past decade or so has real real-time data analysis and digital communication been possible. Real real-time applications deliver information immediately — in milliseconds or less.
When we think about what qualifies as “real-time data,” it’s important to distinguish between that which is merely called real-time, and that which actually is. Why? Because their applications and their outcomes are entirely different — and this difference often has to be experienced to be fully understood.
Industries that are asking themselves “what would I need real-time data for?” — or those who mistakenly think they already have it — are missing out on vital intelligence and industry-shaking capabilities that they quite simply cannot survive without.
One common use of “real-time” is that of “real-time communication” (RTC), which has minimal latency and no transmission delays, with no data stored before the information is received. RTC is possible with widespread technologies like the internet, landlines and mobile phones, instant message, and video conferencing. Similarly, so-called real-time applications in the form of web technologies are integral to the user experience of many web-based services, including social media platforms, chat apps, and customer service transactions.
The term “real-time analytics” suggests that data is both accessible in the same moment that it is created, and is being recorded over time for post-facto historical analysis. Increasingly, the emphasis is on the former half of that definition. As IoT-powered sensors and cameras generate more data than ever, low-latency processing becomes an issue of both function and safety. Imagine, for example, a self-driving car that has to make a split-second decision with data that takes two split seconds to process. In these cases, latency can mean the difference between life and death.
The working definition(s) of real-time often depend on the context in which the term is used. In computer processing, sub-second response times are crucial for effective performance. For humans, however, real-time can mean as much as a few seconds: think of how long you would wait for a web page to load before you noticed the delay. Real-time can also refer to processes that are triggered by other events — if a change is made, a real-time response would occur just after that change, but before another change occurs. For Complete Event Processing (CEP), combining data from multiple sources may take slightly longer, but allows for the detection of patterns in what is considered real-time for that specific application.
The ambiguity of the phrase real-time may pose an issue if businesses mistakenly believe their processes provide instantaneous information. Recording data continuously over time doesn’t imply real-time analysis: in many cases, much of this data is being stored immediately, and is only analyzed after the fact.
Shipping sensors, for instance, may gather temperature or location data while on the road for 12 hours, but that data often isn’t processed or even viewed until the next day. It doesn’t matter if data is generated throughout an entire process — if it isn’t made viewable in the moment, it isn’t real-time information, and is therefore only useful in the generation of retroactive insights.
When it comes to data analytics, there is a consequential difference between truly real-time and near real-time. Near real-time means “almost now,” but that can entail a few minutes to a few hours. In this case, data is stored and batch processed every so often. The resulting insights are therefore decidedly not instantaneous.
This is not a problem in all cases, however — certain applications may see no further benefit from an increase in data transmission speeds. But for first responders, high-precision manufacturing, shipping & logistics and many others — latencies of even just a few seconds can render otherwise valuable data practically useless. Needless to say, the consequences can be serious.
Real real-time means instantaneous insight and a proactive position. Of course, real-time insights don’t just happen: they are built through powerful technologies, platforms and applications designed to transform disparate data inputs into formats that users can leverage right away. This means visualizing real-time data into actionable intelligence.
The real real-time is here, and the capabilities enabled by its arrival are becoming apparent across many industries. Platforms designed to leverage this data can provide solutions across nearly every vertical — from public safety and the military to transportation and hospitality. And these industries are only just beginning to tap into the transformative level of situational awareness this data enables.
In transportation logistics, where timely data can mean the difference between a failed shipment and a successful one, the possibilities are vast. Real-time temperature information can help safeguard perishable cargo, while real-time location and traffic information can optimize shipping routes. Moreover, when this data is shared with all relevant stakeholders (i.e. truckers, shippers, and retailers) through a common operational picture, entirely new capabilities are made available.
In security scenarios, where rapid communication and information-sharing are crucial, real-time data holds similarly transformational prospects. This remains true whether you’re protecting business assets in a warehouse, securing a border, or ensuring the safety of sports fans. These threats demand immediate action. Security cameras and gunshot detection devices won’t have their intended impact with a five-minute or hour-long delay. Security teams need that data to be accurate and up-to-date as the situations unfold.
As it stands, the term “real-time” is defined in context — based on the demands of the situation, and the technologies used. For now, this makes sense. But as industries start to recognize the unprecedented value of truly instantaneous data, they’ll start replacing this collection of contextual definitions with the one — the real real-time — the paradigm that will fundamentally transform the global economy.