4 ways for joining the real-time enterprise revolution

These days, there's a constant stream of chatter about the importance of being a "real-time enterprise," with the ability to sense and respond to any event or request in an instantaneous manner. Of course, there's a technical definition of real-time, but to the business, being real-time goes beyond processing to the ability to simply act fast and respond at the time a response is need to a given situation, such as a customer needing a product upgrade. 

So, what's it really mean to be real-time? I ran this question past industry experts, and here are some of their insights. The bottom line is joining the real-time revolution requires business sense, data savvy, attention to the edge, and adoption of modern technologies. 

Real-time Technology is critical to organizations going forward into the 2020s, because in today's fast-paced world, real time decision-making is a competitive differentiator. "Almost all modern applications require real-time capabilities," says Dr. Vikram Ahmed, director of enterprise information systems at Stetson University. "This is because with the advent of mobile devices and systems, users need access to up-to-date information at their fingertips. In recent times, the concept of the Internet of Things has sky rocketed, and real-time data is what drives this concept."   

The question is, then, how ready are enterprises for this shift? As it stands now, "most organizations remain further down the real-time streaming analytics maturity curve," says Steve Sparano, principal product manager of IoT and event stream processing at SAS. "Generating real-time insights requires the ability to ingest data in real-time, structure, analyze, and append that data to customer profiles, and then act on it accordingly. Core resources and technologies include streaming analytics to move from near-real time to processing data in real time, as events are happening, while still re-directing and storing the data in traditional databases for reporting, visualizations, and model development." 

Technology itself "has not kept up with the explosion of data and the stress it can put on critical systems," Josh Odmark, CTO and co-founder of Pandio, points out.  "Most technologies in existence today were built for big data analytics. Analytics only scratches the surface of what can be done with data, and as companies expand out from analytics, they require technologies that can handle more of everything. More data, more compute, more bandwidth, more labor, more connectivity, more operational support, or more insight."

As a result, getting to a world full of real-time applications is still a work in progress, especially for mid-size and small companies that do not have the budgets and infrastructure in place. "These organizations may still be relying on end-of-the day processing -- rather than real time -- and semi-automated data transfer protocols," Ahmed says. 

Of course, any technology wave is pushed and propelled by the applications that users need, and this is a key factor in the building momentum to real-time. "Real-time capabilities are in high demand in most analytical applications today," says Odmark. At this time, "industries that are highly affected by time are driving innovation," he says. "Machine learning applications are starting to explore real-time capabilities as they deal with inaccuracies with time series data and the gap between when a model can be trained and deployed."

Sparano sees real-time applications arising with all industries and processes.