Continuous intelligence, according to the Gartner report, Top 10 Data and Analytics Technology Trends That Will Change Your Business, “combines data and analytics with transactional business processes and other real-time interactions.” It’s a way for systems to know what’s happening in real time – and do something about it.
Defining Continuous Intelligence
We already experience continuous intelligence in mundane apps like mobile maps. The map continuously updates our position and expected time of arrival based on traffic data, our speed and other factors. What’s new today is the ability to implement this kind of functionality into a far broader set of applications. And, it’s getting more sophisticated. As Gartner put it, continuous intelligence “leverages augmented analytics, event stream processing, optimization, business rule management and ML.”
For example, consider what’s currently happening in medical practice with decision support systems. Whereas in the old days, a doctor could order an MRI by scratching some instructions on a prescription pad, today he or she must input all sorts of diagnostic criteria into a software program that either supports, questions or denies the recommended service. There’s a good reason for this, namely that some medical procedures are not necessary. Insurance companies and government payers (e.g. Medicare) are now starting to mandate use of decision support systems for services they cover.
The decision support system is fairly static, however. It relies on medical criteria determined by experts over a period of years, e.g. from the American College of Radiology. With continuous intelligence, though, the decision support system could become a diagnostic witch ball. It can achieve situational awareness of the patient. Like, if the patient’s most recent lab work reveals an allergy to a dye agent used in the MRI, the doctor can instantly be informed of this and change the details of the MRI order. This is the advantage of continuous real-time or near-real-time ingestion of streaming data.
Two types of continuous intelligence systems are expected to predominate:
- Proactive push systems – these are “always-on” continuous intelligence (monitoring) systems that “listen” to events as they occur. When they detect a threat or opportunity situation that requires a response, they “push” updates to dashboards, or trigger automated responses.
- On-demand – in this case, a user invokes real-time analytics. When a process reaches a decision point it triggers a process or decision. The medical example falls into this category.
What’s Driving the Rise of Continuous Intelligence?
Gartner projects that over half of major new business systems will incorporate continuous intelligence by 2022. While organizations have long sought the digital witch ball, continuous intelligence is becoming viable now because of recent shifts in data analytics and related technologies. One driver is the increasing availability of augmented analytics.
Augmented analytics is accompanied by advances in ML, AI and stream analytics as well as in decision management software and time-series database management system (DBMS) software. Other factors include the availability of inexpensive sensor data from ubiquitous Internet of Things (IoT) sources. This data, in turn, is made possible by the prevalence of low-cost, high performance CPUs, GPUs, memory, storage, networks, cloud computing and mobile devices.
Benefits of Continuous Intelligence
Gartner envisions at least three benefits for continuous intelligence:
- For one, like the witch ball, continuous intelligence promises to turn a historical 360-degree view of the customers into a real-time 360-degree view – enabling more precise and effective customer support and special offers.
- They expect that continuous intelligence will help in implementing condition-based, predictive maintenance for equipment.
- Gartner also foresees the technology serving at the core of “enterprise nervous systems” in businesses like airlines, railroads and trucking companies. There, continuous intelligence could monitor and optimize resource scheduling decisions and the like.
Other possible beneficial use cases for continuous intelligence include workflows in cybersecurity, fraud detection and stock trading. In security, continuous intelligence can help solve the problem of needing to be proactive without being disruptive. For instance, it’s common for intrusion detection systems to “notice” a potential attack. Normally, this will result in an alert to a human being, who may or may not do something about it, depending on a human brain’s assessment of the seriousness of attack.
What if that security analyst is home asleep or out to lunch? (Or, just busy and overloaded, which is also common...) Continuous intelligence can support a proactive response to the alert. By using AI and ML to assess multiple data points and data streams, a security operations tool supported by continuous intelligence can take action, e.g. closing a firewall port, without the risk of shutting down business operations. Or, it could make an informed recommendation to the human security analyst that saves this person a lot of time and effort.
It’s difficult to predict exactly how the continuous intelligence trend will unfold. Chances are, the capability will be an add-on or upgrade to existing BI and data-driven systems. The challenge will be to pull them together and make them perform in a way that’s meaningful for the business. We have experience in these types of processes and innovations, so if you want to understand how continuous intelligence could positively affect your operations and strategic decision making, let’s talk.