From continuous intelligence to composite AI – a mix for success
Leveraging business intelligence to make decisions has been the core of any company that somehow wants to utilize data. An approach to the ever-growing data volume has been to integrate BI systems into everyday operations. Which is perfectly fine and might work for some. The issues appear when data velocity becomes greater and data starts moving fast, with continuous changes. That’s the reason why we see more and more companies using data streaming tools, like Kafka, and Spark, or in tandem, to get access to real-time data. And in comes something special, continuous intelligence (CI) as a solution to ever-changing business landscapes. Also, as the world shifts, and IT infrastructures move with it, companies are not tied to only one solution in handling and utilizing data. One source of either AI, ML, or analytics tools is no longer enough. A combination of all will most likely occur. Often it was referred to as multidisciplinary AI. But, now a new term has emerged – composite AI. As one of the most prominent and biggest trends in complete AI integration in business processes, as declared by Gartner, composite AI will make some waves. You may look at it as something that is not new, which might be true because this is rather an approach than a new technology. Both CI and composite AI present positive and must-have new approaches to guiding business decisions in a new era. Continuous intelligence in service of real-time insights Continuous intelligence (CI) is the use of different approaches and technologies integrated into business operations to process current and historical data through real-time data streams to perform real-time analytics. What is different about continuous intelligence is that it uses data in motion but also uses historical and batch data. This specificity requires multiple solutions to coordinate together so they can generate proper insights. Firstly, a solution to ingest data in real-time has to be implemented to handle data streaming. Of course, it needs a platform to collect, organize and analyze data. And we must not forget the analysis of historical data that requires some kind of in-memory technology that will speed up data processing. But, it’s not only about analyzing data streaming and batch data, in real-time or historical. It’s about the implementation of AI and machine learning to make this process as automated as possible to remove human bias, lag time, and possible errors. We already mentioned augmented analytics in previous blog posts, and this is also an integral part of continuous intelligence (CI). CI isn’t just a piece of technology. It’s a cluster of them to form a comprehensive design of tools to analyze data in order to precisely generate metrics and insights at a moment’s notice. It’s about continuously learning and adapting to the system it monitors and uses. It should bring data from being static to something you can mold and turn to your advantage at every single point by everyone. Composite AI – the outlook Composite AI is a combination and application of different AI techniques to reach the best results, improve the efficiency of AI tools, increase the level of ability to solve complex problems, and make significant business decisions. It fuses multiple tools and methods such as deep learning, machine learning, natural language processing, knowledge graphs, contextual analysis, analytics, and more, to generate deeper insights and support more precise data-based business actions. The basic proposition is that it creates a unified approach of multiple AI tools to answer one business problem or question. It consists of layered solutions that form a cohesive outlook on a specific business domain aspect. The point is to line up content with context and deepen the understanding of business data. Composite AI should change and supercharge decision intelligence. It doesn’t sound that revolutionary or does it Probably, it does not sound like something new, but it is. Multiple AI tools that one company uses? It doesn’t sound like anything that will make such an impact if it’s already used in one way. But what companies fail to realize is that one tool is not enough with the wide range of dynamics data and all-around business operations. Usually, businesses use AI as a single tool. They forget that under its umbrella there are so many various tools that on their own don’t reach their full potential. So, in comes composite AI as a platform that uses multiple approaches that complement each other to maximize results. It supports and enhances the quality of AI applications by bringing all data and different methods together to reach the same goal. Its basic intention is to bring AI from ordinary to excellent. CI also isn’t something completely new, but it’s a complete approach to data that is not static. Often we look at data streaming and historical data as separate occurrences, but together in CI, they provide the full picture. By using ML and AI it automates those processes and brings them to another level. It’s about generating value from data As one is oriented towards data streaming and the other towards AI and ML, they might seem contradictory in some aspects and separate answers to data challenges. But it doesn’t have to be so. They can both be complimentary in nature since they both focus on implementing AI and ML in optimizing data and business performance. As said above, composite AI is there to improve decision intelligence, or in this case continuous intelligence. Composite AI can target only one specific business problem, whereas CI can be a comprehensive approach weaved throughout the whole business. One leverages more advanced technologies, while the other is formed more to provide insights based on real-time data. This is where we can see that they could complement each other in new ways. Imagine using both in your business to gain valuable information and recommendations on business operations and decisions. Data holds value, so it makes sense to do everything and use everything to maximize what it has to offer. These collections of tools build