From continuous intelligence to composite AI – a mix for success

continuous intelligence

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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 upon each other to make the system smoother, more flexible, and without friction when talking about data flow. 

How do you benefit

There are so many benefits that these solutions could offer. After all, if there weren’t any benefits, CI our composite AI wouldn’t exist. Business operations optimization demands new approaches in a fast-evolving world, especially with data.

High volumes and velocity of data seek faster data processing. There is no time to wait for important insights. Data at one moment in time becomes obsolete fast. So, it’s vital that each piece of information is processed as soon as it can. This is where automation and CI come into play. Data also creates patterns and typical behavior. AI and CI can recognize such instances and predict how they will behave in the future.

But, one benefit that comes as important is comprehensive automated data analysis. AI and ML can minimize manual intervention and speed up data processing, preparation, and analysis. This means that metrics and insights get faster to the user or designated department. Also, it means that all of the relevant data is taken into account in real-time and not just certain aspects which provides a higher level of accuracy and coverage. 

One of the major benefits is that AI and ML are used to make predictions on business operations and their behavior. Based on data they can form future predictions of how certain metrics will go, how will customers behave, whether business operations show growth or not, and similar! This allows for a speedier response to changes and anticipated occurrences. If you know that something will possibly happen, you can react preemptively. Real-time analytics also mean that decisions are made on time. 

Data and system monitoring are extremely important, so composite AI and CI do play a part in that and make it easier to do so. They provide a clear overview of data through augmented data management

Should you choose only one or use both

Well, the decision whether you need or want composite AI or continuous intelligence depends on the type of data you have and which business problems you want to solve. If your data doesn’t support the use of one, then it’s probably a good decision not to go into it. But, if you have such amounts of data that move with great velocity, choose carefully what brings the most value to your company. What is your end goal will determine the path you’ll choose. 

If you need to make instant decisions often, then continuous intelligence is the right way to go. If you have a specific problem that AI and machine learning could solve, then composite AI is your choice. But if you have both of these needs, then use CI and composite AI in tandem, but carefully, of course, so you don’t lose sight of the ultimate objective and go into over-automation and lose the human aspect. 

If you don’t have data coming in at great speed or is not in movement, then you probably won’t need Spark or Kafka in CI. Streaming is not for all. And don’t go into AI just because it’s a buzzword. Choose wisely and employ data companies to guide you through. 

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