Advanced analytics and other AI-driven tools and technologies have been transforming the way organizations function by harnessing valuable information from the largest datasets and providing important insights. With the continued growth of cognitive technologies and increasingly widespread adoption by many industries, what will the future of advanced analytics and AI adoption look like? With the evolution of big data analytics over the past few years, the opportunities to apply this knowledge and to see how different industries are embracing AI and ML has shown tremendous value. However, the evolution and future of analytics doesn't come without challenges. In a recent AI Today podcast interview with Antonio Cotroneo, Director of Technical Content Strategy at OmniSci, spoke about these potential challenges as well as opportunities for industries.

Advanced analytics and other AI-driven tools and technologies have been transforming the way organizations function by harnessing valuable information from the largest datasets and providing important insights. With the continued growth of cognitive technologies and increasingly widespread adoption by many industries, what will the future of advanced analytics and AI adoption look like? With the evolution of big data analytics over the past few years, the opportunities to apply this knowledge and to see how different industries are embracing AI and ML has shown tremendous value. However, the evolution and future of analytics doesn't come without challenges. In a recent AI Today podcast interview with Antonio Cotroneo, Director of Technical Content Strategy at OmniSci, spoke about these potential challenges as well as opportunities for industries.

How is it possible to harness high volumes of data on a planetary scale to discover spatial and temporal patterns that escape human perception? The convergence of technologies such as LIDAR and machine learning is allowing for the creation of macroscopes, which have many applications in monitoring and risk analysis for enterprises and governments. Microscopes have been around for centuries, and they are tools that allow individuals to visualize and research phenomena that are too small to be perceived by the human eye. Macroscopes can be thought of as carrying out the opposite function; they are systems that are designed to uncover spatial and temporal patterns that are too large or slow to be perceived by humans. In order to function, they require both the ability to gather planetary-scale information over specified periods of time, as well as the compute technologies that can deal with such data and provide interactive visualization.

As humankind begins its coexistence with artificial intelligence, it's worthy to note that one of the most important methods of advanced learning, at least in the data realm, dates back more than a half-century. Exploratory Data Analysis (EDA), a term coined by renowned statistician John Tukey, is a technique for initially understanding and developing a view of a particular set of data before deep inquiry starts. In EDA, statistical techniques are used to describe the characteristics of the data in order to generate initial hypotheses. In the age of big data, when datasets routinely grow to petabytes in size, EDA is more important than ever. Most repositories of information today are simply too large, complex and diverse to explore by rote numerical analysis.

In 2009, the future founders of Kinetica came up empty when trying to find an existing database that could give the United States Army Intelligence and Security Command (INSCOM) at Fort Belvoir (Virginia) the ability to track millions of different signals in real time to evaluate national security threats. So they built a new database from the ground up, centered on massive parallelization combining the power of the GPU and CPU to explore and visualize data in space and time. By 2014 they were attracting other customers, and in 2016 they incorporated as Kinetica. The current version of this database is the heart of Kinetica 7, now expanded in scope to be the Kinetica Active Analytics Platform. The platform combines historical and streaming data analytics, location intelligence, and machine learning in a high-performance, cloud-ready package.