Most corporations already use the three types of data analytics: descriptive, predictive, and prescriptive. They extract patterns from their data and build mathematical models to better understand what has happened, what will happen, and what they should do next. The process they use is typically some combination of statistical methods, computational tools, and human cognition.
But there is a problem. Data volumes and business requirements are increasing.
The IoT (Internet-of-things) can easily generate terabytes or petabytes of data in a day; the analyses are needed in real-time, and the models are getting more and more complex.
Eventually it is human cognition that becomes the limiting factor
Adding more computational resources helps up to a point, but eventually it is human cognition that becomes the limiting factor. This is already the case in many practical applications.
If we want to keep improving the models and their predictive power, we need to rethink and redesign the process of data analytics so that it can be scaled to suit our growing needs .
This requires three things
Real-time access for big data. First, we need to store and manage our big data so that it can be accessed in real-time. One way to do this is to implement a data lake by using a Hadoop cluster.
Deep learning for pre-processing the data. Second, we need to use semantic engine and deep learning to pre-process the data and extract its higher-level meaning. There is simply too much data to process manually, and the best models are too complex for us to understand them.
Artificial intelligence. Third, due to the complexity of the models, we need to use artificial intelligence (AI) to discover new business insights and give us the results in simple terms so that the incomprehensible once again becomes useful.
Read further on how you can turn your data and information into profit by capturing, enriching, using and managing it.
