Amidst the plethora of incoming data through social network, online web portals, e-care engines, sports live feed, we are assembling big data but are far from utilizing it to the best capacity. According to IDC, 44 zettabytes of data could be created by 2020. There is a dire shortage of data analysts in the market, and as big data becomes even bigger, can data analysts, that companies heavily rely on today to analyze and interpret raw data, simply keep pace with its exponential growth?
Probably not. And that’s why we need AI.
Also known as cognitive science or machine learning, AI adds an intelligence coating to big data to address complex analytical tasks much faster than humans ever could. It will eventually more than requite for the dearth of analytical resources we are facing today. Quintillion bytes of data is being produced every day therefore knowledge of how AI systems collect, utilize, infer and generate data is essential. By leveraging the technology of these systems to handle the issues of Big Data, we can transform numbers to insights. Intelligent systems are built on a foundation of simple and understandable processes.
Unlike what it’s perceived to be, AI is not magic, but the interdisciplinary application of a set of algorithms ruled by data, magnitude, and processing power. AI systems today use three primary components of human reasoning: assessing, inferring, and predicting. Assessing with AI can be done by matching against comparable portfolios and profiles and then creating a preemptive prediction bank with dynamic data, for e.g. Amazon. Inferring with AI is usually done by checking similitude, classification and assembling evidence. Predicting with AI is basically translating the assessment into a prediction.
The cycle of assessing, inferring, and predicting form the foundation of many intelligent systems especially the ones with which humans interact. For any intelligent or a smart system, even humans, the ability to perceive current events, draw an inference about it, and then predict upcoming events based on the inference drawn, is crucial to the ability to predict and plan for the future.
For example, Tesco, the famous retail store could see that individual consumers were buying wine and bread on weekends, but it could not discern that customers were not buying cheese. It could see consumers buying toothbrushes, but was unable to see that they were not buying toothpaste.
Evidently corresponding purchases were being made elsewhere, and narrow AI could have been utilized to investigate this episode and provide some answers. Tesco could then have resolved to coupon based promotional activities to bridge the purchasing gaps.
Data is only as precious as the insight you can draw from it. Successively, the insights are only precious if they are easily understood on time; mechanically converting thousands of metrics into a fewer thousand graphs doesn’t achieve this. Hence, today businesses like ours use AI not only to draw insight from data, but successfully transcribe big data into value for both the organization and the customer by providing it in an easy to use format – in human language.