Both big data and AI are path breaking technologies in their own right. However, when big data meets AI, the two complement each other, helping us analyze and implement large data sets in unique and unexplored ways.
AI is the simulation of human intelligence by computers. By applying machine learning algorithms, we can make ‘intelligent’ machines, which can employ cognitive reasoning to make decisions based on the data fed to them. Big Data, on the other hand, is a blanket term for computational strategies and techniques applied to large sets of data to mine information from them. Big data technology includes capturing and storing the data, and then analyzing data to make strategic decisions and improve business outcomes. Most companies deploy big data and AI in silos to structure their existing data sets and to develop machines which can think for themselves. But, big data is in reality the raw material for AI. So, when big data meets AI, they have the potential to transform both, the way data is structured and the way machines learn.
Big Data meets AI to create new possibilities
Corporations analyze and manage extensive data sets every day. Client information, employee details, business statistics, all put together, can be a huge collection of unstructured data that can be sorted and studied for business optimization. Big data provides solutions to collect and store data in a robust manner, while AI, with its machine learning techniques, learns from the data sets to make better decisions in the future.
The retail brand Walmart recently used big data with AI to revise their business structure. With over millions of customers accessing their online and offline stores every single day, Walmart collects customer data in the range of petabytes. Big data analysts work on the vast data set, helping their machine learning algorithms master the decision-making skills. Studying the trending products on the site, patterns in customer buying habits, and relations between the demand and supply of goods, helped Walmart remodel its website and inventory to suit the needs of their customers, thus boosting their business.
AI algorithms usually work on sample data sets to in the machine’s initial stages of learning. However, clubbing the algorithms with live data allows machines to learn from actual data sets rather than sample ones. Thus, we can efficiently train our machines to make better decisions right from the learning stage.
An excellent example of this comes from the meteorology department. Servers in weather observatories receive data in the form of text, images, and videos from satellites, weather stations, and relay boards from all over the world. Big data coupled with AI is used in these domains to efficiently store the data and then work on it using image and video processing techniques for weather predictions.
MetLife recently launched two of its insurtech programs, which deploys big data with AI for optimizing the business structure. These programs gather customer health data from various sources, like tracking devices, wearables, and mobile applications, to help them improve their insurance coverage, efficiently settle claims, and effectively predict risks.
Although computers cannot match human brains on a cognitive level, they are essential to sort and organize the vast data sets we deal with in the modern world. By merging AI and big data, we can obtain a structured real-time database, which can further be used in a variety of applications. Though the merger of these two domains is still in progress, we can expect rapid breakthroughs in the way we handle extensive data sets in businesses and in everyday lives.
