Artificial Intelligence is taking over a large chunk of technological advancement. Currently, the perfect match it preaches is that of Big Data. The reason being that Artificial Intelligence is taking large processing of complex data, much better than the traditional human processing way. Market Research is pointing a number of ways where the match will perfectly suit future technological domains.
The banking sector, for example, is using the foremost of AI and Big Data. Consequently, applications of bank systems are streaming lot of concurrent data region within a fraction of second. Furthermore, if any anonymous activity prevails the manpower involvement won’t gather much security protocols. The reason being, people cannot possibly process and analyze a large volume of data in seconds, like an A.I do. Activities like fraud and theft require much more attention and involvement than mere human decision-making capabilities. With hundreds of humans performing a task, the outcome still goes less than a single A.I.
A. I surely put forwards assignments in a much easier way than a human can. Market research ideas even put forward the predictions of new data reasoning in several domains such as healthcare and technology. Ordinarily, AI systems get smarter and driving data is easy as compared to stringent anomalies over the time frame.
Below are some technologies put forward by Market Research where AI is getting ahead with the Big Data power.
Anomaly Detection process:
This is a different process which has a name as outlier detection. The process consists of identifying items and events of certain observations which are hard to analyse and do not confirm a single and dominant pattern. Preferably, the anomaly detection process can detect many bank fraud cases. Other domains include fault detection process, system health monitoring process, network sensors and ecosystem abnormalities.
The graph theory is a term used to define the study of mathematical structures. It subsequently put forwards the models which have pairwise relations between objects. The graph present in the context consists of vertices, nodes and points connected by edges, arcs and lines. This overall thing is at times too complex and requires steady hands other than a human’s. Consequently, with the advent of a graph theory, the insights into the relationships between data can be easier to obtain. One such example includes a bottleneck in the network which can cause a variety of problems and a root cause to the particular bottleneck can be put forward by an A.I.
This is one of a kind of complex process where the pair of AI and Big data is useful. The process of extrapolation is done for an estimation beyond the value of an original observation range. The value is on a base with relationship beyond other variables. Situations like determining a trend is done, where executives of an organisation put forward views of the sustainability of that aim. This process can be largely put forward by Extrapolation. Linear trends make it a simple one and a simple line of the chart will make it tangible. The algorithms consist of polynomial, conic and curve equations.
Market Research is ruling several other processes which are holding the pair of Big Data and A.I on a prevailing scale.