There is a lot of talk in the industry about how big data is shaping the future of business and everyday life, but what is big data and what does machine learning have to do with it? Big data refers to the large scale sets of data that have been collected and stored for future analysis. It has been defined by big data experts as the four V’s: Volume, Velocity, Variety, and Variability. Volume refers to the large amounts of data being collected about transactions, social media interactions, and machine communication. Velocity of the data being collected as well as the velocity of interpretation must be taken into consideration. The variety of these data is also distinctive because these data come from all different formats; from numeric data to text, email, audio, video, and transactional data. Finally the variability must be considered. Not only do the data come in different forms, they also come at different rates and different times daily, seasonally, or around a certain event. More recently the main focus has switched from collecting all the data to interpreting, analyzing and organizing it. Since the scale of the data is so large, it is difficult to analyze with a single set algorithm. A machine learning expert is integral to interpreting big data.
Machine learning experts classify the process into three different categories. The first, supervised learning, is where the search algorithm is trained by using labeled examples. A machine learning expert gives the machine a set of inputs with corresponding outputs to compare to its own outputs to find errors. This method is used in detecting credit card fraud, for example; the machine is given a set of instances classified as normal or fraudulent and when a new purchase is made, it can categorize the new data in real time.
Unsupervised learning, on the other hand, is defined by the algorithm using no prior markers to find structure within a data set. Online retailers such as amazon use this process to come up with a list of other items you may like. They curate this list by assessing data from other customers who also bought similar products.
Similar to unsupervised, reinforcement learning is done without a set of labels provided by a machine learning expert. In this form of machine learning, it is not told what to do, but the algorithm attempts different actions to find which one yields the best result. This trial-and-error process is most common in robotics and some smart cars.
These types of machine learning can aide big data experts in their analysis of their ever-growing pool of data. Companies can use these data to gain a competitive edge. For example, they are able to suggest products or services tailored to a customer’s specific needs, re-develop their products, and even preform predictive risk analysis by looking at data from many different fields.
The switch from analog storage to digital, during the birth of the digital age, brought about an exponential increase in the amount of information collected, as shown in the image below. Continuing in this trend is the internet of things. In the future world of internet of things (IoT), where everyday objects communicate wirelessly, the amount of data collected will also increase exponentially. Although estimates on the number of “things” vary, big data experts all agree that this number will increase dramatically. With so much more data, there becomes an even greater need to analyze it in order to predict the next trend and keep a competitive advantage in such a rapidly changing, more connected world. View our news and insights pages for more blog articles and technology white papers.
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