Big Data and Machine Learning are two concepts that have now become quite common and well-known: there is no doubt that these are systems and technologies that have completely overturned the traditional business logics of previous decades, and today they are the main drivers of a radical change affecting all productive sectors.
As important and well-known as these terms are, it is good to know their characteristics and differences, and that is what we will do in this article. We will explain what Big Data is, what Machine Learning is, and why these two technologies are so closely related.
In conclusion, we will review the development potential that these two realities can offer.
Big Data refers to the technology of aggregating and processing an infinite amount of data from an infinite number of sources. The complexity given by the size of this information makes it impossible to calculate with traditional processing techniques.
But why is Big Data so important? The potential of this tool is extremely high!
The analysis of such a large amount of data makes it possible to make sense of it, discovering and developing new trends and patterns. At a time when networking and data exchange have become predominant in everyday life, being able to collect, channel and interpret all this information is an important opportunity for the development of business strategies. Thanks to Big Data, organisations can gather valuable information about their target audience, carry out increasingly accurate segmentations, analyse objectives and simulate future scenarios, thus being able to outline increasingly effective and profitable development strategies!
If Big Data represents the aggregation of a huge amount of available data, Machine Learning describes the ways in which this data can be analysed and used. We can say that machine learning is a sort of subset of Artificial Intelligence (AI), i.e. the technology that allows devices and machines to 'learn' in total autonomy.
Thanks to Machine Learning, devices become truly smart, and just as if they were a growing child, they learn new notions, and from these they are able to understand behaviours, usage scenarios, user habits etc. Machines can really develop their own intelligence thanks to the use of complex algorithms that learn autonomously, and to do so they 'feed' on data, lots of data: Big Data!
After understanding the difference between Big Data and Machine Learning, the question we now have to answer is: what is the connection between the two?
As we have seen, Machine Learning allows devices to develop their own intelligence and learn new things step by step, but how does this process take place? In order to teach a machine to develop its own learning capacity, it is necessary to provide it with preliminary information, through which it can then formulate 'reasoning' and elaborate an answer.
The information that machines need is provided by Big Data. The latter are, in fact, an inhomogeneous and disconnected set of data and informationi, which must therefore be aggregated, reorganised and processed in order to be useful for elaborating new models and hypothesising future development scenarios.
Machine Learning operates precisely from this point of view: it collects an infinite amount of data at its disposal, processes them and develops a response in the form of output.
On the basis of this process, a device can, for example, learn to distinguish between stimuli and information collected by its sensors; it can distinguish animals, shapes, colours or people, and on the basis of this information develop an appropriate and context-specific response!
To summarise, Big Data is the source through which a machine 'experiences' time. The more data available, the more sources, the greater the ability of a device to learn and master a given condition. To do this, it uses algorithms, comparable to human neurons, which then process the information received and develop new capabilities.
Machine Learning may seem like a complex concept, but it is actually an application that we interact with on a daily basis. In fact, all modern technology makes use of machine learning logic, such as voice assistants, which are also present in our smartphones. These tools have an intelligence of their own that enables them to learn the user's habits and lifestyle, offering feedback that is more and more precise and personalised according to the user's needs.
The same could be said for demotic devices, which are able to collect a great deal of information on their use, and develop scenarios or recommendations to be given back to the user to reduce energy consumption, for example.
Machine Learning logic is behind the navigation of any website or social network we use every day. The data collected is such that it allows algorithms to understand our preferences or purchase intentions, delivering increasingly engaging and personalised ads and advertisements.
Machine Learning systems are therefore much closer to us than we might think, they are a concrete reality already today, and are destined to develop more and more to offer new experiences and services to users.