Big Data: The Importance of Ai for Its Effective Use

If we take into account that almost 90% of the world’s data (Structured and unstructured) has been created in the last ten years, it is consistent to think that this trend will continue to increase in the coming years. However, in an environment like the current situation, in which the development of 5G and the Internet of “things” (also extendable to people) will mean the connection of billions of devices generating data 24 hours a day, 7 hours a day.

This enormous combination of data or data sets is called ” Big Data.” Beyond the volume of data, which can range from 50Tb to several petabytes, it is also characterized by the variability of sources, their complexity, and speed of growth.

Most of today’s technologies (Internet and social networks, audio and video data, geolocation…) generate unstructured data internal and external to organizations, such as opinions, quotes, and likes, which are challenging to analyze and encompass by the teams of any company. No matter how big it is:

Imagine a call centre of a large insurance operator that records thousands of calls daily where customers give their opinions about the quality of a particular service. The operator wants to have an image of how many customers are satisfied with it to prevent turnover and loss of customers and thus take preventive actions. Can someone listen to all the recorded calls and then draw some valuable conclusions? … No. Usually, there will be a tendency to “give up” on the richness that Big data offers us, taking approximations such as surveys with samples that are sometimes too small, which generates the feeling of being analyzed at least “something”…

There is no doubt that big data and its potential granularity offer excellent opportunities that companies have never before had within their reach. In addition to cost reduction, adaptation to the market, customer, and loyalty, the most significant contribution of effective use of big data is to detect problems that have not been suspected. However, this is not without challenges:

The most significant lies in how to go from data to information and from information to knowledge, that is, in being able to collect this massive amount of numbers, organize them, and analyze them to obtain patterns and trends that lead to action.

Another serious problem is the lack of quality and homogeneity of the data collected, which leads to the principle of “garbage in, garbage out”: If you analyze erroneous data, you reach the wrong conclusions.

The significant advance of the last five years in Big data is in  AI (Artificial Intelligence) and in the development of new software and programs specially designed to process, store and analyze all the internal and external data that arrives at the company on a continuous basis. And through several channels simultaneously, considerably facilitating the process of converting this data into insights and, therefore, concrete objectives and actions.

This new generation and software development is beneficial for large companies, which need to analyze a massive volume of data on a large scale daily to act accordingly. For example, a hypermarket chain will be able to know much more accurately the behaviour of its customers to predict which products are going to sell best. A telecommunications company will be able to maintain a much more direct and reliable feed with its customers and be aware of where it may be failing in critical aspects such as loyalty or data protection regulatory compliance. This is possible if the company effectively integrates AI into its data governance strategy as a crucial analysis tool to scale towards success.