Ana Jiménez: "we have to create new data analysis methods"
24 de June de 2019
24 de June de 2019
Ana Jiménez Castellano, the Director of the Minor in Big Data & Analytics and lecturer on Machine Learning on the Master in Business Intelligence and Technological Innovation at EAE, led an Alumni Focused Program entitled "Big data: Data and Business Opportunities".
The Artificial Intelligence and Data Science expert began the session held in EAE's Event Hall by asking the participants to consider some images that call one of the problems of data prediction into question: "how do you put results into action when you transform data into something useful, as Artificial Intelligence only represents patterns", explained Ana.
Over the last 50 years, technology has advanced very quickly. In fact, one of the main objectives of technological companies is keeping up-to-date, which is no easy challenge. There have been four industrial revolutions, caused respectively by the emergence of machines, electricity, computers and, last but not least Artificial Intelligence. "It is a change not only in terms of technologies but also in society itself. The way we work has changed, our mindset has evolved and new tools have emerged", explained Ana. Machines are increasingly faster and the web is changing a great deal. We have moved from on the static 1.0 web to the 2.0 connections, then on to the semantic network 3.0. "In just a few years' time, we will reach ubiquitous 4.0 networks, which are the future because they connect intelligence", explained the Big Data expert.
Right now, a huge amount of data is generated and the physical world cannot deal with so much information. Therefore, the Data Scientist explained that the technological revolution puts human beings at the centre of the equation, and the foundations are being laid for us to coexist with machines. Moreover, she made a distinction between data, information and knowledge because "we need to move from data to information and, from there, to knowledge", summarized Ana Jiménez.
In Ana's opinion, a piece of data is the minimum semantic unit. Although information technologies have made a great contribution in terms of data gathering, unstructured data has to converted into categorical or numeric compilations so that the Artificial Intelligence algorithm can make predictions. "This problem is yet to be resolved", explained the AI expert, while also emphasizing the importance of the quality of the data. Data has to be of good quality, complete, credible, accurate, consistent and interpretable. "There is a lot of talk about data, which is one of the problems of processing information".
"The Data-Information-Knowledge-Action-Result-Value cycle has to go full circle so that companies can take action and not stay stuck on the dashboard", explained the Director of the EAE Minor. To achieve this, we have to optimize processes such as data gathering, storage and semantics, because "the more data we have, the harder it is to find the pattern". We have to design new methods, create a data-based culture and use data as a tool to continue moving forward.
The lecturer then went on to give us an overview of the differences between Business Intelligence and Big Data. The former focuses on strategies, tools, administration and knowledge creation through the analysis of data that already exists in an organization. This is "structured data with problems to work on in real time". The latter, Big Data, "has become a fashionable term related to innovation in companies". There are not currently any Big Data suppliers that can differentiate between data sources, tools to organize the information, insights or analytical elements and, last but not least, approaches focused on decision-making and taking action. Ain the EY partner's opinion, "we have to change technology, the processes, the culture and people to achieve a good result".
Technology poses challenges that are yet to be resolved, such as the fact that each organization has a specific problem and the tools available can only resolve generic problems. A total of 52% of companies suffer from a technology gap, 33% do not have adequate profiles and 19% do not know how to integrate Big Data. In this transformation phase, it is essential to talk about data quality, analysis and modelling. This is where Data Science comes in to apply intelligence in data analysis. "There is still a long way to go to make progress towards successfully representing information", emphasized Ana. In short, the path forward involves transforming the real world with data, applying human knowledge to achieve it.