5 evolutions of analytic computing that mark Programmer’s Day

In 1946, North American scientists John Eckert and John Mauchly introduced the world to the ENIAC, which is considered the first large-scale electronic digital computer. The acronym, which stands for Electronic Numerical Integrator Computer (or Electrical Numerical Integrator Analyzer and Computer, in English), represented at the time the maximum computational capacity to process and analyze data.

75 years after ENIAC’s achievement, analytic computing has gone through several evolutions. The processing power and the sources of information and data allowed the invention of Eckert and Mauchly to reach a wide range of applications. To show how this is being done today, we’ve brought together several experts from SAS, the world’s leading analytics company, to address 5 facets of today’s analytics computing that facilitate the daily work of programmers around the world. Look:

Artificial Intelligence – Artificial intelligence (AI) allows machines to learn from experiences, adjust to new data inputs and perform tasks like human beings, as explained by Larissa Lima, Customer Advisory at SAS Brasil. “AI involves the grouping of various technologies, such as deep learning and natural language processing, which can simulate human capabilities linked to intelligence”, says the expert. “With these technologies, computers can be trained to perform tasks when processing large amounts of data.”

Big Data – Big data is a term that describes a large volume of data, both structured and unstructured, which is everywhere, says Deivison Santos, SAS systems engineer. “Big Data is basically composed of three ‘Vs’, which are volume, speed, and variety”, he says. “Thinking, for example, of financial institutions that receive a high volume of customer registration data. All this information needs to be stored in some way. When consuming this information, the faster the better. And with a greater variety of information, structured or not. With this combination, it is possible to gain relevant insights from use”, he adds.

Machine Learning – Machine learning (or machine learning, in English) is a method that automates the construction of analytical models. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention, explains Gustavo Peixinho, Customer Advisory at SAS Brasil. “In other words, it is a set of techniques capable of learning from historical data, detecting patterns in the information and making predictions”, he highlights.

Analytics – Analytics is a comprehensive and multidimensional field that uses mathematical, statistical, predictive modeling, and machine learning techniques to find significant patterns and knowledge in data, says Lívia Moraes, Pre-Sales consultant at SAS Brasil. “The analytics is important because, with it, we can discover if what we think is really true, in addition to producing answers to questions that we never thought of asking”, he exemplifies.

Internet of Things (IoT) – The Internet of Things (IoT) refers to the possibility that “things” connected to the Internet share data with other “things” – home automation, agricultural machinery, wearables, cars, industrial equipment, appliances, among a multitude of things, says Lyse Nogueira, Customer Advisory at SAS Brasil. “For example, you can have a robot vacuum cleaner connected to the internet at home and use your smartphone to turn it on and off, even if you’re not at home,” he explains. “In a factory, it is possible to have sensors connected, collecting information in real-time, so that decisions can be made based on this data.”

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