One of the key challenges of modern technology is understanding the distinction between artificial intelligence, machine learning, big data, data science, deep learning, among others. Although they are all closely related, each industry has its own goals and solutions for different problems.

In recent years, the popularity of Big Data and Artificial Intelligence has grown so much that many companies have come to appreciate their importance at the highest levels and are closely considering how to introduce them to grow their business. However, there are many misconceptions about these technologies even among the specialists of such companies. This article will help you to figure it out.


What is Big Data?

At its most basic level, Big Data captures, processes, and analyzes vast amounts of data for various purposes. For example, if you have a media and entertainment website and a user opens multiple pages with products or categories, you have created data. Ideally, this data will be used by a specialist to understand your behavior, offer personalized products, and retargeted advertising to convince you to buy or consume based on what you have seen. This is one of the simplest applications of data science, but it is becoming increasingly complex. Big Data involves processes of data extraction, purification, analysis and visualization for the practical application of data.

A data analyst is responsible for meticulously working datasets to carry out the most complex business communications. Much insight goes unseen in a huge flow of information, and it is the data science that can shed new light on consumer behavior, operational failures, supply chain cycles, and predictive analytics. A smart data strategy is critical for businesses looking to retain consumers and stay in business.


What is Artificial Intelligence?

Artificial intelligence is the concept of machines performing tasks that once could only be performed by human intelligence. Many people use the terms AI, machine learning (ML), and deep learning (DL) interchangeably, but there are key differences between them. AI broadly covers the entire field, of which ML and DL are sub-segments. Artificial intelligence can be divided into two distinct areas. Applied AI refers to an application optimized for a specific task, such as suggesting a movie or optimizing a driving route. General AI includes broader applications of AI, such as a computer that learns different tasks and how to solve problems like a human. Machine learning is the process of creating machines or programs that can access data.


Differences between Big Data and Artificial Intelligence

Artificial Intelligence and Big Data complement each other, but AI has the ability to collect and analyze much more information and act on it.

AI uses algorithms and statistics to work with data generated and extracted from multiple sources. Most of the time, the volume of information is so great that it cannot be treated solely by a data analyst. This is where machine learning comes in: it enables the system to master the ability to process datasets autonomously, without human intervention. This is achieved using complex algorithms and techniques such as regression, supervised classification, naive bayes classifier, etc.

Netflix, for example, uses AI technology to recommend viewers series or movies based on their preferences, tastes and interests, often right after they have viewed content.


For this reason, Jump Data Driven , a business data management platform specifically designed for video service players pursues business optimization opportunities through Big Data and Artificial Intelligence technologies to increase the ROI of media and entertainment streaming companies.