Description:
In MIS 2502: Data and Analytics, we have learned a variety of topics relevant to the discovery, research, tools of communication, and systems of interpretation regarding data. In this course, there are a few specific topics that are touched on but not dug into deep enough compared to the focal material of the course. One of these is the topic and relevance of Artificial Intelligence in the world of data. Artificial Intelligence, also well known as AI, is the developed intelligence composed of variations within computer science demonstrated by machines that are designed to perform tasks that are typically done by a human being. AI is relevant to analytical science and data through its assistance in conducting a more in-depth analysis of data in specialized areas of study that is not naked to the human eye.
Generally speaking, AI allows organizations to have a more advanced and effective base when making important decisions, developing core business strategies, and increasing the agility of computational operations for these organizations at lower costs. An example of how artificial intelligence aids data-related situations is with big data, data that is too large or complex and needs to be condensed in some form (Surya). Usually in these cases, since such data can grow exponentially over time, traditional data-processing application software is unable to minimize the data due to its high levels of complexity, thus, AI resolves the issue. Some analytical operations AI runs include identifying connections through different databases, locating errors and coding malfunctions, and recognizing advanced computational knowledge to further assist data systems in executing their sensitive information properly (Dixon). As AI is designed to replicate an advanced version of the human mind, it has the ability to work faster and for longer compared to humans, demonstrating the effectiveness of its existence in the world of analytical data.
Without the presence of Artificial intelligence when creating and using data analysis systems, it would be far more difficult to develop accurate databases at the speed it currently takes utilizing AI. With the inclusion of such advanced intelligence, organizations are set to succeed and are less likely to generate faulty systems and interpretational tools. In the future, it would perhaps be beneficial for students to understand the purpose and importance of the relationship between AI and the processes within data analysis by further expanding on the concept in the course.
