(And while we’re at it…what are Big Data, Business Intelligence, Artificial Intelligence, Data Science and IoT?)
To the newly initiated, introducing one’s self to the field data analytics can be intimidating. Navigating through a dizzying array of terms can be a difficult and tedious task. In this post, we bring to you a brief laymen’s glossary to many of the new words and phrases that are sure to become a part of your everyday vocabulary.
Data Analytics – In its most basic form, Data Analytics refers to the practice of using data to draw conclusions that may help inform a decision or a future business practice. One type of data analytics, Predictive Analytics, refers to the practice of using data collected about past events to predict the likelihood of various possible future events. For example, employers may use predictive analytics to predict who is most likely to leave their organization in the future based on an analysis of the characteristics of those who have left their organization in the past. Still confused? Watch Moneyball® – it’s a fantastic movie.
Big Data – Perhaps the term that is thrown around with most abandon, Big Data refers to massive collections of data that, due almost entirely to their volume, require special methods and technologies to manage and analyze them. This term is often used generically to describe large or complex data sets.
Business Intelligence – Generally refers to the tools and methods used by an organization to analyze data from various sources for the purposes of optimizing business decisions. For example, a company may analyze the nature and source of its revenue stream to better inform sales strategies.
Artificial Intelligence – Phrase often used to describe complex processes or systems that are capable of performing tasks that are typically thought of as requiring human intervention or intelligence. An example includes speech recognition. Don’t believe us? Ask Siri® or Alexa®.
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