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Core Data Terms - Essential Definitions for Data

For anyone engaging with data, whether it’s for business, research, or personal projects, it’s crucial to understand the various terms and classifications used. In this part of the Navigating Data Language series, we present key definitions for different types of data you’ll encounter. These definitions will serve as foundational knowledge for our upcoming articles.

PII Data

Personally Identifiable Information (PII) Data is any information that can be used to identify an individual. This category includes, but is not limited to, names, addresses, student or staff ID numbers, social security numbers, email addresses, and phone numbers.

Operational Data

This type of data is real-time information used in the daily functions of an organization. It is often transient and not stored for extended periods. Examples include student demographic data, course data, enrollment data, and more.

Transactional Data

Data that records events, changes, or transactions within a system. This could range from student data changes in an SIS setting to updates in a database. It is generally structured and forms the basis for analytics and reporting.

Longitudinal Data

Data collected from the same subjects repeatedly over a specific time period. This data allows for tracking changes, identifying trends, and making predictions. It is frequently used in long-term research projects across various fields such as medicine, education, and social sciences.

Big Data

A term for data sets so large or complex that traditional data processing methods are inadequate. Big Data is characterized by the Three Vs: Volume, Velocity, and Variety. Specialized tools like Hadoop and Spark are often required for handling Big Data.

Understanding these definitions is the first step in grasping the complex world of data. Each term offers a different lens through which to view, analyze, and utilize information effectively. As we progress in the Navigating Data Language series, these definitions will provide the groundwork for a deeper understanding of data-related topics.

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