InfoGuides: Finding and Working with Data: Data Terminology

Last Updated: Oct 7, 2024 11:26 AM

Terminology

Aggregation
Collecting data from multiple sources for purposes of reporting or analysis
Algorithm
A mathematical formula that can perform certain analyses on data
Analytics
The discovery of insights in data; can be descriptive, predictive, or prescriptive
Anonymization
Act of making data anonymous; removing all data points that could identify a person
Database
Digital collection of data and the structure around which the data is organized
Data mining
Deriving patterns or knowledge from large datasets; often using methods that combine machine learning and statistical approaches
Data model
Defines the structure of the data for the purpose of communicating between functional and technical people
Data Modeling
The analysis of data objects using data modelling techniques to create insights from the data
Data point
Individual item on a graph or chart
Demographic Data
Data relating to the characteristics of a human population
Metadata
Data about other data; can be descriptive, structural, or administrative
Query
Asking for information to answer a specific question
Schema
Structure that defines the organization of data in a database system
Sentiment analysis
Using algorithms to determine how people feel about certain topics, products, or companies
Structured Query Language (SQL)
Programming language for retrieving data from a relational database
Statistics
Results of data analysis - interpretation and presentation; finding statistics may lead you to data sources
Structured Data
Data that is organized in structure like rows and columns; often in relational databases and organized so that it relates to other data via tables.
Text Analysis
Applying statistical, linguistic, and machine learning techniques on text-based sources to derive meaning or insight
Unstructured Data
Generally text-heavy data that is not organized in tables. Examples: email messages, social media posts, and recorded human speech
XML databases
XML databases are often linked to document-oriented databases. The data stored in an XML database can be queried, exported and serialized into any format needed.