Data Visualization for Business Intelligence

These flashcards cover key concepts in data analytics and data analysis as presented in the lecture notes, focusing on definitions and differences.

Data Analytics

A set of processes, tools, and technologies that help manage qualitative and quantitative data to enable discovery, simplify organization, support governance, and generate insights for a business.

Data Analysis

The process of dissecting a given data set into its component pieces and analyzing each one separately, as well as how the parts relate to one another.

Descriptive Data Analytics

Examines past data to explain what had happened; it's the most straightforward data analytics technique.

Diagnostic Data Analytics

Examines past data to explain the cause of an anomaly; answers 'why did this happen?' from descriptive analytics results.

Predictive Data Analytics

Uses current or historical data to predict future actions; often employs machine learning and statistical modeling.

Prescriptive Data Analytics

Involves selecting the best solution for a problem from available options, examining results from prior analytics to guide decision-making.

Real-time Data Analytics

Uses data immediately when entered into the database, analyzing new data on the go.

Augmented Data Analytics

Uses machine learning and natural language processing to analyze data, automating the tedious task of code-based data exploration.

Data Cleaning

The process of finding and correcting data quality problems in the collected data.

Data Governance

Policies that ensure data is used correctly and meets corporate standards.