Chapter 1 Section 1.1 – Business Driven MIS (Vocabulary Flashcards)

Vocabulary flashcards covering core MIS concepts from Chapter 1 Section 1.1, including data, information, BI, knowledge, data types, analytics, and related roles.

Data

Raw facts that describe the characteristics of an event or object.

Big data

Large volumes of data—both structured and unstructured—containing greater variety, increased veracity, and more velocity.

Variety

Different forms of structured and unstructured data.

Veracity

The uncertainty of data, including biases, noise, and abnormalities.

Volume

The scale of data.

Velocity

The analysis of streaming data as it travels around the Internet.

Structured data

Data with a defined length, type, and format (e.g., numbers, dates, or strings such as customer address format).

Unstructured data

Data not defined by a fixed format; typically free-form text such as emails, tweets, and text messages.

Machine-generated structured data

Data created by a machine without human intervention, in a structured format.

Human-generated structured data

Structured data created by humans, using computers.

Machine-generated unstructured data

Unstructured data produced by machines (e.g., satellite images, radar data).

Human-generated unstructured data

Unstructured data produced by humans (e.g., text messages, emails, social media).

Dynamic report

A report that changes automatically during creation.

Static report

A report created once based on data that does not change.

Information

Data converted into a meaningful and useful context.

Business intelligence

Information collected from multiple sources that analyzes patterns, trends, and relationships for strategic decision making.

Knowledge

Skills, experience, and expertise coupled with information and intelligence that creates intellectual resources.

Data democratization

The ability for data to be collected, analyzed, and accessible to all users.

Data silo

A situation where one department cannot freely communicate with others, hindering cross-functional work.

IoT (Internet of Things)

Any device connected to the Internet with the goal of enhancing performance without human intervention.

M2M (Machine-to-Machine)

Two or more connected devices interacting via wireless or wired connections with data sharing and analytics without human intervention.

Descriptive analytics

Describes past performance and history.

Diagnostic analytics

Examines data to answer the question, 'Why did it happen?'

Predictive analytics

Extracts information from data and uses it to predict future trends and identify behavioral patterns.

Prescriptive analytics

Creates models including the best decision to make or course of action to take.