MKTG 121: Decision Support Systems & Big Data

Flashcards covering key concepts from MKTG 121, Chapters 5 and 6, focusing on Decision Support Systems, Marketing Information Systems, Big Data characteristics (the 4 Vs + Value), and types of analytics.

Secondary Data

Data collected by someone else for another purpose, often saving time and money, but may not fit perfectly or lack accuracy.

Marketing Information System (MIS)

A planned system for the ongoing collection and regular reporting of marketing intelligence.

Decision Support System (DSS)

A system that combines data, models, and a user interface to help decision-makers interactively explore data and make decisions.

Dashboards & CRM

Tools that visualize key metrics and make customer insights accessible to employees, often integrated with DSS.

Data System (DSS Component)

The component of a DSS responsible for capturing and storing both internal and external data.

Model System (DSS Component)

The component of a DSS that provides analytical tools for manipulating and interpreting data.

Dialog System (DSS Component)

The user interface component of a DSS that allows managers to interact with the system and generate custom reports.

Knowledge Management

A practice that goes beyond systems by tapping into employees' expertise and sharing it across the organization.

Information System Implementation Challenges

Obstacles to implementing new information systems, including inertia (resistance to change), ego (decision-makers' reluctance to share methods), and cost.

Big Data

More than just 'lots of data'; it is characterized by its Volume, Velocity, Variety, Veracity, and Value.

Volume (of Big Data)

Refers to the sheer size and amount of data being generated and stored.

Velocity (of Big Data)

Refers to the speed at which data flows into and out of systems.

Variety (of Big Data)

Refers to the diverse types of data, including structured and unstructured formats.

Veracity (of Big Data)

Refers to the trustworthiness, quality, and accuracy of data, which can be a significant challenge (garbage in, garbage out).

Value (of Big Data)

The 5th V of big data, concerning the ability to turn raw data into actionable insights for decision-making.

Structured Data

Data that is organized in a highly formatted and easily searchable manner, such as transaction records and registrations.

Unstructured Data

Data that lacks a predefined format or organization, such as social media posts, videos, and customer reviews.

Descriptive Analytics

An analytical approach that identifies trends and patterns in past data to understand what happened.

Predictive Analytics

An analytical approach that uses statistical techniques to forecast customer behavior or market trends to understand what might happen.

Prescriptive Analytics

An analytical approach that recommends the best actions to improve performance, guiding what should be done.

Golden Rule of Data Collection

A principle stating that one should know how each piece of data will be used before collecting it to ensure its value and avoid collecting unnecessary information.