WEEK 4 Data in Organizations, Strategic Decision Making, and Extracting Intelligence

Flashcards covering organizational decision levels, system types, big data characteristics, databases, data warehousing, ETL processes, and business intelligence techniques.

Organizational Decision-Making Levels

The three hierarchical levels in an organization—Operational (base), Managerial (middle), and Strategic (top)—each requiring different types of data and handling distinct decision types.

Operational Decision Making

Decision making performed at the base level by lower management, analysts, and staff, focusing on structured decisions to develop and maintain core day-to-day business operations.

Managerial Decision Making

Decision making performed at the middle level by middle management, managers, and directors, using semi-structured decisions to evaluate operations and adapt to change.

Strategic Decision Making

Decision making performed at the top level by C-Suite executives and senior management, focusing on unstructured, long-term decisions to guide overall organizational goals and competitive strategy.

Structured Decisions

Frequent and repetitive operational decisions made in situations where established processes, rules, or formulas exist to dictate the correct choice.

Semi-structured Decisions

Managerial decisions occurring in situations where a few established processes help evaluate potential solutions, but not enough to give a single definite recommended choice.

Unstructured Decisions

Strategic decisions occurring in situations where no pre-existing procedures or rules exist to guide decision makers toward the correct choice.

Four V's of Big Data

The core characteristics defining Big Data: Volume (data at scale), Variety (data in many structured/unstructured forms), Velocity (data in motion), and Veracity (data uncertainty).

Transaction Processing System (TPS)

A basic operational business system that serves lower-level management and staff by capturing routine event data to assist in making structured decisions.

Online Transaction Processing (OLTP)

The continuous capturing, processing, updating, and storing of transactional and event data in live operational databases.

Decision Support System (DSS)

A system that models information to support middle managers and business professionals during the analytical decision-making process.

Online Analytical Processing (OLAP)

The manipulation and aggregation of stored database information to create business intelligence in support of strategic decision making.

What-If Analysis

A DSS modeling technique that checks the impact of a change in an input variable or assumption on a proposed solution.

Sensitivity Analysis

A DSS study of the impact that changes in one or more parts of a model have on other dependent parts of the model.

Goal-Seeking Analysis

A DSS process that determines the exact input values necessary to achieve a specific target goal or desired level of output.

Optimization Analysis

An extension of goal-seeking analysis that calculates the optimum value for a target variable by repeatedly changing other variables, subject to specified constraints.

Executive Information System (EIS)

A specialized Decision Support System (DSS) tailored for senior executives that utilizes visual displays, dashboards, drill-downs, and slice-and-dice capabilities.

Granularity

The level of detail contained within a data model or decision-making process, ranging from fine transactional data to coarse aggregated summaries.

Database (DB)

A structured central repository that stores and maintains interrelated sets of business data across tables.

Database Management System (DBMS)

Software through which users and applications interact with a database to create, read, update, and delete (CRUD) data.

Big Data

Data of such large scale, velocity, variety, or complexity that traditional data management and analysis tools are ineffective.

Information Overload

The operational difficulty and distraction caused when individuals or organizations are presented with too much information to make effective decisions.

Data Sift

The challenge of separating useful, high-value data from irrelevant or low-value information within massive data streams.

Business Intelligence (BI)

Suites of applications, technologies, and practices used to mine business insights and knowledge from large collections of organizational data.

Market Basket Analysis

An association rule data mining technique that analyzes consumer purchasing patterns to evaluate the co-purchase likelihood of specific items.

Data Warehouse

A logical, aggregated collection of historical and analytical information gathered from multiple operational and external databases into a single offline repository.

ETL Framework

A three-stage data integration process consisting of Extracting data from databases, Transforming it into common enterprise definitions through cleansing, and Loading it into a data warehouse.

Information Cleansing (Scrubbing)

The critical step during data transformation that weed out, fixes, or discards incomplete, inaccurate, inconsistent, or duplicate information.

Data Mining

A process of analyzing datasets to extract hidden patterns, associations, or knowledge not provided by raw data alone.

1 Exabyte

A unit of data storage equivalent to $1,073,741,824\,GB$.