idm 2020 midterm

Vocabulary flashcards covering core definitions, taxonomies, models, and architectures of Business Intelligence, Analytics, Data Science, and Data Warehousing.

Data mining

The process of extracting hidden patterns, correlations, and useful information from large datasets using statistics, machine learning, and database systems.

Decision Support Systems (DSS)

Systems designed to assist managers with semistructured decision-making using data and models.

Business intelligence (BI)

An umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies to enable interactive access and manipulation of data for decision-making.

Online Transaction Processing Systems (OLTP)

Systems responsible for capturing and storing data related to day-to-day business functions and handling a company's routine ongoing business.

Online Analytical Processing (OLAP)

An information system that analyzes large volumes of historical data to generate insights and reports, allowing users to query the system and conduct analysis.

Analytics

The process of developing actionable decisions or recommendations for actions based on insights generated from historical data.

Descriptive Analytics

The stage of analytics focused on defining business problems and opportunities by answering questions like 'What happened?' and 'What is happening?' through reporting, dashboards, scorecards, and data warehousing.

Predictive Analytics

The stage of analytics aimed at producing accurate projections of future events and outcomes by answering 'What will happen?' and 'Why will it happen?' using data mining, forecasting, text mining, and web mining.

Prescriptive Analytics

Also known as decision or normative analytics, it determines the best possible business decisions and actions by answering 'What should I do?' and 'Why should I do it?' through optimization, simulation, decision modeling, and expert systems.

Business Analytics Framework

A visual framework showing business analytics spanning descriptive analytics (business intelligence) to advanced analytics consisting of predictive and prescriptive analytics, detailing their respective questions, enablers, and outcomes.

Artificial intelligence (AI)

(1) The study of human thought processes to understand what intelligence is, and (2) the representation and duplication of those thought processes in machines so that they can perform tasks intelligently like people do.

Augmented (enhanced) Intelligence

AI technology that enhances computer tasks to extend human cognitive abilities, resulting in high performance.

Intelligence Agent (IA)

An autonomous, relatively small computer software program that observes and acts upon changes in its environment by running specific tasks autonomously.

Machine Learning

A branch of AI focusing on the use of data and algorithms to imitate the way humans learn, gradually improving its accuracy.

Deep Learning

A type of machine learning that tries to mimic how the human brain works using artificial neural technology.

Computer Vision

Technology that acquires or processes, analyzes, and interprets digital images and produces meaningful information for decision-making to give computers high-level understanding.

Cognitive Computing

The application of knowledge derived from cognitive science and computer science theories to simulate human thought processes so computers can exhibit or support decision-making and problem-solving.

Agentic AI

Autonomous artificial intelligence systems powered by large language models that can perceive their environment, reason, plan, use tools, and execute multi-step workflows with limited human supervision.

RAG (Retrieval-Augmented Generation)

An AI technique that improves large language models (LLMs) by letting them look up facts from external sources before answering.

Data Taxonomy in Analytics

The taxonomy classifying analytical data into structured data (categorical: nominal, ordinal; numerical: interval, ratio) and unstructured or semistructured data (textual, multimedia: image, audio, video; and XML/JSON).

Data source reliability

An analytics-ready data quality metric evaluating the originality and appropriateness of the storage medium where data is obtained.

Data accessibility

A metric indicating that data are easily and readily obtainable, the urgency of which is reflected in the popularity of Information Assurance degree and certificate programs.

Data currency / data timeliness

A metric verifying that data is up-to-date and recorded at or near the time of the event to prevent time-delay-related misrepresentations.

Data granularity

The level of detail at which variables and data values are defined for their intended use, describing how finely data is divided within a dataset.

Interval Data

Variables that can be measured on interval scales without an absolute zero point, such as temperature on the Celsius scale with units of $\frac{1}{100}$.

Ratio Data

Quantitative numerical data measured on a scale that has equal distances between adjacent values and an absolute true zero point, commonly found in physical measures such as mass, length, time, plane angle, energy, and electric charge.

Data Wranglers

Specialists tasked with finding, acquiring, blending, and unifying raw data sources into structured formats while ensuring quality and consistency during the data consolidation phase.

MapReduce

A programming model that breaks processing into small units that run in parallel across hundreds or thousands of nodes, distributing the processing of large multistructured data files across a machine cluster.

Hadoop

An open-source framework for storing and analyzing massive amounts of unstructured data that breaks Big Data into multiple parts to allow simultaneous processing across machines.

Mean Absolute Deviation (MAD)

A measure of overall dispersion calculated by summing the absolute values of the differences between each individual data point and the mean.

Logistical Regression

A supervised machine learning and statistical algorithm used to predict the probability of a categorical dependent variable, typically binary such as $0$ or $1$, Yes or No, or True or False.

Data Warehouse (DW)

A subject-oriented, integrated, time-variant, and nonvolatile repository of current and historical data constructed to support managerial decision-making.

Operational Data Stores (ODS)

An interim staging area for a data warehouse whose contents are updated throughout the course of business operations.

Enterprise Data Warehouse (EDW)

A large-scale, company-wide repository that provides relevant summarized and detailed information originating from multiple sources to support decision analysis across an entire enterprise.

Metadata

Data about other data that describes the structure, meaning, and rules of data sets, enabling IT personnel and users to index, search, and assess the contents.

Three-tier vs. Two-tier DW Architecture

A structural comparison where 3-tier separates the client workstation (Tier 1), application server (Tier 2), and database server (Tier 3), whereas 2-tier combines the application and database engines onto a single server (Tier 2).

Inmon Model

A top-down data warehouse development approach that builds an enterprise data warehouse (EDW) first before creating departmental data marts, prioritizing full integration and a single source of truth.

Kimball Model

A bottom-up data warehouse development approach centered on a 'plan big, build small' philosophy where subject-oriented data marts are constructed first before integrating them.

Star schema

The most common dimensional modeling design, consisting of a central fact table surrounded by and linked directly to several dimension tables.

Snowflake schema

An extension of the star schema where dimension tables are normalized into multiple related tables, creating a diagram resembling a snowflake.

Enterprise Application Integration (EAI)

A process-driven mechanism for pushing data from source systems into a data warehouse by integrating system functionality and sharing operational procedures rather than sharing raw data.

Enterprise Information Integration (EII)

A data-driven integration approach that enables real-time access and integration of data from relational databases, Web services, and multidimensional sources.

Data lakes

A centralized repository holding vast amounts of raw data in native formats using schema-on-read to apply structure during analysis.

Dashboards

A single-screen visual display presenting consolidated critical performance metrics, key performance indicators (KPIs), and operational data for digestion at a single glance.