Introduction to Business Analytics (Chapter 1)

Vocabulary flashcards covering key concepts from the lecture notes on business analytics and big data.

Business Analytics

The scientific process of transforming data into insight to enable better, data-driven decisions; spans from simple reports to advanced optimization, simulation, and data mining.

Decision Making

The process by which managers plan, organize, coordinate, and lead to achieve organizational goals; involves identifying problems, generating alternatives, and selecting among them.

Strategic Decisions

High-level choices about the overall direction of the organization.

Tactical Decisions

Medium-term choices on how to achieve strategic goals.

Operational Decisions

Short-term, day-to-day decisions on running operations.

Identify the problem

First step in decision making: clearly defining the problem to be solved.

Alternative solutions

Possible options or approaches to solve the problem.

Evaluation criteria

Standards used to judge and compare alternative solutions.

Evaluate alternatives

Process of comparing options against criteria to select the best solution.

Descriptive Analytics

Techniques that summarize what happened in historical data.

Predictive Analytics

Models based on past data to forecast future outcomes; includes regression and time-series methods.

Prescriptive Analytics

Recommends actions; combines predictions with rules to optimize decisions.

Data Queries

Requests for specific information from a database.

Data Dashboards

Real-time charts, tables, and metrics that update automatically.

Data Mining

Techniques for discovering hidden patterns or relationships in large datasets.

Linear Regression

A predictive technique using the relationship between variables to estimate a value.

Time Series Analysis

Forecasting method using historical data indexed by time to predict future values.

Simulation (Risk Analysis)

Using probability and statistics to model uncertainty and assess risk.

Hadoop

Open-source framework for distributed storage and processing of big data across clusters.

MapReduce

Programming model in Hadoop; Map step distributes data to nodes; Reduce step aggregates results.

Big Data

Data sets that are too large or complex for standard data-processing tools; involves scale, complexity, data type, and processing requirements.

Volume

Amount of data.

Velocity

Speed of data creation and processing.

Variety

Different types and formats of data.

Veracity

Trustworthiness and quality of data.

The Four V of Big Data

Volume, Velocity, Variety, Veracity — IBM framework describing big data characteristics.

Opportunities of Big Data

Competitive advantage from better insights, personalized services, and efficiency gains.

Challenges of Big Data

Data storage and processing infrastructure; data security and privacy; shortage of skilled analysts.

Financial Analytics

Forecast performance, assess investment risk, optimize portfolios, and budget capital; uses predictive and prescriptive models.

HR Analytics (People Analytics)

Manage skills, hire top talent, retain employees, achieve diversity goals; example: Google.

Marketing Analytics

Understand consumer behavior, improve pricing, advertising, product-line management, demand forecasting, and customer loyalty.

Health Care Analytics

Control costs, improve treatment effectiveness, optimize scheduling and inventory.

Supply-Chain Analytics

Improve logistics, routing, scheduling, inventory, and efficiency.

Government & Nonprofit Analytics

Increase efficiency, accountability, and effectiveness.

Sports Analytics

Player evaluation, on-field strategy, contract negotiations, fan experience.

Web Analytics

Analyze online user behavior to improve site design, ad placement, and sales.

Decision Types

Strategic (long-term), Tactical (mid-term), Operational (daily).

Three Types of Analytics

Descriptive (describes past), Predictive (forecasts future), Prescriptive (recommends optimal actions).