Analytical Business Applications

• Three major events spurred explosive growth for analytical methods in business applications

First Development

• Tech advancements produce huge amounts of data for businesses via scanner tech and e-commerce
• Companies want to improve profitability, understand customers, price products effectively, and gain a competitive advantage using data

Second Development

• Ongoing research resulted in methodological developments
• Computational approaches handle and explore mass amounts of data
• Faster algorithms exist today for optimization and simulation
• Approaches now available are way more effective for visualizing data

Third Development

• Methodological developments paired with explosions in computing power and storage
• Improved computing hardware, parallel computing, and cloud computing enables businesses to solve major problems faster, and more accurately

Decision Making

• Managers are responsible for making strategic, tactical, and operational decisions
• Strategic decisions involve the overall direction of an organization
• Strategic decisions define the organization's goals and aspirations
• Tactical decisions concern how an organization achieves goals set by its strategy
• Tactical decisions are usually the responsibility of the mid-level management
• Operational decisions impact how the firm is run daily
• Operational decisions are the domain of operations managers closest to the customer

Decision Making Process

• Identify and define the problem
• Determine the criteria
• Determine alternative solutions
• Evaluate the alternatives
• Choose an alternative

Approaches to Making Decisions

• Tradition
• Intuition
• Rules of Thumb
• Using Data

Business Analytics

• Transforms data into insight, which allows for better decision-making
• Data-driven or fact-based decision-making is more objective than other alternatives

Tools to Enhance Decisions

• Creating insights
• Improve forecasting
• Quantify risk
• Yield better alternatives through optimization and analysis

Analytical Methods and Models

Descriptive Analytics

• Encompasses techniques describing what has happened in the past
• Data queries
• Reports
• Descriptive Statistics
• Data visualization, including data dashboards
• Data-mining techniques
• Basic "what-if" spreadsheet models

Data Query

• A request for database information with characteristics

Data Dashboards

• Collections of tables, maps, charts, and summary statistics that are updated
• Dashboards help management monitor company performance related to decision-making
• Corporate-level managers use dashboards to summarize sales by region and inventory levels across the company
• Front-line managers use dashboards that use staffing levels, local inventory, and short-term sales forecasts

Data Mining

• Utilizes analytical techniques to better understand relationships and patterns in large data sets
• Cluster analysis
• Sentiment analysis

Predictive Analytics

• Constructs models using historical data to predict the future
• Surveys and purchase behavior may predict a market share of a new product
• Linear regression and time series analysis are frequently used
• Data mining identifies relationships in large databases
• Simulation uses probability and statistics to construct a computer model and study the impact of uncertainty

Prescriptive Analytics

• This shows what course of action needs to be implemented
• Forecasting/prediction in conjunction with a rule becomes a prescriptive model
• Rule-based models rely on a rule or set of rules

Prescriptive Analytics Examples

• Portfolio models determine high-yield investments while controlling risk
• Supply network design identifies the right distribution center locations for customer service requirements
• Price markdown models use historical data to maximize revenue from discounts
• Optimization models give the best decision subject to the situation's constraints
• Simulation optimization combines probability and statistics to find good decisions in highly uncertain settings
• Decision analysis develops the best strategy when faced with decision alternatives

Big Data

• Data sets get too big and too complex to be processed with desktop software or techniques
• IBM used the "Four V's" to describe the phenomenon of big data

Four V's

• Volume: Collect data electronically
• Velocity: Real-time data capture and analysis lead to unique challenges
• Variety: Complicated data is valuable to businesses
• Veracity: The impact of uncertainty

Data Examples

• Text data is collected by monitoring social media
• Audio data come from service calls
• Video data comes from cameras and is used to analyze behavior

Data Characteristics

• Presents opportunities
• Presents challenges in data storage, processing, security, and analytical resources
• Hadoop: Open-source programming environment supporting big data processing through distributed storage and processing on clusters
• MapReduce: Programming model inside Hadoop that performs the map and reduce steps
• Data security is critical given the rise of data-related crimes
• Analysts in high demand due to companies needing assistance to process and analyze mass amounts of data
• Data scientists know how to process and analyze data and are in high demand
• Internet of Things sends data from machine sensors to be stored and analyzed online

Business Analytics in Practice

• Financial Analytics
• Human Resource (HR) Analytics
• Marketing Analytics
• Health Care Analytics
• Supply-Chain Analytics
• Analytics for Government and Nonprofits
• Sports Analytics
• Web Analytics
• Predictive/prescriptive analytics are forms of "Advanced Analytics"

Financial Analytics

• Forecast investment performance
• Assess risk
• Construct derivatives
• Construct optimal portfolios of investment
• Allocate assets
• Create capital budgeting plans
• Assess risk through simulation

Human Resources Analytics

• Supports the mix of skills in the organization needed to meet needs
• Hires the best talent
• Provides environment for retention of talent
• Achieve organizational diversity goals

Marketing Analytics

• One of the fastest-growing application areas
• Understand how consumer behavior impacts the use of scanner and social media data
• Impacts advertising budgets
• Impacts pricing strategies
• Improves forecasting and product line management
• Increases customer satisfaction and loyalty

Health Care Analytics

• Used to improve scheduling of patients, staff, and facilities
• Improves patient flow
• Use for purchasing and inventory control
• Used for diagnosis and treatment

Supply-Chain Analytics

• The core is efficient delivery
• Optimizes sorting goods
• Optimizes vehicle and staff scheduling
• Optimizes profitability for logistics companies such as UPS and FedEx
• Improves the chain through better inventories

Government and Nonprofits Analytics

• Drives of inefficiencies
• Increases effectiveness and accountability
• Ensures effectiveness

Sports Analytics

• Assesses players for drafts
• Decides contract agreements
• Optimizes racing teams
• Assists with on-field decision making
• Off-the-field decision-making is increasing rapidly
• Prescriptive analytics dynamically adjusts ticket prices