Business Intelligence & Decision Support

• Companies are moving aggressively to computerized support for their operations via business intelligence.
• The Business Pressures-Responses-Support Model is used

Business Pressures

• Business pressures result from today's competitive business climate.
• Globalization, customer demand, government regulations, market conditions, and competition are examples of business environmental factors.
• Opportunities respond to pressures.

Organization's Responses

• Strategy, partners' collaboration, real-time responses, agility, increased productivity, new vendors, and new business models are examples of the organization's responses.

Decisions and Support

• Analysis, Predictions, and Decisions are forms of decisions and support
• Actions are integrated with computerized decision support and business intelligence.

Closing the Strategy Gap

• A major objective of computerized decision support is to facilitate closing the gap between an organization's current performance and its desired performance.
• Desired performance is expressed in the organization's mission, objectives, and goals, and the strategy to achieve them.

Managerial Decision Making

• Management is a process to achieve organizational goals using resources.
• Inputs are resources.
• The output is the attainment of goals.
• A measure of success is outputs/inputs.
• Management is decision-making.
• Decision-making is selecting the best solution from two or more alternatives.

Decision-Making Process

• Managers usually make decisions by following a four-step process:
• Define the problem (or opportunity)
• Construct a model that describes the real-world problem.
• Identify possible solutions to the modeled problem and evaluate the solutions.
• Compare, choose, and recommend a potential solution to the problem.

Information Systems Support for Decision Making

• Information systems provide group communication and collaboration
• Enable improved data management.
• Information systems manage data warehouses and Big Data.
• Provide analytical support.
• Help overcome cognitive limits in processing and storing information.
• Support knowledge management.
• Offer support anywhere, anytime.

Early Decision Support Framework (Gorry and Scott-Morten, 1971)

• Decisions are classified as highly structured (a.k.a. programmed), semi-structured or highly unstructured.

Types of Control (Anthony, 1965)

• Strategic planning is top-level, long-range
• Management control is tactical planning.
• Operational control is also used

The Concept of DSS

• DSS refers to interactive computer-based systems that help decision-makers utilize data and models to solve unstructured problems (Gorry and Scott-Morton, 1971).
• Decision support systems couple individuals' intellectual resources with the capabilities of the computer to improve the quality of decisions.
• DS is an Umbrella Term
• Decision Support has evolved into Business Intelligence

Business Intelligence (BI)

• Business Intelligence is an evolution of decision support concepts over time.
• It has evolved from Executive Information Systems to Everybody's Information System (BI).
• BI systems are enhanced with visualizations, alerts, and performance measurement capabilities.
• The term BI emerged from industry.
• BI is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies.
• BI is a content-free expression, so it means different things to different people.
• Business Intelligence's major objective is to enable easy access to data (and models) to provide business managers with the ability to conduct analysis.
• BI helps transform data to information (and knowledge), to decisions, and finally to action.

A Brief History of BI

• The term BI was coined by the Gartner Group in the mid-1990s, but the concept is much older.
• 1970s: MIS reporting was static/periodic.
• 1980s: Executive Information Systems (EIS)
• 1990s: OLAP, dynamic, multidimensional, ad-hoc reporting led to the coining of the term "BI"
• 2010s: Inclusion of AI and Data/Text Mining capabilities; Web-based Portals/Dashboards, Big Data, Social Media, and Analytics became factors.
• 2020s: Was yet to be seen

The Architecture of BI

• A BI system is comprised of the following components:
• A data warehouse and, data sources.
• Business analytics: tools for manipulating, mining, and analyzing data in the data warehouse.
• Business performance management (BPM) for monitoring and analyzing performance.
• A user interface (e.g., dashboard).

Business Value of BI Analytical Applications

• BI Analytical Applications provide customer segmentation, propensity to buy
• Can increase customer profitability and improve fraud detection
• Reduce rates of customer attrition.
• Channel optimization

Analytics Overview

• A simple taxonomy of analytics:
• Descriptive Analytics
• Predictive Analytics
• Prescriptive Analytics

Introduction to Big Data Analytics

• It includes volume, variety, and velocity.