Business Pressures-Responses-Support Model

• Companies are aggressively adopting computerized support, known as Business Intelligence (BI), for their operations.
• The Business Pressures-Responses-Support Model involves addressing business pressures with strategic responses, facilitated by support systems.
• Business pressures are a result of a competitive business climate.
• Responses are actions taken to counter business pressures.
• Support is implemented to better facilitate business operations.

Business Pressures-Responses-Support Model

• Business Environmental Factors lead to Pressures and Opportunities, which drive Organizational Responses and enable Decisions and Support.
• Business Environmental Factors examples include Globalization, Customer Demand, Government Regulations, Market Conditions, and Competition.
• Responses include Strategy, Partner Collaboration, Real-time Response, Agility, Increased Productivity, New Vendors, and New Business Models.
• Decisions and Support include analyses, predictions, integrated computerized decision support, and business intelligence.

Strategy Gap

• Computerized decision support aims to bridge the gap between an organization's current and desired performance.
• Desired performance is based on the organization's mission, objectives, goals, and strategies.

Managerial Decision Making

• Management involves using resources to achieve organizational goals.
• Inputs are resources.
• Output is the attainment of goals.
• The measure of success is the ratio of outputs to inputs.
• Management is essentially decision-making.
• Decision-making involves selecting the best solution from multiple alternatives.

Decision-Making Process

• Managers typically follow a four-step decision-making process, also known as the scientific approach.
• The steps are defining the problem or opportunity, constructing a model, identifying and evaluating solutions, and recommending a solution.

Information Systems Support

• Information systems help with group communication and collaboration.
• Information systems facilitate improved data management and the management of data warehouses and Big Data.
• Information systems provide analytical support.
• Information systems help in overcoming cognitive limits in processing and storing information.
• Information systems enable knowledge management.
• Information systems provide anywhere, anytime support.

Early Decision Support Framework

• Gorry and Scott-Morten developed an early decision support framework in 1971.
• Decisions can be structured, semistructured, or unstructured.
• Types of control can be operational, managerial, or strategic planning.
• Accounts receivable, accounts payable, and order entry are examples of structured decisions.

Degree of Structuredness

• Simon classified decisions based on their level of structure in 1977.
• Decisions can be highly structured (programmed), semi-structured, or highly unstructured (nonprogrammed).
• Strategic planning is top-level and long-range.
• Management control involves tactical planning.
• Operational control is also considered.

Concept of DSS

• Decision Support Systems (DSS) are interactive, computer-based systems.
• DSS helps decision-makers use data and models to solve unstructured problems by Gorry and Scott-Morton in 1971.
• DSS combines individual intellectual resources and computer capabilities to improve decision quality.
• "DS" is an umbrella term.
• The concept of DS has evolved into Business Intelligence.

Business Intelligence (BI) Framework

• BI is an evolution of decision support concepts.
• BI evolved from Executive Information Systems to Everybody's Information System.
• BI systems include visualizations, alerts, and performance measurement tools.
• The term "BI" emerged from industry.

Definition of BI

• BI is an umbrella term covering architectures, tools, databases, analytical tools, applications, and methodologies.
• BI is content-free.
• A key objective of BI is to provide easy access to data (and models) for analysis by business managers.
• BI transforms data into information (and knowledge), decisions, and actions.

Brief History of BI

• The term "BI" was coined by the Gartner Group in the mid-1990s.
• The concept of BI is older.
• The 1970s focused on MIS reporting with static/periodic reports.
• The 1980s featured Executive Information Systems (EIS).
• The 1990s introduced OLAP, dynamic, multidimensional, ad-hoc reporting, and the coining of the term "BI".
• The 2010s included AI and Data/Text Mining, web-based portals/dashboards, Big Data, Social Media, and Analytics.
• The 2020s are yet to be seen.

Architecture of BI

• A BI system has four key components.
• The components are a data warehouse with source data, business analytics tools, business performance management (BPM) for performance monitoring, and a user interface like a dashboard.

High Level Architecture of BI

• Data Sources are built into a Data Warehouse by technical staff in a Data Warehouse Environment
• Business user then access the data via the Business Analytics Environment for manipulation
• This then leads to Manager/Executive BPM strategy

Business Value of BI Analytical Applications

• BI applications provide business value.
• They can be used for customer segmentation, propensity to buy analysis, customer profitability assessment, fraud detection, customer attrition management, and channel optimization.

DSS-BI Connections

• DSS and BI have similarities and differences.
• The similarity is in architectures and data focus.
• DSS provides direct support, while BI offers indirect support.
• They cater to different target audiences.
• The origin is industry vs. academia.

Analytics Overview

• Analytics is a new term for analytics.
• INFORMS proposes a simple analytics taxonomy.
• Descriptive Analytics
• Predictive Analytics
• Prescriptive Analytics

Analytics Details

• Business Analytics has 3 key sections
• Descriptive: What happened? What is happening?
• Business reporting and data warehousing are enablers
• Accurate projections of the future states
• Predictive: What will happen? Why will it happen?
• Data Mining is an enabler
• Prescriptive: What should I do? Why should I do it?
• Decision Modeling is an enabler

Introduction to Big Data Analytics

• Big Data is characterized by the 3 Vs
• Volume: the size of the data
• Variety: the different forms the data can take
• Velocity: the speed at which the data can be processed