Chapter 1 An Overview of Business Intelligence Analytics

Chapter 1: An Overview of Business Intelligence, Analytics, and Decision Support

Learning Objectives • Understand today’s turbulent business environment and describe how organizations survive and even excel in such an environment • Understand the need for computerized support of managerial decision making • Understand an early framework for managerial decision making • Learn the conceptual foundations of the decision support systems (DSS) methodology

Learning Objectives • Describe the business intelligence (BI) methodology and concepts and relate them to DSS • Describe the concept of work systems and its relationship to decision support • List the major tools of computerized decision support • Understand the major issues in implementing computerized support systems

Changing Business Environment & Computerized Decision Support • Companies are moving aggressively to computerized support of their operations Business Intelligence Business Pressures–Responses–Support Model – Business pressures result of today's competitive business climate – Responses to counter the pressures – Support to better facilitate the process

Managerial Decision Making

The Business Environment • The environment in which organizations operate today is becoming more and more complex, creating – opportunities, and – problems. – Example: globalization. • Business environment factors: – markets, consumer demands, technology, and societal…

Business Environment Factors

Organizational Responses • Be Reactive, Anticipative, Adaptive, and Proactive • Managers may take actions, such as – – – Employ strategic planning. Use new and innovative business models. Restructure business processes. Participate in business alliances. Improve corporate information systems. … more [in your book]

Closing the Strategy Gap • One of the major objectives of computerized decision support is to facilitate closing the gap between the current performance of an organization and its desired performance, as expressed in its mission, objectives, and goals, and the strategy to achieve them.

Managerial Decision Making • The nature of managers’ work – Interpersonal – Informational – Decisional

The Nature of Managers’ Work Mintzberg's 10 Managerial Roles Interpersonal 1. Figurehead 2. Leader 3. Liaison Informational 4. Monitor 5. Disseminator 6. Spokesperson Decisional 7. Entrepreneur 8. Disturbance handler 9. Resource allocator 10. Negotiator

Managerial Decision Making • Management is a process by which organizational goals are achieved by using resources. – Inputs: resources – Output: attainment of goals – Measure of success: outputs / inputs • Management Decision Making • Decision making: selecting the best solution from two or more alternatives

Managerial Decision Making • The process of decision making 1. Defining the problem (a decision situation that may deal with some difficulty or with an opportunity) 2. Constructing a model that describes the real-world problem 3. Identifying possible solutions to the modeled problem and evaluating the solutions 4. Comparing, choosing, and recommending a potential solution to the problem

How hard it is to follow the process? • To follow the process: – Sufficient alternatives solutions are being considered – These alternatives should be reasonably predicted – Comparisons should be done properly – However!!

Difficulties in following the decision process • Technology, information systems, advanced search engines, and globalization result in more and more alternatives • Government regulations, changing, consumer demand, political instability = more uncertainty • Trial and error learning is costly and difficult • Environments are growing more complex • The need for computer support is vital! >>

Managerial Decision Making • Decision support systems (DSS) A conceptual framework for a process of supporting managerial decision- making, usually by modeling problems and employing quantitative models for solution analysis

Computerized Support for Decision Making • Why use computerized decision support systems – Speedy computations – Improved communication and collaboration – Increased productivity of group members – Improved data management – Managing giant data warehouses

Computerized Support for Decision Making • Why use computerized decision support systems – Quality support – Agility support – Overcoming cognitive limits in processing and storing information – Using the Web – Anywhere, anytime support

An Early Decision Support Framework (Gorry and Scott-Mortin 1971) • Degree of Structuredness (Simon, 1977) – Decisions are classified as • Highly structured (a. k. a. programmed) • Semi-structured • Highly unstructured (i. e. , nonprogrammed) • Types of Control (Anthony, 1965) – Strategic planning (top-level, long-range) – Management control (tactical planning) – Operational control

An Early Framework for Computerized Decision Support

An Early Framework for Computerized Decision Support – Degree of structuredness • Four-phase decision making process – – Intelligence Design Choice Implementation

An Early Framework for Computerized Decision Support

An Early Framework for Computerized Decision Support • Computer support for structured decisions – Management science (MS) or operations research (OR) The application of a scientific approach and mathematical models to the analysis and solution of managerial decision situations (e. g. , problems, opportunities) - Since 1960’s

An Early Framework for Computerized Decision Support • Computer support for structured decisions – Automated decision systems (ADS) A business rules-based system that uses intelligence to recommend solutions to repetitive decisions (such as pricing)

An Early Framework for Computerized Decision Support

An Early Framework for Computerized Decision Support – The decision support matrix • For semistructured decisions and unstructured decisions, conventional MIS and management sciences (MS) tools are insufficient • Supportive information systems = they called: Decision support systems (DSS)

The Concept of DSS • DSS - interactive computer-based systems, which help decision makers utilize data and models to solve unstructured problems (Gorry and Scott-Morton, 1971) • Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. • DS as an Umbrella Term • Evolution of DS into Business Intelligence

The Concept of Decision Support Systems (DSS) • DSS as an Umbrella term: Describes any computerized system that supports decision making in an organization – DSS as a specific application – The architecture of DSS • • • Data Models manipulate data as related to a specific situation Knowledge component Users User interface

The Concept of Decision Support Systems (DSS)

The Concept of Decision Support Systems (DSS) – Types of DSS • model-oriented DSS: quantitative models used to generate a recommended solution to a problem • data-oriented DSS: support ad-hoc reporting and queries

A Framework for Business Intelligence (BI) • Business intelligence (BI) An umbrella term that combines architectures, tools, databases, applications, and methodologies • Evolution of BI>>>

A Framework for Business Intelligence (BI) • BI is an evolution of decision support concepts over time • Then: Executive Information System • Now: Everybody’s Information System (BI) • BI systems are enhanced with additional visualizations, alerts, and performance measurement capabilities • The term BI emerged from industry

A Brief History of BI • The term BI was coined by the Gartner Group in the mid-1990 s • However, the concept is much older – 1970 s - MIS reporting - static/periodic reports – 1980 s - Executive Information Systems (EIS) – 1990 s - OLAP, dynamic, multidimensional, ad-hoc reporting -> coining of the term “BI” – 2010 s - Inclusion of AI and Data/Text Mining capabilities; Web-based Portals/Dashboards, Big Data, Social Media, Analytics – 2020 s - yet to be seen

Definition of BI • BI is a content-free expression, so it means different things to different people • BI'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 Framework for Business Intelligence (BI)

The Architecture of BI • A BI system has four major components – a data warehouse, with its source data – business analytics, a collection of tools for manipulating, mining, and analyzing the data in the data warehouse – business performance management (BPM) for monitoring and analyzing performance – a user interface (e. g. , dashboard)

A High-Level Architecture of BI

A Framework for Business Intelligence (BI) – Data warehouse • Originally, included historical data that were organized and summarize, so end users could easily view or manipulate data and information • Today, some data warehouses include current data as well, so they can provide real time decision support

A Framework for Business Intelligence (BI) • Business analytics – Reporting and queries – Advanced analytics – Data, text and Web mining and other sophisticated mathematical and statistical tools

A Framework for Business Intelligence (BI) • Data mining A process of searching for unknown relationships or information in large databases or data warehouses, using intelligent tools such as neural computing, predictive analytics techniques, or advanced statistical methods

A Framework for Business Intelligence (BI) • Business performance management (BPM) An advanced performance measurement and analysis approach that embraces planning and strategy – BPM extends the monitoring, measuring, and comparing of sales, profit, cost, profitability, and other performance indicators by introducing the concept of “management and feedback – BPM provides a top-down enforcement of corporatewide strategy

A Framework for Business Intelligence (BI) • Business performance management – User interface – Dashboard A visual presentation of critical data for executives to view. It allows executives to see hot spots in seconds and explore the situation – Dashboards integrate information from multiple business areas • Visualization tools

Business Value of BI Analytical Applications • • • Customer segmentation Propensity to buy Customer profitability Fraud detection Customer attrition Channel optimization

A Framework for Business Intelligence (BI) – Styles of BI 1. 2. 3. 4. 5. Report Delivery and Alerting Enterprise Reporting (dashboard, scorecard) Cube Analysis (Slice and Dice Analysis) Ad-hoc Query Statistics and Data Mining

A Framework for Business Intelligence (BI) • The DSS-BI connection 1. The architecture is very similar since BI evolved from DSS 2. DSS are constructed to directly support specific decision making; BI systems are geared to provide accurate and timely information (indirect support) 3. BI has an executive and strategy orientation while DSS has been oriented toward analysts

A Framework for Business Intelligence (BI) • The DSS-BI connection 4. BI systems are constructed with commercially available tools and components that are fitted to the needs of organizations; DSS more programming is used to construct custom solutions to very unstructured problems 5. DSS were developed mostly in the academic world; BI were developed mostly by software companies 6. Many tools used by BI are also considered DSS tools (e. g. , data mining and predictive analysis )

A Multimedia Exercise in Business Intelligence • Teradata University Network (TUN) www. teradatauniversitynetwork. com • BSI Videos (Business Scenario Investigations) www. youtube. com/watch? v=NXEL 5 F 4_a. KA • Also look for other BSI Videos at TUN

Takeaways? • You can use data to make decisions • The decisions may be complex – with tradeoffs because of capacity - which is why you may need to automate the decisions • The rules you use will evolve over time, be refined • You can measure the consequences of decisions – good vs. bad • You can tie decisions to business goals

Analytics Overview • Analytics? – Something new or just a new name for … • SAS Institute Inc. proposed eight levels of analytics. • The Institute for Operations Research and Management Sciences (INFORMS). Has created major initiative to organize and promote analytics.

Analytics Overview • A Simple Taxonomy of Analytics (proposed by INFORMS) – Descriptive Analytics – Predictive Analytics – Prescriptive Analytics • Analytics or Data Science?

Analytics Overview

Introduction to Big Data Analytics • Big Data? – Not just big! – Volume – Variety – Velocity • More of Big Data and related analytics tools and techniques are covered in Chapter 13.

Plan of the Book • Part I - Decision Making and Analytics: An Overview – (Chapters 1 & 2) • Part II - Descriptive Analytics – (Chapters 3 & 4) • Part III - Predictive Analytics – Chapters 5 - 8 • Part IV - Prescriptive Analytics – Chapter 9 - 12 • Part V - Big Data and Future Directions for Business Analytics – Chapters 13 & 14

End of the Chapter • Questions / Comments…

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