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Business Intelligence, Analytics, and Data Science: A Managerial Perspective Fourth Edition Chapter 1 An Overview of Business Intelligence, Analytics, and Data Science Co...

Business Intelligence, Analytics, and Data Science: A Managerial Perspective Fourth Edition Chapter 1 An Overview of Business Intelligence, Analytics, and Data Science Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved OPENING VIGNETTE Sports Analytics— An Exciting Frontier for Learning and Understanding Applications of Analytics Sports analytics is becoming a specialty within analytics Sports is a big business – Generating $145B in revenues annually – Additional $100B in legal and $300B in illegal gambling Analytic in sports popularized by the Moneyball book by Michael Lewis in 2003 – About Oakland A’s – And the follow-on movie in 2011 Nowadays, analytics is used in many facets of sports Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette Questions 1.What are three factors that might be part of a predictive model for season ticket renewals? 2.What are techniques that football teams can use to do the opponent analysis? 3.How can wearables improve player health and safety? What kinds of new analytics can trainers use? 4.What other analytics uses can you envision in sports? Soccer, Cricket, Motor Sports, Baseball, Tennis, etc. Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved OPENING VIGNETTE Sports Analytics— An Exciting Frontier for Learning and Understanding Applications of Analytics Example 1: The Business Office FIGURE 1.1 Season Ticket Renewals—Survey Scores Percentage that renewed after responding…. Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Figure 1.2 Dynamic Pricing Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opponent Analysis Football Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved OPENING VIGNETTE Sports Analytics— An Exciting Frontier for Learning and Understanding Applications of Analytics BSI Precision Football case ***Also check out the book “Mathletics: How Gamblers, Managers, and Sports Enthusiasts Use Mathematics in Baseball, Basketball, and Football” by Wayne L. Winston Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Riddell football helmets are an interesting case study in technology innovation Product Innovation Partnership with Carbon to custom design a 3-D printed helmet liner based on individual head shapes and measurements (no more S, M, L, XL) Improved impact data with sensors Business Model innovation Insite software – industry leader in impact monitoring (leveraging the data they collect) – Impact analytics Source: https://www.riddell.com/riddell/en/ – Coaching tool – Training prescriptive analytics Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Changing Business Environments and Evolving Needs for Decision Support and Analytics Increased hardware, software, and network capabilities Group communication and collaboration Improved data management Managing giant data warehouses and Big Data Analytical support Overcoming cognitive limits in processing and storing information Knowledge management Anywhere, anytime support Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Evolution of Computerized Decision Support to Analytics/Data Science FIGURE 1.8 Evolution of Decision Support, Business Intelligence, and Analytics Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved A Framework for Business Intelligence DSS  EIS  BI Definition of Business Intelligence – [Broad Definition] An umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies – [Narrow Definition] Descriptive analytics tools and techniques (i.e., reporting tools) A Brief History of BI – 1970s  1980s  1990s … The Origins and Drivers of BI (See Figure 1.9) The Architecture of BI (See Figure 1.10) Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved A Framework for Business Intelligence FIGURE 1.9 Evolution of Business Intelligence (BI)  Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved A Framework for Business Intelligence The Architecture of BI FIGURE 1.10 A High-Level Architecture of BI Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved A Multimedia Exercise in Business Intelligence TUN (TeradataUniversityNetwork.com) – BSI Videos (Business Scenario Investigations)  Analogues to CSI (Crime Scene Investigation) Go To – www.youtube.com/watch?v=NXEL5F4_aKA See the – www.slideshare.net/teradata/bsi-how-we-did-it-the-case-of-the- misconnecting-passengers.slides Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Transaction Processing versus Analytic Processing Online Transaction Processing (OLTP) – Operational databases – ERP, SCM, CRM, … – Goal: data capture Online Analytical Processing (OLAP) – Data warehouses – Goal: decision support What is the relationship between OLTP and OLAP? Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Transaction Processing Versus Analytic Processing Transaction processing systems (OLTP) are constantly involved in handling updates (add/edit/delete) to what we might call operational databases – ATM withdrawal transaction, sales order entry via an ecommerce site – updates DBs – OLTP – handles routine on-going business – ERP, SCM, CRM systems generate and store data in OLTP systems – The main goal is to have high efficiency Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Transaction Processing Versus Analytic Processing Online analytic processing (OLAP) systems are involved in extracting information from data stored by OLTP systems – Routine sales reports by product, by region, by sales person, by … – Often built on top of a data warehouse where the data is not transactional – Main goal is the effectiveness (and then, efficiency) – provide correct information in a timely manner – More on OLAP will be covered in Chapter 2 Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Appropriate Planning and Alignment with the Business Strategy Planning and Execution  Business, Organization, Functionality, and Infrastructure Functions served by BI Competency Center – How BI is linked to strategy and execution of strategy – Encourage interaction between the potential business user communities and the IS organization – Serve as a repository and disseminator of best BI practices between and among the different lines of business. – Standards of excellence in BI practices can be advocated and encouraged throughout the company –… Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Real-Time, On-Demand BI Is Attainable Emergence of real-time BI applications Justifying the need – Is there a need for real-time [is it worth the additional expense]? Leveraging the enablers – RFID – Web services – Intelligent agents Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Critical BI System Considerations Developing or Acquiring BI Systems – Make versus buy – BI shells Justification and Cost–Benefit Analysis – A challenging endeavor, why? Security Protection of Privacy Integration to Other Systems and Applications Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Plan of the Book What should What will happen? happen? What did happen? FIGURE 1.15 Plan of the Book Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Resources Teradata University Network (TUN) TeradataUniversityNetwork.com Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved End of session 1 Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Analytics Overview Analytics…a relatively new term/buzz-word Analytics…the process of developing actionable decisions or recommendations for actions based on insights generated from historical data According to the Institute for Operations Research and Management Science (INFORMS) – Analytics represents the combination of computer technology, management science techniques, and statistics to solve real problems. Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Business Analytics FIGURE 1.11 Three Types of Analytics Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Descriptive Analytics Descriptive or reporting analytics Answering the question of what happened Retrospective analysis of historic data Enablers – OLAP / DW – Data visualization  Dashboards and Scorecards – Descriptive statistics Application cases 1.2 and 1.3 p 24-25 Dundas BI Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Predictive Analytics Aims to determine what is likely to happen in the future (foreseeing the future events) Looking at the past data to predict the future Enablers – Data mining – Text mining / Web mining – Forecasting (i.e., time series) Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Prescriptive Analytics Aims to determine the best possible decision Uses both descriptive and predictive to create the alternatives, and then determines the best one Enablers – Optimization – Simulation – Multi-Criteria Decision Modeling – Heuristic Programming Analytics Applied to Many Domains Analytics or Data Science? Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Analytics Examples in Selected Domains Analytics in Retail Value Chain FIGURE 1.12 Example of Analytics Applications in a Retail Value Chain Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Analytics Examples in Retail Value Chain For the complete table, refer to your textbook -- also section 1.6 p 29-35 Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved A Brief Introduction to Big Data Analytics What Is Big Data? (Is it just “big”?) – Big Data is data that cannot be stored or processed easily using traditional tools/means – Big Data typically refers to data that comes in many different forms: large, structured, unstructured, continuous  3Vs – Volume, Variety, Velocity – Data (Big Data or otherwise) is worthless if it does not provide business value (and for it to provide business value, it has to be analyzed) More on Big Data Analytics is in Chapter 7 Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Application Case 1.6, p. 37 CenterPoint Energy uses real-time big data to improve customer service Outage prediction with weather and smart meters, fuel savings IBM video https://www.ibmbigdatahub.com/video/centerpoint- energy-talks-real-time-big-data-analytics Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved An Overview of the Analytics Ecosystem What are the key players in analytics industry? What do they do? Is there a place for you to be a part of it? There is a need to classify different industry participants in the broader view of analytics to – Identify providers (as an analytics consumer) – Identify roles to play (as a potential provider) – Identify job opportunities – Identify investment/entrepreneurial opportunities – Understand the landscape and the future of computerized decision sport systems Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved An Overview of the Analytics Ecosystem FIGURE 1.13 Analytics Ecosystem Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved An Overview of the Analytics Ecosystem Data Generation Infrastructure Providers Data Management Infrastructure Providers Data Warehouse Providers Middleware Providers Data Service Providers Analytics Focused Software Developers Application Developers: Industry Specific or General Analytics Industry Analysts and Influencers Matt Turck (First Mark) Analytics ecosystem (2018) Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved An Overview of the Analytics Ecosystem Academic Institutions and Certification Agencies – Certificates – Masters programs – Undergraduate programs – Offered by  MIS, Engineering  Marketing, Statistics  Computer Science  … Regulators and Policy Makers Analytics User Organizations Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved New and evolving tools to support BI Hadoop  Apache Hadoop is a software framework that supports data-intensive distributed applications under a free license (Open source). It enables applications to work with thousands of nodes and large quantities (petabytes) of data. – Inspired by Google’s file system and Google’s MapReduce – http://en.wikipedia.org/wiki/MapReduce  http://hadoop.apache.org  Use in BI support is evolving. – http://www.pentaho.com/hadoop Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved New and evolving tools to support BI Apache Spark – Open source massive data processing tool Different than Hadoop:  Hadoop is designed to effectively and efficiently distribute massive data & Apache is designed to process data distributed on Hadoop type frameworks (Spark doesn’t store data)  Can use Hadoop without Spark and vice versa (although Spark does require some type of big data file processing platform)  Spark is typically faster than MapReduce and acts on the entire data set at one time rather than on a cluster of the data set at one time – some estimates of up to 100x faster Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved New and evolving tools to support BI Vendors in the sensor market space  SmartBin pretty cool example at Good Will Industries  SIKO Products Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Major BI Vendors (not in book) In the last several years, the landscape of BI vendors has changed – Cognos acquired by IBM in 2008  IBM also acquired SPSS in 2009 – Hyperion acquired by Oracle in 2008 – SAP’s BI/A suite (acquired Business Objects in 2009) Microstrategy – May be the only independent large BI vendor Others include Microsoft, SAS Teradata (mostly considered a DW vendor) Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Application Case 1.5 – real time ATP Page 27 of book Discussion Noodle.ai video https://www.youtube.com/watch?v=xuKoyGrzMqU Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved End of Chapter 1 Questions / Comments Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved