Kecerdasan Bisnis Terapan Business Intelligence, Analytics, and Data Science Husni Lab. Riset JTIF UTM Sumber awal: http://mail.tku.edu.tw/myday/teaching/1071/BI/1071BI02_Business_Intelligence.pptx Business Intelligence (BI) 1 Introduction to BI and Data Science 2 Descriptive Analytics 3 Predictive Analytics 4 Prescriptive Analytics 5 Big Data Analytics 6 Future Trends 2 Outline • Business Intelligence (BI) • Analytics • Data Science 3 Business Intelligence (BI) 4 Evolution of Decision Support, Business Intelligence, and Analytics Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson 5 Changing Business Environments and Evolving Needs for Decision Support and Analytics 1. 2. 3. 4. 5. Group communication and collaboration Improved data management Managing giant data warehouses and Big Data Analytical support Overcoming cognitive limits in processing and storing information 6. Knowledge management 7. Anywhere, anytime support Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson 6 Decision Support Systems (DSS) (Gorry and Scott-Morton, 1971) “interactive computer-based systems, which help decision makers utilize data and models to solve unstructured problems” Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson 7 Decision Support Systems (DSS) (Keen and Scott-Morton, 1978) “Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer-based support system for management decision makers who deal with semistructured problems.” Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson 8 Evolution of Business Intelligence (BI) Chapter 1 • An Overview of Business Intelligence, Analytics, and D Quer ying and r epor t ing ETL M et adat a Dat a war ehouse DSS EIS/ESS Dat a mar t s Financial r epor t ing Spr eadsheet s (M S Excel) OLAP Digit al cockpit s and dashboar ds Business Int elligence Scor ecar ds and dashboar ds Wor kflow Aler t s and not ificat ions Dat a & t ext mining Pr edict ive analyt ics Br oadcast ing t ools Por t als Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), FIGU RE 1.9Business Evolution ofSource: Business Intelligence (BI). Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson 9 mining Br oadcast ing t ools Predict ive analyt ics Por t als A High-Level Architecture of BI FIGU RE 1.9 Evolution of Business Intelligence (BI). Dat a Warehouse Environment Dat a Sources Business Analyt ics Environment Technical st af f Business user s Build t he dat a w arehouse - Organizing - Summarizing - St andardizing Access Dat a warehouse Performance and St rat egy M anagers/execut ives BPM st rat egies M anipulat ion, result s User int erface Fut ure component : Int elligent syst ems - Browser - Port al - Dashboard FIGU RE 1.10 A High-Level Architecture of BI. (Source: Based on W. Eckerson, Smart Companies in the 21st Century: The Secrets of Creating Successful Intelligent Solutions. Data Warehousing Institute, Seattle, W A, Source: Business Ramesh Sharda, Dursun Delen, andThe Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson 10 Business Intelligence (BI) Infrastructure Source: Kenneth C. Laudon & Jane P. Laudon (2014), Management Information Systems: Managing the Digital Firm, Thirteenth Edition, Pearson. 11 Business Intelligence and Data Mining Increasing potential to support business decisions End User Decision Making Data Presentation Visualization Techniques Business Analyst Data Mining Information Discovery Data Analyst Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems Source: Jiawei Han and Micheline Kamber (2006), Data Mining: Concepts and Techniques, Second Edition, Elsevier DBA 12 Architecture of Big Data Analytics Big Data Sources Big Data Transformation Middleware * Internal * External * Multiple formats * Multiple locations * Multiple applications Big Data Platforms & Tools Raw Data Hadoop Transformed MapReduce Pig Data Extract Hive Transform Jaql Load Zookeeper Hbase Data Cassandra Warehouse Oozie Avro Mahout Traditional Others Format CSV, Tables Big Data Analytics Applications Queries Big Data Analytics Reports OLAP Data Mining Source: Stephan Kudyba (2014), Big Data, Mining, and Analytics: Components of Strategic Decision Making, Auerbach Publications 13 Architecture of Big Data Analytics Big Data Sources * Internal * External * Multiple formats * Multiple locations * Multiple applications Big Data Transformation Big Data Platforms & Tools Data Mining Big Data Analytics Applications Middleware Raw Data Hadoop Transformed MapReduce Pig Data Extract Hive Transform Jaql Load Zookeeper Hbase Data Cassandra Warehouse Oozie Avro Mahout Traditional Others Format CSV, Tables Big Data Analytics Applications Queries Big Data Analytics Reports OLAP Data Mining Source: Stephan Kudyba (2014), Big Data, Mining, and Analytics: Components of Strategic Decision Making, Auerbach Publications 14 Analytics 15 Three Types of Analytics Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson 16 Three Types of Business Analytics • Prescriptive Analytics • Predictive Analytics • Descriptive Analytics Source: Thomas H. Davenport, "Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data", FT Press, 2012 17 Three Types of Business Analytics Optimization Randomized Testing “What’s the best that can happen?” Prescriptive Analytics “What if we try this?” Predictive Modeling / Forecasting “What will happen next?” Statistical Modeling “Why is this happening?” Alerts “What actions are needed?” Query / Drill Down “What exactly is the problem?” Ad hoc Reports / Scorecards “How many, how often, where?” Standard Report “What happened?” Predictive Analytics Descriptive Analytics Source: Thomas H. Davenport, "Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data", FT Press, 2012 18 Business Intelligence and Enterprise Analytics • • • • • Predictive analytics Data mining Business analytics Web analytics Big-data analytics Source: Thomas H. Davenport, "Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data", FT Press, 2012 19 Data Science 20 Data Analyst • Data analyst is just another term for professionals who were doing BI in the form of data compilation, cleaning, reporting, and perhaps some visualization. • Their skill sets included Excel, some SQL knowledge, and reporting. • You would recognize those capabilities as descriptive or reporting analytics. Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson 21 Data Scientist • Data scientist is responsible for predictive analysis, statistical analysis, and more advanced analytical tools and algorithms. • They may have a deeper knowledge of algorithms and may recognize them under various labels—data mining, knowledge discovery, or machine learning. • Some of these professionals may also need deeper programming knowledge to be able to write code for data cleaning/analysis in current Web-oriented languages such as Java or Python and statistical languages such as R. • Many analytics professionals also need to build significant expertise in statistical modeling, experimentation, and analysis. Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson 22 Data Science and Business Intelligence Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015 23 Data Science and Business Intelligence Predictive Analytics and Data Mining (Data Science) Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015 24 Predictive Analytics Data Science and and Data Mining Business Intelligence (Data Science) Structured/unstructured data, many types of sources, very large datasets Optimization, predictive modeling, forecasting statistical analysis What if…? What’s the optimal scenario for our business? What will happen next? What if these trends countinue? Why is this happening? Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015 25 Profile of a Data Scientist • Quantitative – mathematics or statistics • Technical – software engineering, machine learning, and programming skills • Skeptical mind-set and critical thinking • Curious and creative • Communicative and collaborative Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015 26 Data Scientist Profile Quantitative Technical Data Scientist Skeptical Curious and Creative Communicative and Collaborative Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015 27 Big Data Analytics Lifecycle 28 Key Roles for a Successful Analytics Project Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015 29 Overview of Data Analytics Lifecycle Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015 30 Overview of Data Analytics Lifecycle 1. Discovery 2. Data preparation 3. Model planning 4. Model building 5. Communicate results 6. Operationalize Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015 31 Key Outputs from a Successful Analytics Project Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015 32 Example of Analytics Applications in a Retail Value Chain Retail Value Chain Critical needs at every touch point of the Retail Value Chain Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson 33 Analytics Ecosystem Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson 34 Job Titles of Analytics Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson 35 Google Colab https://colab.research.google.com/notebooks/welcome.ipynb 36 Summary • Business Intelligence (BI) • Analytics • Data Science 37 References • Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson. • Kenneth C. Laudon & Jane P. Laudon (2014), Management Information Systems: Managing the Digital Firm, Thirteenth Edition, Pearson. • Jiawei Han and Micheline Kamber (2006), Data Mining: Concepts and Techniques, Second Edition, Elsevier. • Stephan Kudyba (2014), Big Data, Mining, and Analytics: Components of Strategic Decision Making, Auerbach Publications. • EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015. 38