《商务智能：数据分析的管理视角 Business Intelligence, Analytics, and Data Science：A Managerial Perspective》教学资源（PPT课件，原书第4版）02 Descriptive Analytics I：Nature of Data, Statistical Modeling, and Visualization

Business Intelligence, Analytics, and Data Science: A Managerial Perspective Fourth Edition BUSINESS INTELLIGENCE ANALYTICS Chapter 2 AND DATA SCIENCE Descriptive Analytics I A Managerial Nature of data Statistical Modeling and visualization Ramesh Sharda Dursun Delen Efraim Turban PEarson Pearson Copyright 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Business Intelligence, Analytics, and Data Science: A Managerial Perspective Fourth Edition Chapter 2 Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Slides in this presentation contain hyperlinks. JAWS users should be able to get a list of links by using INSERT+F7

Learning Objectives (1 of 2) 2.1 Understand the nature of data as it relates to business intelligence(Bl)and analytics 2.2 Learn the methods used to make real-world data analytics ready 2.3 Describe statistical modeling and its relationship to business analytics 2. 4 Learn about descriptive and inferential statistics 2.5 Define business reporting and understand its historical evolution Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Learning Objectives(1 of 2) 2.1 Understand the nature of data as it relates to business intelligence (BI) and analytics 2.2 Learn the methods used to make real-world data analytics ready 2.3 Describe statistical modeling and its relationship to business analytics 2.4 Learn about descriptive and inferential statistics 2.5 Define business reporting, and understand its historical evolution

Learning Objectives (2 of 2) 2.6 Understand the importance of data/information visualization 2.7 Learn different types of visualization techniques 2.8 Appreciate the value that visual analytics brings to business analytics 2.9 Know the capabilities and limitations of dashboards Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Learning Objectives (2 of 2) 2.6 Understand the importance of data/information visualization 2.7 Learn different types of visualization techniques 2.8 Appreciate the value that visual analytics brings to business analytics 2.9 Know the capabilities and limitations of dashboards

Opening vignette Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing 1. What does sirius XM do? In what type of market does it conduct its business 2. What were the challenges? Comment on both technology and data related challenges 3. What were the proposed solutions? 4. How did they implement the proposed solutions? Did they face any implementation challenges? 5. What were the results and benefits? Were they worth the effort /investment? Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing 1. What does Sirius XM do? In what type of market does it conduct its business? 2. What were the challenges? Comment on both technology and data￾related challenges. 3. What were the proposed solutions? 4. How did they implement the proposed solutions? Did they face any implementation challenges? 5. What were the results and benefits? Were they worth the effort/investment?

The Nature of Data (1 of 2) Data: a collection of facts usually obtained as the result of experiences observations, or experiments Data may consist of numbers, words, images, Data is the lowest level of abstraction(from which information and knowledge are derived) Data is the source for information and knowledge Data quality and data integrity-critical to analytics Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved The Nature of Data (1 of 2) • Data: a collection of facts – usually obtained as the result of experiences, observations, or experiments • Data may consist of numbers, words, images, … • Data is the lowest level of abstraction (from which information and knowledge are derived) • Data is the source for information and knowledge • Data quality and data integrity → critical to analytics

The Nature of Data (2 of 2) Business Process ERP SCM nternet/Social Media Data protection Instagram End Users interest Twitter Linked In YouTube Flicker Data Storage Tumblr Analytics Reddit Applications Cloud Storage and Computing Machines/Internet of Things Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved The Nature of Data (2 of 2)

Metrics for Analytics Ready Data Data source reliability Data content accuracy Data accessibility Data security and data privacy Data richness Data consistency Data currency/data timeliness Data granularity Data validity and data relevancy Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Metrics for Analytics Ready Data • Data source reliability • Data content accuracy • Data accessibility • Data security and data privacy • Data richness • Data consistency • Data currency/data timeliness • Data granularity • Data validity and data relevancy

A Simple Taxonomy of Data (1 of 2) Data (datum-singular form of data: facts Structured data Targeted for computers to process Numeric versus nominal Unstructured/textual data Targeted for humans to process/digest Semi-structured data? XML, HTML, Log files, etc Data taxonomy Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved A Simple Taxonomy of Data (1 of 2) • Data (datum—singular form of data): facts • Structured data – Targeted for computers to process – Numeric versus nominal • Unstructured/textual data – Targeted for humans to process/digest • Semi-structured data? – XML, HTML, Log files, etc. • Data taxonomy…

A Simple taxonomy of Data (2 of2 Data in Analytics Structured Data Unstructured or Semistructured Data Categorical Numerical Textua Image omina Interval Multimedia Audio Video Ordinal Ratio XMLJSON Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved A Simple Taxonomy of Data (2 of 2)

pplication Case 2.1 Medical Device Company Ensures Product Quality While Saving Money Questions for Discussion 1. What were the main challenges for the medical device company? Were they market or technology driven? 2. What was the proposed solution? 3. What were the results? What do you think was the real return on investment(ROD? Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Application Case 2.1 Medical Device Company Ensures Product Quality While Saving Money Questions for Discussion 1. What were the main challenges for the medical device company? Were they market or technology driven? 2. What was the proposed solution? 3. What were the results? What do you think was the real return on investment (ROI)?