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Describe how DSS/BI technologies and tools can aid in each phase of decision making.Please review attachments belowNote: Using APA in discussion posts is very similar to using APA in a paper. You need to cite your sources in your discussion post both in-text and in a references section.300-400 words sharda_11e_full_accessible_ppt_03.pptx sharda_11e_full_accessible_ppt_04.pptx Unformatted Attachment Preview Analytics, Data Science and AI: Systems for Decision Support Eleventh Edition Chapter 3 Nature of Data, Statistical Modeling and Visualization Slide in this Presentation Contain Hyperlinks. JAWS users should be able to get a list of links by using INSERT+F77 Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Learning Objectives (1 of 2) 3.1 Understand the nature of data as it relates to business intelligence (BI) and analytics 3.2 Learn the methods used to make real-world data analytics ready 3.3 Describe statistical modeling and its relationship to business analytics 3.4 Learn about descriptive and inferential statistics 3.5 Define business reporting, and understand its historical evolution Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Learning Objectives (2 of 2) 3.6 Understand the importance of data/information visualization 3.7 Learn different types of visualization techniques 3.8 Appreciate the value that visual analytics brings to business analytics 3.9 Know the capabilities and limitations of dashboards Copyright © 2020, 2015, 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? Copyright © 2020, 2015, 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 Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved The Nature of Data (2 of 2) Copyright © 2020, 2015, 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 Copyright © 2020, 2015, 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… Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved A Simple Taxonomy of Data (2 of 2) Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Application Case 3.1 Verizon Answers the Call for Innovation: The Nation’s Largest Network Provider uses Advanced Analytics to Bring the Future to its Customers Questions for Discussion: 1. What was the challenge Verizon was facing? 2. What was the data-driven solution proposed for Verizon’s business units? 3. What were the results? Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved The Art and Science of Data Preprocessing (1 of 2) • The real-world data is dirty, misaligned, overly complex, and inaccurate – Not ready for analytics! • Readying the data for analytics is needed – Data preprocessing ▪ Data consolidation ▪ Data cleaning ▪ Data transformation ▪ Data reduction • Art – it develops and improves with experience Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved The Art and Science of Data Preprocessing (2 of 2) • Data reduction 1. Variables – Dimensional reduction – Variable selection 2. Cases/samples – Sampling – Balancing / stratification Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Data Preprocessing Tasks and Methods Table 3.1 A Summary of Data Preprocessing Tasks and Potential Methods. Main Task Subtasks Popular Methods Data consolidation Access and collect the data Select and filter the data Integrate and unify the data SQL queries, software agents, Web services. Domain expertise, SQL queries, statistical tests. SQL queries, domain expertise, ontology-driven data mapping. Data cleaning Handle missing values in the data Fill in missing values (imputations) with most appropriate values (mean, median, min/max, mode, etc.); recode the missing values with a constant such as “ML”; remove the record of the missing value; do nothing. Blank Identify and reduce noise in the data Identify the outliers in data with simple statistical techniques (such as averages and standard deviations) or with cluster analysis; once identified, either remove the outliers or smooth them by using binning, regression, or simple averages. Blank Find and eliminate erroneous data Identify the erroneous values in data (other than outliers), such as odd values, inconsistent class labels, odd distributions; once identified, use domain expertise to correct the values or remove the records holding the erroneous values. Data transformation Normalize the data Reduce the range of values in each numerically valued variable to a standard range (e.g., 0 to 1 or −1 to +1) by using a variety of normalization or scaling techniques. Blank Discretize or aggregate the data If needed, convert the numeric variables into discrete representations using rangeor frequency-based binning techniques; for categorical variables, reduce the number of values by applying proper concept hierarchies. Blank Construct new attributes Derive new and more informative variables from the existing ones using a wide range of mathematical functions (as simple as addition and multiplication or as complex as a hybrid combination of log transformations). Data reduction Reduce number of attributes Use principal component analysis, independent component analysis, chi-square testing, correlation analysis, and decision tree induction. Blank Reduce number of records Perform random sampling, stratified sampling, expert-knowledge-driven purposeful sampling. Blank Balance skewed data Oversample the less represented or undersample the more represented classes. Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Application Case 3.2 (1 of 4) Improving Student Retention with Data-Driven Analytics Questions for Discussion: 1. What is student attrition, and why is it an important problem in higher education? 2. What were the traditional methods to deal with the attrition problem? 3. List and discuss the data-related challenges within context of this case study. 4. What was the proposed solution? And, what were the results? Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Application Case 3.2 (2 of 4) Improving Student Retention with Data-Driven Analytics • Student retention – Freshmen class • Why it is important? • What are the common techniques to deal with student attrition? • Analytics versus theoretical approaches to student retention problem Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Application Case 3.2 (3 of 4) Improving Student Retention with Data-Driven Analytics • Data imbalance problem Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Application Case 3.2 (4 of 4) Improving Student Retention with Data-Driven Analytics • Results… Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Statistical Modeling for Business Analytics (1 of 2) Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Statistical Modeling for Business Analytics (2 of 2) • Statistics – A collection of mathematical techniques to characterize and interpret data • Descriptive Statistics – Describing the data (as it is) • Inferential statistics – Drawing inferences about the population based on a sample data • Descriptive statistics for descriptive analytics Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Descriptive Statistics Measures of Centrality Tendency (1 of 2) • Arithmetic mean x1 + x2 + ... + xn x= n  x= n i =1 xi n • Median – The number in the middle • Mode – The most frequent observation Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Descriptive Statistics Measures of Dispersion (1 of 2) • Dispersion – Degree of variation in a given variable • Range – Max - Min • Variance Standard Deviation  i=1 ( xi − x )2 n s2 = n −1 2 ( x − x )  i=1 i n s= n −1 • Mean Absolute Deviation (MAD) – Average absolute deviation from the mean Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Descriptive Statistics Measures of Dispersion (2 of 2) • Quartiles • Box-and-Whiskers Plot – a.k.a. box-plot – Versatile / informative Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Descriptive Statistics Measures of Centrality Tendency (2 of 2) • Histogram - frequency chart • Skewness – Measure of asymmetry  skewness = s = n 3 ( x − x ) i i =1 (n − 1) s 3 • Kurtosis – Peak/tall/skinny nature of the distribution  kurtosis = K = n 4 ( x − x ) i i =1 ns 4 −3 Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Relationship Between Dispersion and Shape Properties Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Technology Insights 3.1 – Descriptive Statistics in Excel Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Technology Insights 3.1 – Descriptive Statistics in Excel Creating box-plot in Microsoft Excel Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Application Case 3.3 Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and Detect Problems Questions for Discussion: 1. What were the challenges the Town of Cary was facing? 2. What was the proposed solution? 3. What were the results? 4. What other problems and data analytics solutions do you foresee for towns like Cary? Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Regression Modeling for Inferential Statistics • Regression – A part of inferential statistics – The most widely known and used analytics technique in statistics – Used to characterize relationship between explanatory (input) and response (output) variable • It can be used for – Hypothesis testing (explanation) – Forecasting (prediction) Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Regression Modeling (1 of 3) • Correlation versus Regression – What is the difference (or relationship)? • Simple Regression versus Multiple Regression – Base on number of input variables • How do we develop linear regression models? – Scatter plots (visualization—for simple regression) – Ordinary least squares method ▪ A line that minimizes squared of the errors Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Regression Modeling (2 of 3) Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Regression Modeling (3 of 3) • x: input, y: output • Simple Linear Regression y =  0 + 1 x • Multiple Linear Regression y =  0 + 1 x1 +  2 x2 +  3 x3 + ... +  n xn • The meaning of Beta ( ) coefficients – Sign (+ or −) and magnitude Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Process of Developing a Regression Model How do we know if the model is good enough? – R2 (R-Square) – p Values – Error measures (for prediction problems) ▪ MSE, MAD, RMSE Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Regression Modeling Assumptions • Linearity • Independence • Normality (Normal Distribution) • Constant Variance • Multicollinearity • What happens if the assumptions do NOT hold? – What do we do then? Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Logistic Regression Modeling (1 of 2) • A very popular statistics-based classification algorithm • Employs supervised learning • Developed in 1940s • The difference between Linear Regression and Logistic Regression – In Logistic Regression Output/Target variable is a binomial (binary classification) variable (as supposed to numeric variable) Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Logistic Regression Modeling (2 of 2) f ( y) = 1 1 + e − ( 0 + 1 x ) Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Application Case 3.4 (1 of 4) Predicting NCA A Bowl Game Outcomes Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Application Case 3.4 (2 of 4) Predicting NCA A Bowl Game Outcomes • The analytics process to develop prediction models (both regression and classification type) for NCA A Bowl Game outcomes Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Application Case 3.4 (3 of 4) Predicting NCA A Bowl Game Outcomes Prediction Results 1. Classification 2. Regression Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Application Case 3.4 (4 of 4) Predicting NCA A Bowl Game Outcomes Questions for Discussion: 1. What are the foreseeable challenges in predicting sporting event outcomes (e.g., college bowl games)? 2. How did the researchers formulate/design the prediction problem (i.e., what were the inputs and output, and what was the representation of a single sample—row of data)? 3. How successful were the prediction results? What else can they do to improve the accuracy? Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Time Series Forecasting • Is it different than Simple Linear Regression? How? Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Business Reporting Definitions and Concepts • Report = Information → Decision • Report? – Any communication artifact prepared to convey specific information • A report can fulfill many functions – To ensure proper departmental functioning – To provide information – To provide the results of an analysis – To persuade others to act – To create an organizational memory… Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved What is a Business Report? • A written document that contains information regarding business matters. • Purpose: to improve managerial decisions • Source: data from inside and outside the organization (via the use of ETL) • Format: text + tables + graphs/charts • Distribution: in-print, email, portal/intranet Data acquisition → Information generation → Decision making → Process management Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Business Reporting Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Types of Business Reports • Metric Management Reports – Help manage business performance through metrics (SL As for externals; KPIs for internals) – Can be used as part of Six Sigma and/or TQM • Dashboard-Type Reports – Graphical presentation of several performance indicators in a single page using dials/gauges • Balanced Scorecard–Type Reports – Include financial, customer, business process, and learning & growth indicators Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Application Case 3.5 Flood of Paper Ends at F E MA Questions for Discussion: 1. What is FEMA, and what does it do? 2. What are the main challenges that FEMA faces? 3. How did FEMA improve its inefficient reporting practices? Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Data Visualization “The use of visual representations to explore, make sense of, and communicate data.” • Data visualization vs. Information visualization • Information = aggregation, summarization, and contextualization of data • Related to information graphics, scientific visualization, and statistical graphics • Often includes charts, graphs, illustrations, … Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved A Brief History of Data Visualization • Data visualization can date back to the second century AD • Most developments have occurred in the last two and a half centuries • Until recently it was not recognized as a discipline • Today’s most popular visual forms date back a few centuries Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved The First Pie Chart Created by William Playfair in 1801 William Playfair is widely credited as the inventor of the modern chart, having created the first line and pie charts. Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Decimation of Napoleon’s Army During the 1812 Russian Campaign By Charles Joseph Minard • Arguably the most popular multi-dimensional chart Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Application Case 3.6 Macfarlan Smith Improves Operational Performance Insight with Tableau Online Questions for Discussion: 1. What were the data and reporting related challenges Macfarlan Smith facing? 2. What was the solution and the obtained results and/or benefits? Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Which Chart or Graph Should You Use? Figure 3.21 A Taxonomy of Charts and Graphs. Source: Adapted from Abela, A. (2008). Advanced Presentations by Design: Creating Communication That Drives Action. New York: Wiley. Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved An Example Gapminder Chart: Wealth and Health of Nations Figure 3.22 A Gapminder Chart That Shows the Wealth and Health of Nations. Source: gapminder.org. Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved The Emergence of Data Visualization And Visual Analytics (1 of 2) Figure 3.23 Magic Quadrant for Business Intelligence and Analytics Platforms. • Magic Quadrant for Business Intelligence and Analytics Platforms (Source: Gartner.com) • Many data visualization companies are in the 4th quadrant • There is a move towards visualization Source: Used with permission from Gartner Inc. Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved The Emergence of Data Visualization And Visual Analytics (2 of 2) • Emergence of new companies – Tableau, Spotfire, QlikView, … • Increased focus by the big players – MicroStrategy improved Visual Insight – SAP launched Visual Intelligence – SAS launched Visual Analytics – Microsoft bolstered PowerPivot with Power View – IBM launched Cognos Insight – Oracle acquired Endeca Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Visual Analytics • A recently coined term – Information visualization + predictive analytics • Information visualization – Descriptive, backward focused – “what happened” “what is happening” • Predictive analytics – Predictive, future focused – “what will happen” “why will it happen” • There is a strong move toward visual analytics Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Visual Analytics by SAS Institute (1 of 2) Figure 3.25 An Overview ... Purchase answer to see full attachment