1 chapter 1: overview 1.1 overview of business analytics 1.2 software used in this course 1.3 data...
TRANSCRIPT
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Chapter 1: Overview
1.1 Overview of Business Analytics
1.2 Software Used in This Course
1.3 Data Management
1.4 Recommended Reading
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Chapter 1: Overview
1.1 Overview of Business Analytics
1.2 Software Used in This Course
1.3 Data Management
1.4 Recommended Reading
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Objectives Define analytics and data mining. Explain the proliferation of data and how this impacts
the need for good analytics. Identify some of the key challenges of data mining. Name some applications where analytics are helpful. Name some applications where analytics are not
helpful. Explain some of the common pitfalls of analytical
practice.
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Analytics“The extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.”
Davenport and Harris (2007)
Competing on Analytics:
The New Science of Winning
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Basic Reporting What happened?
Ad Hoc Reporting How many, how often, where?
Dynamic Reporting Where exactly are the problems?
Reporting with Early Warning What actions are needed?
Basic Statistical Analysis Why is this happening?
Forecasting What if these trends continue?
Predictive Modeling What will happen next?
Decision Optimization What is the best decision?
Data Information Intelligence
Advanced Analytics
Basic Analytics
Reporting
Decision Support Decision Guidance
Achieving Success with Analytics
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Data Deluge
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Three Consequences of the Data Deluge1. Every problem will generate data eventually.
2. Every company will need analytics eventually.
3. Everyone will need analytics eventually.
...
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Three Consequences of the Data Deluge1. Every problem will generate data eventually.
Proactively defining a data collection protocol will result in more useful information, leading to more useful analytics.
2. Every company will need analytics eventually.
3. Everyone will need analytics eventually.
...
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Three Consequences of the Data Deluge1. Every problem will generate data eventually.
Proactively defining a data collection protocol will result in more useful information, leading to more useful analytics.
2. Every company will need analytics eventually.Proactively analytical companies will compete more effectively.
3. Everyone will need analytics eventually.
...
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Three Consequences of the Data Deluge1. Every problem will generate data eventually.
Proactively defining a data collection protocol will result in more useful information, leading to more useful analytics.
2. Every company will need analytics eventually.Proactively analytical companies will compete more effectively.
3. Everyone will need analytics eventually.Proactively analytical people will be more marketable and more successful in their work.
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The Business Analytics ChallengeGetting anything useful out of tons and tons of data
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Hope for the Data Deluge
= actionable knowledge
+ analytical tools
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Changes in the Analytical Landscape
Analytical Modelers Management
Historically…
Historically, analytics have typically been handled in the “back office,” and information was shared only by a few individuals.
Models
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Changes in the Analytical LandscapeHistorical Changes Executive Dashboarding – Static reports on business
processes Total Quality Management (TQM) – Customer focused Six Sigma – Voice of the process, Voice of the
customer Customer Relationship Management (CRM) – The
right offer to the right person at the right time Forecasting and Predicting – 360-degree customer
view
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Changes in the Analytical LandscapeRelational Databases
Enterprise Resource Planning (ERP) Systems
Point of Sale (POS) Systems
Data Warehousing
Decision Support Systems Reporting and Ad Hoc Queries Online Analytical Processing (OLAP)
Performance Management Systems Executive Information Systems (EIS) Balanced Scorecard Dashboard
Business Intelligence
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CRM EvolutionTotal Quality Management (TQM)
Product Centric Quality: Six Sigma Total Customer Satisfaction Mass Marketing
One-to-One Marketing
Customer Relationship Wallet Share of Customer Customer Retention
Customer Relationship Management (CRM)
Customer Centric Strategy Process Technology
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Changes in the Analytical Landscape
Analytical Modelers
CustomerService
Retail
Logistics
Promotions
OPERATIONS TARGET
Customers
Stockholders
Suppliers
Employees
Now…
Now analytics are being pushed out to the “front office” and are directly impacting company performance. There are clear, tangible benefits that management will track. Data mining is a critical part of business analytics.
Proliferation of Models
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Idiosyncrasies of Business Analytics1. The Data
– Massive, operational, and opportunistic
2. The Users and Sponsors– Business decision support
3. The Methodology– Computer-intensive adhockery– Multidisciplinary lineage
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The DataExperimental Opportunistic
Purpose Research Operational
Value Scientific Commercial
Generation Actively Passivelycontrolled observed
Size Small Massive
Hygiene Clean Dirty
State Static Dynamic
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The Data: Disparate Business Units
Marketing Invoicing Risk
Acquisitions Operations Sales
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Opportunistic Data Operational data is typically not collected with data
analysis in mind. Multiple business units produce a silo-based data
system. This makes business analytics different from
experimental statistics and especially challenging.
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The Methodology: What We Learned Not to DoPrediction is more important than inference. Metrics are used “because they work,” not based on
theory. p-values are rough guides rather than firm decision
cutoffs. Interpretation of a model might be irrelevant. The preliminary value of a model is determined by its
ability to predict a holdout sample. Long-term value of a model is determined by its ability
to continue to perform well on new data over time. Models are retired as customer behavior shifts, market
trends emerge, and so on.
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Using Analytics IntelligentlyIntelligent use of analytics results in the following: Better understanding of how technological, economic,
and marketplace shifts affect business performance Ability to consistently and reliably distinguish
between effective and ineffective interventions Efficient use of assets, reduced waste in supplies, and
better management of time and resources Risk-reduction via measurable outcomes and
reproducible findings Early detection of market trends hidden in massive
data Continuous improvement in decision making over time
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Simple ReportingExamples: OLAP, RFM, QC, descriptive statistics, extrapolation
Answer questions such as
Where are my targets now?
Where were my targets last week?
Is the current process behaving like normal?
What’s likely to happen tomorrow?
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Proactive Analytical Investigation Examples: inferential statistics, experimentation, empirical validation, forecasting, optimization
Answer questions such as
What does a change in the market mean for my targets?
What do other factors tell me about what I can expect from my target?
What is the best combination of factors to give me the most efficient use of resources and maximum profitability?
What is the highest price the market will tolerate?
What will happen in six months if I do nothing? What if I implement an alternative strategy?
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Data StalemateMany companies have data that they do not use or that is used by third parties. These third parties might even resell the data and any derived metrics back to the original company!
Example: retail grocery POS card
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Every Little Bit…Taking an analytical approach to only a few key business problems with reliable metrics tangible benefit.
The benefits and savings derived from early analytical successes managerial support for further analytical efforts.
Everyone has data.
Analytics can connect data to smart decisions.
Proactively analytical companies outpace competition.
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Areas Where Analytics Are Often UsedNew customer acquisition
Customer loyalty
Cross-sell / Up-sell
Pricing tolerance
Supply optimization
Staffing optimization
Financial forecasting
Product placement
Churn
Insurance rate setting
Fraud detection
…
Which residents in a ZIP code should receive a coupon in the mail for a new store location?
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Areas Where Analytics Are Often UsedNew customer acquisition
Customer loyalty
Cross-sell / Up-sell
Pricing tolerance
Supply optimization
Staffing optimization
Financial forecasting
Product placement
Churn
Insurance rate setting
Fraud detection
…
What advertising strategy best elicits positive sentiment toward the brand?
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Areas Where Analytics Are Often UsedNew customer acquisition
Customer loyalty
Cross-sell / Up-sell
Pricing tolerance
Supply optimization
Staffing optimization
Financial forecasting
Product placement
Churn
Insurance rate setting
Fraud detection
…
What is the best next product for this customer?
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Areas Where Analytics Are Often UsedNew customer acquisition
Customer loyalty
Cross-sell / Up-sell
Pricing tolerance
Supply optimization
Staffing optimization
Financial forecasting
Product placement
Churn
Insurance rate setting
Fraud detection
…
What is the highest price that the market will bear without substantial loss of demand?
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Areas Where Analytics Are Often UsedNew customer acquisition
Customer loyalty
Cross-sell / Up-sell
Pricing tolerance
Supply optimization
Staffing optimization
Financial forecasting
Product placement
Churn
Insurance rate setting
Fraud detection
…
How many 60-inch HDTVs should be in stock? (Too many is expensive; too few is lost revenue.)
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Areas Where Analytics Are Often UsedNew customer acquisition
Customer loyalty
Cross-sell / Up-sell
Pricing tolerance
Supply optimization
Staffing optimization
Financial forecasting
Product placement
Churn
Insurance rate setting
Fraud detection
…
What are the best times and best days to have technical experts on the showroom floor?
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Areas Where Analytics Are Often UsedNew customer acquisition
Customer loyalty
Cross-sell / Up-sell
Pricing tolerance
Supply optimization
Staffing optimization
Financial forecasting
Product placement
Churn
Insurance rate setting
Fraud detection
…
What revenue increase can be expected after the Mother’s Day sale?
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Areas Where Analytics Are Often UsedNew customer acquisition
Customer loyalty
Cross-sell / Up-sell
Pricing tolerance
Supply optimization
Staffing optimization
Financial forecasting
Product placement
Churn
Insurance rate setting
Fraud detection
…
Will oatmeal sell better near granola bars or near baby food?
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Areas Where Analytics Are Often UsedNew customer acquisition
Customer loyalty
Cross-sell / Up-sell
Pricing tolerance
Supply optimization
Staffing optimization
Financial forecasting
Product placement
Churn
Insurance rate setting
Fraud detection
…
Which customers are most likely to switch to a different wireless provider in the next six months?
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Areas Where Analytics Are Often UsedNew customer acquisition
Customer loyalty
Cross-sell / Up-sell
Pricing tolerance
Supply optimization
Staffing optimization
Financial forecasting
Product placement
Churn
Insurance rate setting
Fraud detection
…
How likely is it that this individual will have a claim?
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Areas Where Analytics Are Often UsedNew customer acquisition
Customer loyalty
Cross-sell / Up-sell
Pricing tolerance
Supply optimization
Staffing optimization
Financial forecasting
Product placement
Churn
Insurance rate setting
Fraud detection
…How can I identify a fraudulent purchase?
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When Analytics Are Not HelpfulSnap decisions required
Novel approach (no previous data possible)
Most salient factors are rare (making decisions to work around unlikely obstacles or miracles)
Expert analysis suggests a particular path
Metrics are inappropriate
Naïve implementation of analytics
Confirming what you already know
Deciding when to run from danger
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When Analytics Are Not HelpfulSnap decisions required
Novel approach (no previous data possible)
Most salient factors are rare (making decisions to work around unlikely obstacles or miracles)
Expert analysis suggests a particular path
Metrics are inappropriate
Naïve implementation of analytics
Confirming what you already know
Predicting the adoption of a new technology
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When Analytics Are Not HelpfulSnap decisions required
Novel approach (no previous data possible)
Most salient factors are rare (making decisions to work around unlikely obstacles or miracles)
Expert analysis suggests a particular path
Metrics are inappropriate
Naïve implementation of analytics
Confirming what you already know
Planning contingencies for employees winning the lottery
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When Analytics Are Not HelpfulSnap decisions required
Novel approach (no previous data possible)
Most salient factors are rare (making decisions to work around unlikely obstacles or miracles)
Expert analysis suggests a particular path
Metrics are inappropriate
Naïve implementation of analytics
Confirming what you already know
The seasoned art critic can recognize a fake
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When Analytics Are Not HelpfulSnap decisions required
Novel approach (no previous data possible)
Most salient factors are rare (making decisions to work around unlikely obstacles or miracles)
Expert analysis suggests a particular path
Metrics are inappropriate
Naïve implementation of analytics
Confirming what you already know
Predicting athletes’ salaries or quantifying love
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When Analytics Are Not HelpfulSnap decisions required
Novel approach (no previous data possible)
Most salient factors are rare (making decisions to work around unlikely obstacles or miracles)
Expert analysis suggests a particular path
Metrics are inappropriate
Naïve implementation of analytics
Confirming what you already knowOnly looking at one variable at a time
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When Analytics Are Not HelpfulSnap-decisions required
Novel approach (no previous data possible)
Most salient factors are rare (making decisions to work around unlikely obstacles or miracles)
Expert analysis suggests a particular path
Metrics are inappropriate
Naïve implementation of analytics
Confirming what you already know Ignoring variables that might be important
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Naïve AnalyticsMany companies implementing analytical programs such as Six Sigma demonstrate tremendous success.
However, it is important to use analytics in a meaningful way.
For example: It might not be possible to establish a Six Sigma
process with low production volume. Producer-centric metrics might not give useful
information about customer satisfaction, and the Six Sigma process might still fail to meet customer specifications.
Simplistic reporting on massive data might hide complex patterns and is generally unsuccessful.
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The Fallacy of Univariate Thinking
What is the most important cause of churn?
Prob(churn)
InternationalUsage
DaytimeUsage
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Expectations Leading the AnalysisEven sophisticated analytics are not immune to personal bias such as selectively fitting models with variables because they
place someone’s opinion or agenda in a positive light ignoring information that might disprove a hypothesis.
Personal bias in model fitting, whether intentional or otherwise, can diminish the usefulness of your analytical efforts.
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Trustworthy AnalyticsLet the data guide your conclusions.
Ask the following questions: Are my assumptions about the causes of my data
patterns warranted? Should I be trying something different?
Assign a cynic to the analytical team whose purpose is to question the assumptions. What would my critic say is the flaw with my analysis? Investigate the data in such a way that a critic’s
concerns can be ruled out.
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Chapter 1: Overview
1.1 Overview of Business Analytics
1.2 Software Used in This Course
1.3 Data Management
1.4 Recommended Reading
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Objectives Identify and describe several software tools for
business analytics, including – SAS Enterprise Guide– SAS Enterprise Miner– SAS Forecast Studio.
Describe several of the key features of these programs.
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Tasks for Data Analysis Access data sources Combine tables Transform variables Explore and describe data Visualize patterns in the data Analyze and model data Validate models Score Update models
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The Tools of Data Analysis
...
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The Tools of Data Analysis
...
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The Tools of Data Analysis
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Introduction to the SAS SystemThe SAS System is driven by SAS programs. SAS programs are composed of commands in the form
of DATA steps and PROC (or procedure) steps. The SAS System features point-and-click interfaces to
SAS that write programs and perform additional functions automatically. Three interfaces featured in this course are– SAS Enterprise Guide– SAS Enterprise Miner– SAS Forecast Studio.
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Introduction to SAS Enterprise Guide
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Introduction to SAS Enterprise GuideSAS Enterprise Guide provides a point-and-click interface for managing data and generating reports.
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Introduction to SAS Enterprise GuideBehind the scenes, SAS Enterprise Guide generates SAS programs that are submitted to SAS, and results are returned to SAS Enterprise Guide.
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SAS Enterprise Guide InterfaceSAS Enterprise Guide also includes a full programming interface that can be used to write, edit, and submit SAS programs.
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SAS Enterprise Guide Interface: The ProjectA project serves asa collection of data sources SAS programs
and logs results from
tasks and queries informational notes
for documentation.
You can control the contents, sequencing, and updating of a project.
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Building a SAS Enterprise Guide ProjectTo begin work with SAS Enterprise Guide, you
1. create a new project
2. add data to the project
3. run tasks against the data.
In addition, you can
4. customize results
5. automate the process.
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Behind the ScenesAs you build tasks, SAS Enterprise Guide generates SAS programs.
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The SAS Help FacilityUse the SAS Enterprise Guide Help facility for additional direction about SAS Enterprise Guide. Additional help is available online.
http://support.sas.com/documentation/onlinedoc/guide
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SAS Enterprise Miner
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SAS Enterprise Miner: Interface Tour
Menu bar and shortcut buttons
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SAS Enterprise Miner: Interface Tour
Project panel
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SAS Enterprise Miner: Interface Tour
Properties panel
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SAS Enterprise Miner: Interface Tour
Help panel
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SAS Enterprise Miner: Interface Tour
Diagram workspace
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SAS Enterprise Miner: Interface Tour
Process flow
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SAS Enterprise Miner: Interface Tour
Node
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SAS Enterprise Miner: Interface Tour
SEMMA tools palette
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SAS Enterprise Miner Analytic Strengths
Predictive Modeling
Pattern Discovery
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SAS Software for Forecasting Base SAS
– DATA step programming SAS/STAT
– Ordinary least squares regression– Regression with correlated errors
SAS/ETS– Univariate and multivariate time series forecasting– Dynamic regression with correlated errors– Econometric modeling– Spectral analysis– Time series cross-sectional modeling
Forecast Server– High performance large-scale forecasting
...
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SAS Software for Forecasting Base SAS
– DATA step programming SAS/STAT
– Ordinary least squares regression– Regression with correlated errors
SAS/ETS– Univariate and multivariate time series forecasting– Dynamic regression with correlated errors– Econometric modeling– Spectral analysis– Time series cross-sectional modeling
Forecast Server– High-performance large-scale forecasting
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Environments for Forecasting Using SAS The SAS windowing environment The Time Series Forecasting System SAS Enterprise Guide SAS Forecast Studio
...
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Environments for Forecasting Using SAS The SAS windowing environment The Time Series Forecasting System SAS Enterprise Guide SAS Forecast Studio
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SAS Forecast Studio: Interface Tour
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SAS Forecast Studio: Interface Tour Menu bar and shortcut buttons
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SAS Forecast Studio: Interface Tour
Active series
Overview panel
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SAS Forecast Studio: Interface Tour
Four view tabs
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SAS Forecast Studio: Interface Tour
The Forecasting view
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SAS Forecast Studio: Interface Tour
The Modeling view
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SAS Forecast Studio: Interface Tour
The Scenario Analysis view
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SAS Forecast Studio: Interface Tour
The Series view
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Large-Scale Forecasting Scenario
Time Series Data
80% can be forecast automatically.
10% require extra effort.
10% cannot be forecast accurately.
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IDEA EXCHANGE Identify several business problems that you could address
with analytics. You might think about case studies you are using in other classes, projects you work on at your job, examples from the media, or others.
Describe the goal, whether the variables can be measured, how the data could be obtained, and what types of specific questions you would like to address with analytics.
For now, entertain several examples. Later in the course, you might consider how to collect or download) the relevant data, perform a data analysis, and report your findings to others in an organization.
What kind of data do you think you would need? How could you obtain it?
What software tools do you think you are likely to use?
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Chapter 1: Overview
1.1 Overview of Business Analytics
1.2 Software Used in This Course
1.3 Data Management
1.4 Recommended Reading
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Objectives Explain the concept of data integration. Describe SAS Enterprise Guide and how it fits in to
data integration and management for business analytics.
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Data Management Business AnalyticsData management brings together data components that can exist on multiple machines, from different software vendors, throughout the organization.
Data management is the foundation for business analytics. Without correctly consolidated data, those working in the analytics, reporting, and solutions areas might not be working with the most current, accurate data.
Advanced Analytics
Basic Analytics
Reporting
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Managing Data for Business Analytics Business analytics requires data management
activities such as data access, movement, transformation, aggregation, and augmentation.
These tasks can involve many different types of data (for example, simple flat files, files with comma-separated values, Microsoft Excel files, SAS tables, and Oracle tables).
The data likely combines individual transactions, customer summaries, product summaries, and/or other levels of data granularity.
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Planning from the Top Down
What data will help you answer these questions?
What mission-critical questions must be answered?
What data do you havethat will help you buildthe needed data?
...
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Implementing from the Bottom Up
Define Target Data
Identify Source Data
Create Reports
...
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Collaboration Is Key to Business Analytics
Business Expert IT Expert Analytical Expert
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Data Marts: Tying Questions to DataIn a very basic form, data marts are implemented at organizations because there are questions that must be answered.
Data is typically collected in daily operations but might not be organized in a way that answers the questions.
An IT professional can use the questions and the data collected from daily operations to construct the tables for a data warehouse or data mart.
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Building a Data MartFoundation of a Data Mart
Create target tables.
Identify target tables.
Identify source tables.
Building the foundation of the data mart consists of the three basic steps listed above.
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Orion Star Sports & Outdoors
Orion Star Sports & Outdoors is a fictitious global sports and outdoors retailer with traditional stores, an online store, and a large catalog business.
The corporate headquarters is located in the United States with offices and stores in many countries throughout the world.
Orion Star has about 1,000 employees and 90,000 customers. It processes approximately 150,000 orders annually and purchases products from 64 suppliers.
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Orion Star DataAs is the case with most organizations, Orion Star has a large amount of data about its customers, suppliers, products, and employees. Much of this information is stored in transactional systems in various formats.
Using SAS Enterprise Guide, this transactional information will be extracted, transformed, and loaded into a data mart for the Marketing Department.
You continue to work with this data set for some basic exploratory analysis and reporting.
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Orion Star Source Data Normalized ModelThe Orion Star source data is organized as a normalized model.
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A Target Star SchemaOne goal of creating a data mart is to produce, from the source data, a dimensional data model that is a star schema.
Fact TableProduct
Dimension
TimeDimension
OrganizationDimension
CustomerDimension
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Orion Star Target Star SchemaThe analyst can produce, from the Orion Star source data, a dimensional data model that is a star schema.
OrderFactFact Table
ProductDimension
TimeDimension
OrganizationDimension
CustomerDimension
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OrderFact Table DiagramThe OrderFact table process diagram was sketched to be this.
The source tables, target table, andjob metadata objects were already created (used for the change management demonstration), andthe target table loaded.
Customer Orders
OrderFact
Order_Item
Computed Columns
NONE
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Product Dimension Table DiagramThe ProdDim table (representing the product dimension) process diagram was sketched to be this.
The target table and source table metadata objects must be created, as well as the job that loads the ProdDim table.
Product_List Supplier
ProdDim
Product_CategoryProduct_GroupProduct_Line
Computed Columns
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Customer Dimension Table DiagramThe CustDim table (representing the customer dimension) process diagram was sketched to be this.
The target table and source table metadata objects must be created, as well as the job that loads the CustDim table.
Customer Customer_Type
CustDim
Customer_AgeCustomer_Age_Group
Computed Columns
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Organization Dimension Table Diagram
Organization Staff
OrgDim
CompanyDepartment
GroupSection
The OrgDim table (representing the organization dimension) process diagram was sketched to be this.
The target table and source table metadata objects must be created, as well as the job that loads the OrgDim table.
Computed Columns
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Time Dimension Table Diagram
TimeDim
All Columns!!(User-written code
generates theTimeDim table.)
The TimeDim table (representing the time dimension) process diagram was sketched to be this.
The target table metadata object must be created, as well as the job that loads the TimeDim table.
Computed Columns
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Advantages of Data Marts There is one version of the truth. Downstream tables are updated as source data is
updated, so analyses are always based on the latest information.
The problem of a proliferation of spreadsheets is avoided.
Information is clearly identified by standardized variable names and data types.
Multiple users can access the same data.
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SAS Enterprise Guide OverviewSAS Enterprise Guide can be used for data management, as well as a wide variety of other tasks: data exploration querying and reporting graphical analysis statistical analysis scoring
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Example: Orion Star Data ManagementThe head of marketing wants to know which products are most popular in different regions. Should marketing efforts be applied to specific regions for certain products?
Create part of an analytical data mart by combining information from three tables: transactional order data, customer records, and product information.
The data for this example can be found in two types of file: SAS data sets Microsoft Excel workbook
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Input FilesOrderinformation.sas7bdat
Productinformation.sas7bdat
Customerinformation.xls
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Final Data
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A Data Management Process Using SAS Enterprise Guide
Sporting Goods Case Study
Task: Add different types of source data to a process flow using SAS Enterprise Guide.
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Explore the Data and Create a ReportInvestigate the products sold by country. Create a report of product category revenues by
country.
Which product categories sell the greatest quantity of products in each country? Which account for the highest revenue in each country? Create a graph of total quantity for each country. Create a graph of total revenue for each country.
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Exploratory Analysis
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Exploring the Data and Creating a Basic Report
Sporting Goods Case Study
Task: Create a tabular report of sales in each product category by country, a plot of number of products sold in each country and a plot of total revenue per country. Produce an HTML report to give to management.
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IDEA EXCHANGE What conclusions would you draw from this basic data
exploration? Are there additional plots or reports you would like to explore from the orders data to help you determine where your marketing budget should be allocated?
What additional data would you need to help you make a case to the head of the Marketing Department that Marketing dollars should be spent in a particular way?
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Chapter 1: Overview
1.1 Overview of Business Analytics
1.2 Software Used in This Course
1.3 Data Management
1.4 Recommended Reading
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Recommended ReadingDavenport, Thomas H. and Jeanne G. Harris. “The Dark Side of Customer Analytics.” Harvard Business Review. May 2007. Plus commentary from George L. Jones, Katherine N. Lemon, David Norton, and Michael B. McCallister.
https://hbr.org/download/2707842/R0705A-PDF-ENG/R0705A-PDF-ENG.PDF
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Recommended ReadingDavenport, Thomas H., Jeanne G. Harris, and Robert Morison. 2010. Analytics at Work: Smarter Decisions, Better Results. Boston: Harvard Business Press. Chapter 1, pp. 1-17; part 2 pp. 23-43 (overview and
data); pp. 19-22 optional
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Recommended ReadingHaque, Umair. “The Case for Being Disruptively Good.” Harvard Business Review blogs. April 12, 2010. http://blogs.hbr.org/haque/2010/04/the_case_for_being_disruptivel.html