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SAP Applications and the Modern Data Scientist –
Predictive Analytics for the End User

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Introductions
What is Predictive Analytics
SAP Predictive Analytics 2.3 Overview
Where SAP is in the Advanced Analytics Market
System Demonstration
Use Case: Association Analysis
Use Case: Regression
Questions/Next Steps

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On the Phone:
Rob Jerome
Vice President, Innovation + Technology
rob.j@dickinson-assoc.com
Todd Siedlecki
Consultant, Predictive Analytics Practice Lead
todd.s@dickinson-assoc.com
Olavo Figueiredo
Consultant
olavo.f@dickinson-assoc.com

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We Are:
Focus: Delivery of quality SAP Business Suite, BI/Analytics,
and Mobility consulting services to customers across
North America, Europe, and Asia.
Our People: A team of 140+ full-time SAP professionals reflects the
ideal mix of years of relevant business knowledge, very
strong SAP credentials, and solid communication skills.
Our team has an average of 15 years SAP and 19 years
business experience.
Offices: Chicago, IL (Headquarters)
Satellites: New York, NY | Scottsdale, AZ | Cincinnati, OH
We are:

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Experience
What Sets Us Apart? Our People.
 Experienced consultants with strong SAP knowledge, sound project
management capability, and years of industry experience.
 Proven experience in delivering innovative ERP solutions with
minimal disruption to the business.
 An open corporate culture that makes us
“big enough to deliver value and small enough to care”.
 We carefully create each project team or support team
to match the client objectives and its culture.
 Most important, we understand and believe strongly that
Companies don’t implement SAP… People Do.
No.TeamMembers
0 – 3yrs 3 – 8yrs 8 – 14yrs 14+ yrs

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Partnership and Designations
 SAP Gold Channel Partner
 SAP Services Partner
 SAP All-in-One Certified Solutions
 SAP-Qualified Partner for RDS
 Business Objects
 Sybase Partner
 SuccessFactors Partner

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Service Offerings
SAP Strategy
Implementation
Process
Optimization
Services
SAP
Upgrade
Services
Application
Support
Professional
Staffing

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What is Predictive Analytics?

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Predictive Analytics Defined

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SAP Predictive Analytics - Myths
 Requires a Ph.D. to implement
 Hard to execute without technical
expertise
 Does not require business input
 Only for large companies

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Why do we need it?

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Value of Predictive Analytics

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Value of Predictive Analytics

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Users of Predictive Analytics

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Users of Predictive Analytics

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Applications of Predictive Analytics

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Applications of Predictive Analytics

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Use Cases by Line of Business

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Use Cases by Industry

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Predictive Analytics Process
Model deployment,
scoring, monitoring
Define the objectives of
the analysis;
Understanding the
business problem
Data selection,
cleansing,
transformation; initial
data exploration
Model building, training,
testing, evaluation
Reiterate

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Classes of Applications
 Time Series Analysis
 Classification Analysis
 Cluster Analysis
 Association Analysis
 Outlier Analysis

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Time Series Analysis
 Use past data points as the basis for projecting future
values
 Variable = Data (i.e. Sales or Headcount) with a series of
values over time
 Historical patterns of past data are used to make
predictions

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Classification Analysis
 Goal is to predict a variable (a.k.a. target or dependent
variable) using the data of other variables
 Largest group of applications of predictive analysis
 Examples: churn analysis, target marketing, predictive
maintenance

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Cluster Analysis
 Takes the data set and groups it into segments (clusters)
that have similar attributes
 Application is often used to subset a large data set in
order to better understand the attributes of the smaller
subsets
 Helps to find patterns and explanations for relationships
 Examples: customer segmentation

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Association Analysis
 Find associations between items
 Example: Shopping basket and product
recommendations

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Outlier Analysis
 This class of applications seeks unusual or unexpected
values in the dataset
 Possible significant impact on predictive models, so it’s
used in the context of all other classes of predictive
applications
 Could be genuine variations or errors
 Example: fraud detection

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SAP Predictive Analytics 2.3

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SAP Predictive Analytics 2.3 - Overview
 Automate data prep, predictive modeling, and
deployment – and easily retain models
 Harness in-database predictive scoring for a wide variety
of target systems
 Leverage advanced visualization capabilities to quickly
reveal insights
 Integrate with R to a enable a large number of algorithms
and custom R scripts
 Deploy SAP Predictive Analytics stand-alone or with
SAP HANA

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SAP Predictive Analytics 2.3 – System Requirements
 Server Requirements
 300 MB of disk space
 2GB of RAM
 Client Hardware Requirements
 150 MB of disk space
 512 MG of RAM
 30 day free trial available
 http://go.sap.com/product/analytics/predictive-analytics.html

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SAP Predictive Analytics 2.3 – Automated vs. Expert
Automated Analytics
 Designed for business
analyst or super user
 Drag and drop/Point and
click tool
 Preps data for the user
 Automatically selects
appropriate model
Expert Analytics
 Designed for statisticians
 Robust functionality with
statistical software R
 Create your own algorithms
 Compare effectiveness of
models

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Demo 1 – Predictive Maintenance

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Demo 1 – Business Problem
Background
 A manufacturing company is seeking to lower their
preventative maintenance costs on certain machines

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Demo 1 – Predictive Maintenance
 Maintenance scheduled
according to set time period
Future State – Predictive
Maintenance
 Maintenance scheduled
according to data analysis
Current State – Preventative
Maintenance

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Demo 2 – Employee Turnover

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Demo 2 – Business Problem
 A marketing company is experiencing a high rate of
turnover among employees
 When an employee leaves, the process is very
expensive due to the following:
 Lost Knowledge
 Training Costs
 Interviewing Costs
 Lowered Productivity

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Demo 2 – Analytics to Improve HR
 HR would like to use analytics to know not only which
employees will be likely to leave, but also take a more
refined approach by grouping employees with similar
characteristics together
 Goals:
 Segment out employees into different groups
 Determine which groups are most likely to have a high turnover rate
 Analyze data to determine what incentives could be best offered to
keep employees from leaving

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Questions and Next Steps

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What’s Next?
 Q+A
 Contact Todd Siedlecki to discuss how SAP
Predictive Analytics may fit in to your analytics
strategy
 Email – todd.s@dickinson-assoc.com