Predictive analytics applying it in your organization - bocx 2014

© 2014 Broadstreet Data & NexJ Systems Inc. Confidential and Proprietary.© 2014 Broadstreet Data & NexJ Systems Inc. Confidential and Proprietary.
Predictive Analytics
Applying It In Your Organization

© 2014 Broadstreet Data & NexJ Systems Inc. Confidential and Proprietary.
Agenda
• Introduction to Predictive Analytics
• SAP Predictive Analysis Overview
• Demo Scenarios
• Q&A
• Getting Help
• Appendix

Introduction to Predictive Analysis

Why Use Predictive Analytics
Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data
mining that analyze current and historical facts to make predictions about future, or otherwise unknown,
events.

Extend Analytics Capabilities

SAP Predictive Analysis Overview

SAP Predictive Analysis
Acquire data
from various
data sources,
• Excel
• CSV
• SAP
HANA
• Universe
• RDBMS
Perform data
enrichment
operations,
• Data
manipulation
• Data cleansing
• Semantic
enrichment
Get insight into
data prediction
using statistical
algorithms,
• Clustering
• Decision
Tree
• Trending
• Association
• Regression
Build
visualization
using wide
selection of
charts and
graphs.
Explore and
analyze row
and predictive
datasets using
visualizations.
Publish and
share
predictive
datasets to,
• SAP HANA
• SAP
Explorer
• SAP
Streamwork
• Excel
• CSV
• RDBMS
• Email
• PDF

Predictive Analysis Architecture
• The R open source statistics language and its 3,500
plus packages or algorithms is the most popular
predictive analysis tool according to the Rexer
Analytics 2011 Data Mining Survey, close to half of
all data miners (47%) using it.
• R resides on a separate server, side by side with
SAP HANA. Data stored in SAP HANA tables is
passed to the R server and transformed into R data
vectors or data frames, which is the data format
used by R. R script code is embedded in SAP
HANA SQLScript and is also passed over to R for
processing on the R server. The results, in the form
of R data vectors or data frames, are transferred
back to SAP HANA, which then converts it back to
an SAP HANA table. All the communication, data
transfer and transformation tasks are performed by
the SAP HANA Platform.

Predictive Analysis Algorithms
Supervised Learning: Target information and examples (training sets) provided
to the learner (component)
•Unsupervised, no training sets
•Find patterns in transactional datasets for generating association rules
•Predict future transacting behavior
Association
•Cluster observations into groups without prior knowledge of those
relationships
•Input to decision making process
Cluster
•Supervised, training sets for error correction
•Predict class membership through probability
•Scoring models for churn, defect, triage
Classification
•Structured, training sets, k+1 forecast
•Predicts the value of a variable based on supplied data
•Forecasting with or without trending or seasonal adjustments
Regression
•Find objects that do not comply with the general behavior or model
•Churn, fraud, fault analysis
Outliers

Create New Custom-R Component

Demo Scenarios

Customer Segment Analysis - Retail
Business Scenario
• A consumer clothing retail company wants to
perform a targeted marketing campaign to increase
particular product sales
• Marketing team wants to understand the
demographic of their customer
• Company wants to target specific customer segment
to achieve maximum success from the campaign

Targeted Customer Offering - Insurance
Business Scenario
• An insurance company wants to increase their
revenue by extending product offering to targeted
customers
• Company wants to know the likelihood of their
customers to buy particular product offering
• Company wants to focus their agents sales initiatives
to targeted customers

Hospital Staffing Forecast - Healthcare
Business Scenario
• Hospital management team wants to manage
staffing assignments based on forecasted patient
demand
• Historical statistics for Available Beds, Occupied Beds,
Inpatient Admissions and Day Case Admissions by
month is available
• Management wants to use the historical data to
predict and forecast hospital staffing needs for next 5
years

Customer Retention - Banking
Business Scenario
• Identify the attributes of those customers who have
been retained, in contrast to those who have left or
churned.
• Develop strategies to maximize the retention of
customers.

Q & A

Getting Help
• SAP Analytics Training on youTube
o Create an Analysis: SAP Predictive Analysis 1.14
o Create a Custom R Component: SAP Predictive Analysis 1.14
o Install and Configure Open Source R Algorithms: SAP Predictive Analysis 1.14
o Save, Export and Import Data Models: SAP Predictive Analysis 1.14
SAP BigData http://www.sapbigdata.com/analytics/predictive/
SAP BI Blog http://sapbiblog.com/category/predictive-analytics/
SAP Community
Network
http://scn.sap.com/community/predictive-analysis
SAP Help Portal https://help.sap.com/pa
SAP HANA http://www.saphana.com/community/learn/solutions/
predictive-analysis
SAP Insider http://sapinsider.wispubs.com/Search?query=SAP%20
Predictive%20Analytics
SAP Support Portal http://service.sap.com/xsearch
SAP Training https://www.youtube.com/user/SAPAnalyticsTraining

Appendix

PA 1.15 What’s New

PA 1.15 PAM – Desktop & Server

PA 1.15 PAM – Database Connectivity

PA Supported Algorithms
Type Brief Algorithm R
Association Find associations between entities based on transactions involving them
(aka Market Basket Analysis)
Apriori R-Apriori
Cluster Group individuals in a population together by their similarity, but not
driven by any specific purpose
K-Means
Self-Organizing Maps
R-K-Means
Classification Predict by individual element, which mutually exclusive class this
individual belongs to
ABC Analysis
KNN
Naïve Bayes
Weighted Score Analysis
-
Decision Trees Classification of individual elements through a hierarchical form – useful
for model scoring
C4.5
CHAID
R-CNR Tree
Regression Classification to estimate or predict, for each individual, the numerical
value of some variable
Exponential
Geometric
Logarithmic
Logistic
Multiple Linear
Polynomial
R-Multiple
Linear
Regression
Time Series A time series is set of consecutive points collected in uniform time
intervals. Smoothing provides forecasting abilities based on collected
averages, double with trend, triple with seasonality.
Exponential Smoothing:
Double
Single
Triple
R-Triple
Exponential
Smoothing
Outlier Material difference from or inconsistent with the remaining set of data,
are called anomalies or outlier
Anomaly Detection
Inter-quartile Range
Variance Test
-

PA Algorithm Descriptions
Time Series Analysis
• In this group we use past data points as the basis for
projecting future ones. The data is a variable, such as
sales or headcount or telemetry readings, with a series
of values over time. We use historical patterns in the
known data to make predictions about future values.
Association Analysis
• This class of applications looks for associations between
things, with the best known example being the analysis
of shopping baskets and the prediction that people
who purchased this product are most likely to purchase
these other products. It is the analysis is of a basket of
items. Other examples include the analysis of telecom
services purchased by subscribers; financial policies held
by households; telephone calling patterns; visitor paths
through a website.

PA Algorithm Descriptions (Cont.)
Cluster Analysis
• This class of application is concerned with grouping the
data into clusters or segments that have similar attributes.
It is often used to subset a large data set, as we may be
able to better understand the attributes of the smaller
subsets. We also cluster data so that we can focus on
specific groups within a dataset. The best example of this
is customer segmentation where we want to develop
specific sales strategies for particular customer groups.
Classification Analysis
• This is the largest group of applications of predictive
analysis. To predict a variable using the data of other
variables that we believe affect the values of the variable
that we are trying to predict. The variable that we wish to
predict is variously known as the output variable, the
target variable, or the dependent variable. The latter
name comes from the assertion that it depends on the
values of the independent variables or input variables on
Analysis.

PA Algorithm Descriptions (Cont.)
Outlier Analysis
• This class of application of predictive analysis looks for the
unusual and the unexpected: unusual in the sense that
values in a data set are very different from other values,
and unexpected in the sense that the values are very
different from what was predicted. The outliers could be
as a result of errors in the data or genuine variations in the
data, which the predictive model needs to take into
account. Outlier or anomaly detection has a major
application in the area of fraud detection.
5. Outlier Analysis

PA Task to Algorithm Sample

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Predictive analytics applying it in your organization - bocx 2014

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