Predictive Analytics: A New Wealth of Options

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CONFIDENTIAL PREDICTIVE ANALYTICS WITH SAP SYBASE IQ JOYDEEP DAS PRODUCT MANAGEMENT APRIL 17, 2012

Transcript of Predictive Analytics: A New Wealth of Options

Page 1: Predictive Analytics: A New Wealth of Options

CONFIDENTIAL

PREDICTIVE ANALYTICS WITH SAP SYBASE IQ JOYDEEP DAS PRODUCT MANAGEMENT

APRIL 17, 2012

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©  2012 SAP AG. All rights reserved. 2

BUZZ IN THE INDUSTRY - I Unleashes Business Value

Operational Efficiencies

Revenue Growth

New Strategies & Business Models

Business Value*

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BUZZ IN THE INDUSTRY - II New Demands, New Opportunities

Mining New Sources of Data •  Large volumes of data beyond structured

•  Text, social, clickstream, geospatial •  Interactive, on-the-fly analysis •  New methodologies

•  e.g. social network analysis

Proliferation •  Increased usage by non-technical

business users •  Embedded in applications

•  e.g. recommendation engines in CRM

•  Platform has to be accessible and support more users!

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SAP Sybase IQ A Powerful Platform For Predictive Analytics – Sample Case Study #1

Sybase  IQ  significantly  enhanced  AOK  Hessen’s  ability  to  handle  complex  business  predic0ons  involving  mul0-­‐dimensional  analysis  of  many  input  variables.  

AOK  Hessen,  a  large  health  insurance  company,  searches  for  pa+erns  across  all  exis8ng  informa8on  –  medical  treatment,  prescrip8on,  insurance  benefits  –  simultaneously.  

“The  divisions  currently  using  the  tool  run  a  significantly  greater  number  of  analyses  than  ever  before.  They  keep  discovering  new  ways  of  drilling  down  into  data  while  working  with  the  soCware.”    

-­‐  Michael  Shimmelpfennig,  Service  Manager,  IT-­‐Business  Department

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SAP Sybase IQ A Powerful Platform For Predictive Analytics – Sample Case Study #2

Sybase  IQ  enables  Playphone  to  gain  significant  compe00ve  advantage  through  new  capabili>es  in  customer  targe0ng,  opera0onal  efficiency  and  fraud  detec0on.    

Playphone,  a  leading  global  mobile  entertainment  and  media  company,  enables  customer  analy8cs  for  large  scale  marke;ng  campaigns  using  advanced  predic8ve  models  on  Sybase  IQ  -­‐  crunching  through  

customer  behavior,  purchasing  history,  and  many  other  relevant  metrics.    

“Sybase  IQ  is  a  brilliant  analy;cs  engine.  I  don’t  know  that  the  business  would  s8ll  be  here  today  without  our  Sybase  solu8ons.”    

-­‐  Simon  Rose,  Director  Of  Infrastructure  

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SAP Sybase IQ Our Objective

Find buried signal in time! Or, get buried in Data!

Model Diversity Big Data Complex Alternatives

Deal  With  It    

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SAP Sybase IQ PlexQ Technology: Versatile With Multiple Options For Predictive Analytics

Pull Out Push Down

Data Mining Tools  

BI/DM Tools

Federated Push - Pull

BI Tools

Accelerated Access Drivers

Embedded libraries

Method I Method II Method III

Full  Mesh  High  Speed  Interconnect

IBM  SPSS    

SAS   R  

Panopticon  

KXEN  

Hadoop  

R  

Fuzzy Logix   Visual Numerics  

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SAP Sybase IQ Method I: Traditional Pull To Client

Pull Out

Data Mining Tools  

Accelerated Access Drivers

Method I

Full  Mesh  High  Speed  Interconnect

IBM  SPSS    

SAS   R  

Standard Drivers  Optimized Drivers  

  Pervasive Method

  Myriad Tools

  Available Skillset

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SAP Sybase IQ

       Fetch  data  from  database  

Create  datasets  for  analy>cal  packages  

Analyze  data  using  sta>s>cal  func>ons  on  proprietary  plaMorms  

Store  results  from  datasets  back  into  database  

                                       Generate  reports  

Time  consuming  process  Could  run  into  memory  constraints  with  large  data  sets  

Proprietary  plaMorms  make  it  very  difficult  to  embed  in  applica8ons    

Another  8me  consuming  process  which  could  slow  down  the  delivery  of  results  to  end-­‐users    

Data    

Volume  

Processing  Time  

Accuracy  

Compromise  on  at  least  one  key  area  

Visualization Database Server Logic/Filtering Applied Outside Database Server

Visualization Logic/Filtering Applied Inside Database Server

!  

Overcoming Method I: Avoid Pull

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SAP Sybase IQ

Models per month

2000

2005

2010 2015

Factory Analysis

Craftsman Analysis

Fully automated modeling process

•  Regression

•  Classification

•  Segmentation

•  Time series forecasting

•  Association rules

Identify key variables

Generate Sybase IQ specific SQL code

Executive and operational reports

Easy to Use Time to Market More Models

In-database Push down

KXEN  

Push down (Method II): Solution #1 with KXEN

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SAP Sybase IQ Push down (Method II): Solution #2 with Fuzzy Logix

Visualization Fuzzy Logix Libraries Executed Inside Sybase IQ

Application Algorithms Direct  Marke0ng:  Op>mize  the  performance  of  Internet  Marke>ng  Customer  Reten>on  

K-­‐  Means,  Correla>on,  PorKolio  Op>miza>on,  PCA,  Logis>c  Regression  

Marke0ng  Services:  Accelerate  the  speed  of  model  development  for  their  clients  

Sparse  Matrix  Calcula>ons,  Correla>on,  Euclidean  Distance    

Health  Insurance:  Risk  Management  and  Client  Management  Teams  -­‐  Scoring  models  to  assess  the  quality  and  efficiency  of  care    

Sparse  Matrix  Calcula>ons,  Correla>on,  Euclidean  Distance    

Banking:  Risk  Management   Correla>on,  Simula>on,  Regression,  Cubic  Spline,  Matrix  Opera>ons  

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SAP Sybase IQ Push down (Method II): Solution #3 with Zementis

Database  Server  Sybase  IQ  

Applica>ons  

PMML  (models)  PMML  (models)  PMML  (models)  

Zemen>s  PMML  Preprocessor  (convert  &  validate)  

Universal  Plug-­‐In  

SQL  

Predic>ons  

UDFs  

Bridge  

PMML  

Express  Complex  Computa0ons  In  Industry  Standard  Predic0ve  Modeling  Markup  Language  (PMML),  Plug  In  Models  Close  To  data  for  execu0on  

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SAP Sybase IQ Push down (Method II): Solution #3 with Zementis (contd)

Delivers a wide range of model types for high performance scoring, including: •  Decision Trees for classification and regression •  Neural Network Models: Back-Propagation, Radial-Basis Function, and Neural-Gas •  Support Vector Machines for regression, binary and multi-class classification •  Linear and Logistic Regression (binary and multinomial) •  Naïve Bayes Classifiers •  General and Generalized Linear Models •  Cox Regression Models •  Rule Set Models (flat decision trees) •  Clustering Models: Distribution-Based, Center-Based, and 2-Step Clustering •  Scorecards (point allocation for categorical, continuous and complex attributes) •  Association Rules •  Also implements data dictionary, missing / invalid values handling and data pre-processing

Handles most SAS models publishable in PMML from SAS Enterprise Miner

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SAP Sybase IQ Push down (Method III): Solution #1 Federated Push-Pull With “R”

PUSH-­‐PULL  FEDERATION:  

-­‐  UDF  Bridge  Between  Sybase  IQ  and  “R”  server  

-­‐  Fire  SQL  against  Sybase  IQ  that  pushes  “R”  models  embedded  in  UDFs  to  “R”  server  for  execu>on  

-­‐  UDFs  pulls  results  back  for  combining  with  rest  of  SQL  query  results  in  Sybase  IQ  

-­‐  Supports  most  “R”  models  

C++  UDF    “R”  Server  

SQL  Queries    

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SAP Sybase IQ Push down (Method III): Solution #2 Client Side Push-Pull

Sybase IQ Predictive Analytics

Job Results

$

QUEST Toad for Cloud Databases

Hadoop Hive MR Job Results

•  Ideal for bringing together predictive analytics computations from different domains

• Better performance when computation from each domain is pushed down to each branch and then pulled together

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SAP Sybase IQ Push down (Method III): Solution #3 Data Federation Push-Pull

•  Ideal for combining subsets of HDFS data with Sybase IQ data for operational analytics

• HDFS data not implicitly stored in Sybase IQ: Fetched into Sybase IQ In-memory tables on the fly as part of query fired at Sybase IQ

Predictive Queries

UDF Bridge Hadoop

Distributed File System

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SAP Sybase IQ Push down (Method III): Solution #4 Query Federation Push-Pull

Predictive Queries

UDF Bridge Hadoop

MapReduce Jobs

•  Ideal for combining subsets of Hadoop MapReduce job results with Sybase IQ data for operational analytics

• Hadoop MapReduce results not implicitly stored in Sybase IQ: Fetched into Sybase IQ In-memory tables on the fly as part of query fired at Sybase IQ

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SAP Sybase IQ Push down (Method III): Solution #5 Federated Push-Pull Text Analytics

Data Files

Text Index

(ISYS)

BLO

B

Web

Service

SAP BusinessObjects

Data Services Sybase IQ

1. Limit Textual Corpus 2. Extract Concepts/Entity Relationships 3. Mine Resulting Schemas

iSYS-­‐Search  Document  Filters  

Kapow  So[ware  

FourSquare  

Twitter

Amazon  

Facebook

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SAP Sybase IQ

Data  Discovery  (Data  Scien0sts)  

Applica>on  Modeling  (Business  Analysts)  

Reports/Dashboards    (BI  Programmers)  

Business  Decisions  (Business  End  Users)  

Infrastructure  Management  

(DBAs)  

Full  Mesh  High  Speed  Interconnect

A Versatile Platform For Predictive Analytics

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SAP Sybase IQ Summary – transform your business

• Predictive Analytics going mainstream

• Many options available

• SAP Sybase IQ has a broad and comprehensive support

THANK YOU!

[email protected]

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