Welcome to SAS New South Wales User Group Q2 2015 Groups...Welcome to SAS New South Wales User Group...

Welcome toSAS New South Wales User Group

Q2 2015Committee:Scott Bass

Peter StaggBhupendra Pant

Patrick CubaChris Latham

James Enoch (SAS)Marna Smit (SAS)

5:15pm Registration

5:30pm Welcome & Introduction James Enoch, SAS

5:35pm Big Data Analytics – The Future of analysisCarmen Gallagher - SAS

5:55pm How to get Money from StrangersKim Gill & Dr. Con Menictas

6:15pm Dynamic Reporting Systems using ODS & ExcelPeter Stagg – Decision Informatics

6:35pm Wrap up and Lucky draw

6:50pm Drinks

8:00pm Event Concludes

Welcome & Introduction James Enoch, SAS

Tips & Tricks From the Committee Scott Bass, Senior Information Analyst, Bupa

Big Data Analytics – The Future of analysisCarmen Gallagher - SAS

Copyright © 2015, SAS Institute Inc. All rights reserved.

SNUG meeting –Big Data AnalyticsCarmen GallagherHead of Information Management & Big DataSAS Australia/New Zealand

Copyright © 2014, SAS Institute Inc. All rights reserved.Copyright © 2012, SAS Institute Inc. All rights reserved.

Big Data Analytics WHERE WE ARE NOW

2005 2007 2009 2011 2013

ANALYTICSBIG DATA HADOOPLots of data Processing

PowerAccurate /Decisions

WHO CARES?

LG R‐S73CT refrigerator 2001. 730 liter, 15.1 inch full color TFT‐LD with touch screen, internet enabled

2000 LG introduces it’s Internet Enabled Refrigerator

2015 Amazon introduces “Amazon Dash Button”

BIG DATA

Place it. Press it. Get it.When you're running low, simply press Dash Button, and Amazon quickly delivers so you can skip the last‐minute trip to the store.

Copyright © 2015, SAS Institute Inc. All rights reserved.

BIG DATA IT’S ALL TALK OVER HERE

By 2017, 90 percent of all Samsung hardware will be Internet-connected. Five years from now, every

Samsung product will be part of the Internet of things, no matter whether it’s an air

purifier or a vacuum cleaner.

Samsung CEO and president BK Yoon, Jan 2015

Copyright © 2015, SAS Institute Inc. All r ights reserved.

BIG DATA WHAT DOES IT MEAN FOR ME

Copyright © 2015, SAS Institute Inc. All r ights reserved.

SAS as you know it isn’t going anywhere

The way your company uses data to report won’t change any time soon

You still use all the same proceedures they’re being re-written to also work in Hadoop

Co-existence of two different types of architecture to enable your organisation

Copyr igh t © 2014 , SAS Ins t i t u te I nc . A l l r i gh t s reserved .

Hadoop will soon become a replacement complement to: Business Intelligence; Data Warehousing; Data Integration; Analytics.

SOURCE: Integrating Hadoop with BI and DW - TDWI Best Practices Report

WHY HADOOP?

HADOOP IN PRODUCTION:

10%

28%

13%10%

12%

27%

YES

< 12 MONTHS

< 24 MONTHS

< 36 MONTHS

3+YEARS

NEVER


SAS and Hadoop – The big Picture

SAS and Hadoop are made for each other

SAS and Hadoop will help you answer bigger questions

SAS is making it easy for our users to leverage Hadoop


What is Hadoop ? An Apache Software Foundation project

• Open-source• Origins in early 2000s with contributions from Google, Yahoo! and

Facebook

Framework/Ecosystem of tools for processing Big Data1. Hadoop Common: The common utilities that support the other Hadoop

subprojects2. Hadoop Distributed File System (HDFS): A distributed file system that

provides high –througput access to application data3. Hadoop MapReduce/YARN: A software framework for distributed

processing of large data sets on compute clusters


Idea #1 - HDFS. Never forgets!

Head Node Node 1 Node 2 Node 3 Node 4…MYFILE.TXT..block1 -> block1copy1..block2 -> block2copy2..block3 -> block3copy3


Idea #1 - HDFS. Never forgets!

Head Node Node 1 Node 2 Node 3 Node 4…MYFILE.TXT..block1 -> block1copy1 block1copy2..block2 -> block2copy2 block2copy2..block3 -> block3copy2 block3copy3


Idea #2 - MapReduce

We Want the Minimum Age in the Room

Each Row in the audience is a data node

I’ll be the coordinator• From outside to center, accumulate MIN• Sweep from back to front. Youngest Advances


SAS® Within the HADOOP ECOSYSTEM

Next-Gen SAS® User

User Interface

Metadata

Data Access

DataProcessing

FileSystem

SAS® User

MPI Based

SAS® LASR™ Analytic

Server

SAS® High-Performance

Analytic Procedures

HDFS

Base SAS & SAS/ACCESS® to Hadoop™

SAS Metadata

Pig

Map Reduce/YARN

In-Memory Data Access

SAS® Visual Analytics

SAS®

Enterprise Miner™

SAS® Data Integration

SAS® Data Loader for Hadoop

HiveSAS Embedded

Process

SAS® In-Memory Statistics for

Hadoop


DATA TO DECISION LIFECYCLE on Hadoop

TEXT

MANAGE DATA

EXPLOR

E

DATA

DEVELOP MODELS

DEP

LOY

&

MO

NIT

OR

• SAS/ACCESS (Hadoop/Impala) • SAS Data Management• SAS Federation Server

• SAS Data Quality Accelerator for Hadoop

• SAS Code Accelerator for Hadoop• SAS Data Loader for Hadoop • SAS Visual Analytics

• SAS In-memory Statistics for Hadoop

• SAS HPA Products• SAS Visual Statistics

• SAS In-memory Statistics for Hadoop

• Model Manager

• SAS Scoring Accelerator for Hadoop


MANAGE DATA READ/WRITE TO HDFS

/* Create directory on HDFS*/ filename cfg "C:\Sample_Data\hadoop_config.xml";proc hadoop options=cfg username="hadoop" password="hadoop";

hdfs mkdir="/user/hadoop/testfolder" ;run;/* Copy file from local SAS to HDFS*/filename cfg "C:\Sample_Data\hadoop_config.xml";proc hadoop options=cfg username="hadoop" password="hadoop";

hdfs copyfromlocal="C:\Sample_data\dept.txt"out="/user/hadoop/testfolder/";run;/* Copy file from HDFS to local SAS */filename cfg "C:\Sample_Data\hadoop_config.xml";proc hadoop options=cfg username="hadoop" password="hadoop";

hdfs copytolocal="/user/hadoop/testfolder" out="C:\Sample_data\" ;run;


MANAGE DATA MAPREDUCE

/*Invoke MapReduce Word Count program*/filename cfg "C:\Sample_Data\hadoop_config.xml";proc hadoop options=cfg username="hadoop" password="hadoop" verbose; hdfs delete="/user/hadoop/output_MR1";

mapreduce input="/user/hadoop/gutenberg“ output="/user/hadoop/output_MR1"jar="C:\Sample_data\hadoop-examples-2.0.0-mr1-cdh4.1.2.jar"outputkey="org.apache.hadoop.io.Text"outputvalue="org.apache.hadoop.io.IntWritable"reduce="org.apache.hadoop.examples.WordCount$IntSumReducer"combine="org.apache.hadoop.examples.WordCount$IntSumReducer"map="org.apache.hadoop.examples.WordCount$TokenizerMapper"reducetasks=0;

run;


MANAGE DATA SAS/ACCESS

• Base SAS Procedures executed in-database for Hadoop• FREQ, REPORT, SORT, SUMMARY/MEANS, TABULATE

• Supported Hadoop distributions & combinations*• Cloudera CDH 5.0 running Hive/Hive2• Hortonworks HDP 2.0 running HiveServer2• IBM InfoSphere BigInsights 2.1 running Hive• MapR M5 2.0.1 running Hive• Pivotal/Greenplum HD running Hive• Pivotal/Greenplum MR 2.0.1 running Hive


MANAGE DATA SAS/ACCESSLIBNAME cdh_hdp HADOOP PORT=10000 SERVER=sascldserv02 user=hadoop password=hadoop ;/* Create new table*/proc sql;connect to hadoop(PORT=10000 SERVER=sascldserv02 USER=hadoop PASSWORD="hadoop"); exec( create table cars_prc (make string, model string, msrp double) ) byhadoop; quit;

/* Copy from another table*/proc sql;

insert into cdh_hdp.cars_prc select make, model, msrp from sashelp.cars ;

quit;/* List contents*/proc sql; select * from cdh_hdp.cars_prc; quit;


MODEL DEVELOPMENT SAS HIGH PERFORMANCE ANALYTICS

High-Performance Text Mining

• HPTMINE

• HPTMSCORE

High-Performance Data Mining1

• HPREDUCE

• HPNEURAL

• HPFOREST

• HP4SCORE

• HPDECIDE

• HPCLUS

• HPSVM

• HPBNET

• HPTSDR

High-Performance Econometrics

• HPCOUNTREG

• HPSEVERITY

• HPQLIM

• HPPANEL

• HPCOPULA

• HPCDM

High-Performance Optimization

• OPTLSO

• Select features in

• OPTMILP

• OPTLP

• OPTMODEL

• OPTGRAPH

High-Performance Statistics

• HPLOGISTIC

• HPREG

• HPLMIXED

• HPNLMOD

• HPSPLIT

• HPGENSELECT

• HPFMM

• HPCANDISC

• HPPRINCOMP

• HPPLS

• HPQUANTSELECT

1Includes SAS High-Performance Statistics

Common Set (HPDS2, HP DMDB, HPSAMPLE, HPSUMMARY, HPIMPUTE, HPBIN, HPCORR)#


DEPLOY & MONITOR SAS SCORING ACCELERATOR FOR HADOOP

• Publish SAS® Enterprise Miner™ models or SAS/STAT linear models inside the Hadoop

• Fully integrated with SAS® Model Manager to streamline registration, validation and performance monitoring

• Reduced data movement and improve data governance by streamlining model deployment processes within Hadoop


FINDING THE VALUE BIG DATA INNOVATION LAB

SAS BIG DATA INNOVATION LAB• 12 WEEKS• YOUR SCENARIO• YOUR DATA• OUR SOFTWARE • INFRASTRUCTURE (CLOUD/HARDWARE) INCLUDED


BIG DATA BIG PLAY OFFERING13 MAY 2015


THANK YOU!4 short years and…

• Hadoop is on the opening session, 2 Hadoop leaders’s share the panel discussion at Exec Track

• 50% of the people attending an executive conference talk have an active Hadoop project

• SAS has built a family of applications that take full advantage of Hadoop as an analytics platform

For more Information: http://www.sas.com/hadoop


[email protected]

@datalysis

carmengallagher Thank You!

http://www.sas.com/au/sashadoop

How to get Money from StrangersKim Gill & Dr. Con Menictas

“How to Get Money From Strangers!”

Dr Con MenictasKim Gill

Mark Grosser

The Charity Problem

1. How do we get people to give?

2. How do we keep them giving?

3. How do we make them advocates to our cause?

Individual Giving

Direct Mail Appeals

Online donations

Telethon responders

Peer to Peer fundraising

Cash DonorsCash Donors

Monthly support

Regular GivingRegular Giving

Supporter Engagement Strategies

F2F Fundraisers

Telemarketing

Online

PillarsPillars

Welcome Packs

Milestone Comms

Calls to Upgrade

Retention Strategies

ToolsTools

Challenges

Low level analytics and data

Only transactional data (RP data)

Might know the what but not the why

Hardware not a priority

Limited budget

The Ideal Feedback Loop

What Can Be An Ideal Starting Point?

is the utility (or net benefit or well-being) a person obtains from choosing

alternative . The behavior of the person is utility-maximizing i.e., person

chooses the alternative that provides the highest utility.

Behavioural Economics and Experimental Psychology

The Psychology of Judgment and Decision Making

Daniel Kahneman

Awarded the 2002 Nobel Memorial Prize in Economics

for his work on Discrete Choice Modelling

Humans have problems rating or ranking objects.

Humans utility maximise so asking them what they like the most fails.

Humans want all the benefits and none of the costs.

Choice modelling delve deeper into the mechanics of human choice.

Choice modelling forces trade-offs between products or services

Within products and services forces attributes and levels to compete.

What is Choice Modelling?

Choice experiments vary the characteristics (levels) of each brand factor (attribute).

We use a systematic approach known as optimal experimental designs, which are orthogonal and efficient.

The efficiency of the design ensures that the standard errors of the parameter estimates are going to be minimized.

Experimental Designs and Orthogonality

Decision Strategies

Decision Strategy Properties

Decision Assumptions

Choice Complexity Aspects

Advantages of Discrete Choice Experiments

Force respondents to consider trade-off between alternatives.

Estimate the level of customer demand for alternative products.

Enable implicit prices to be estimable for factors and their levels.

Simulate competitive, retaliatory and strategic responses.

Disadvantages of Discrete Choice Experiments

Complex choice experiments may occasion cognitive computational burden.

Respondents might be unable to articulate attitudes toward new concepts.

They may feel forced to think about issues they would otherwise not think of.

Poorly designed studies may over-value emotional or preference variables.

Choice modelling does not take into account the number items per purchase.

The Problem for Today

How can we use choice experimentation and modelling to overcome the charity dilemma?

Is there a coarse preference elicitation for Charity?

Should we consider an overall likelihood of donation?

Is it feasible to assume minimal heterogeneity?

SAS QC:

The FACTEX procedure constructs factorial designs.

The OPTEX procedure constructs optimal designs when factorial designs are not available.

FACTEX

Full factorial designs, with and without blocking

Fractional factorial designs, with and without blocking

Three-level designs, with and without blocking

Mixed-level factorial designs, such as 4x3 designs, with and without blocking

Randomized complete block designs

Factorial designs with outer arrays

Hyper-Graeco Latin square designs

Construct orthogonally confounded full and fractional factorial designs, with and without blocking, including 2**k

orthogonal array designs

Construct q**k factorial designs, where q is a prime. These can be used with the SAS DATA step to construct many

complex designs, such as mixed-level factorial and balanced incomplete block designs.

No limitation on the number of factors or factor levels designs can be chosen to have minimum size, maximum

resolution, maximum block size, or minimum aberration

OPTEX

Searches for D-optimal or A-optimal designs

Intended for situations in which a standard design is not appropriate

Add points to an existing design

Augment preexisting covariates

Evaluate an existing design

Improve an existing design

Various search algorithms and selection criteria are available

Flexible GLM-type models are available incomplete block designs.

No limitation on the number of factors or factor levels

Designs can be chosen to have minimum size, maximum resolution, maximum block size, or

minimum aberration

TYPES OF OPTIMALITY

A-optimality: minimizes the average variance of the estimates of the regression coefficients.

C-optimality: minimizes the variance of a best linear unbiased estimator of a predetermined linear

combination of model parameters.

D-optimality (determinant): maximizes the differential Shannon information content of the parameter

estimates.

E-optimality (eigenvalue): maximizes the minimum eigenvalue of the information matrix.

T-optimality (trace): maximizes the trace of the information matrix.

G-optimality: minimizes the maximum variance of the predicted values.

I-optimality (integrated): minimizes the average prediction variance over the design space.

V-optimality (variance): minimizes the average prediction variance over a set of m specific points.

THE OPTEX PROCEDURE

%mktdes(factors=x1-x2=2 x3-x5=3, n=18)

Design Number D-Efficiency A-Efficiency G-EfficiencyAve of the Predicted

SEs

1 99.0008 97.9412 93.5502 0.7145

2 99.0008 97.9412 88.6512 0.7145

3 98.6998 97.2973 94.8683 0.7169

4 98.6998 97.2973 94.8683 0.7169

5 98.6998 97.2973 94.8683 0.7169

Attribute and Level Matrix

Performance

Dr Con Menictas

[email protected]

0425 339 790

+61 425 339 790

6:15pm Dynamic Reporting Systems using ODS & Excel

Peter Stagg – Decision Informatics

60

Dynamic Reporting Systems Using ODS and Excel

Peter Stagg60

61

Discussion

• Requirements• What we have• Data• Dynamic Macro Code• Excel• Output

62

Requirement• Quality Assurance (QA) over 400 separate

sources of data from 6 states and 2 territories• Conduct activity comparisons with previous

year’s data• Ensure consistency of the summarised data.• Individual reports for each data source• Written to be used in future QA episodes• Fast turnaround

63

Requirement

• Used in all sites across the county• Allows editing and responses to be added• Multiple page output

Rules Out

• PDF – Too many pages– No response entry capability

• HTML/Web Reports– No response capability– Administration overheads

64

EXCELSo that leaves

65

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Data

• Values in the data provided in inconsistent datasets between years– Each year new measures or changes in

measures• Site data amalgamated into one set of state

data (2 datasets per state)• 3 million to 780 million records in size

– Raw format.

67

Data

• Datasets need to be summarised before comparison for the report

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Current Report is wide and shallowKeeps on going

SO HOW DO WE MAKE IT READABLE?

Narrow and deep

69

WHAT DO WE NEED TO DO?

70

Key Points

• Dynamic macro code • Transpose data• Create warning flags in data• Insert formulas into the sheet• Write out as Proc Report

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Dynamic Macro Code

• Set up macro vars at beginning of process• Create dynamic formats• Create warning flags• No changes required in macro code for

each round

Dynamic Format Code%let prev_rnd = 16;%let curr_rnd = 17;

value $po1_col_fmt“R_&prev_rnd._LOS_SUM" = "Rd &prev_rnd_Days""R_&prev_rnd._NUMCASES_SUM" = "Rd &prev_rnd Seps/OOS""R_&prev_rnd._DIR_COST_SUM" = "Rd &prev_rnd Direct $""R_&prev_rnd._OH_COST_SUM" = "Rd &prev_rnd O/Head $""R_&curr_rnd._LOS_SUM" = "Rd &curr_rnd Days""R_&curr_rnd._NUMCASES_SUM" = "Rd &curr_rnd Seps/OOS""R_&curr_rnd._DIR_COST_SUM" = "Rd &curr_rnd Direct $""R_&curr_rnd._OH_COST_SUM" = "Rd &curr_rnd O/Head $"

Tip

Excel Formulas Data dsname;Set dsname2;order = put(tr_col,$po1_order_fmt.);if order =0 then

total = 'Total';else if order = 1 then

total = ' ';else if order in (1,2,3,4,5,6,9,10,11,12,13) then

total = '=SUM(RC[-19]:RC[-1])-RC[-9]';else if order in (7) then

total = '=R[-1]C/R[-5]C';else if order in (8) then

total = '=R[-2]C/R[-5]C';

Tip

Report dataset columnsExcel

FormulasOriginalColumnNames

PresentationOrder

NewColumnNamesasRownames

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ODS to Excel

• Many ways to create spreadsheet via ODS

• Chose ODS tagsets.ExcelXP– Allows formatting– Traffic lighting– Sheet presentation options.

77

ODS Excel

• Set up file output and first sheet

• Options

• New worksheet• Options

• Close workbook

• ODS tagsets.excelXP file = “directory\filename.xls”

• Sheetname• Filter

• ODS tagsets.excelXP [no file dest]

• Sheetname• Filter

• ODS tagsets.excelXP close

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ODS Excel statementsODS TAGSETS.EXCELXP FILE="&_pathoutputfile./report_%trim(&value)_%trim(&rundate)_.xls" style=meadow

OPTIONS(EMBEDDED_TITLES='YES‘FROZEN_HEADERS= "&frozen_head" /* row number */ EMBEDDED_footnotes='YES' AUTOFIT_HEIGHT='YES' AUTOFILTER='YES' /*filter to be showing */SHEET_name='summary' ) ; < Titles ><SAS Statements>ODS tagsets.excelXP close

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Proc report

• Usedtooutputinastandardlayout.• FormatswrittentomatchExcelstyle

• tagattr='format:#,##0’• tagattr='format:###.00%‘• tagattr='format:#,##0.00'

Tip

Output codeODS TAGSETS.EXCELXP NOTE if more than one sheet output the filename is not needed after the first statement

FILE="&_pathoutputfile./report_%trim(&value)_%trim(&rundate)_.xls" style=meadowOPTIONS(EMBEDDED_TITLES='YES' FROZEN_HEADERS= "&frozen_head" EMBEDDED_footnotes='YES' AUTOFIT_HEIGHT='YES' AUTOFILTER='NO' SHEET_name='P01' ) ;

title1 j=CENTER c=black height=4 bold ‘Report Title' ;

proc report data= main_p01_report split='*' noheader;

where filter_variable= &Macro_Value;column order COL_NAME &prodList total;

define order /order noprint ;define col_name /DISPLAY style = [ background=lightgrey TEXTALIGN = LEFT font_weight = bold];%do i = 1 %to &prod_count;

define %sysfunc(scan(&prodlist , &i, ' ')) /DISPLAY width = 10; /* column names for Products */%end;

define total /display "Total Cost $"; /* Excel Formulas */

compute col_name;

CALL DEFINE (_COL_, "STYLE","style = [ background=lightgrey TEXTALIGN = C font_weight = bold]");if col_name = 'Comments' then CALL DEFINE (_ROW_, "STYLE","style = [ background=$comm.]");

if order in (0,1) then CALL DEFINE (_ROW_, "STYLE","style = [ background=lightgrey TEXTALIGN = C font_weight = bold]");else if order in (2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22) then CALL DEFINE (_ROW_, 'STYLE',"style =[tagattr='format:#,##0' TEXTALIGN = R ]");else if order in (23,24,25,26,27,28,29) then CALL DEFINE (_ROW_, 'STYLE',"style=[tagattr='format:###.00%' TEXTALIGN = R ]");endcompute;

footnote1 j=left c=black height=1 " Footnote";

Output code (Header)ODS TAGSETS.EXCELXP FILE="&_pathoutputfile./report_%trim(&value)_%trim(&rundate)_.xls" style=meadowOPTIONS(EMBEDDED_TITLES='YES' FROZEN_HEADERS= "&frozen_head" EMBEDDED_footnotes='YES‘AUTOFIT_HEIGHT='YES' AUTOFILTER='NO' SHEET_name='P01' ) ; title1 j=CENTER c=black height=4 bold Report Title' ;

Output code (Body)

proc report data= main_p01_report split='*' noheader;where filter_variable= &Macro_Value;

column order COL_NAME &prodList total;

define order /order noprint ;define col_name /DISPLAY style = [ background=lightgreyTEXTALIGN = LEFT font_weight = bold];/* This is the Product List */%do i = 1 %to &prod_count;define %sysfunc(scan(&prodlist , &i, ' ')) /DISPLAY width = 10; /* column names for Products */%end;

define total /display "Total Cost $"; /* Excel Formulas */

Output code (compute)compute col_name;CALL DEFINE (_COL_, "STYLE","style = [ background=lightgrey TEXTALIGN = C font_weight = bold]");

if col_name = 'Comments' then CALL DEFINE (_ROW_, "STYLE","style = [ background=$comm.]");

if order in (1) then CALL DEFINE (_ROW_, "STYLE","style = [ background=lightgrey TEXTALIGN = C font_weight = bold]");else if order in (2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22) then CALL DEFINE (_ROW_, 'STYLE',"style =[tagattr='format:#,##0' TEXTALIGN = R ]");

else if order in (23,24,25,26,27,28,29) then CALL DEFINE (_ROW_, 'STYLE',"style=[tagattr='format:###.00%' TEXTALIGN = R ]");endcompute;

footnote1 j=left c=black height=1 " Footnote";ods tagsets.excelxp close;

Building the call stepsproc sql;select count (DISTINCT(SITE)) into :counterfrom x;select distinct(site) into :var_lisT1 -:var_list%trim(&counter)from x;quit;

Note: SAS 9.4 does not require the end macro var. It creates the vars dynamically. If doing multiple runs and various site numbers this way ensures no false macro varsare passed

%macro rept;%do I = 1 %to &counter;%output(site = &var_list&i);%end;%mend;%rept;

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References• http://support.sas.com/rnd/base/ods/odsmarkup/TipSheet_ods_xl_xp.pdf• http://support.sas.com/rnd/base/ods/odsmarkup/excelxp_demo.html• http://support.sas.com/resources/papers/proceedings10/031-2010.pdf• http://support.sas.com/resources/papers/proceedings15/SAS1700-2015.pdf

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Contact

Peter Stagg

Director

[email protected]

Wrap Up

• Survey – Please complete• Best Presentation Award

• Q2 – Ron Elazar & Dharmik Jeena from Westpac• Wins iPod Nano & goes into draw for trip to SAS Global Forum 2015• 2013 Winner – Bob Whitehead just returned from Washington

• SANZOC – SAS Australia & New Zealand Online Community• Lucky Draw• Attendance – No Shows

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