IBM Systems Magazine, Mainframe - November/December 2015 - 16

CURRENTS

looking to inject intelligence
into operations with subsecond
response time, analytics must
more tightly integrate with operational business processes.

Why Predictive
Analytics?

Predictive analytics is the go-to
technology for operational
decisions. Predictive models are
designed to detect patterns of
behavior and examine how well
transactions match up to these
SDWWHUQVÃ$ÃWUDQVDFWLRQÃFORVHO\Ã
matching a certain buying pattern
might trigger an upsell or cross-sell
action, while a transaction that
closely matches a pattern of fraud
might cause a payment request
WRÃEHÃFDQFHOHGÃ$ÃVHULHVÃRIÃWUDQVactions that heavily deviate from
known patterns might indicate the
emergence of new patterns that can
then be figured into the models.
You might try to do this analysis
with business rules, but rules aren't
nearly as flexible as predictive
models. In a rules-based system, a
sequence of, say, 15 rules could be
established to determine if a transaction is likely to be fraudulent.
Unless all of the rules fire, the transaction is processed. With predictive
modeling, several indicators would
generate a probabilistic determination of how well a transaction fits
NQRZQÃSDWWHUQVÃRIÃIUDXGÃ$ÃWKUHVKold can be set-say 85 percent-that
the business deems the point at
which a fraud prevention action
must be taken. It's possible that a
fraudulent transaction would pass
some of the rules but still generate
a score of 85 percent.
Predictive models, like the real
world, are not binary, and probabilistic pattern matching is a far
more accurate indicator of opportunity and intent.

Antiquated Thinking
So why aren't all operational
systems scoring each transaction?

In the early days of automated business processes,
complex queries against transactional data could
severely degrade operations. This gave rise to the
data warehouse mentality that still holds. But the
business-criticality of analytics has risen sharply, to the
point that all operations must be informed by some sort
of analytic for the enterprise to remain competitive.
Most clients, in their engagements with the z Systems
%LJÃ'DWDÃDQGÃ$QDO\WLFVÃWHDPÃH[SUHVVÃDÃGHVLUHÃWRÃPDNHÃ
analytics a more integral part of operations, but the tactics used still reflect the bifurcated thinking of the past.
Typically one of three scenarios is encountered:
1. Clients rely on simpler techniques alone, such as
business rules. But predictive analytics deliver
more accurate results.
2. If clients use predictive analytics, they typically
perform the analysis post-transaction. For example, a healthcare provider may run an end-of-day
analysis of claims paid, determine which ones were
likely fraudulent, then forward that information to
their special investigation unit to try and recover
the money. Once money is paid, the recovery rate is
very low (acfe.com/rttn-summary.aspx).
3. If clients are using more-advanced analytics for
operations, they typically only analyze a subset
of transactions for fear of missing extremely tight
6/$VÃGXHÃWRÃODWHQF\ÃRIÃGLVWULEXWHGÃDUFKLWHFWXUHV
In all of these cases, the models or scores are derived
on copies of transactional data, so decisions are made
on aged information. Fortunately, a better way exists.

Predictive Decision
Engine Choices for z/OS
7KHÃ]Ã6\VWHPVÃ%LJÃ'DWDÃDQGÃ$QDO\WLFVÃWHDPÃDGYRFDWHVÃ
an architecture that combines three technologies for
producing superior decision management in the context of operational decisions, which are:
ĀÃ

$QÃRUFKHVWUDWLRQÃOD\HUÃZKLFKÃLVÃDÃSURFHVVÃIORZÃ
engine or similar structure for mediating the interaction between transactional and analytic elements.
ĀÃ $ÃSUHGLFWLYHÃDQDO\WLFVÃHQJLQHÃ$QÃLQWUDQVDFWLRQÃ
predictive engine can score every transaction

More Resources
Business Analytics and Big Data Solutions:
ibm.com/systems/z/solutions/data.html
Zementis for IBM z Systems: zementis.
com/products/uppi/z4ibmzsystems/

16 // NOVEMBER/DECEMBER 2015 ibmsystemsmag.com

and deliver valuable information about risk, clustering,
segmentation, propensity, etc.
ĀÃ $ÃEXVLQHVVÃUXOHVÃHQJLQHÃ$SSO\Ã
business policy, best practices,
know-how and regulatory
requirements against the
probabilities generated by the
predictive engine.
Two technologies are available
on z/OS for delivering predictive
scores within the scope of an
operational transaction.
IBM SPSS Modeler with
Scoring Adapter for z Systems
The SPSS* predictive analytics
engine works with models built in
IBM SPSS Modeler product. It can
also ingest models built using the
,%0Ã'%ŪÃ$QDO\WLFVÃ$FFHOHUDWRUÃ
(Read about this in "Personal
Shopper," page 24.) Exclusively
for z Systems, the SPSS scoring
adapter can be installed directly
within DB2 z/OS via the DB2
user-defined function mechanism.
During execution of a transaction,
an SQL call to the adapter will
yield a nearly instantaneous score.
Zementis for z Systems
IBM business partner Zementis
has made their predictive
analytics engine available on z/
OS. Zementis is a Predictive Model
Markup Language (PMML) engine,
so it can ingest models built with
WKHÃ6366Ã0RGHOHUÃDQGÃ$QDO\WLFVÃ
$FFHOHUDWRUÃDVÃZHOOÃDVÃPRGHOVÃEXLOWÃ
with other popular tools such
DVÃ.1,0(Ã3\WKRQÃ5ÃDQGÃ6$6Ã
Zementis takes a different tack
for in-transaction scoring with
the same result. The Zementis
engine is a highly efficient Java*
executable that can be invoked
from any z/OS Java application,
and has been tested with both
CICS* and WebSphere* for z/OS.
Which do you choose? Zementis
installs in the application, and
IBM SPSS installs in the database.
Zementis supports any PMMLbased model, while IBM SPSS


http://www.acfe.com/rttn-summary.aspx http://www.ibm.com/systems/z/solutions/data.html http://zementis.com/products/uppi/z4ibmzsystems/ http://zementis.com/products/uppi/z4ibmzsystems/ http://www.ibmsystemsmag.com

IBM Systems Magazine, Mainframe - November/December 2015

Table of Contents for the Digital Edition of IBM Systems Magazine, Mainframe - November/December 2015

IBM Systems Magazine, Mainframe - November/December 2015
Table of Contents
IBM Perspective: Opening new options for real-time analytics
Editor's Desk: Predictive analytics enhances purchases
Infographic: Breaking down the mainframe skills gap
Partner PoV: IBM z Systems clients must manage existing and emerging assets together to get the most value
Focus on Storage: The IBM Spectrum Storage family of software-defined solutions simplifies storage management
Currents: Architect real-time analytics into operational systems for competitive advantage
Cover Story: Dynamic Spark: IBM brings the Apache project's analytics power to Linux and z Systems servers
Feature: Personal Shopper: Retailer optimizes analytics to drive a more customized client experience
Tech Showcase: In a roundtable, distributors discuss how IBM LinuxONE enables flexibility for users
Tech Corner: Runtime Diagnostics and Predictive Failure Analysis V2.2 improve z/OS availability
Solutions: Strobe 5.2
Stop Run: IBMer volunteers time to educate people about malaria and technology
Reference Point - Global Events, Education, Resources for Mainframe
2016 Mainframe Solutions Edition Product Index
IBM Systems Magazine, Mainframe - November/December 2015 - Intro
IBM Systems Magazine, Mainframe - November/December 2015 - IBM Systems Magazine, Mainframe - November/December 2015
IBM Systems Magazine, Mainframe - November/December 2015 - Cover2
IBM Systems Magazine, Mainframe - November/December 2015 - 1
IBM Systems Magazine, Mainframe - November/December 2015 - Table of Contents
IBM Systems Magazine, Mainframe - November/December 2015 - 3
IBM Systems Magazine, Mainframe - November/December 2015 - 4
IBM Systems Magazine, Mainframe - November/December 2015 - IBM Perspective: Opening new options for real-time analytics
IBM Systems Magazine, Mainframe - November/December 2015 - Editor's Desk: Predictive analytics enhances purchases
IBM Systems Magazine, Mainframe - November/December 2015 - 7
IBM Systems Magazine, Mainframe - November/December 2015 - Infographic: Breaking down the mainframe skills gap
IBM Systems Magazine, Mainframe - November/December 2015 - Partner PoV: IBM z Systems clients must manage existing and emerging assets together to get the most value
IBM Systems Magazine, Mainframe - November/December 2015 - 10
IBM Systems Magazine, Mainframe - November/December 2015 - Focus on Storage: The IBM Spectrum Storage family of software-defined solutions simplifies storage management
IBM Systems Magazine, Mainframe - November/December 2015 - 12
IBM Systems Magazine, Mainframe - November/December 2015 - 13
IBM Systems Magazine, Mainframe - November/December 2015 - 14
IBM Systems Magazine, Mainframe - November/December 2015 - Currents: Architect real-time analytics into operational systems for competitive advantage
IBM Systems Magazine, Mainframe - November/December 2015 - 16
IBM Systems Magazine, Mainframe - November/December 2015 - 17
IBM Systems Magazine, Mainframe - November/December 2015 - Cover Story: Dynamic Spark: IBM brings the Apache project's analytics power to Linux and z Systems servers
IBM Systems Magazine, Mainframe - November/December 2015 - 19
IBM Systems Magazine, Mainframe - November/December 2015 - 20
IBM Systems Magazine, Mainframe - November/December 2015 - 21
IBM Systems Magazine, Mainframe - November/December 2015 - 22
IBM Systems Magazine, Mainframe - November/December 2015 - 23
IBM Systems Magazine, Mainframe - November/December 2015 - Feature: Personal Shopper: Retailer optimizes analytics to drive a more customized client experience
IBM Systems Magazine, Mainframe - November/December 2015 - 25
IBM Systems Magazine, Mainframe - November/December 2015 - 26
IBM Systems Magazine, Mainframe - November/December 2015 - 27
IBM Systems Magazine, Mainframe - November/December 2015 - 28
IBM Systems Magazine, Mainframe - November/December 2015 - Tech Showcase: In a roundtable, distributors discuss how IBM LinuxONE enables flexibility for users
IBM Systems Magazine, Mainframe - November/December 2015 - 30
IBM Systems Magazine, Mainframe - November/December 2015 - 31
IBM Systems Magazine, Mainframe - November/December 2015 - 32
IBM Systems Magazine, Mainframe - November/December 2015 - 33
IBM Systems Magazine, Mainframe - November/December 2015 - Tech Corner: Runtime Diagnostics and Predictive Failure Analysis V2.2 improve z/OS availability
IBM Systems Magazine, Mainframe - November/December 2015 - 35
IBM Systems Magazine, Mainframe - November/December 2015 - 36
IBM Systems Magazine, Mainframe - November/December 2015 - 37
IBM Systems Magazine, Mainframe - November/December 2015 - Solutions: Strobe 5.2
IBM Systems Magazine, Mainframe - November/December 2015 - 39
IBM Systems Magazine, Mainframe - November/December 2015 - Stop Run: IBMer volunteers time to educate people about malaria and technology
IBM Systems Magazine, Mainframe - November/December 2015 - Cover3
IBM Systems Magazine, Mainframe - November/December 2015 - Cover4
IBM Systems Magazine, Mainframe - November/December 2015 - RF1
IBM Systems Magazine, Mainframe - November/December 2015 - SE1
IBM Systems Magazine, Mainframe - November/December 2015 - SE2
IBM Systems Magazine, Mainframe - November/December 2015 - SE3
IBM Systems Magazine, Mainframe - November/December 2015 - SE4
IBM Systems Magazine, Mainframe - November/December 2015 - SE5
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