tag:blogger.com,1999:blog-10620566142001087412024-02-02T12:25:02.812-08:00System z Chief Data Officer: Lessons from the FieldShantan Kethireddy's Blog: System z Chief Data Officer; Lessons from the FieldShantan Kethireddyhttp://www.blogger.com/profile/05506577167753584974noreply@blogger.comBlogger5125tag:blogger.com,1999:blog-1062056614200108741.post-55613493931360802592017-07-05T07:19:00.000-07:002017-07-17T16:59:51.036-07:00Transactional Machine Learning and Analytics: Industry Example<style id="dynCom" type="text/css"><!-- --></style>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><b style="mso-bidi-font-weight: normal;"><u><span style="color: black; font-size: 14.0pt;">What is Machine Learning for z/OS?</span></u></b></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt;">Machine Learning is a type of
artificial intelligence (AI) that provides computers with the ability to learn
without being explicitly programmed.<span style="mso-spacerun: yes;"> </span></span><span style="font-size: 11.0pt; mso-fareast-font-family: "Times New Roman";">Using
algorithms that iteratively learn from data, machine learning allows computers
to find hidden insights in the data without being explicitly programmed where
to look</span><span style="font-size: 11.0pt;">.<span style="mso-spacerun: yes;">
</span>Machine learning systems can find correlations in data and recognize
patterns to provide early detection and to predict events before they
happen.<span style="mso-spacerun: yes;"> </span>This can mean early detection of
healthcare conditions, prediction of factors that lead to better patient
adherence or better clinical outcomes, or algorithms to reach new heights of
personalized care and tailored treatment protocols.<span style="mso-spacerun: yes;"> </span></span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt;">Machine learning projects
generally include tasks such as data cleansing and ingestion, data feature
engineering and selection, data transformation, model training, model
evaluation, model deployment, scoring, re-evaluation, and re-training (feedback
loop). Many of these tasks need to be performed iteratively to get to desired
results. Each task requires heavy engagement from experienced analytics
personas across the organization from data scientist and/or software/data
engineers to application developers. As such, a machine learning project
usually takes weeks to months before a usable model could be generated and
deployed in production.</span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt;">IBM Machine Learning for z/OS
(Machine Learning for z/OS) is an end to end enterprise machine learning
platform that will help to simplify and significantly reduce the time for
creation and deployment of machine learning models by:</span></span></div>
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<li><span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11pt; line-height: 115%;"><span style="mso-list: Ignore;"><span style="font-feature-settings: normal; font-kerning: auto; font-language-override: normal; font-size-adjust: none; font-size: 7pt; font-stretch: normal; font-style: normal; font-synthesis: weight style; font-variant: normal; font-weight: normal; line-height: normal;"></span></span></span><span style="font-size: 11pt; line-height: 115%;">Integrating all the tools and
functions needed for machine learning and automating the machine learning
workflow.</span></span></li>
<li><span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11pt; line-height: 115%;"><span style="mso-list: Ignore;"><span style="font-feature-settings: normal; font-kerning: auto; font-language-override: normal; font-size-adjust: none; font-size: 7pt; font-stretch: normal; font-style: normal; font-synthesis: weight style; font-variant: normal; font-weight: normal; line-height: normal;"></span></span></span><span style="font-size: 11pt; line-height: 115%;">Providing a platform with freedom
of choice and productivity for better collaboration across different personas
including data scientist, data engineer, business analyst and application
developers, for a successful machine learning project.</span></span></li>
<li><span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11pt; line-height: 115%;"><span style="mso-list: Ignore;"><span style="font-feature-settings: normal; font-kerning: auto; font-language-override: normal; font-size-adjust: none; font-size: 7pt; font-stretch: normal; font-style: normal; font-synthesis: weight style; font-variant: normal; font-weight: normal; line-height: normal;"></span></span></span><span style="font-size: 11pt; line-height: 115%;">Infusing cognitive capabilities
into the machine learning workflow to help determine when model results
deteriorate and need to be tuned and provide suggestions for updates or
changes. </span></span></li>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt; mso-bidi-font-weight: bold;">Machine
learning is needed where business rules are rapidly changing, or where
application development can’t keep pace with changes that need to be made, or
where applications need to be continually tuned. Instead of writing lots of
complex business rules you would use machine learning, select the appropriate
algorithm and parameters to build the model. Once the model is created, it can
be trained on historical data and deployed to recognize patterns to make future
predictions.<span style="mso-spacerun: yes;"> </span>Predictions are retained
and compared to actual result as part of model monitoring. As environment
evolves, model results may deteriorate at which time, the data scientist can
choose to retrain the model with stored feedback data. By simplifying model
management, Machine Learning for z/OS reduces the amount or maintenance in an
application because the model is "aware" and always learning,
becoming smarter over time. </span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><b style="mso-bidi-font-weight: normal;"><u><span style="font-size: 14.0pt; mso-bidi-font-size: 12.0pt;">Why is
Machine Learning Important to zEnterprise Customers? </span></u></b></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt;">Many of our enterprise
customers have expressed an interest in leveraging the latest analytics technology
with the flexibility to deploy on premise, in the cloud or in a hybrid
environment.<span style="mso-spacerun: yes;"> </span>That said, many of our z
Systems customers are not yet ready to move their most sensitive data to the
cloud.<span style="mso-spacerun: yes;"> </span>They want to take advantage of
their existing significant investment in infrastructure, minimize costly data
movement and ensure data governance/security. <span style="mso-bidi-font-weight: bold;"><span style="mso-spacerun: yes;"> </span>For some customers this will be
their first entry into the machine learning domain.<span style="mso-spacerun: yes;"> </span>For them we have made this process much
simpler by lowering the bar for development and maintenance of predictive
behavior models.<span style="mso-spacerun: yes;"> </span>For some customers,
with already extensive data science expertise we have simplified the
development and maintenance process by providing cognitive expertise to build
behavioral models and automation to maintain those models over time -- freeing
up their data developers and data scientists to work on enhancing their
existing models and to bring data science to new areas of the business.</span></span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt; mso-bidi-font-weight: bold;">Machine
Learning for z/OS also offers RESTful APIs and programming APIs to perform
tasks such as transactional scoring.<span style="mso-spacerun: yes;">
</span>Scoring allows zEnterprise customers to evaluate a transaction against a
machine learning model to determine in real time e.g. risk of pre-diabetes,
likelihood of medication adherence/compliance, risk of over-payment prior to
claims payment, and to make real time decisions based on these information
(e.g. elastic drug pricing).<span style="mso-spacerun: yes;"> </span>This type
of real time scoring requires access to the actual transactional data which
means the model scoring engine should be collocated with the transactions to
meet transactional SLAs.<span style="mso-spacerun: yes;"> </span>Machine
Learning for z/OS includes the various tools and functions needed to train and
deploy machine learning models and automating machine learning workflows.<span style="mso-spacerun: yes;"> </span>It includes collaboration features for
personas such as data scientists and application developers.<span style="mso-spacerun: yes;"> </span>It also includes capabilities to determine
when models need to be tuned and advise changes.<span style="mso-spacerun: yes;"> </span>Through its web UI, RESTful APIs and
programming APIs, it provides a suite of functions to ingest all types of
zEnterprise data, transform and cleanse the data, train models with a selected
algorithm using the data, evaluate a trained model, select optimal models/algorithms
through the Cognitive Assistant for Data Scientist (CADS) interface, manage
models, deploy models into production, automate feedback to ingest new data and
re-train models, monitor model status and resource utilization, RESTful APIs to
call for online scoring with models, a data scientist notebook interface to use
machine learning APIs in interactive mode.</span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt;">IBM makes it possible for
customers to satisfy these requirements while benefiting from the latest
analytics advancement like Machine Learning for z/OS. They can access z Systems
data in place and combine that data with other sources of information, such as
structured and unstructured data from other systems. They can then build models
to predict customer behavior to make the most optimal business decisions. And
by accessing live data they can be more agile. This is exactly what our large
customers want to do.</span></span></div>
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IBM DB2 Analytics Accelerator?</span></u></b></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt;">The IBM DB2 Analytics
Accelerator for z/OS (the Accelerator) is a high-performance appliance for DB2
z/OS that deeply integrates Netezza balanced and highly parallelized asymmetric
massively parallel processing technology with IBM z Systems technology at the database
kernel level.<span style="mso-spacerun: yes;"> </span>The accelerator allows DB2
to offload data-intensive and complex static and dynamic DB2 queries (e.g. data
warehousing, business intelligence, and analytic workloads) to the accelerator
without any application changes. With the accelerator, these queries can be
executed significantly faster than was previously possible, while avoiding expensive
general purpose CPU (GP) utilization in DB2 for z/OS. The performance and cost savings
of the Accelerator opens up unprecedented opportunities for organizations to
make use of their data on the zEnterprise platform.<span style="mso-spacerun: yes;"> </span></span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt;">The analytics accelerator is
conceptually the same as a hybrid automobile.<span style="mso-spacerun: yes;">
</span>The hybrid automobile has a standard vehicle user interface (e.g.
steering wheel, brake, accelerator pedal).<span style="mso-spacerun: yes;">
</span>A hybrid automobile may at any given time run using its gasoline or
electrical power source to optimize fuel economy.<span style="mso-spacerun: yes;"> </span>The switching between power sources to
optimize fuel efficiency is done by the automobile itself without requiring
constant manual intervention by the user or a change in the standard vehicle
API’s.</span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt; mso-bidi-font-family: Times;">With
the DB2 Analytics Accelerator, DB2 for z/OS can offload data-intensive and
complex static and dynamic DB2 for z/OS queries, such as data warehousing,
business intelligence and analytic workloads, transparently to the application.
The DB2 Analytics Accelerator then executes these queries significantly faster
than previously possible—all while avoiding CPU utilization by DB2 for
z/OS.<span style="mso-spacerun: yes;"> </span>It allows users to run workloads
that historically were offloaded from z Systems, or run queries that were
governed or shunted in DB2 for z/OS such as ad hoc queries whose performance
characteristics are typically unknown at runtime. And IT administrators can
allow DB2 for z/OS to choose where to run these queries, or they can force
these queries to the DB2 Analytics Accelerator to prevent additional DB2 for
z/OS consumption.</span></span></div>
<ul>
<li><span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11pt; line-height: 115%;"><span style="mso-list: Ignore;"><span style="font-feature-settings: normal; font-kerning: auto; font-language-override: normal; font-size-adjust: none; font-size: 7pt; font-stretch: normal; font-style: normal; font-synthesis: weight style; font-variant: normal; font-weight: normal; line-height: normal;"></span></span></span><span style="font-size: 11pt; line-height: 115%;">The accelerator delivers dramatic
improvement in response time on unpredictable, complex, and long-running
dynamic and static query workloads.<span style="mso-spacerun: yes;"> </span>It
helps in meeting SLAs and shortening batch windows by offloading complex query
workloads.<span style="mso-spacerun: yes;"> </span>The idea is to keep what’s
working well in DB2 and improve response times for CPU intensive queries. </span><span style="font-size: 11pt; line-height: 115%;"> </span></span></li>
<li><span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11pt; line-height: 115%;">The accelerator allows users to
run new workloads that had previously not been considered for the MF or run
queries that had previously been governed or shunted in DB2 (e.g. Ad-hoc
queries whose performance characteristics are typically unknown at
runtime).<span style="mso-spacerun: yes;"> </span>Clients can allow DB2 to
choose where to run these queries, or they can force these types of queries to
the accelerator to prevent additional DB2 consumption.</span><span style="font-size: 11pt; line-height: 115%;"> </span></span></li>
<li><span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11pt; line-height: 115%;">By offloading resource intensive
queries and the associated processing onto the accelerator, clients can lower
MSU consumption.<span style="mso-spacerun: yes;"> </span>Additionally, they can
reduce the cost of storing, managing, and processing historical data with a near
line storage solution.</span><span style="font-size: 11pt; line-height: 115%;"> </span></span></li>
<li><span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11pt; line-height: 115%;">There is also the reduction in
costs associated with the time it takes to perform general tuning and
administration tasks associated with supporting and improving performance for
resource intensive workloads in DB2 for System z.<span style="mso-spacerun: yes;"> </span></span></span></li>
<li>
<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11pt; line-height: 115%;">Clients can also lower or
eliminate the cost of acquiring HW and SW for data warehousing and analytics as
well as lowering or eliminating the cost incurred from data movement,
transformation, landing, storage, and maintenance of systems.<span style="mso-spacerun: yes;"> </span>With the accelerator, clients can consolidate
disparate data to their existing zEnterprise platform while benefiting from
integrated operational BI.</span></span></li>
<li>
<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11pt; line-height: 115%;">With Accelerator-only tables and
in-DB transformation capabilities, data can be <b style="mso-bidi-font-weight: normal;">E</b>xtracted from a number of source systems, <b style="mso-bidi-font-weight: normal;">L</b>oaded into the Accelerator, and <b style="mso-bidi-font-weight: normal;">T</b>ransformed within the Accelerator (ELT).<span style="mso-spacerun: yes;"> </span>Applications directly access the transformed
data through DB2 for z/OS.<span style="mso-spacerun: yes;">
</span>Accelerator-only tables can be used to store transformed data ‘only’ in
the Accelerator and not maintain a second copy in z/OS.</span></span></li>
<li>
<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11pt; line-height: 115%;"><span style="mso-list: Ignore;"></span></span><span style="font-size: 11pt; line-height: 115%;">Increased organization agility by
being able to more rapidly respond with immediate, accurate information and
deliver new insights to business users.</span></span></li>
<li><span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11pt; line-height: 115%;"><span style="mso-list: Ignore;"><span style="font-feature-settings: normal; font-kerning: auto; font-language-override: normal; font-size-adjust: none; font-size: 7pt; font-stretch: normal; font-style: normal; font-synthesis: weight style; font-variant: normal; font-weight: normal; line-height: normal;"></span></span></span><span style="font-size: 11pt; line-height: 115%;">Reporting is consolidated on
zEnterprise where the majority of the data being analyzed lives, while
retaining zEnterprise security and reliability.</span></span></li>
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Accelerator Complement and Improve the Enterprise Data Lake Strategy?</span></u></b></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt; mso-bidi-font-family: Arial;">The
Analytics Accelerator was designed to be used in concert with DB2 z/OS with a
vision to become the first true Hybrid Transactional and Analytics Processing
Engine (HTAP).<span style="mso-spacerun: yes;"> </span>The Analytics Accelerator
was intended to be complementary to a zEnterprise data lake strategy and not
competitive.<span style="mso-spacerun: yes;"> </span>Several new features within
the Analytics Accelerator actually reduce the costs of data movement to the
data lake AND improve the data latency of the data that is landed in the data
lake.<span style="mso-spacerun: yes;"> </span></span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt; mso-bidi-font-family: Arial;">In
2017, two new features will further the Analytics
Accelerator’s ability to complement a zEnterprise data lake strategy.</span></span></div>
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<li><span style="font-family: "arial" , "helvetica" , sans-serif;"><b style="mso-bidi-font-weight: normal;"><span style="color: black; font-size: 11pt;">Transactional consistency
in the Analytics Accelerator:</span></b><span style="color: black; font-size: 11pt;"> With this feature, </span><span style="color: black; font-size: 11pt;">DB2 applications
will no longer need to be concerned with data currency within the Analytics
Accelerator: <span style="background-color: yellow;"><span style="mso-bidi-font-weight: normal;">the most current result
set will be guaranteed</span></span>. This removes the largest obstacle for much
broader use of the Analytics Accelerator. Today many customers hesitate to use
the Analytics Accelerator because they cannot guarantee that the queries can
tolerate potentially stale data.<span style="mso-spacerun: yes;"> </span><span style="background-color: yellow;"><span style="mso-bidi-font-weight: normal;">With this feature, there will be no
difference in latency between data returned by DB2 and by the Analytics
Accelerator</span></span></span><span style="color: black; font-size: 11pt;">.<span style="mso-spacerun: yes;"> </span>This will make DB2 + </span><span style="font-size: 11.0pt; mso-bidi-font-family: Arial;">the Analytics Accelerator</span><span style="color: black; font-size: 11pt;"> the only true Hybrid
Transactional and Analytics Processing Engine (HTAP) solution in the
market.<span style="mso-spacerun: yes;"> </span></span><span style="font-size: 11pt;"></span><b style="mso-bidi-font-weight: normal;"><span style="color: black; font-size: 11pt;"> </span></b></span></li>
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<li><span style="font-family: "arial" , "helvetica" , sans-serif;"><b style="mso-bidi-font-weight: normal;"><span style="color: black; font-size: 11pt;">Remove the cost of
replication to the Analytics Accelerator from the 4HRA:</span></b><span style="color: black; font-size: 11pt;"> When customers say '<i>we
can replicate to other environments</i>', there will be 2 major advantages with
the Analytics Accelerator.<span style="mso-spacerun: yes;"> </span>First is that
they cannot guarantee transactional consistency when replicating to a separate
environment (see above).<span style="mso-spacerun: yes;"> </span>Second; when
sending data to an external environment, replication and ETL has a cost on z/OS
on top of the standard People, Process, Infrastructure, Liability of Data
Breach costs from maintaining 2 copies with 2 separate access points.<span style="mso-spacerun: yes;"> </span>See our 'Cost of ETL' Calculator.<span style="mso-spacerun: yes;"> </span>With this feature, the cost of replication to
the Analytics Accelerator will be removed from the 4HRA.<span style="mso-spacerun: yes;"> </span>Any other replication or ETL to disparate
environments will impact the 4HRA and thus lead to additional costs.</span><span style="font-size: 11pt;"></span></span></li>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt; mso-bidi-font-family: Arial;">As
was mentioned above, the Analytics Accelerator also supports Accelerator Only
Tables (AoT’s).<span style="mso-spacerun: yes;"> </span></span><span style="font-size: 11.0pt;">With Accelerator-only tables and in-DB transformation
capabilities, data can be <b style="mso-bidi-font-weight: normal;">E</b>xtracted
from a number of source systems, <b style="mso-bidi-font-weight: normal;">L</b>oaded
into the Accelerator, and <b style="mso-bidi-font-weight: normal;">T</b>ransformed
within the Accelerator (ELT).<span style="mso-spacerun: yes;">
</span>Applications directly access the transformed data through DB2 for
z/OS.<span style="mso-spacerun: yes;"> </span>Accelerator-only tables can be
used to store transformed data ‘only’ in the Accelerator.</span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt;">What this all means is that </span><span style="font-size: 11.0pt; mso-bidi-font-family: Arial;">the Analytics Accelerator </span><span style="font-size: 11.0pt;">data will be transactionally consistent with DB2
data.<span style="mso-spacerun: yes;"> </span>The replication of data from DB2 to
</span><span style="font-size: 11.0pt; mso-bidi-font-family: Arial;">the Analytics
Accelerator</span><span style="font-size: 11.0pt;"> will be $0 cost.<span style="mso-spacerun: yes;"> </span>The Analytics Accelerator will support in
accelerator transformations of data.<span style="mso-spacerun: yes;">
</span>Therefore, data can be replicated to the accelerator, transformed to
match the structure of data in the data lake, and extracted with 0 latency from
the DB2 data without incurring any costs in DB2 AND without having to extract
to a ETL server in between to do the transformations.<span style="mso-spacerun: yes;"> </span>Such a solution avoids the high cost of
extraction of data from DB2 for System z, the cost of maintaining a set of ETL
servers and complex ETL flows (Test, Prod), the additional liability of data
breach from maintaining additional data copies and interfaces to these copies,
the latency in moving this data to disparate systems before landing to the data
lake, etc.<span style="mso-spacerun: yes;"> </span>Many customers are already
using federation technologies between DB2 + the Analytics Accelerator and the
data lake (Big SQL, Impala) to reduce data movement processes.<span style="mso-spacerun: yes;"> </span>With HTAP, $0 cost of replication to the
Analytics Accelerator, and AoT’s, the Analytics Accelerator is completely
complementary to the enterprise data lake strategy and reduces costs, liability
of data breach, and latency associated with getting data from System z to the
data lake.</span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><a href="https://www.blogger.com/null" style="mso-comment-date: 20170417T1309; mso-comment-reference: SK_1;"><b style="mso-bidi-font-weight: normal;"><u><span style="font-size: 14.0pt; mso-bidi-font-size: 12.0pt;">How Can Machine Learning and
the Analytics Accelerator Be Used?</span></u></b></a><span style="mso-bidi-font-weight: normal;"><span style="font-size: 14.0pt; mso-bidi-font-size: 12.0pt;"> </span></span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt;">The proposed solution
architecture, with Machine Learning for z/OS and the IBM DB2 Analytics
Accelerator at its core, is intended to drive substantial new analytics driven
revenue for clients while reducing existing people, process, and infrastructure
costs.<span style="mso-spacerun: yes;"> </span>This solution provides the
tooling to derive a tremendous amount of actionable insight from its
transactional data (monetize its transactional data), reduces existing costs by
reducing data/infrastructure sprawl across the enterprise, improves existing
Service Level Agreements (SLAs), reduces data latency for analytics
initiatives, improves data governance, etc.<span style="mso-spacerun: yes;">
</span>Ultimately, the goal of Machine Learning for clients is to take new,
transactional AI solutions to the market in an efficient and scalable
manner.<span style="mso-spacerun: yes;"> </span>In the case of a 'Transparent Pharmaceutical Benefits Manager (PBM)', machine learning
and the analytics accelerator can serve as the transactional analytics engine
that deliver new revenue opportunities to a consumer.<span style="mso-spacerun: yes;"> </span>Showcasing state of the art analytics and AI
solutions may also attract new PBM opportunities (e.g. marketing machine
learning based formulary and rebate management processes to earn new claims
adjudication business).<span style="mso-spacerun: yes;"> </span>Some examples of
opportunities for Machine Learning and the Analytics Accelerator in the PBM example are:</span></span>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><b style="mso-bidi-font-weight: normal;">Example 1: Health Outcomes Optimization; ex Diabetes</b></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="color: black; font-size: 11.0pt;">Most health conditions being treated have metrics associated with
success.<span style="mso-spacerun: yes;"> </span>Conditions can be segmented
into common chronic (i.e. diabetes, asthma, high cholesterol, high blood
pressure, heart disease, arthritis, etc.) and uncommon high cost/needing
specialty medications (i.e. RA, Crohns, multiple sclerosis, cancers).<span style="mso-spacerun: yes;"> </span>Diabetes has very clear metrics tied to
success (ABC:<span style="mso-spacerun: yes;"> </span>A1c = average blood sugar;
B=Blood pressure; and C = cholesterol).<span style="mso-spacerun: yes;">
</span>Unfortunately, payers and providers have limited views on the successful
metrics for a given population.<span style="mso-spacerun: yes;"> </span>A PBM can build out a predictive risk model to provide a health score for
patients with Diabetes and thereby segment the diabetes population into well
controlled, moderate control and poor control.<span style="mso-spacerun: yes;">
</span>By having this information available for real time analysis inside<span style="background-color: white;"> <span style="background-attachment: scroll; background-clip: border-box; background-image: none; background-origin: padding-box; background-position: 0% 0%; background-repeat: repeat; background-size: auto auto;">it’s Db2 adjudication system</span></span>, a PBM can enable its health plan clients to “treat/manage” these segments
differently – i.e. someone who is poorly controlled may receive additional
counseling at the pharmacy, have a different copay for the member or have a
different message to the physician.<span style="mso-spacerun: yes;"> </span>At
both the point of care in the doctor’s office and the point of sale, the PBM
would measure the adherence to medications.<span style="mso-spacerun: yes;">
</span>If someone is not at goal, and was not taking their medications
regularly, an adherence program could be implemented.<span style="mso-spacerun: yes;"> </span>If the patient was taking their medications,
then a more potent medication or a new medication may be needed. </span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="color: black; font-size: 11.0pt;">The value of doing this type of analysis to consumers is that the PBM can help patients meet clinical goals and drive lower copay's to the
consumer.<span style="mso-spacerun: yes;"> </span>For the physicians, this type
of analysis can be used to drive pay for performance programs.<span style="mso-spacerun: yes;"> </span>This analysis can also be used to drive value
between the health plans and pharmaceutical companies.<span style="mso-spacerun: yes;"> </span>By leveraging the concept of differential
rebates, this technology can help members achieve clinical goals.<span style="mso-spacerun: yes;"> </span>By increasing achievement in clinical goals,
the pharmaceutical companies get paid more, and the health care systems can
reduce costs.<span style="mso-spacerun: yes;"> </span>A PBM can monetize this by further aligning itself
with the health systems (increased value to the health system from better
clinical outcomes, more effective transactional scoring and auditing within
fast pass and e-Prior Authorization control processes, etc.) and potentially
driving increased revenue through its ‘prescription outcomes’ contracts. </span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><b style="mso-bidi-font-weight: normal;">Example 2: Major changes in “risk” - Resource Utilization Bands (RUBS)</b></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt;">The Johns Hopkins ACG uses
regression based modeling primarily from historical pharmacy and medical claims
to profile and predict risk for a population.<span style="mso-spacerun: yes;">
</span>Each member in a population receives a variety of risk scores.<span style="mso-spacerun: yes;"> </span>These patients are also lumped together into
1 of 6 RUBS (resource utilization bands) – no data, healthy, low risk, medium
risk, high risk and very high risk.<span style="mso-spacerun: yes;"> </span>As
the amount of data inputs increases beyond medical and pharmacy claims to
include behavioral data, care management data, EMR data, and consumer data, we
can use ML to more timely and accurately predict changes in risk.<span style="mso-spacerun: yes;"> </span>For example, 60% of people who take a chronic
medication have at least one 30 day gap in a<span style="mso-spacerun: yes;"> </span>year.<span style="mso-spacerun: yes;"> </span>Some resume the medication
after a few months whereas many stop altogether.<span style="mso-spacerun: yes;"> </span>ML techniques can be used to identify the
correlation between non compliance and hospitalizations for certain diseases
(i.e. for high cholesterol unlike to have correlation whereas for health
failure, likely to have high correlation).<span style="mso-spacerun: yes;">
</span>This machine learning based modeling helps identify potential causes for
changes to patient morbidity risk.<span style="mso-spacerun: yes;"> </span>The
machine learning modeling analyzes changes to Resource Utilization Bands across
consecutive periods and attempts to find correlations with a number of patient
related features.<span style="mso-spacerun: yes;"> </span>Clearly, uncovering
factors that may predict changes in morbidity risk can be used to alert
providers and health systems to potentially increasing morbidity risk and provide
possible interventions in reducing this risk.<span style="mso-spacerun: yes;">
</span></span>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="background-color: white;"><b style="mso-bidi-font-weight: normal;"><span style="background-attachment: scroll; background-clip: border-box; background-image: none; background-origin: padding-box; background-position: 0% 0%; background-repeat: repeat; background-size: auto auto;">Example 3:
Showcasing the Value of Machine Learning Driven Insight to Existing Clients<span style="mso-spacerun: yes;"> </span></span></b></span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt; mso-bidi-font-family: Arial;">With
the ability to access medical data files from existing customers, a PBM can:</span></span></div>
<ul>
<li><span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11pt;"><span style="mso-list: Ignore;"><span style="font-feature-settings: normal; font-kerning: auto; font-language-override: normal; font-size-adjust: none; font-size: 7pt; font-stretch: normal; font-style: normal; font-synthesis: weight style; font-variant: normal; font-weight: normal; line-height: normal;"></span></span></span><span style="font-size: 11pt;">Use ML
capabilities to show correlations (e.g. patient attributes and co-morbidity)
using medical/health data</span><span style="font-size: 11pt;"></span></span></li>
<li><span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11pt;">Apply Johns
Hopkins ACG functions to this data</span><span style="font-size: 11pt;"></span></span></li>
<li><span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11pt;">Show clients the
value of ML to clinical outcomes</span><span style="font-size: 11pt;"></span></span></li>
<li><span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11pt;">Integrate ML
features into the existing application (e.g. via a Bot)</span><span style="font-size: 11pt;"></span></span></li>
<li><span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11pt;">Sell this new
application as a service to clients </span><span style="font-size: 11pt;"></span></span></li>
</ul>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt;">Other
clients may have other interesting data sources.<span style="mso-spacerun: yes;"> </span>For example, some customers may engage </span><span style="font-size: 11.0pt; mso-bidi-font-family: Arial;">human coaching companies
who have a wealth of data, interactivity with member, and a wealth of
asynchronous communications that can be leveraged in Machine Learning modeling.</span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><b style="mso-bidi-font-weight: normal;"><span style="font-size: 11pt;">Example 4: Fast Pass, e-Prior
Authorization, Alternative Drug Recommendation<span style="mso-spacerun: yes;"> </span><span style="background: yellow; mso-highlight: yellow;"></span></span></b></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt;">This is a case where the drug is covered
(i.e. it is the preferred drug).<span style="mso-spacerun: yes;"> </span>Machine
Learning can be used to determine cases additional situations where fast pass
is appropriate vs additional controls.<span style="mso-spacerun: yes;">
</span>The second case is related to the process of recommending alternative
drugs that require the pharmacy to contact the provider.<span style="mso-spacerun: yes;"> </span>The third case exists within the e-Prior
Authorization control mechanisms.<span style="mso-spacerun: yes;"> </span>Again,
streamlining these processes and determining where additional controls make sense
(or do not make sense) is something that Machine Learning models can help obviate.<span style="mso-spacerun: yes;"> </span>This is value add to the pharmacy, the
providers, the consumers, and the health plans.</span><b style="mso-bidi-font-weight: normal;"><span style="font-size: 14.0pt;"></span></b>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><b style="mso-bidi-font-weight: normal;"><span style="font-size: 11pt;">Example 5: Drive new
revenue at Hospital Systems</span></b></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt; mso-bidi-font-family: Arial;">There are several immediate potential opportunities
that exist within small to medium hospital health systems using Machine
Learning.<span style="mso-spacerun: yes;"> </span></span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt; mso-bidi-font-family: Arial;">The first opportunity is with employee health at
these hospital systems.<span style="mso-spacerun: yes;"> </span>Small to medium
systems may have 50K employees.<span style="mso-spacerun: yes;"> </span>In the
case of employee health, every 10K employees represents $100M in employee
spend.<span style="mso-spacerun: yes;"> </span>Machine learning driven insight
can be used to show these hospital systems how a PBM can help save 5-7% on
employee health costs and improve the qualities of service for its employees. <span style="mso-spacerun: yes;"></span></span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt; mso-bidi-font-family: Arial;">The second potentially large opportunity is to use
machine learning to help hospital systems optimize revenue for specialty
products.<span style="mso-spacerun: yes;"> </span>A transparent PBM typically wants to align with the hospital systems.<span style="mso-spacerun: yes;"> </span>For example, there are cases where hospital
systems are treating patients that require expensive drugs (MS, HIV).<span style="mso-spacerun: yes;"> </span>Historically, some of these health systems
started prescribing the drugs and sending them out to a 3rd party who would
handle the filling of the medication.<span style="mso-spacerun: yes;">
</span>These drugs often represented $50K of medication.<span style="mso-spacerun: yes;"> </span>This presents an </span><span style="font-size: 11.0pt; mso-bidi-font-family: Arial;">opportunity for the PBM to showcase what they can do as a
partner and sell new core services to the hospital health system.</span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt; mso-bidi-font-family: Arial;">Smaller hospital health systems may also be more
interested in population health management.<span style="mso-spacerun: yes;">
</span>For instance, understanding the factors that lead to some people taking
medications and others skipping or not filling their medication.<span style="mso-spacerun: yes;"> </span>Machine Learning is key to uncovering factors
that humans may not have previously considered.</span></span></div>
<br />
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><b style="mso-bidi-font-weight: normal;"><span style="font-size: 11pt;">Example 6: Drive Value
to Retail Clinics/Stores</span></b></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: 11.0pt; mso-bidi-font-family: Times;">Promote patient medication adherence using other
financial motivators such as free co-pay cards to use with retail pharmacy’s or
retail grocery store coupons for health food options.</span><span style="font-size: 15pt;"></span></span></div>
Shantan Kethireddyhttp://www.blogger.com/profile/05506577167753584974noreply@blogger.com2tag:blogger.com,1999:blog-1062056614200108741.post-72445480443843335022017-06-17T07:47:00.000-07:002017-07-17T16:59:51.050-07:00CSI: DB2 Historical Data Forensics On Demand for Audit Defense<style>
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</style>Imagine that you get audited by the IRS for claiming a large
business loss due to your online retail business facing some unforeseen
competition. You try to recall the details of all your business expenses such
as the times you used your car and home for business purposes. You wish
you had kept log records of all your business activities neatly organized and
indexed on your computer for quick analysis. instead, you attempt to
cobble the details together to put forth some semblance of proof. Every
detail that you cannot prove costs you money.<br />
<br />
Now imagine you are the Risk Officer at a $30 Billion/year
Enterprise that services some of the most sensitive transactional data in the
world. This could be Social Security numbers, medical records/lab results,
credit card numbers, account balances. Changes to this data are under
constant scrutiny by regulatory bodies in each industry sector. Many
organizations devote significant financial and technical resources on risk
management. For example, internal governance rules may require housing
20+ years of historical records in case of a law suit. Audits related to
government regulations (HIPAA, SEC Rule 17a-4) may not only require maintenance
of historical data, but also a view of all data changes. In order to do
this, organizations may:<br />
<ul>
<li><span style="mso-fareast-font-family: "Times New Roman";">Transform
all transactional ‘Update’ operations into ‘Insert’ and ‘Delete’ pairs to
retain before-and-after images of records. </span></li>
<li><span style="mso-fareast-font-family: "Times New Roman";">Employ
procedural code (e.g. triggers) to keep track of changes. </span></li>
<li><span style="mso-fareast-font-family: "Times New Roman";"></span><span style="mso-fareast-font-family: "Times New Roman";">Create
copies of the historical data on external systems which may increase the
liability of data breach and lead to additional costs related to data copying,
transformation, storage, and maintenance. </span>
</li>
</ul>
<div class="MsoNormal">
<br /></div>
Performing these tasks may cost Millions in yearly costs
associated with additional transactions, additional procedural computing,
increased storage, copying data to external environments, etc. <b>But what if
there was a way to:</b><br />
<ul>
<li><span style="mso-fareast-font-family: "Times New Roman";">Keep
an entire history of changes to the data without manually changing the
transactions themselves (i.e. without requiring code to transform updates into
insert/delete pairs)</span></li>
<li><span style="mso-fareast-font-family: "Times New Roman";"></span><span style="mso-fareast-font-family: "Times New Roman";">Automatically
maintain beginning and end timestamps for each row of data where the timestamps
indicate the “life” of the data (i.e. without requiring procedural code)</span><span style="mso-fareast-font-family: "Times New Roman";"> </span></li>
<li><span style="mso-fareast-font-family: "Times New Roman";">Access
and analyze this data via the transactional systems (without impacting
resources on these transactional systems)</span></li>
<li><span style="mso-fareast-font-family: "Times New Roman";">C</span><span style="mso-fareast-font-family: "Times New Roman";">reate
a snapshot of the data as it existed at any point in time or range(s) of time
with massive parallelism (without creating separate data connections and
credentials </span> </li>
</ul>
All of these "data forensic" enabling features are
made possible on System z through two technologies. The first is a
capability within DB2 for z/OS called Temporal Tables. The second is
through a technology called the IBM DB2 Analytics Accelerator (The
Accelerator).<span style="mso-spacerun: yes;"> </span>Please see the following <b style="mso-bidi-font-weight: normal;">paper (published soon)</b> for details on
using Temporal tables and the Accelerator for 'Historical Data Forensic'
capabilities On Demand!
Shantan Kethireddyhttp://www.blogger.com/profile/05506577167753584974noreply@blogger.com0tag:blogger.com,1999:blog-1062056614200108741.post-39563565629892870922017-06-15T18:37:00.000-07:002017-07-17T16:59:51.030-07:00New opportunities to drive analytics value into business operations: IBM DB2 Analytics AcceleratorToday, many System z clients are using the IBM DB2 Analytics Accelerator (the Accelerator) to help their organizations gain even greater insight and value from their data. Organizations can offload data-intensive and complex DB2 for z/OS queries to the Accelerator in order to support data warehousing, business intelligence and analytic workloads. The Accelerator executes these queries quickly, without requiring CPU utilization by DB2 for z/OS. The Accelerator is a logical extension of DB2 for z/OS, so DB2 manages and regulates all access to the Accelerator. DB2 for z/OS directly processes relevant workloads, such as OLTP queries and operational analytics. Queries that run more efficiently in a massively parallel processing (MPP) environment are seamlessly rerouted by DB2 for z/OS to the Accelerator. There is one set of credentials that is governed by RACF security, and all access flows through DB2 for z/OS. Users often first see the business value of the Accelerator in handling long-running queries, but many are also finding that the Accelerator can drive cost savings in areas such as administration, storage and consolidation as well as delivering real-time analytics. <br /><br />
This <a href="https://public.dhe.ibm.com/common/ssi/ecm/im/en/imw14875usen/IMW14875USEN.PDF" target="_blank">white paper</a> discusses how organizations can improve analytic insight with the IBM DB2 Analytics Accelerator. It offers guidance to help organizations more quickly uncover new opportunity areas where the Accelerator can have the greatest impact. The paper covers topic areas including: <br /><br /> • Accessing enterprise data in place <br /> • Gaining advocates from IT, application teams and Lines of Business <br /> • Uncovering and expanding opportunities for the DB2 Analytics Accelerator <br /> • Measuring the business value of the DB2 Analytics Accelerator <br /> • Case studies <br /> • The potential for the DB2 Analytics Accelerator to provide even greater ROI Shantan Kethireddyhttp://www.blogger.com/profile/05506577167753584974noreply@blogger.com0tag:blogger.com,1999:blog-1062056614200108741.post-71531076763412199092015-09-14T18:31:00.003-07:002017-07-17T16:59:51.046-07:00z Analytics Business Value Validation MethodologyAre you considering an investment in a z Systems Analytics solution? How will you evaluate the Return on Investment (ROI) that will be realized using this solution? Does the measurement of 'Return' align with your business objectives? The z Systems 'Business value validation workshop' offered by IBM will validate both technically and financially if/how a z Systems centric solution can help you meet your key business objectives. Typical business objectives include cost savings, cost avoidance, new customer value, customer satisfaction, reduced liability, increased security. <br />
<br />
For example, a fictional company 'Acme Systec' is focused on reducing costs and reduce data sprawl. This assessment would be used to explore the savings, efficiencies, and new value that can be gained by reducing data sprawl within Acme Systec's IT infrastructure through use case definition, requirement gathering, technical validation and a cost benefit analysis. The workshop would focus on Acme Systec's specific environment and business requirements, forging a partnership between the application teams, infrastructure teams, and key decision makers. The application teams provide relevant insight into use cases and business usage, while the infrastructure teams provide insight into current costs and technical configurations. The workshop recommendations provide a holistic approach to both technical architecture improvement and financial cost reduction. <br />
<br />
The following link contains a sample offering focused on determining the cost savings that can be realized through DB2 z/OS + the IBM DB2 Analytics Accelerator: <a href="https://www.ibm.com/developerworks/community/blogs/42acc52f-ec39-4667-867e-9404d4f53bd0/entry/IBM_DB2_Analytics_Accelerator_Cost_Benefit_Analysis_of_Consolidation_Workshop?lang=en" target="_blank">IDAA Cost Benefit Analysis Link</a>. For more information about the Business Value Validation Methodology for z Systems, please contact your local IBM z Systems sales specialist.<br />
<br />
-ShantanShantan Kethireddyhttp://www.blogger.com/profile/05506577167753584974noreply@blogger.com0tag:blogger.com,1999:blog-1062056614200108741.post-61458485473997231292015-09-11T18:13:00.000-07:002017-07-17T16:59:51.041-07:00Could your analytics strategy cost your business USD 100 million?<h3>
<b>How new technologies can help protect your analytics data and your bottom line</b></h3>
<br />
Technology trends and forces such as cloud, mobile and big data can represent big opportunities to bring analytic insight to the enterprise. They can also represent big risks if proper data security and governance controls are not in place. In 2015, one of the largest health benefits companies in the United States reported that its systems were the target of a massive data breach. This exposed millions of records containing sensitive consumer information such as social security numbers, medical IDs and income information. Various sources, including The Insurance Insider, suggest that this company's USD 100 million cyber-insurance policy would be depleted by the costs of notifying consumers of the breach and providing credit monitoring services—and that doesn’t consider other significant costs associated with a breach such as lost business, regulatory fines and lawsuits. <br />
Data is now so important that it is has a value on the balance sheet. Cyber criminals know this. Without exception, every industry has been under attack and suffered data breaches – healthcare, government, banking, insurance, retail, telco. Once a company has been breached, hackers focus on other companies in that same industry to exploit similar vulnerabilities. In 2015 the average cost of a data breach was US$ 3.79M, causing long term damage to the brand, loss of faith and customer churn. <br />
As you think about the impacts of this and other data security breaches occurring at organizations worldwide, consider this question: how exposed is your business to a similar type of breach? To answer this question, you must first ask, “Where does the data that feeds our analytics processes originate?”<br />
<br />
See my full paper <a href="http://www.ibm.com/common/ssi/cgi-bin/ssialias?subtype=WH&infotype=SA&htmlfid=IMW14836USEN&attachment=IMW14836USEN.PDF" target="_blank">here</a> <br />
<br />
-ShantanShantan Kethireddyhttp://www.blogger.com/profile/05506577167753584974noreply@blogger.com1