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Your Competitive Advantage: The IBM Client Center for Advanced Analytics (CCAA)TSB-7171CCAA Speakers:

Magesh “Max” Narayanan, Lead Solution Architect

Joe Palaia, Data Scientist

Brian Van Bibber, Business Solutions

© 2014 IBM Corporation

Agenda

• Introduction to the Client Center for Advanced Analytics

• Analytics and Insight – deriving business value

• Case Studies and Demo – using SPSS and BigInsights

• Data, Capabilities and Infrastructure – bringing it all together

• Getting Started with CCAA

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IBM Client Center for Advanced Analytics

• Established in 2011 CCAA provides services based on client demand:

� Domestic analytics services for both advanced analytics as well as foundational data management.

� Partner to support a client analytics journey from strategy through implementation

� Provider of products, technology and hosting options to explore analytic capabilities

� Talent in advanced analytics, visualization, big data, cloud and integration

� Resources for integrated and accelerated delivery

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Analytics and Insight

Deriving Business Value

Discovering the value to the business using analytics

• The focus and understanding of a business challenge in the form of a question; is a good way to carve out a use case and applying analytics to answer the question.

• The questions vary across industries and even across companies in the same industry

• How can I predict policy cancellations? What can I do to prevent them from happening?

• How can I measure the effectiveness of my fall product line marketing campaigns? And what can I do to make them more effective?

• How can I improve Supply Chain for my business? Faster movement of product? Right product? Right location?

• What does consumer group want 5 years from now? How will they be shopping? Should I continue to invest in my existing platform or move to self-service?

Overcoming challenges to answer the business

Challenges:

• New more complex data has emerged and is being generated at rates never seen before

• Social network data, web logs, archived data and sensor data are all new data sources of attracting analytical attention

• Data and analytical workload complexity is growing

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Netting it out...

Identify Use Case(s)

Determine Business Value Measurement

Identify the data needed to analyze

Ingest Data Model Data AnalyzeAssess Findings

MeasureImpact and

Identify Plan to Scale

Case Studies and Demo

Using SPSS and BigInsights

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Proof of Value Engagement Lifecycle

• Data understanding and

business case alignment

• Environment Setup (typically a

2-5 day setup depending on

the requirements)

• Data ingestion and loading

into the environments as

needed (i.e. Netezza, DB2,

BigInsights)

• Data Mapping

• Detailed analysis and

validation

• Perform detailed iterative

data analysis jointly with

Client team

• Creating of reports, models

• Populate & load data list

• Conduct joint sessions to

review output and modify as

necessary

• Prepare final report

• Review final report with client

team

• Data Assessment

• Use Case Definition

• Data Dictionary for the data source

• Preliminary data analysis results

• POC Final ReportKEY DELIVERABLES

1-2 weeks 4 weeks 1-2 weeks

Project Kick-off Review Point Review Point Final presentation

Planning Phase Analysis & Modeling Phase Completion Phase

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The Engagement Lifecycle scope and duration will vary by project based on scope, complexity and business outcome measurement. The following outlines a typical 3 phase approach for Proof of Value Engagement.

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Large Automotive Manufacturer expanding business and operations required a deep assessment of how to best apply Big Data and Analytics for optimization and predictive maintenance.

Business problem: Leading Automotive Manufacturer is in need of improving maintenance and operations of the overall assembly line and wanted to understand how can analytics and big data help identify problematic areas and how and when to take action.

Solution: IBM conducted Business Value Assessment that resulted in 5 core areas of business that could be improved by applying analytics with a Roadmap to address the areas beginning with PoC’s to solution roadmap for implementation. Through CCAA the first PoC as part of the Pilot program is underway applying SPSS and PMQ. It is a collaborative team and is focused on the use of Predicative Monitoring and Assessment to improve operations in General Welding.

Predictive analytics and assessment leveraging existing data to predict and prevent assembly block and holds.

An Analytics Platform utilizing SPSS and Predictive Maintenance Quality assets.

Predictive

Maintenance

and Quality

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Leading Pharmaceutical Distributer increases profit through a Signature Savings solution.

Business problem: Client challenge was to improve profitability based on non-generic and generic drugs in prep for annual sales meeting.

Solution: The IBM team (SWG, GBS, CCAA) put a system in place for them called Signature Savings this demonstrated a profit increase of over $5m. We are now working on numerous initiatives launched to address additional areas of savings to essentially apply and replicate the model of savings.

A Signature Savings Solution demonstrating a profit increase of over $5m.

A commerce platform that will improve brand position and drive stronger sales, as well as improve marketing, campaign management and customer service and intimacy.

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Insurance Company can now apply statistical and predictive models to predict cancellation behaviors and extract business insights to increase policy holder retention..

Business problem: Inability to utilize existing data to apply analytics to build at-risk customer models. Identified the need for analytics and need to secure funding for larger IBM-driven transformation project

Solution: CCAA and the IBM Insurance Industry SME’s guided the customer to a pragmatic approach to utilize 9 years of mixed historical data sets to build early warning models of customers at risk of attrition. The client received a set of actionable models that forecast what customers, by segment, are most at risk of cancelling their policies.

Big Data analytics to identify patterns of attrition to improve customer retention

IBM’s Big Data platform processed a large amount of historical data from various sources leveraging Big Insights w/Hadoop and SPSS for predictive and statistical analytics.

Big Data

A demonstration on analytics for client engagement

• Based on the shared client cases the following of what is happening behind the scenes such as:

� Data Ingestion – the landing zone

� BigInsights – Hadooprunning on Softlayer

� SPSS – models and use of IBM’s Action Clusters

� Cognos – Reporting and Visualization of insights

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Data, Capabilities and Hosting

Bringing it all together

Pulling it all together on Softlayer…

• CCAA Hosts products, technology and assets on Softlayer and leverages traditional hosting for appliances.

Analytics on the CloudAnalytics on the Cloud

Traditional Hosting Traditional Hosting

IBM GTS Cloud Marketplace

IBM DST Hosted

Environments

Getting Started with CCAA

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Rapid Analytics Program

A program provided to quickly on-board clients to execute on analytics, two options of engagement:

�One-Day Visioning Workshop: Explore the “art of the possible”, identify and prioritize business outcomes that Big Data and Analytics can help the client achieve. Develop high-level Roadmap and practical next steps.

�Quick Hit Analytics: Provides the opportunity to clients to prove out the value proposition of Big Data and Analytics using their data, along with IBM Industry Expertise and technology and software.

What is it?

When to use it?

What is the value?

How does it work?

� Explore the applicability of Big Data and

identified areas of business value

� Apply the technology, subject matter expertise

hosted by IBM to confirm business outcome

� Support a transformational plan that includes

transition or application of Big Data and analytics

Gather and send a sample data set to

CCAA for processing and analysis

Work in collaboration with the IBM

Industry Teams and top analytic

resources in CCAA on applying IBM

technology and leveraging the IBM

environments to execute on the PoC

Review and confirm results as a team.

Ensure business goals are met and that

the strategic placement of this capability

in the enterprise is understood.

Identify the question or business

challenge that needs to be addressed

through scoping and planning

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Rapid Analytics Program - Advantages

� Leverage IBM analytics “know-how”� Agile methodology approach� Benefit from cross-industry experience to pick the

right use cases� Gain immediate access to latest technologies,

analytical techniques & methods, and unique data sources

� Alleviates organizational stress and distractions for executing analytical projects

Expertise & Methodology

Cloud and Softlayer Services

Capabilities

Data

� Makes high volume data handling available as necessary

� Deep cycle computing available as needed on CPU-expensive data preparation and/or model building

� Model building in SPSS / R allow for commonly deployed tools to catch work

� Accelerated Time to Value and differentiated outcomes

� An acutely focused environment for incubating and testing new concepts and strategies

� Increased collaboration and ideation� Provides factory approach for sourcing, confirming

and supplying data for downstream consumption� Collaborative teaming to identify and grow skill and

resources to support business needs� Access to IBM Global Research teams for

innovation and unique capabilities for differentiation

� Leverages SoftLayer to make environment spin up easy and elastic to meet your needs

� Creates opportunities for you to keep running analytics on an on-going basis

� Leverages IBM’s DST environment for Appliance hosting integrated with Cloud

� Use the data you already have in new and innovative ways

� While you need to explain your data to us you do not need to have to worry about schemas

The Rapid Analytics Results Program

• Outcome based Analytics “Quick Hit” program• 4 – 8 week duration, depending on Scope• Utilizing

• Customer’s data• IBM’s Existing Environment• IBM’s Technology• IBM’s Talent on the Ground

Which customers should I target?

How do I get my equipment to last longer?

What is causing my

attrition levels to rise?

What’s the right product mix for

maximum profit?

How do I lower my inventory levels without

negative results?

How do I understand my

customer better?

The Rapid Analytics Results Program

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Use Case Selection

Use Case 1

Use Case 2

Use Case 3

Selected Use Case

Visioning Workshop

Analytics Outcome

Resources / Env’t

Complexity

Scoping Session

Raw Data

SoftLayer

Send the Data

Statement of Work

RankingComplexity• Deployment• Skill Set• AdoptionValue • Cost Avoidance• Gross Margin• Throughput

Filter• Executive

Sponsorship• Level of Business

Value• Level of Impact

The “Quick Hit”

Client Center for Advanced AnalyticsColumbus, Ohio

After the Quick Hit

• Continuing Run Services – Analytics Foundry• Scheduled Data Feed• IBM Runs the Model & Returns the Results

• Analytics Partnership• The next Outcome Based Project(s) (“Quick Hits”)• Potential Foundry Opportunity for Future Projects

• Foundational Analytics Bundles• ETL & MDM• Warehouse Architecture & Support• Dashboarding and Visualization

Technology Tools Talent

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Brian Van BibberBusiness Solutions Leader

brian.vanbibber@us.ibm.com614-230-4193

Max NarayananSolution Architect

maxnarayanan@us.ibm.com614-339-9928

Teresa HamidChief Technology Officer

teresah@us.ibm.com614.315.5976

Thank You

Color Palette

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