Let Big Data connect the dots in your business

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Let Big Data connect

the dots in your business

Big Data Conven-on -‐ September 25, 2014 – Golden Tulip Brussels Airport

Falke Van Onacker

Segment Leader for Big Data Analy4cs

IBM SoIware Group – Belgium & Luxembourg

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4 Pop up slides

CuNng-‐Edge « Freemium » soIware for YOU



 

Go to

hVps://www.ibm.com/analy-cs/watson-‐analy-cs/sign-‐up/

index.html?SOURCE=default&S_CMP=az-‐neo

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6 Many Industries are in the middle of all kinds of Hurricanes

Fiat Chrysler’s boss, Sergio Marchionne, is worried that

it will cost his company money to

“provide a venue to host other people’s par=es”

Source: The Economist – “The Connected Car” September 6, 2014

Digital disrup-on is now in full bloom at European, Australian

newspapers

If newspaper companies cannot produce suﬃcient revenues from digital, if they cannot

produce exci4ng, engaging oﬀerings for both readers and adver4sers, they are des4ned to

oﬀer mediocre products with

nothing to diﬀeren=ate

them from the mass of faux news.

Source: « Poynter » -‐ online ar4cle June 9, 2014

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7 Automotive & Transport Industries are in the middle of all kinds of Hurricanes

Copyright by Boston Consul4ng Group

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12 !

Operations Analysis

"

!

Only vendor combining at-rest

vehicle data with real time

data-in-use from vehicles for single,

integrated view and analysis

within and outside of Hadoop

environment

• Predict demand for replacement

parts and service

• Monetize telematics data

• Provide drivers assistance

Advanced Condition

Monitoring

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13

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14 Introduction – IBM Big data architectural overview

Paradigm shifts enabled by big data

Leverage all data being captured

Reduce effort to leverage data

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15 Big Data is Changing the Value Equation

Analyzing MORE Data will provide MORE Value

Cost outweighs value of

analyzing more data

Jump in value curve from

new data sources and types

Reduction in incremental costs from

new Big Data technologies

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16 of every big data & analytics project is spent

Finding

Data

Understanding

Data

Cleansing

Data

Integrating

Data

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17 Actionable insight

Data Marts

Data types

Transaction and application data Predictive analytics and modeling Reporting and analysis

Operational

systems

Archive

Enterprise

Warehouse

Staging area

Better information through transformation

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18 Actionable insight

Reporting &

interactive

analysis

Data types

Operational

systems

Archive

Enterprise

Warehouse

Staging area

Better information through transformation

Leverage column-store and in-memory capabilities to improve performance and enable reporting &

analysis directly against operational data

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19 Actionable insight

Reporting &

interactive

analysis

Deep

analytics &

modeling

Data types

Operational

systems

Archive

Enterprise

Warehouse

Staging area

Better information through transformation

Provide dedicated analytics processing for faster, deeper analysis

and modeling

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20 Actionable insight

Exploration and

landing

Trusted data

Reporting &

interactive

analysis

Deep

analytics &

modeling

Data types

Transaction and application data Enterprise content Social data Image and video

Third-party data Predictive analytics and modeling Reporting, analysis, content analytics Discovery and exploration

Operational

systems

Archive

Better information through transformation

Leverage Hadoop to capture operational data, leverage additional data types and enable exploration

of data prior to normalization

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21 Actionable insight

Exploration,

landing and

archive

Trusted data

Reporting &

interactive

analysis

Deep

analytics &

modeling

Data types

Transaction and application data Enterprise content Social data Image and video

Third-party data Predictive analytics and modeling Reporting, analysis, content analytics Discovery and exploration

Operational

systems

Archive

Better information through transformation

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22 Actionable insight

Exploration,

landing and

archive

Trusted data

Reporting &

interactive

analysis

Deep

analytics &

modeling

Data types

Real-time processing & analytics

Transaction and application data Machine and sensor data Enterprise content Social data Image and video

Third-party data Decision management Predictive analytics and modeling Reporting, analysis, content analytics Discovery and exploration

Operational

systems

Better information through transformation

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23 Information Integration & Governance

Actionable insight

Exploration,

landing and

archive

Trusted data

Reporting &

interactive

analysis

Deep

analytics &

modeling

Data types

Real-time processing & analytics

Third-party data Decision management Predictive analytics and modeling Reporting, analysis, content analytics Discovery and exploration

Operational

systems

Information

Integration

Data Matching

& MDM

Security &

Privacy

Lifecycle

Management

Metadata &

Lineage

Better information through transformation

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24 Information Integration & Governance

Exploration,

landing and

archive

Trusted data

Reporting &

interactive

analysis

Deep

analytics &

modeling

Data types

Real-time processing & analytics

Third-party data

Operational

systems

Actionable insight

Decision management Predictive analytics and modeling Reporting, analysis, content analytics Discovery and exploration

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25 Information Integration & Governance

Logical Data Warehouse

Exploration,

landing and

archive

Trusted data

Reporting &

interactive

analysis

Deep

analytics &

modeling

Data types

Real-time processing & analytics

Third-party data

Operational

systems

Actionable insight

Decision management Predictive analytics and modeling Reporting, analysis, content analytics Discovery and exploration

The Logical Data Warehouse

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26 Information Integration & Governance

INFORMATION SERVER, MDM, G2, GUARDIUM, OPTIM

Exploration,

landing and

archive

Trusted data

Reporting &

interactive

analysis

Deep

analytics &

modeling

Data types

Real-time processing & analytics

_{INFOSPHERE STREAMS}

Third-party data

Operational

systems

INFOSPHERE BIG INSIGHTS DB2, INFORMIX PUREDATA TRANSACTIONS PUREDATA ANALYTICS PUREDATA ANALYTICS

Actionable insight

Decision management Predictive analytics and modeling Reporting, analysis, content analytics Discovery and exploration SPSS MODELER COGNOS BI COGNOS TM1 SPSS MODELER GOLD

IBM Big Data & Analytics offerings

PUREDATA ANALYTICS

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4

3

5

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2

3

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5 More than Hadoop

Greater resiliency and recoverability

Advanced workload management & multi-tenancy

Enhanced, flexible storage management (GPFS)

Enhanced data access (BigSQL, Search)

Analytics accelerators & visualization

Enterprise-ready security framework

Data in Motion

Enterprise class stream processing & analytics

Analytics Everywhere

Richest set of analytics capabilities

Ability to analyze data in place

Governance Everywhere

Complete integration & governance capabilities

Ability to govern all data where ever it is

Complete Portfolio

End-to-end capabilities to address all needs

Ability to grow and address future needs

Remains open to work with existing investments

3

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28 IBM Client Value Engagement

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29 CVE for Big Data Analytics: Methodology

Identify Technical &

Business Initiatives

Determine

Current

State

Process &

Costs (As-Is)

Determine

Future State,

Process &

Costs

(To-Be)

Technical

Solution

Blueprint

CVE

Final Results

+ Definition

of Scope for

PoC

Define & Identify

technical & business

Problems / Challenges

Identify Future Process

& Costs with the

Solution (

To-Be

)

CVE

Engagement

Summary &

Final Analysis

Identify Current State,

Process & Related

Costs (

As-Is

)

Measure the

Difference Between

As-Is & To-Be

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30 CVE Roles: Client Participation

Required Client Roles

Role Description

Client Executive Sponsor

 

Details top client organizational priorities

 

Provides high-level view of top organizational challenges

 

Supplies key decision-making criteria (In scope solution)

Client CVE Coordinator

 

Responsible for scheduling interviews,

 

Resolves and/or elevating any client process issues

 

Helps to facilitate any challenges during the CVE process

 

Communicates the Executive Sponsor’s top priorities

Client Interview Roles:

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CVE Offering:

Time to Value

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CVE Offering:

Reduced Infra. Costs

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CVE Offering:

Value of New Data Sources

Business Level Discussions

 

Business Analysts

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Depending on Analysis Business Area (Representatives from)

–  Marketing –  Finance –  Sales –  Security –  Accounting

IT Level Discussions

–  CIO, Enterprise Architects, Application Managers: –  Database Administrator & System Administrator –  Analytic Modelers, BI Developers, Report Writers –  ETL and DW Developers

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31 Big Data Analytics CVE: Timeline

Tasks

Week 1

Week 2

Week 3

Week 4

Client Introduction

Develop CVE Charter

Conduct CVE Interviews

Validate CVE Analysis + Define

PoC Scope

Final Presentation

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Time estimates are adjustable based on client time lines & priorities

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The average CVE is completed in 2-3 weeks from start to finish



 

Average client resource time is 2-4 hours



 

CVE requires minimal client resource time

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Q

UESTIONS ?

Falke Van Onacker

Segment Leader for Big Data Analy4cs

IBM Belgium & Luxembourg

[email protected]

Mobile: 0032/496.57.59.13

Let Big Data connect the dots in your business