Cloud and business agility

Driving Business Agility Through Cloud-based Analytic Innovation

Michael O’RourkeIBM Cloud Data Services

© 2015 IBM Corporation

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"Without big data analytics, companies are blind and deaf, wandering out onto the web like a deer on a freeway." - Geoffrey Moore

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Business Agility Through Data

Requirements Based Top-Down Design Integration and Reuse Competence Centers Better Decisions Enterprise Focus

Opportunity-Oriented Experimentation Throwaway Hackathons Business Innovation Functional Focus

Traditional Big Data

Glo

bal

Dat

a V

olu

me

in E

xab

ytes

Sens

ors

(Inte

rnet

of T

hing

s)

Multiple sources: IDC,Cisco

100

90

80

70

60

50

40

30

20

10

Agg

rega

te U

nce

rtai

nty

%

VoIP

9000

8000

7000

6000

5000

4000

3000

2000

1000

0

2005 2012 2017

By 2017 the number of networked devices will be more than double the entire global population.

Social Media

(video, audio and text)

The total number of social media accounts exceeds the entire global population.

Enterprise Data

The uncertainty is growing alongside data complexity

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To manage risk and create agility, embrace all data….uncertainty of new information is growing alongside its complexity

Volume Variety Velocity

Data at Scale

Terabytes topetabytes of data

Data in Many Forms

Structured, unstructured, text,

multimedia

Data in MotionAnalysis of

streaming data to enable decisions

within fractions of a second

Veracity

Data Uncertainty

Managing the reliability and

predictability of inherently imprecise

data types

001010110101010101010010101010101010101010101010101011101000101010101010101110101010101101010101110101

001010110101010101010010101010101010101010101010101011101000101010101010101110101010101101010101110101

Organizations that embrace data in all its forms can unlock greater insight

Analyze Data

(Integrate & Interconnect)

Apply Insight

(Intelligent Outcomes)

Capture Data

(Instrument)

Identify Patterns

(Unlock Insight)

But, Most of the data you might need… you do not own

60% of determinants of health Volume, Variety, Velocity, Veracity

30% of determinants of healthVolume

10% of determinants of health

Variety

Clinical data

Genomics data

Exogenous data(Behavior, Socio-economic,

Environmental, ...)

1100 Terabytes Generated per lifetime

6 TBPer lifetime

0.4 TBPer lifetime

Source: "The Relative Contribution of Multiple Determinants to Health Outcomes", Lauren McGover et al., Health Affairs, 33, no.2 (2014)

Big Data

Who are my brand advocates, fence sitters and adversaries?

Are my employees effective at engaging with customers?

Which customers are likely to defect to my competitor?

How are my customers and prospects engaging with my products and

services?

What is the customer sentiment regarding my brand?

What new products and features does my customer desire?

How do my customers feel about my competitors products?

Fuels Insights That Enable For the Enterprise

Higher Sales Conversion Rates

Sales

Marketing

Customer Service

Product Development

Workforce Optimization

Improved Customer Service

Higher Loyalty

Enhanced Online Accuracy

New Product Innovation

New Demand Generation

Risk Mitigation

The Business Agility Process

… Align big data to business outcomes

But How?But How?

Is Information Accurate?

Takes too Long Can’t find the right

information

Data Quality Problems

Ease of Use Integration of

Different Systems

Traditional Barriers

Big Data Barriers

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The 3 R’s of Success with Big Data Analytics

• Revolution• Responsiveness• Resiliency

“If you are not moving at the speed of the marketplace

you’re already dead – you just haven’t stopped

breathing yet”

Jack Welch

Revolution – Managing Disruption

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Data Products Need to Be Data Products Need to Be Built DifferentlyBuilt Differently

Give Data Back in Powerful Give Data Back in Powerful WaysWays

We Don’t Have Time to Do It Right, We Don’t Have Time to Do It Right, But We Have Time to Do It OverBut We Have Time to Do It Over

Decide on Where to Start Decide on Where to Start Building Your ApplicationBuilding Your Application

Create and Die By Your Product Create and Die By Your Product Pre-Flight ChecklistPre-Flight Checklist

- DJ Patel, US Chief Data Scientist- Ruslan Belkin, VP Engineering Salesforce

Responsiveness – Data Sensitivity

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In-House DataTypically Structured

External DataUnstructured, but

converted to structured.

Unfamiliar External DataLeveraged As-Is

Homemade DataSolution Augmentation

Big Data - Examples

In-House Data

External Data.

Unfamiliar External Data

Homemade Data

Leads Most Likely to Generate New Sales

Analysis of Customer Transactions Over Time

Understanding Customer Loyalty Patterns

Market Basket Analysis on Short & Long Term Behavior

Targeted Advertising Use Browsing History

Targeted Discounts via Phone Recognition of Possible Attrition

Social Media Sentiment / Buzz on Your Reputation

Pharmaceutical Drug Analytics Through Refill Patterns

“Personalized” Credit Offers per Customer

Hospital and Physician Quality Ratings

Experimentation for Customized Landing Pages

Patient Claim Analysis Based on Proximity to “poor” Locales

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Elastic Provisioning

Pay-as-You-Go

Manage High Volume External Data Sources

Self-Service Through a Browser

SQL / NOSQL – Unstructured Data

Access Data Anywhere, Anytime

Leverage Current Cloud Apps

Resiliency – Leveraging Cloud Elasticity

Big Data Analytics – Reference Architecture

Sensors

Internet

Social MediaServices

CustomerConversations

Public and Internal Sources

BackOffice Applications

Old and New Sources

InformationIngestion

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Data Connection / Movement

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Data Shaping / Cleansing

Real-TimeData

Streaming

Distributed Messaging

System

DataWorks

ApacheKafka

Streams


Analytic Sources

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Logical Data

Warehouse

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Interactive Queries and Iterative\

Data Processing

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BatchProcessingFramework

Real-TimeData

Streaming


System

Sensors

Internet





Old and New Sources

DashDB

Cloudant

Postgres DB2

MongoDB

ReThinkDB

Redis


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Analytic Sources InteractiveAnalytics

Alerting, Reporting and Planning

Visualization & Collaboration

Real-Time Decision Mgmt.

Systems of Engagement

AcceleratorsMetadataCatalog

InsightHub

ActivityHub

ContentHub

Master &Reference Data Hubs

InformationInteraction

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Logical Data

Warehouse

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Interactive Queries and Iterative\

Data Processing

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BatchProcessingFramework

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Real-TimeData

Streaming


System

Sensors

Internet





Old and New Sources

PredictiveAnalytics

D3

EmbeddableReporting

ApacheZeppelin

DashDB

Cloudant

Postgres DB2

MongoDB

ReThinkDB

Redis

DataWorks

ApacheKafka

Streams


Example – Ford: Integrated Health Management Platform

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Analytic Sources

MetadataCatalog

InsightHub

ActivityHub

ContentHub



Vehicle DeviceSensors

Dongle Information

from Parking Spots

Vehicle & UserInformation

MaintenanceHistory

Old and New Sources

Streams

Cloudant

PredictiveAnalytics

Example – Integrated Health Management Platform

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Analytic Sources

MetadataCatalog

InsightHub

ActivityHub

ContentHub



Clinical andWearable Device

Sensors

Fitbit, JawboneDevice Data

Lab Results and Patient

Conversations

Health Records from RDBMS

Old and New Sources

DataWorks

Streams

DashDB

Cloudant

D3

Has Been BridgedHas Been Bridged

The GapThe Gap

Cloud and business agility

Business

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