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Cognitive Revolution
TODAY’S AGENDA…
Julien
Redmond
Samuel
Williams
Wireless
Smart Devices
Anywhere
Anytime
Anything
2015: One mobile
device for every
human
20011000 songs
2014900 million
GAME CHANGINGSINCE…
28 MAY 2010
On Demand
Unlimited
Processing
Power
Creating
New
Platforms
Cloud
Computing
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Example:
Wine
Pairing
Example Wine
Pairing
Example:
Resume
Accelerated
learning platform…
OBSERVATION #ONE
Where it will lead
I don’t know.
However I
believe…
Recommendation
It is the intersection between AI + BI
OBSERVATION
#TWO
• Human
• Machine
• Expert
OBSERVATION
#THREE
Creating entirely new
possibilities… not yet
thought of
OBSERVATION
#FOUR
OBSERVATION #FIVE
Enabling entirely
different
approaches to:
• Building
applications
• Mine data
• Achieve
meaningful
outcomes
RIGHT BRAIN
CONCLUSIONS
1. Accelerated learning platform
2. This is the intersection between AI + BI
3. Human + Machine + Expert
4. Creating entirely new possibilities not yet
thought of
5. Enabling entirely different approaches to
building applications, mining data +
achieving business outcomes…. staying
competitive
IBM Watson
Analytics
Cognitive products
and servicesCognitive products and services can sense,
reason and learn so they can adapt and
develop new capabilities not previously
imaginable.
THE POSSIBILITIES OF COGNITIVE:
Elemental Path, a Watson ecosystem partner,
developed Cognitoy, a dinosaur toy, that answers its
playmate’s questions, and even learns their sense of
humor by listening to and adapting its personality to
play differently with each child.
Results: Cognitoy is able to take on a unique
personality that evolves over time based on the
child’s interactions and helps her learn rhyming,
spelling, vocabulary, mathematics and more.
WHERE WE ARE NOW:
Decision management
platforms will expand at a
CAGR of
By 2018
Apps with advanced and
predictive analytics
are growing
of all consumers will
regularly interact with
services based on cognitive.
50%
faster than apps without
predictive functionality.
65%
60%through 2019, in response
to the need for greater
consistency and
knowledge retention.
It’s an “always-on” world
A Do-It-Yourself mentality now prevails
Expectations from technology have never been higher
Our work and personal lives have blurred
Making decisions rapidly is no longer a goal; it’s an
imperative
The desire to make data-driven decisions is prevalent
Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute for Business Value study. Copyright © Massachusetts Institute of Technology
Access to required data sources is critical while maintaining governed
standards
34% can not find time to analyze data
38% have a limited understanding of
how to use analytics
Leveraging analytics still faces many obstacles
24% find it difficult to get data
Even a simple analytics project has multiple steps and people
Data Access
Data Preparation
Analysis
Validation
Collaboration
Reporting
Business
Analysts
Business
Users
Data Scientists
and
Statisticians
IT
And it’s rarely a straightforward process
Business
Users Data Scientists
and
Statisticians
IT
Data Access
Analysis
ValidationCollaboration
Reporting
Data Preparation
Business
Analysts
Think Ahead
Tell a StoryUnderstand Your
Business
Get Better Data
Mobile Ready Secure
Embedded information services provide
data access and refinement
Automated intelligence accelerates your
ability to answer questions
Predictive analytics reveals insights and
opportunities
Visualizations support your decisions and
communicate results
Put analytics in the hands of a broad range of users
Make data access and refinement easier
Deliver through the cloud for agility and speed
Business Analysts Data Scientists IT
Self-service analytics for business users and
experts alike
Business Users
Empowering the business for success
PrioritizingAccounts
Receivable
Employee Retention
HelpdeskCase
Analysis
CampaignPlanning and ROI
WarrantyAnalysis
Customer Retention
Finance HRITMarketing OperationsSales
“Which accounts are most likely to be paid?
How can collections be more efficient? How
does it affect revenue and profit when we lose
customers? How can the sales pipeline help
me forecast revenue?”
Justin Chen, Finance Manager
Justin ChenCustomer attrition
Watson Analytics analyzes
customer data to identify which
customers are likely to leave and
why and predicts the effect on
revenue. Justin can then
determine which customer
investments will lead to more
profitable growth.
Natural language dialogue
Cloud-based agility
Data discovery
Quick start intuitive interface
Mobile-ready
Unified analytics experience
Visual storytelling
Intelligent automation
Data access and refinement
Report and dashboard
creation
Integrated social business
Guided analytic
discovery
Single Analytics Experience
Fully Automated Intelligence
Natural Language Dialogue
Guided Analytic Discovery
Visit WatsonAnalytics.com and get started for free
1. A cognitive strategy
Determine what data you need, which experts will train the system;
where you must build more human engagement; which products,
services, processes and operations should be infused with cognition,
and which parts of the unstructured 80% of data you
most need to focus on to make discoveries for the future.
2. A foundation of data and analytics
Collect and curate the right data—data you own, data from
others, data available to all; both structured and unstructured. Apply
cognitive technologies to this data in order to sense, learn and
adapt, thereby creating competitive advantage.3. Cloud services optimized for industry,
data and cognitive APIs
The building blocks for products and services are code, APIs and
diverse data sets. The platform you choose to develop on, and the agile
development culture and methods you embrace, will be critical to your
success.
4. IT infrastructure tuned for
cognitive workloads
Architect a new kind of IT core—a heterogeneous infrastructure
that serves as the backbone of your enterprise. Do this rapidly
and affordably by harmonizing technologies from public, private
and hybrid cloud with distributed devices, IoT instrumentation
and your existing systems.
5. Security for a Cognitive Era
As cognition makes its way into cars, buildings, roadways, business
processes, fleets, supply chains—securing
every transaction, piece of data, and interaction becomes
essential to ensure trust in the entire system—and in your brand
and reputation.
Relationship
Extraction
Questions
&
Answers
Language
Detection
Personality
Insights
Keyword
ExtractionImage Link
Extraction
Feed
Detection
Visual
Recognition
Concept
Expansion
Concept
Insights
Dialog Sentiment
Analysis
Text to
Speech
Tradeoff
Analytics
Natural
Language
Classifier
Author
Extraction
Speech to
Text
Retrieve
&
Rank
Watson
News
Language
Translation
Entity
Extraction
Tone
Analyzer
Concept
Tagging
Taxonomy
Text
Extraction
Message
Resonance
Image
Tagging
Face
Detection
Answer
Generation
Usage
Insights
Fusion Q&A
Video
Augmentation
Decision
Optimization
Knowledge
Graph
Risk
Stratification
Policy
Identification
Emotion
Analysis
Decision
Support
Criteria
Classification
Knowledge
Canvas
Easy
Adaptation
Knowledge
Studio Service
Statistical
Dialog
Q&A
Qualification
Factoid
PipelineCase
Evaluation
The Waston that competed on
Jeopardy! in 2011 comprised what
is now a single API—Q&A—built
on five underlying technologies.
Since then, Watson has grown to
a family of 28 APIs.
By the end of 2016, there will
be nearly 50 Watson APIs—
with more added every year.
Natural Language
Processing
Machine Learning
Question Analysis
Feature Engineering
Ontology Analysis
CERTUS CUSTOMER EXAMPLES
Bringing technology, people and processes together
Certus are helping customers build an
Information Architecture that stores,
explores and analyses data in new
ways.
New Architecture to Leverage All Data and Analytics
Data inMotion
Data atRest
Data inMany Forms
Information
Ingestion and
Operational
Information
Decision
Management
BI and Predictive
Analytics
Navigation
and Discovery
Intelligence
Analysis
Landing Area,
Analytics Zone
and Archive
Raw DataStructured DataText AnalyticsData MiningEntity AnalyticsMachine Learning
Real-time
Analytics Video/Audio
Network/Sensor
Entity Analytics
Predictive
Exploration,
Integrated Warehouse,
and Mart Zones
Discovery
Deep Reflection
Operational
Predictive Stream Processing Data Integration Master Data
Streams
Information Governance, Security and Business Continuity
BigInsights HadoopData Platform
Sources
Information Server Data Integration
Watson Data Exploration BI / Analytics
Break the “Waterfall Style” Lifecycle• Empowers LOB to access data sooner
• Eliminates slow modeling prior to staging
Analytics Performance Booster• Leveraging Teradata for its strength
• Less wasted process, fewer batch windows
Lower Cost Staging, ELT and Deep Data• Commodity infrastructure for data platform
• Automated data transformation and governance
Fresh Capacity and Cost Avoidance• Recoup previously displaced disk
• Defer costly upgrades for production uses
Improving Integration for Analytics
Certus are using cutting edge
matching to give companies a 360
degree view of customers, students,
products or assets
Maximize 1:1 consumer relationshipsDeliver personalized offers aligned to unique behaviors, needs and desires
Brand reputationRight message every time in market
Marketing productivityIncreased breadth of digital channels,
emphasis on cross-sell / up-sell / right-
sell opportunities, understanding and
embracing ROMI
Deliver value across all touch pointsBuild opportunity for revenue growth throughout marketing value chain
360 Degree View of the CustomerUnderstanding, responding and maximizing each unique customer relationship
Optimize marketing mix Model and plan balancing needs of channels, probability of ROI success and resource constraints
Customer growth and retention Demanding customers, commoditized products and crowded competitive marketplace
Define MDM Value
Consuming Applications
Contact Warehouse CRM MarketingPortal
Kate Lamb
32 George Street
Perth, 6000
Kate Jones
Perth, WA 6000
12/06/1970
Catherine Jones
44 Station Street
Perth, WA
Mrs K Lamb
32 St. George
06/12/1970
Dr Katherine Lamb
23 George St
Perth, 6000
06/12/1970
Miss C Jones
Station Street, Perth
Western Australia, 6000
12/06/1970
Person Entity
Dr. Katherine Lamb
Composite View
Dr Katherine Lamb
32 George St, Perth, WA 6000
DOB: 12/06/1970
Probabilistic Matching
Household relationships
› Inspect potential household members and link to confirm relationships.
Employment Relationships
› Inspect relationships between companies and staff.
Joseph’sHousehold
Wife of
Daughterof
Sonof
Is the Subsidiary of
SuppliesProduct to
Is Married to
Is theOwnerof
Has anAccount
with
Is Employed by
Entity Relationships
Big Match › The same MDM Match engine runs on Hadoop to connect
more sources of information about the customer.
Increased engagement
Increased revenue
Decreased risk
Less ‘gut feel’
More data (when used effectively)
Increase on Churn retention rate (no
discounting required)
More newsletter article clicks
More articles read per session
Lookalike acquisition model
increasing conversion
Strong Ad revenue growth 20%
10%
Linkage: audience connectionsAny hard links across accounts, Consumer & Household, Fuzzy matching, Enrichment (Single Customer View)
MDM Value – News Corp
Presentation to IBM SolutionConnect Event Sydney 2014
Certus are helping customers put in
successful data stewardship models to
trust the results they get from reporting
and analytics.
Technology Driven
Process Driven
1) Define
Business
Problem
2) Obtain
Executive
Sponsorship
3) Conduct
Maturity
Assessment
4) Build
Roadmap
6) Build
Business
Glossary
7) Understand
Data
8) Create
Metadata
Repository
5) Establish
Organizational
Blueprint
9) Define
Metrics
11) Govern
Master Data
10) Govern
Data Quality
14) Govern
Analytics
13) Govern
Security &
Privacy
12) Govern
Lifecycle of
Information
15) Measure
Results
Business Glossaries that Extend to Big Data
Monitoring Quality and Stewardship Tasks
Data Quality Dashboard
› Data Quality Metrics across lines of business that makes quality relevant to everyone.
Stewardship Center
› Manage a team of stewards and allocates data quality investigation and data remediation tasks.
› Certus has helped
customers manage
data quality via a
policy driven
approach.
Data Quality Scorecards
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How do I trust a report?
Quality and Lineage behind a report
Data lineage
track back,
quality of each
asset in lineage
Data Quality
trends over timeQuality Metrics
QSuper Success Story
IBM Insight Conference 2015
Operational Impact› Productivity improvements
through automation of data checking reporting.
› Data checking is consistency applied
› Increased confidence in data› Workloads can be managed and
anticipated› KPI’s can be applied
Regulatory Impact
› Risk and Compliance reporting is measurable and can be monitored
› Mitigating risk of penalties and fines through more accurate reporting
› Industry leader in data quality activities.
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