Consider this: Data flows from every device, replacing guessing and approximations with precise information. Yet 80% of this data is unstructured; therefore, invisible to computers and of limited use to business.

1. BUILDING THE BUSINESS CASE
FOR WHY YOU NEED TO INVEST
IN YOUR DATA

2. HEALTHCARE DATA GOVERNMENT & EDUCATION DATA
99% 88% 94% 84%
Healthcare data comes from sources
such as:
Government & education data comes
from sources such as:
Patient
Sensors
Electronic
Medical
Records
Test
Results
Vehicle Fleet
Sensors
Traffic
Sensors
Student
Evaluations
UTILITIES DATA MEDIA DATA
93% 84% 97% 82%
Utilities data comes from sources
such as:
Media data comes from sources
such as:
Utility
Sensors
Employee
Sensors
Location
Data
Video
and Film
Images Audio
growth by 2017 unstructured growth by 2017 unstructured
growth by 2017 unstructured growth by 2017 unstructured
Data flows from every device, replacing
guessing and approximations with precise
information. Yet 80% of this data is
unstructured; therefore, invisible to
computers and of limited use to business.
By 2020,
of new information will be created
every second for every human
being on the planet.
1.7 MB
Consider:

3. The world is being rewritten in software
code, and cloud is the platform on which
the new digital builders—from developers
to business professionals—are
reimagining everything from banking to
retail to healthcare.
100,000,000
lines of code
in a new car
5,000,000
lines of code
in smart appliances
1,200,000
lines of code
in a smartphone
80,000
lines of code
in a pacemaker
Consider:
of B2B
collaboration
will take place
through web
APIs next year.
50%
Smart TVs
represented 27% of
all TV sales in 2012;
by 2018, they will
represent 82%.
Smart LED lighting
will grow from 6M
units in 2015 to 570M
units in 2020, used for
safety communication,
health, pollution and
personalized services.
By 2017, there will be
1B connected things
in smart homes,
including appliances,
smoke detectors and
cameras.
Sensors for industrial asset
monitoring and management will
grow from just over 15M units in
2014 to over 40M units in 2018
Smart traffic sensors
and other devices
installed in smart
cities will grow from
237M units in 2015 to
371M in 2017.
Revenues for
smart grid sensors
will grow ten-fold from
2014 to 2021.
Code Tools
Analytics
Data
APIs
By 2020, there will be
925M smart meters
installed worldwide,
more than double the
400M in 2014.

4. So where do we want to go from here?
Predictive Prescriptive Cognitive

5. Cognitive systems can understand the world through sensing and
interaction, reason using hypotheses and arguments and learn from experts
and through data. Watson is the most advanced such system.
Today, businesses in
countries across.
There are
Watson ecosystem partner
companies, with
78%
of business and IT executives
believe that successful business will
manage employees alongside
intelligent machines.
On average there are
36
17
industries are applying cognitive
technologies.
350+
100
of those have taken their product to market.
1.3B
Watson API calls a month
and growing.
Among C-Suite executives familiar
with cognitive computing:
96%
84%
94%
89%
in insurance intend to invest in cognitive
capabilities.
in healthcare believe it will play a
disruptive role in the industry, and 60%
believe they lack the skilled professionals
and technical experience to achieve it.
in retail intend to invest in cognitive
capabilities.
in telecommunications believe
it will have a critical impact on the future of
their business.
Consider:

6. Watson Winning and Jeopardy
4 Letter Word for
a Vantage Point
or a belief
“and anytime you feel the
pain, hey” this guy “refrain,
don’t carry the world upon
your shoulders”
Watson defeated two all
time Jeopardy champions
over two nights on US TV
You’re just a little stiff! You
don’t have this painful
mosquito-borne joint illness
with a Swahili name

7. Relationship
Extraction
Questions
&
Answers
Language
Detection
Personal
ity
Insights
Keyword
Extraction
Image 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
Pipeline
Case
Evaluation
The Watson 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
IBM Analytics Campus
IBM Watson

8. Asset Data is Rich
Asset Management Modules
• Assets
• Condition Monitoring
• Failure Codes
• Locations
• Meters and Meter Groups
Work Management Modules
• Assignment Manager
• Job Plans
• Lock-Out/Tag-Out
• Labor Tracking
• PreventiveMaintenanceMaster PM
• Qualifications
• Safety
• Service Items and Requests
• Tools/Crafts
Inventory Management Modules
•Item Master
•Storerooms
•Lot Management
•Kitting
•Issues & Transfers
•Condition Codes
•Stocked Tools
•Service Items
Procurement Management Modules
•Desktop Requisitions
•Purchase Orders
•Purchase Requisitions
•Receiving & Receiving Inspections
•Request for Quotation
Contract Management Modules
•Labor Rate Contracts
•Lease/Rental Contracts
•Master Contracts
•Payment Schedules
•Purchase Contracts
•Warranty Contracts
Service Management Modules
•Service Catalogs
•Service Level Agreement (SLA)
•Incidents
•Problems
•Changes
•Releases
•Solutions
Maximo is a treasure trove of unstructured and semi structured notes, guides, messages,
instructions etc.

9. Important Information can be Hidden
Maximo attachment example – an external document that helps
complete work orders.
1.0 GENERAL INSTRUCTIONS
1.1 Worksite Access and Egress
Access to the worksite shall be in accordance with Worksite Access and Egress.
Thunderstorm risk must be checked prior to access.
1.2 Instructions prior to commencing work
Either Maintenance Control or the Traffic Control Room should be notified via O & M radio
prior to the commencement of any work, and again at the end of the works. If any alarms
are to be activated, the Traffic Control room should also be informed.
1.3 Defects
All Defects found are to be reported to the Traffic Control Room or the Leading Hand and
listed on the defect register
1.4 Qualifications / Licences
This procedure should be carried out by BOCS personnel experienced in VR sensor
certification procedures and familiar with the operation of Kapsch Tolling Equipment.
1.5 Overview
VR sensor replacement is carried out prior to the certification process. Before replacement
of the sensor, the certification entity (SGS) needs to place seals on the VR sensor and
take pictures of the VR sensor to record serial number and MAC address of the
replacement sensor.
Certification information and Images are forwarded to the certification entity and kept by
them for their records.

10. Watson on Diagnosis
Watson at work
After Jeopardy IBM developed Watson to become a business application – some of the
first use cases were helping health care workers diagnosis.
"On Jeopardy it was not
necessarily critical to know
how Watson arrived at its
answer,” says Eric Brown,
IBM Research Director of
Watson Technologies. "But
doctors or domain experts in
any field will want to
understand what information
sources Watson consulted,
what logic it applied and what
inferences it made in arriving
at a recommendation.”

11. How Watson Works
Answer
Scoring
Models
Responses with
Confidence
Inquiry
Evidence
Sources
Models
Models
Models
Models
ModelsPrimary
Search
Candidate
Answer
Generation
Hypothesis
Generation
Hypothesis and Evidence
Scoring
Final Confidence
Merging & Ranking
Synthesis
Answer
Sources
Inquiry/Topic
Analysis
Evidence
Retrieval
Deep
Evidence
Scoring
Learned Models
help combine and
weigh the Evidence
Hypothesis
Generation
Hypothesis and Evidence
Scoring
Inquiry
Decomposition
1000’s of
Pieces of Evidence
Multiple
Interpretations
of a question
100,000’s Scores from
many Deep Analysis
Algorithms
100’s
sources
100’s Possible
Answers
Balance
& Combine
How Watson works: DeepQA architecture

12. So where are we all right now?
Structured
Semi-
structured
Unstructured

13. • Unfortunately, 40% to 50% of data warehouse initiatives end in costly failure.
International Association for Computer Information Systems
• Gartner estimates in 2012 that by 2014, fewer than 30% of business intelligence
(BI) initiatives will align analytics completely with enterprise business drivers,
despite alignment being the foremost BI challenge
• LinkedIn poll in 2013 asked BI experts how much of a BI project was spent in just
getting the data right. The answer came back as around 75–80%.
• Data warehouses are replaced on average every 3–5 years!
The issues with a traditional approach

14. What if we could increase and improve each of these?
Structured
Semi-
structured
Unstructured

15. So we need a new architecture
and a new approach

16. Systems Security
On premise, Cloud, As a service
Storage
IBM Watson Foundations
IBM Big Data & Analytics Infrastructure
New/Enhanced
Applications
All Data
Real-time
analytics
zone
Enterprise
warehouse
data mart
and analytic
appliances
zone
Information governance zone
Exploration,
landing
and
archive zone
Information
ingestion
and
operational
information
zone
What could
happen?
Predictive analytics
and modeling
What action
should I take?
Decision
management
What is
happening?
Discovery and
exploration
Why did it
happen?
Reporting, analysis,
content analytics
Cognitive
Fabric
Different approaches require different systems:

17. • Capturing any type of information, fast/slow, big/small, structured,
unstructured, text/images etc
• The ability to visualise, report. predict and action
BUT to make this a reality we need to apply:
• Retained auditability/integrity
• Flexible access
• Appropriate security
• Single view of an entity
• Just enough governance
• Just enough modelling
• Automation
• Avoiding re-engineering
New tools & methods are providing new capabilities to:

18. Operational Data Store
Bring data together
in real time.
Create an alternative
to querying operational
databases.
Make operational data easier
to access and use.
Time Machine
Go back to any point in time
and re-run a query.
Troubleshoot old decisions
made on old report.
Analyse change over time.
Semantic Layer
Store data so that reporting
and analytic tools can use it.
Model and name information
so it can be understood
and used safely.

19. • Seamlessly allow SQL and noSQL data integration
• Only 3 ELT patterns to load ALL data into a warehouse
• Avoids all re-engineering
• Enables full automation of changes into data warehouse
• Enables true agile reporting and analytics
• Specific constructs to deal with business rules
• Retains auditability and security
• Linear scaling in process and development
Methods like Data Vaulting that:

20. New/Enhanced
Applications
All Data
Information Integration and Governance
Information Governance and compliance reporting
Data
Quality
Entity
Management
Information
Lifecycle
Management
Information
Integration
On-Premises, As-A-Service (IBM DataWorks)
and Cloud (SoftLayer)
Unlocks key insights, by providing trust and comprehensive view
Information integration and governance

21. MDM is essential

22. Everyone’s traditional MDM Build Process
23

23. Implementing a Total Information Quality Management System across People,
Processes and Tools to create a continuous data quality improvement culture.
Certus Data Quality Framework

24. Data Profiling and Assessment
A lot of 1977 and 1970
in Installation Date
Some legacy values in
Allow Work Order field

25. Defining Business Terms
Who can help
me?
What is important
to know to use it?

26. Defining Rules

27. Data Quality Management is essential
Pass Fail rate Potential Cost of Failures Cost to fix errors
Errors by
data domain
and quality
category
Errors by
criticality

28. A Stewardship Function is essential
Business Process Management
for manual and automated
workflows required to implement
information governance and
manage data quality.

29. So back to the business case:
Governance
• Make Stewards more
efficient
• Spend money wisely
• Minimise Data as a
cause of accidents
• Forecast and budget
more accurately
• Ensure crews can
access sites
Analytics
• Predict replacement
and maintenance
dates with greater
accuracy
• Anticipate equipment
failure
• Improve the
relationship with the
customer
• Optimise the use of
resources
Cognitive
• Ensure all personnel
know how to react to
a condition code
• Use the corpus for
better actions and
resolutions
• Make it easier to get
answers from data

30. Why an MDM?
31
Maximize 1:1 consumer
relationships
Deliver personalized offers or
discounts aligned to unique
behaviors, needs and desires
Brand reputation
Right message every time in market
and consistent communication with
customers
Marketing productivity
Increased breadth of digital
channels, emphasis on cross-sell /
up-sell / right-sell opportunities,
understanding and embracing ROMI
Deliver value across all
touch points
Build opportunity for revenue
growth throughout marketing
value chain
360 Degree View of the Customer
Understanding, 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

31. Becoming an analytically driven or
cognitive business is a journey.
Businesses will be able to rapidly capitalise
on new opportunities if they have invested
in the foundations of their information
management systems.

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