Telematics data provides a wealth of new, actionable insights, particularly when integrated with other enterprise data. But where do you start? How do you prioritize? What is the roadmap? In an interactive workshop learn how to derive more from data so you can do more in your business. - Find the value of integrating telematics data with traditional data elements, including financial, customer, manufacturing, location and weather data - How integrated telematics data can improve customer satisfaction, lifecycle management, warranty reserves, supply chain performance, and even engineering & design choices - Gain practical examples from top manufacturers to improve operational efficiencies, develop new revenue streams, create customer insights, and better understand product performance

1. It’s Not Enough to Just Collect Data

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Conversations Business and IT Leaders are Having
New types of data
present new
opportunities
Reduce complexity of
big data analytics
Empower existing
resources to generate
value from big data
Use next generation
analytics to discover
insight
Gain
unmatched
competitive
advantage
using Big
Data

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Big Data: Traditional + New Data Types
Business Transactions
(orders, payroll,
purchases, trades)
Observations
(sensors, meters,
geolocation)
Source: IDC, Gartner
Interactions
(emails, “likes”,
tweets, weblogs)
+ +

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Enterprise Analytical Architectures are evolving: why?
DISCOVERY PLATFORM
DATA WAREHOUSE
DATA
PLATFORM
The Data Mart Era The EDW Era The Logical Data Warehouse Era
”Just Give Me Any Old
Data – And Fast!” (Never
our advocated approach!)
“Centralise the data that
are widely re-used and
shared - but integrate all
of the data and the
analytics.”
“Give me integrated, high
quality data that enables
me to optimise end-to-
end business processes
cost-effectively.”

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Discovery Platform Requirements
1
2
3
4
All Data
Multiple Analytic Methods
Diverse Enterprise Analysts
Rapid Exploration

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Why
• Attain Zero unplanned downtime.
• Efficient service allocation – calls, parts &
common components, skills.
• Provide a feedback loop to engineering.
Impact
• Significant cost reductions.
• Improved machine up-time.
• Improved customer satisfaction.
Role of Sensor Data
• Improved analysis, faster algorithm
development using machine diagnostic data
and field service logs.
• Maximize customer satisfaction and machine
in-service time.
• Understand root cause of failures.
Art of the Possible – Predictive Failure Modeling
Remote Equipment

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Why
• Understand if certain variants of vehicle
configurations have a higher occurrence of
repair codes, operation codes or Diagnostic
Trouble Codes (DTCs).
Impact
• Faster problem identification, leading to
improved dealer performance and increased
profitability and customer satisfaction.
• Reduce known failures & repairs required in
future configurations.
Role of Sensor Data
• Predict which configurations lead to more
repairs by finding common patterns in repair
sequences.
• Aid future design/build of configurations.
Art of the Possible – Vehicle Configuration Dependent Faults
Automotive OEM

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A Car Company Powered by Data | Phase 1
Connected Car
Diagnostic Trouble
Code (DTC)
Control, Monitoring
and Diagnostics
Engine Control
Unit (ECU)
Dealer Scheduled
Service or Repair
Reference of all
Mechanical and
Electric Failures
Across all Models
over Time
Manufacturer
Context
400 Discrete
Measurements
such as fault thresholds, wear
factors, operating parameters
Design
Warranty
Quality
Manufacturing

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A Car Company Powered by Data | Benefits
• Document Environmental Innovation
> Track actual fuel efficiency performance
against design objectives and investigate
causal variances
> Understand balanced use of engine braking
impact to recharge the battery without
overcharging
• Enable Regulatory Compliance
> TREAD Act reporting
• Cost Reductions
> 2/3 reduction in infrastructure costs with
data mart elimination and standardization
and simplification of the IT landscape
> Process improvements and accelerations
supported by a data-driven design culture
> Improved analytical performance, expanded
user access, accelerated problem response
• Quality and Functionality throughout the
Product Lifecycle
> Trace quality problems to the production
process
> Prioritize, target and expedite problem
response efforts
> Trace mechanical faults to their root
causes
> Model failure rates over time
> Correlate mechanical failures with
location-specific conditions
> Resolve quality issues within the current
production run
• Warranty Reimbursement Accuracy
> Identify sources of dealer data quality
issues, for example in warranty mileage
reporting

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A Car Company Powered by Data | Lessons
It’s all about business value
• Win and keep management support with a strong business case
• Business value is always the highest priority
• IT cost savings are a bonus
It’s all about people
• Find the people with strong statistical and mathematical skills (6-Sigma)
• Insight into numbers leads to improvements
• Involve the business at the pilot stage to create ownership
It’s all about data
• An enterprise data model based on detailed data saves time and supports the
EDW
• Save all your data–new uses will arise
• Plan for capacity, demand WILL grow

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Is Big Data Delivering Business Value Today?
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Are the people in your organization able to directly ask
and get answers for the big data questions they want?
How much time does it take to answer a new business
question with big data?
Are you able to able to iterate and operationalize your
discoveries from big data analytics?
Need right technologies to realize business value of big data

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