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

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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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