Analytics 3.0 in

Transport

Thomas H. Davenport

Babson/MIT/IIA/Deloitte

SAS Transportation Summit

June 10, 2014

Big data begins at

online firms

& startups

No technical or

organizational

infrastructure to

co-exist with

Working wonders for

Google, eBay, & LinkedIn

…but what about

everyone else?

What happens in

20 big companies when

analytics are

well-entrenched?

Findings show evolution

of a new analytics

paradigm

Analytics 1.0 Traditional Analytics

• Primarily descriptive analytics

and reporting

• Internally sourced, relatively small, structured

data

• “Back room” teams of analysts

• Internal decision support focus

• Slowly-developed models

• One method typically employed

1.0

Analytics 1.0 in Transport Why They Fall Short

• Gap between analytics and decision execution

• Models created once, used for years

• Little use of external data

• Analytics benefited internal ops, not customers

• Most managers rely primarily on experience

Analytics 1.0 Data Warehouse for Everything

ERP

CRM

Legacy

3rd Party Apps

Reporting

OLAP

Ad Hoc

Modeling

• Spreadsheets

• BI and analytics “packages”

• ETL tools

• OLAP cubes

• On-premise servers

• Out-of-database/memory

analytics

Analytics 1.0 Other Technologies

Keep inside the

sheltering confines of

the IT organization

Take your time—

nobody’s that interested

in your results anyway

Focus on the past,

where the real threats to

your business are

Analytics 2.0 The Big Data era

• Complex, large, unstructured data

• New analytical and computational

capabilities

• “Data Scientists” emerge

• Online and startup firms create data and

analytics-based products and services

• Transport and other big data analytics

primarily for consumers/passengers

2.0

2.0 Data ProductsFrom Online Firms

• Google—Search, AdSense, Books, Maps, Scholar, etc., etc.

• LinkedIn—People You May Know, Jobs You May Like, Groups You May Be

Interested In, etc.

• Netflix—Cinematch, Max, etc.

• Zillow—Zestimates, rent Zestimates, Home Value Index, Underwater Index, etc.

• Kayak—Price Predictor

Analytics 2.0 Hadoop for Everything

Map/Reduce

Web Logs

Images & Videos

Social Media

Docs & PDFs

HDFS

Operational Systems

Data Warehouse

Data Marts & ODS

We need to be “on the bridge”

Agile is too slow

Consulting =dead zone

We’re changing the world

Analytics 3.0 Fast, Pervasive Impact in the Age of Smart Machines

• Analytics used for data products and Industrialized

decision processes

• A seamless blend of traditional analytics and big data

• Analytics integral to all business functions

• Rapid, agile insight and model delivery

• Analytical tools available at point and time of decision

• Analytics are everybody’s job

3.0

TODAY

Analytics 3.0 The Data Economy for Transport Firms

• Every transportation company – not just online firms –

can create data and analytics-based products and

services that change the game

• Internet of Things data from trucks, trains, and planes

integrated and processed with the Analytics of Things

• Continuous, real-time analytics to monitor vehicle

status and location

• Need enterprise-level “data teams” good at data

science, operations, new product/service development

• Analytics for dispatching and routing built at scale and

embedded into operational processes

Analytics 3.0 Transport Applications

• Remote monitoring and diagnostics for engines and

other components

• Trip and route optimization for trucks, trains, ships and

planes

• Semi-autonomous vehicles

• Driver safety monitoring and predictive safety analysis

• Dynamic routing and re-routing by shippers and their

customers

• Port efficiency and reliability monitoring for ocean

shippers

Analytics 3.0: Data Types

• Customer profiles

• Organization

contacts

• Billing

• Marketing

• Contracts/orders

• Shipping

• Claims

• Call center

• Customer service

• Purchase history

• Segmentation

• Customer value

• Purchasing behavior

• Recommendations

• Sentiment analysis

• Target marketing

• Satisfaction

• Customer

experience

management

• Service tiers

Message logs

Images

RSSVideos

Hosted applications

Spatial GPS

LinkedIn

Device sensors

Email

Articles

Text messages

Cloud

Mobile devicesXML

Presentations

Blogs

Website activity

Social Feeds

Twitter

Documents

Analytics 3.0 A Highly Diverse Data Environment

Hadoop EDW/Mart

s

Discovery Platform Vertical/Graph/etc.

• Heavy reliance on machine learning

• Ensemble methods

• Better data curation productivity

• In-memory and in-database analytics

• Focus on data discovery

• Open source and commercial products

• Integrated and embedded models

• Analytical “apps” by industry and decision

Analytics 3.0Technology Beyond Just Hadoop

3.0

• Big data champions who have clear business

objectives

• Data architects who can put it all together

• Quants who can make sense of it all

• Translators who can tell stories with data

• Product developers who can build data

products

• Analytical amateurs who can do their jobs

with analytics

Analytics 3.0People Beyond Just “Data Scientists”

• $2B initiative in software, analytics, and

“Industrial Internet”

• Primary focus on data-based products and

services from “things that spin,” including

locomotives and jet engines

• Predictive maintenance reshaping service

agreements

• GE Railcar offers continuous service analytics

to customers—what’s wrong, where to go for

repairs

• GE Transportation offers a variety of trip and

asset optimization systems

• Expecting $1B in Industrial Internet revenues in

2014

GE 3.0

• Forty years of investment in measurement culture,

IT systems, telematics and GPS in vehicles

• Project ORION focused on real-time dynamic

routing of vehicles and packages

• Already saved 85 million gallons of fuel--on track

for $500M/year in labor, fuel, and vehicle savings

• MyChoice is new data and analytics-based service

offering bringing in additional revenue

UPS 3.0

• Invested heavily in sensors on trucks, trailers,

and intermodal containers

• Quality of dispatching and routing decisions has

improved dramatically

• Predictive driver safety algorithm changes job

roles and relationships

• Fuel stop optimization diminishes driver

autonomy

Schneider National 3.0

• Managers, engineers, and customers who don’t understand the value of data

• Unions and drivers who don’t like the impact on jobs

• Multiple data formats that are difficult to integrate

• Lawyers who don’t want to reveal safety analytics

• Managers (and analysts) who don’t like “black box” decisions

• Legislators who don’t know what to do about autonomous vehicles

3.0 ObstaclesBarriers to data products and decisions at scale

Recipe for a 3.0 World

1. Start with basic data management and analytics capabilities and a data-driven culture

2. Add some unstructured, large-volume data

3. Throw some product/service innovation into the mix

4. Add a dash of Hadoop and a pinch of NoSQL

5. Cook up some data in a high-heat convection oven

6. Train your sous chefs in big data and analytics

Thank you!tdavenport@babson.edu