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
Device sensors
Articles
Text messages
Cloud
Mobile devicesXML
Presentations
Blogs
Website activity
Social Feeds
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 [email protected]
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