Post on 15-Apr-2017
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• The obligatory slide: But I will not speak about Teradata
• How it all began: The evolution of data
• Upstream: Sourcing relevant data
• Midstream: Storing Big Data
• Downstream: Analyzing and enabling value
• Reality Check: High Value Reference Cases
Agenda
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• The obligatory slide: But I will not speak about Teradata
• How it all began: The evolution of data
• Upstream: Sourcing relevant data
• Midstream: Storing Big Data
• Downstream: Analyzing and enabling value
• Reality Check: High Value Reference Cases
Agenda
5 © 2014 Teradata
“There were 5 Exabytes of
information created between the
dawn of civilization through 2003,
but that much information is now
created every 2 days.”” (Eric Schmidt, ex Google CEO, 2010)
"Big Data, for better or worse:
90% of world's data generated
over last two years." (ScienceDaily, 22 May 2013)
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• The obligatory slide: But I will not speak about Teradata
• How it all began: The evolution of data
• Upstream: Sourcing relevant data
• Midstream: Storing Big Data
• Downstream: Analyzing and enabling value
• Reality Check: High Value Reference Cases
Agenda
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Upstream: Sourcing relevant heterogenic data in real time & huge volumes
Best in class ingestion engine for IoT data Very modern concept ingest 100s of source near realtime
Teradata Customer examples utilizing Teradata Listener/Kafka
Customer Example LinkedIn: Some key figures
• 220B messages/day
• 3.25M messages/second peak
• 40TB in (70MB/s), 160TB out (400MB/s)
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• The obligatory slide: But I will not speak about Teradata
• How it all began: The evolution of data
• Upstream: Sourcing relevant data
• Midstream: Storing Big Data
• Downstream: Analyzing and enabling value
• Reality Check: High Value Reference Cases
Agenda
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• In the presence of big choice
• Typical Questions are – What platform to use for what
data?
– What are the price points per platform?
– What other criteria need to be matched (e.g. work load management)
• Our Answer:
Midstream: Storing Big Data
The user couldn’t (& shouldn’t) care less
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The Data Intelligence Hub (based on Teradata UDA) is a modular open platform allowing to source, store & analyze huge amounts of maximal heterogenic data in near real time.
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• The obligatory slide: But I will not speak about Teradata
• How it all began: The evolution of data
• Upstream: Sourcing relevant data
• Midstream: Storing Big Data
• Downstream: Analyzing and enabling value
• Reality Check: High Value Reference Cases
Agenda
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Multi-genre Advanced Analytics On-demand
Machine Learning Text Graph
Time Series Pattern
Path
Stats
Multi-genre Advanced Analytics
Transformations Data Access
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Enable Discovery Development and Execution
Single discovery analytics solution with interface for – Business, Analyst, R User & Data Scientist
IDE SELECT n.event_path, count(*)
FROM nPath(
ON (
SELECT *
FROM telco_data td, profile p
WHERE d.customer_id = p.customer_id
)
PARTITION BY customer_id
ORDER BY timestamp
MODE( overlapping )
PATTERN(‘EVENT+.CANCEL_SERVICE_EARLY’)
SYMBOLS(
action<>‘CANCELSERVICE’ASEVENT,
SQL Client
Business /BI User Business Analysts R User Data Scientists
R Client
BI & Open Source Visualization Tools (for Discovery Insights)
Time to Value Acceleration
(actionable insights in hours, days or weeks)
AppCenter & Guided Development Interface (GDI)
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Aster Analytics Evolving Use & Value Examples Affinity & Influencer Analysis: (Product, Service, Social, Warranty)
Predictive Analysis (Behaviors, Components, Social,…)
Behavioral (paths & pattern sequences)
Text Analytics
(sentiment, documents, voice of customer )
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• The obligatory slide: But I will not speak about Teradata
• How it all began: The evolution of data
• Upstream: Sourcing relevant data
• Midstream: Storing Big Data
• Downstream: Analyzing and enabling value
• Reality Check: High Value Reference Cases
Agenda
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Transforming business models
The Internet of Trains
Opportunity • Increase share of travel from plane (renfe)
• Boost NPS & reputation (renfe)
• Change to superior business model (Siemens)
Approach • Move to condition-based, predictive maintenance
• Ensure commercial sustainability by preventing failure on the track
• Enabled thru near real time analytics of sensor data
Results • If delay > 1 h train ticket price will be reimbursed in full
• “Most reliable high-speed train in the whole network”
• Share of plane travel down from 80% to 30%
• Change single asset sale to long term service contract
• New offering (incl. risk share & perf. based contracts) 17 © 2014 Teradata
Similar cases
“It is a whole new business model.
Instead of selling our customers a train, we sell them its performance over a certain period of time.” – Gerhard Kress, Director of Mobility Data Services at Siemens.
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Revolutionizing automotive Connected cars
18 © 2014 Teradata
• 80-90% of cars connected to Volvo cloud, analyzed by Teradata Aster
• Share data within Volvo & with local cities, eg. for road maintenance
• Volvo use cases include failure prediction, early warning system for drivers,
cloud based remote control for the car and the goal of making Volvo cars
“death-proof” (Volvo) by 2020
• Design for future cars is highly influenced by sensor data & AoT
Volvo: “Cars shouldn’t crash…”
• “BMW already has the best car connectivity [record] of any company,”
(BMW), with six million of its cars directly connected to the internet.
• BMW built a data lake based on Teradata involving the whole organization.
• Current & future use cases include autonomous driving, services enhancing
the driving experience through big data analytics
BMW: “A revolution for the car industry”
• “The launch of Pay-As-You-Drive insurance [gives] motorists access to
insurance specifically tailored to them & their driving habits.” (Aviva)
• Teradata enabled Norwich Union rating & managing a much larger set of
customer trips on a daily basis, while better managing the associated risk
Aviva/Norwich Union: “A revolutionary new product”
“When we come to ‘Transportation as a Service’ or ‘Mobility as a Service,’ there’s
a game changer for the whole society. [..] The full ownership of the vehicle will look different at least in the bigger cities and megacities in the future. That is a full change of our
business principles.” – Jan Wassén, Director of Business Analytics, Volvo Cars
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Dr. Stefan Schwarz Director Business Consulting Lead Telco, M&E TERADATA
M: +49-173-74-88381 stefan.schwarz@teradata.com