Big Iron + Big Data = BIG DEAL! Unlock The Power of Your Mainframe Data
BIG DATA, BIG DEAL!
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INTERNAL USE ONLY INTERNAL USE ONLY Big data? Big deal! SAP Forum Istanbul 2014 Dr. Jürgen Brock Vice President Global Marketing [email protected] Fujitsu Technology Solutions GmbH Mies-van-der-Rohe-Str.8, 80807 Munich, Germany.
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SAP FORUM İstanbul 2014 FUJITSU "Big Data nedir? Nasıl bir öneme sahiptir? Fujitsu Global Pazarlama Başkan Yardımcısı Dr. Juergen Brock; örnek bir vaka çalışması eşliğinde Big Data ile işletmenizin sahip olduğu değeri nasıl artırabileceğinizi anlatacak."
Transcript of BIG DATA, BIG DEAL!
0 INTERNAL USE ONLYINTERNAL USE ONLY Copyright 2014 FUJITSU
Big data? Big deal!
SAP Forum Istanbul 2014
Dr. Jürgen Brock
Vice President
Global Marketing
Fujitsu Technology Solutions GmbH
Mies-van-der-Rohe-Str.8, 80807 Munich, Germany.
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Agenda
1. The big data context
2. A big data case…with a twist
3. Synthesis and conclusions
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Agenda
1. The big data context
2. A big data case…with a twist
3. Synthesis and conclusions
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Big data… rise of an old concept
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Big data… rise of an old concept
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The three “Vs” of big data
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So where are we?
Source: Big data survey 2014, ETH Zurich and Fujitsu.
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Agenda
1. The big data context
2. A big data case…with a twist
3. Synthesis and conclusions
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Co-creation in the automotive industry
Stage 1
“Bayesian Virtual Adviser”
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Co-creation in the automotive industry
Stage 2
“Opportunity trigger”
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Co-creation in the automotive industry
Stage 3
“Virtual engineer” Hi, I am Tom your car design engineer. You have specified
requirements that our currently available cars cannot
meet.
For example, you said that you want a full size truck with 4
cylinders. Generally, our smaller trucks come with 4 cylinder engines. With some
further information from you, you can help us design a more
suitable truck.
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Co-creation in the automotive industry
Stage 4
“Opportunity sizing”
Identified business opportunity:
1-2bn $!
The twist?
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Agenda
1. The big data context
2. A big data case…with a twist
3. Synthesis and conclusions
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Synthesis and conclusions
Big data? Big deal! people – information - infrastructure
People From researcher to
data scientist
Information From data to
information
Infrastructure From legacy to
digital transformation
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