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1. An automotiveIT special edition
www.automotiveIT.com
• Big Intelligence: Using and recycling information profitably
• Advancement through knowledge: Interview with Audi CIO Mattias Ulbrich
• Development, production, after-sales: A wealth of data – major opportunities
The Challenge: A Technical Evolution and a Business Revolution
The High-Tech Raw Material
01 2013
Big Data automotive
Exclusive: Technical paper from the Fraunhofer IAIS on the subject of big data
3. Contents · Big Data automotive 3
Special Edition 01 · 2013
_Game Changer. Big Data is chan-
ging automakers' perspective, shifting
it away from products and toward
customers. New methods, tools and
IT infrastructures are helping them
pivot. 4
_Interview. Audi looks at Big Data
across the entire automotive value
chain. CIO Mattias Ulbrich sheds
light on the specific advantages that
departments can expect. 6
_Quotes. Many manufacturers and
suppliers are working already on Big
Data projects or are now in the evalua-
tion and planning phase. automotiveIT
asked board members, CIOs and
executives about them. 10
_Value Chain. Successful Big Data
solutions begin in the business areas.
To realize the maximum ROI in deve-
lopment, production and after sales, IT
has to be tightly linked with business
processes. 12
_SOA platforms. The digital world
produces an abundance of data.
To generate added-value from this
information, auto industry IT decision-
makers have to change the technology
in their computing centers. 14
_Expertise. "Big Data happens on
the street” – that is the thrust of a
technical paper that Hendrik Stange
of Fraunhofer IAIS in Sankt Augus-
tin, Germany, wrote for this special
edition. 16
Interview. Audi CIO Mattias Ulbrich
on the intelligent analysis and interpre-
tation of data 6
Basic concept. 80 percent of the data
that will be generated by 2015 end up
in Hadoop environments 14
Contents
Big Data automotive An automotiveIT Special Edition
Photos:Audi,ClausDickIllustration:SabinaVogelCover:Audi,iStockphoto/ollyIllustrations:SabinaVogel
4. 4 Big Data automotive · Game Changer
Special Edition 01 · 2013
If you want to shape new products and services out of data,
you need the right tools – and a good idea where the path will
take you.
Big Intelligence
Illustration:SabinaVogel
5. Game Changer · Big Data automotive 5
Special Edition 01 · 2013
The search term "Big Data" currently delivers nearly 1.8
billion hits on Google. By contrast, "cloud computing" with
148 million hits seems almost easy to grasp. But the reality is
quite different: many companies are doing tests to see whether
and how the transfer of servers and applications to virtualized
environments could benefit them. On the other hand, Big Data
is still not a top priority on management agendas. So has the
world turned upside down? Numerous market observers and
analyst firms say no. They say it’s the right approach to lay the
groundwork on the infrastructure side for major data growth
in the future and gradually take a close look at old analysis and
reporting processes. Anyone putting the cart before the horse
risks taking a fall. Without a doubt, Big Data holds consider-
able potential for companies in the auto industry and other
sectors. But IT decision-makers need not worry about missing
the connection. The number of projects and applications will
keep growing over the next two years, and the buzzwords will
turn into routine business. The potential areas of application in-
clude the analysis of customer behavior, product optimization,
better service, greater support for operating processes, and
maybe even the creation of new business fields. The first best
practice examples already exist.
Strictly speaking, Big Data is nothing new. The retail sector
has exploited noticeable correlations in the purchase behavior
of its customers since the early 1990s to optimize the physical
presentation of individual product groups. The purchase of
commodities is the key phrase here. Since there are no striking
differences in many competing industrial products, the nuan-
ces of taste are the sole influence on the purchase decision.
Even in the auto sector, it is hard to differentiate the technolo-
gies under the sheet metal. That’s why it is crucial to move the
perspective away from products and more toward customers.
Big Data comes into play precisely at this point. In addition to
the data that internal company applications generate, manage
and analyze, there are external sources that manufacturers
and suppliers have not had on their radar to this point. They
include vehicle sensors, the customer's mobile devices, and
postings and tweets in social media. They can all help compa-
nies pick up moods, recognize market trends, simulate develop-
ments and allow consolidated information to flow into wide-
ranging and strategic corporate decisions.
But companies have to meet basic requirements, namely
structure and organization. Current business systems for goods
and classic business intelligence solutions cannot process un-
structured data in an orderly way. If you want to score points
with Big Data, you need new methods, tools and IT infrastruc-
tures. A huge amount of homework is especially looming for
the data management and integration areas. The wide variety
of informational channels and file formats requires new sys-
tem technologies, new processing concepts, and possibly even
a reorganization of the information flow within the company.
Out with the transactional approach, in with democratic data
assessments where all levels can participate, not just senior
management.
Can the power of algorithms bring an end to the hegemony
of knowledge? “Yes, to a certain extent," said Reimund Willig
of the technology company EMC. “Big Data is measuring the
world of the 21st century all over again,” he said. “Data can
extend our physical selves digitally like the clothing on our
bodies." Freely translated, this means that companies receive
completely new information about their customers, along with
feedback on their products and their competitors. Anyone ma-
stering the technology and using it cleverly will be in a position
to expand his business model profitably. Instead of just earning
income on books or search results, companies will make big
money on customer profiles, which will include personal sensi-
tivities, preferences, needs and behaviors. The new currency in
the world of Big Data is the right information at the right time.
By Ralf Bretting
Data Tsunami: Storing with all your might
Whether from a stationary PC, via a mobile smartphone or
tablet, or machine to machine, the quantity of data flowing
through global networks is growing inexorably. Over the past
10 years, the volume has increased by a factor of 750. And
there is no end to the trend in sight. On the contrary: By
2016, the number of digital packets will again grow fourfold.
Experts see the strongest growth in countries that are hard-
ly connected to the network today. Challenges are looming
for corporate IT departments, too. By 2020, the number of
servers will rise tenfold and the amount of information by a
factor of 50. At that point, companies struggling to bring the
mountain of data under control will need 50 percent more IT
specialists than they have today.
6. 6 Big Data automotive · Interview
Special Edition 01 · 2013
Audi AG's marketing claim translated from the original German
promises “Advancement through Technology.” In an interview,
CIO Mattias Ulbrich describes the contribution that IT - and Big
Data in particular - makes to the success of the company and the
areas where the intelligent analysis and interpretation of data can
fuel the business in the future.
“We look at Big Data along
the entire value chain”
Mr. Ulbrich, at their core, many of the challenges the
auto industry faces have to do with the collection and
intelligent evaluation of data. What role does the con-
cept of Big Data play for you?
We have recognized the importance of data and identified
opportunities associated with this information. Our IT strate-
gy is firmly anchored in Audi Strategy 2020. On the road to
becoming the leading premium brand, our goal is to align Audi
with future challenges and to satisfy customers worldwide. In
the process, data and their use are playing a central role. If you
interpret the data and the facts intelligently and create added
value for the customer, you can develop a decisive competitive
edge. We don't consider Big Data to be a buzzword. We are sup-
porting the business areas by undertaking Big Data and ana-
lytical projects and using the right tools and expertise in data
processing, visualization and interpretation. With our under-
standing of Big Data, we can also advise our colleagues in the
sales and marketing regions.
So let’s put it in concrete terms. You are saying Big
Data is no longer a buzzword for Audi, and it is already
delivering concrete added value.
Yes. We are working on pilot projects based on Big Data. The
fact that the term is becoming more and more prominent is
certainly giving these projects a push. Our business intelligence
infrastructure and competency are already well-developed
when it comes to data management and storage as well as the
evaluation and presentation of data. And we are enhancing
these areas in a targeted way for new data sources. We are
making a comprehensive selection of tools available to the
departments; they can help themselves to them. For example,
we can already link to vehicle sensor data and want to offer the
customer specific added value in the future.
Are you pursuing a particular Big Data strategy?
Our IT strategy naturally takes Big Data elements into account.
Our goal is to strengthen the existing core business and to
develop new business models. The entire management board
supports this idea, especially Luca de Meo, our sales and
marketing chief.
Does Big Data affect IT security?
Data security and data privacy are a top priority for us. We have
extremely high security standards in dealing with customer
and vehicle data. Our new, highly advanced computing center
offers the best possible situation from a technology standpoint.
Our data security experts are already integrated into the con-
ceptualization of services and support the entire development
phase with security analyses. We also employ continual securi-
ty checks to audit current operations.
7. Interview · Big Data automotive 7
Special Edition 01 · 2013
Photos:ClausDick
8. 8 Big Data automotive · Interview
Special Edition 01 · 2013
At what point in the automotive value chain are you
making Big Data applications available to the depart-
ments and divisions today?
We are looking at Big Data along the entire value chain. We are
now testing the first applications. In these pilot projects, we are
gathering crucial experience to develop solutions up to the roll-
out that meet our customers’ premium requirements.
Could you please be more specific? What is Audi focus-
ing on precisely?
We want to primarily use Big Data technologies in the market-
ing and sales field and in quality assurance – and of course im-
prove functions in the vehicle through the use of Big Data. In
addition to internal data sources, we will turn to external data
sources in the medium term to boost the quality of the analysis
and to guarantee the correct interpretation of the data. Here
weather predictions and other environmental data will play a
major role, for example.
Many analysts say that Big Data can especially help auto
manufacturers establish direct relationships with vehicle
buyers and use them intensively in after-sales. Is that
the way you see it and what steps is Audi taking in this
direction?
Yes, we see things similarly and would above all like to create
added value for customers through the use of Big Data tech-
nologies. Thanks to today’s online systems, we already are in
close contact with them. For example, we are now setting up an
online shop for a range of after-sales services. For one thing, cu-
stomers using this shop will be able to arrange an appointment
at their service center with just one click, from right inside their
vehicles.
Networked systems such as Audi connect open up new
business opportunities. They also drive data growth
within the company. In terms of data delivered per
vehicle and per month, what order of magnitude do you
have to gear up for? And how will the technical resour-
ces of your backend cope with it?
Our backend systems are ready for the expected quantities of
data and can adjust to the requirements flexibly. We expect
daily data volumes in the multi-digit gigabyte range. The data
quantity in this environment depends however on many factors
and can fluctuate greatly. The customer’s usage behavior and
the portfolio of services in the vehicle, regardless of the market
and model, have a crucial impact on it. As a result, scalability
is especially important in this context. We are accomplishing
it with private cloud technologies in our in-house Connect
Center. We can adjust computing power and storage volume to
the demand on short notice.
What software and hardware products are you relying on
»We would above all like to create
added value for customers through
the use of Big Data technologies«
9. Interview · Big Data automotive 9
Special Edition 01 · 2013
in the Big Data field? Do you have to modify or perhaps
re-design your IT architecture?
Last year, we strengthened the backbone of Audi IT with our
new computing center, taking a crucial step for our growth
course. But we still constantly check our system landscape. If
you don’t look for potential improvements and exploit innova-
tions continually, you can’t defend your lead. Today we have
a robust, consolidated IT architecture that we can expand for
specific purposes.
Do you have enough experts on your team who are fami-
liar with complex data analysis?
Today we already have high analytic expertise in-house. But we
want to expand it. In doing so, we are aligning ourselves with
the needs of the departments. This year, Audi will hire 1,500
new employees in Germany. Along with experts in lightweight
construction and e-mobility, we are specifically looking for IT
specialists with a data analysis background who want to join us
in shaping the future.
And now finally, let’s look into the crystal ball: What will
we mean by “big” when we talk about quantities of data
in five years?
The number will be at least in the two-digit petabyte range.
Interview by Ralf Bretting and Hilmar Dunker
Data and facts: Audi AG
On track for success: In 2012, Ingolstadt-based Audi
recorded the greatest growth in its history .
in Euro billion 29 .840
2009
35 .441
2010
44. 096
2011
48. 771
2012
Revenue
50 000
40 000
30 000
20 000
10 000
0
Production volume
in million units 0.93
2009
1.15
2010
1.30
2011
1.46
2012
0,9
0,6
0,3
1,2
1,5
0
Employees
58,011
2009
59, 513
2010
62, 806
2011
67 ,231
2012
40 000
30 000
20 000
10 000
50 000
60 000
70 000
0
Mattias Ulbrich has been CIO of Audi AG in Ingolstadt,
Germany, since February 2012. He holds a degree in electrical
engineering and previously worked as manager of IT integration
and services at Volkswagen for six years. He also managed VW's
ITP customer order process. From 2003 to 2006, the 46-year-old
served as manager, information systems and organization at Seat.
He was manager of information systems for product manufacturing
at Audi in Neckarsulm from 1998 to 2003. Ulbrich is married with
two children.
10. 10 Big Data automotive · In their own words
Special Edition 01 · 2013
»ZF has had Big Data on its radar since last year. After the first stirrings in the market,
we are going to look at and investigate serious ZF applications in our IT innovation
management area in the second half of the year. For example, we can imagine the
evaluation of mass data from the production process and products in the field as part
of continuous quality assurance and improvement«
Peter Kraus, informatics manager, ZF, Friedrichshafen, Germany
»Big Data is a catch phrase with literally a sweeping effect. At the same time, it fits
the core of our development: Information management is what Continental's Interior
Division represents. Just as we can only realize new functions in the vehicle today
through the networking of previously separate systems, the use of multifaceted data
sources in the transportation infrastructure will lead to entirely new functions and,
in the end, to an entirely new quality of driving«
Helmut Matschi, member of the management board, Continental AG, Interior Division, Hanover, Germany
»There are about 2 gigabytes of software code and user data in BMW's latest vehicle
generations. In a few years, the amount will increase tenfold. Then, if our models need
an update, our service partners worldwide will need to be able to call up very large
vehicle-specific and operation-critical quantities of data and load the information into
cars. That is a logistical data challenge that we have to prepare for«
Karl-Erich Probst, CIO, BMW Group, Munich, Germany
»Eight currencies, large product families with numerous subcategories, very different
customers with local requirements – the constraints that affect our parts prices in the
Asia-Pacific region are complex. That’s why we want to use a Big Data solution in the
future that supports our analysts’ pricing with key automatically generated figures
from a variety of data sources. Our model is the services that the auto industry has
successfully used to build ties with its customers«
Raymond L. Osgood, manager of Fiat Industrial's parts business in the Asia-Pacific region
Big Data @Work The buzzword has developed into business
projects. Automakers and suppliers are looking at various options.
Starting grid
12. 12 Big Data automotive · Value Chain
Special Edition 01 · 2013
If methods, software tools and IT infrastructures are the right
fit, Big Data can provide answers to the exciting question of
"what if…?" along the entire automotive value chain.
CRTL-S
Photos:LandRoverIllustration:SabinaVogel
13. Value Chain · Big Data automotive 13
Special Edition 01 · 2013
For years, IT departments trained their users to keep their
data sets and centralized reporting tools as lean as possi-
ble. Storage space was expensive, and evaluations took days.
But now Big Data analytics has turned this paradigm on its
head. Companies store every detail they can get their hands on.
Deciding what happens to this information is the next item on
the agenda. Automakers capture the machine parameters in
their factories, look over their sales organization’s shoulders,
want to know what the buyers of their vehicles do, and evalu-
ate the sensors in on-board electronics. A great deal would be
gained if companies could properly channel this diverse input
and feed it back to the first stages of their value chains. Design
and development departments could round out their exper-
tise and gut instincts with valid feedback from the real world.
Senior management could make better decisions. The perfect
feedback loop along the entire product lifecycle does not exist
– at least not yet. But the industry is arguably seeing the first
landmark projects that demonstrate what Big Data can do.
Development
During the development of the Evoque SUV, designers and
engineers at Jaguar Land Rover day after day filled roughly
a terabyte’s worth of disk space with vehicle simulations. Ex-
tensive virtual prototyping and Big Data comparisons not only
left their mark on the Evoque’s external appearance. They also
changed the way Jaguar Land Rover development teams per-
form their jobs. Since design engineers today define all the
characteristics of every new vehicle digitally, they can keep the
time window for changes open longer before they begin the
first work on hardware. Lifestyle trends, studies of the competi-
tion, and feedback from the market will all have an influence on
product development for an even longer period. Daimler also
wants to lengthen the phase before the so-called "design free-
ze." Development chief Thomas Weber says six months or more
is possible as more mock-ups are carried out digitally. “This is
a great opportunity to score points with a globally distributed
development network,” he told automotiveIT at the opening of
the new Mercedes-Benz research and development center in
India. “It makes us faster, more efficient and saves money.”
Production
In manufacturing, a whole new world is being created by the
combination of cost-effective sensor technologies, high-per-
formance IT infrastructures and highly flexible analysis and
planning systems. The consulting firm Experton is proceeding
under the assumption that materials and production flow can
be further improved in the future. The reason is that nearly all
of the input resources can be located and tracked individually.
“The feedback from the demand side migrates in nearly real-
time through the various stages of the supplier and production
chain, providing the best possible control of the output quanti-
ty and the materials and energy sources that are used," wrote
analysts Carlo Velten and Steve Janata in their strategy paper
“Big Data Business Models 2013.” If you look at the productivity
trend in industry in the last 50 years, they said, "you have to ex-
pect that the use of internet technologies in combination with
Big Data processes will trigger more advances in productivity.”
The number of sensors in industrial use is due to triple by 2015.
Product
BMW wants to take the forecast function in its navigation
service to a new level of detail. The calculations are fed a
steady flow of information on personal driving behavior, traffic
light phases, current accident incidence and other factors af-
fecting a selected route. The process depends on correlations
from the various data sources, which are examined and made
available to the driver via ConnectedDrive, in nearly real time.
Sales and after-sales
Detailed analyses of driving behavior can help automakers in
a number of areas. They can set more precise maintenance
intervals, better assure that service visits are based on need,
and actually provide proactive, individualized customer care.
There are millions of diagnostic data points generated daily in
authorized service shops around the world. The information is
already collated to make it easier to locate product defects. The
results can be worth hard cash to an automaker: What service
shops had a similar experience with which models? What so-
lution did they arrive at? The information makes it possible to
isolate a defect’s cause more quickly. Customers with the same
defect can be helped immediately at their local service outlet.
In the past, it took several days or possibly even weeks to collate
the data and experiences and distribute them among service
shops, automakers, suppliers and component makers. In the
age of Big Data, no driver really has to do without his vehicle
for that long.
By Tino Fromme
14. 14 Big Data automotive · SOA platforms
Special Edition 01 · 2013
Big Data, fast data, analytics, intelligence, in-memory – a wealth
of new terms is cropping up on the road to a new IT age. But
none is as important as Hadoop.
Collection basins
Landing zones: The future belongs to software-defined computing centers
CIOs are already thinking about collecting the data distributed across various company entities in a central location and con-
solidating the information with the new Big Data streams they are expecting. These streams must be large, low-priced and
very reliable. They should give IT providers the chance to use their services to bring five critical success factors under one
umbrella:
● The storage of large quantities of data ● Evaluations in real time ● Fast app development ● Coexistence with the legacy
world ● A freely selectable combination of different cloud providers.
Photo:AudiIllustration:SabinaVogel
15. SOA platforms · Big Data automotive 15
Special Edition 01 · 2013
Hadoop is a software framework based on Java. It en-
ables load-intensive processes to be distributed among
thousands of computing hubs and handled in parallel. This
might sounds technical, but it yields tangible advantages. Even
data volumes in the petabyte range are no hindrance to it. And
compared to conventional data warehouses (DWH), Hadoop
systems are very cost-effective because they are based on freely
accessible source code.But there is more: Hadoop can deal with
any format, whether it contains structured data or not. That is
why experts are predicting a great future for the framework:
80 percent of the data that will stream into the global Big Data
universe will land in Hadoop environments.
In view of this development, many CEOs are asking them-
selves whether they might have bet on the wrong horses. The
answer from Germany's high-tech association Bitkom is com-
forting: Companies will combine conventional and new tech-
nologies to gain access to Big Data. Take business intelligence
as an example: It is definitely not dead and remains an impor-
tant aid to business operations. Well-promoted approaches like
the SAP Hana in-memory database can accelerate analyses and
reports many times over. But they remain rooted in the world
of transactional and analytical systems. A co-existence with
the dynamic world of Big Data outside the company boundari-
es now seems to be a more promising approach for companies
to take.
Nonetheless, if companies want to keep pace with the
predicted growth in data, the technology in computing centers
must change. Internet pioneers such as Google, Facebook and
Amazon offer one possible blueprint for action. They have revo-
lutionized the storage and analysis of large quantities of data so
they can constantly make new functions and features available,
no small advantage in this age of social networks and mobile
devices. In most application transactions, several layers of soft-
ware are involved at the same time. And this is a growing trend.
Individual application layers can be found at any given point in
the computing center. They take the form of virtual machines
and can be shifted freely from host to host. If conventional in-
dustrial companies want to keep up, they must try to develop
similar agility piece by piece. In concrete terms, this means they
have to do away with obsolete architectures and bring in new
concepts. Corporate IT must be able to carefully steer the rapid-
ly growing horizontal traffic quickly, with low latency, through
the use of virtualization and multiple transaction layers. “It is
important to have the capacity to quickly analyze data already
stored within the company,” said Paul Maritz, CEO of the EMC
subsidiary Pivotal, which specializes in Big Data and cloud-ba-
sed apps. “But it is even more crucial to have the right concept
to handle the large data stream that is already reaching the new
systems day in and day out.” The key idea here is the internet
of things: Speaking figuratively, practically every technologi-
cal product that we humans manufacture will report its status
to a higher-level control unit in real time. For example, about
30 terabytes of data are produced during a Boeing 777’s trans-
atlantic flight. The information can be examined more close-
ly, and new, enlightening insights can be drawn that improve
airline service, the airplane as a product, and the travel expe-
rience for the passengers. A similar situation is conceivable for
the auto industry if the trend toward the networked vehicle and
car-to-X communication gains strength.
Not every automaker will invest in a cloud infrastructure
with the size and performance capacity that a Google or an
Amazon boasts. Many want to assemble extra computing
capacity with an individualized approach and totally based
on demand. They want to freely select from the cloud services
available on the market. As a result, in most computing cen-
ters, there will be a co-existence for many years between tested,
very efficient mainframe applications as well as new agile apps
in the cloud. Nevertheless, it is a good idea not to implement
one Big Data solution after another. Companies should not
differentiate between content, processes or business areas
either. Instead, the goal must be a central platform that sup-
ports a variety of applications and is available company-wide
as a “shared service.”
By Ralf Bretting
16. 16 Big Data automotive · Expertise
Special Edition 01 · 2013
Increasingly large and multifaceted data volumes are
emerging worldwide from digital processes and networked
value chains. They offer companies an unprecedented oppor-
tunity. When firms proactively analyze the massive streams of
technical data, condense the information into useful knowledge
automatically, and integrate it into their process decisions,
they create a competitive advantage in international competi-
tion. The challenges are as multifaceted as the data volumes
that are being generated and made available at an increasingly
fast pace. Instead of examining individual data silos, the Big
Data approach strives for a holistic, semantic picture from the
data to dynamically support decisions. This requires ways of
merging structured as well as unstructured data, an adaptive,
comprehensive information technology infrastructure as well
as processes for the decentralized analysis of the data streams.
These are just some of the themes that research is now addres-
sing. But one finding in particular has come out of the effort:
Successful solutions not only network data and devices. They
link departments and business processes together as well. Big
Data is not pure technology but rather a strategic issue.
The customer “at the wheel”
Some of the familiar goals that Big Data can re-conceptualize
include understanding the product in the context of its use,
improving and safeguarding production proactively, or develo-
ping innovative new products. An example from the Fraunhofer
Institute for Intelligent Analysis and Information Systems
(IAIS) shows where Big Data can provide support: Intelligent
processes using semantic text analysis are identifying emotions
attached to vehicles, components and manufacturers, all from
user contributions to 30 million posts to an automotive forum.
Global thinking yields individual perceptions, important topics,
and the moods of various markets. If you combine this informa-
tion with data from current production, on-board diagnoses,
or customer and service shop reports, you gain insights that
are just as valuable for market research as they are for product
development or quality management at manufacturers and
suppliers. In this way, customer needs can be addressed more
individually (“social context aware marketing”). When em-
ployed correctly, Big Data has the potential to change our every-
day automotive life in much the same way that the smartphone
is changing other routines today. The main future themes for
the auto sector, worked out in a Fraunhofer IAIS seminar with
industry representatives, show the potential for automotive
innovation that Big Data holds. This includes increasingly in-
dividualized services as well as intermodal utilization models.
There are still more opportunities in supply chain manage-
ment, manufacturing and vehicle development. Here are some
examples:
• resource conservation in manufacturing
• industrial-private partnerships for product development
with customers
• individualized product-service packages
• more efficient management and intelligent process control
(partially automated decision-making in processes)
• early identification and quality assurance during business
operations (from manufacture to use to recycling)
Big Data solutions have a broad technological basis and rely on
special expertise during implementation. The core is made up
of a flexible, scalable IT architecture that combines the various
Big Data tools and frameworks in a task-specific arrangement.In
this process, companies can choose from a series of commercial
or open source tools. Yet making the right choice can frequently
be a challenge. Research offers support in the form of best prac-
tices, living-labs Big Data and the development of specialized
analytic processes:
Photo:FraunhoferIAIS
Roadmap With targeted data evaluations, automakers and sup-
pliers can take the lead in competition. Hendrik Stange of Germany's
Fraunhofer Institute for Intelligent Analysis and Information Systems
(IAIS), highlights the opportunities for processes and products.
Big Data in Motion
17. Expertise · Big Data automotive 17
Special Edition 01 · 2013
• Text analytics is a bundle of technologies to evaluate un-
structured text data (the evaluation of log and report data,
customer dialog and logging analysis, campaign monitoring
• Process analytics provides important insights into processes
and optimized infrastructures (condition monitoring, predic-
tive maintenance, analytical SCM, operational excellence)
• Big Data analytics offers scalable analytical processes and
allows the secure, decentralized monitoring of complex
infrastructures, direct analysis of the data stream (in-stream
and embedded analytics, data mining with integrated data
protection, vehicle sensor analysis)
• Image processing provides processes for automatic extraction
of information from large amounts of image data (traffic
sign recognition, blind spot monitoring, driver assistance)
• Visual analytics puts experts at the controls of an interactive
visualization environment and a real-time dashboard, and
allows ad hoc analyses (for pattern searches, spatiotemporal
analyses, forecasts, etc.).
A number of factors are indispensable to companies wishing to
take advantage of Big Data. They include a comprehensive un-
derstanding of Big Data concepts, technologies, and processes
with an extremely high degree of quality. And it is just as impor-
tant to meet requirements for the protection of sensitive data
covered by compliance rules. Data protection and data security
are a top priority as soon as personal information is integrated.
With “Privacy by Design,” data protection and data security be-
come the fundamental component of any solution.
Summary
Information technology, analytics and industrial controllers are
coming together at an increasingly rapid pace. The associated
paradigm change is expected to make company management,
production and value creation more flexible, creative and net-
worked, without being stymied by the generation of the data.
That said, Big Data is a guiding concept for Industry 4.0. Parti-
cularly intelligent and adaptive systems are laying the corner-
stone for an automotive “Big Data Factory.” In the process, the
networking of the data from business and production processes
is enhanced by the sensor systems and diagnostic capabilities
in the vehicles. For the automotive sector, this has additional
significance. Big Data is happening on the road, too.
Hendrik Stange
Hendrik Stange studied information science with a
focus on data mining and corporate governance at
the Otto von Guericke University in Magdeburg. Since
2007, he has been an analyst in the Knowledge Disco-
very department at Germany's Fraunhofer Institute for
Intelligent Analysis and Information Systems (IAIS),
and has been a project manager there since 2009. His
current research emphasis is on Big Data analytics
and the specialized field of reality monitoring. Stange
helps companies deal strategically with data as a “raw
material” and key competitive factor as they strive to
become “data-driven enterprises.”
18. 18 Big Data automotive · Company information
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