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An automotiveIT special edition

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• 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


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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 AnautomotiveITSpecialEdition

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4 Big Data automotive · Game Changer


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.

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Game Changer · Big Data automotive 5


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 Big Data automotive · Interview


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.

Interview · Big Data automotive 7


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8 Big Data automotive · Interview


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«

Interview · Big Data automotive 9


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 Big Data automotive · In their own words


»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


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12 Big Data automotive · Value Chain


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.

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Value Chain · Big Data automotive 13


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.

DevelopmentDuring 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.”

ProductionIn 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.

ProductBMW 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-salesDetailed 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 Big Data automotive · SOA platforms


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.

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SOA platforms · Big Data automotive 15


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 Big Data automotive · Expertise


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: P

hoto

: Fra

unho

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IAIS

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

Expertise · Big Data automotive 17


•Text analytics is a bundle of technologies to evaluate unstructured 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.

SummaryInformation 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 StangeHendrik 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 Big Data automotive · Company information


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automotiveit/volume Volume 5, 2013, frequency 8 x a year, plus 4 x year as carIT

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