T E A C H I N G C A S E
Understanding the value and organizational implications of big
data analytics: the case of AUDI AG
Christian Dremel1 • Jochen Wulf1 • Annegret Maier2 • Walter Brenner1
Published online: 13 April 2018
� Association for Information Technology Trust 2018
Abstract ‘‘Understanding the value and organizational
implications of big data analytics: the case of AUDI AG’’
presents the case of AUDI AG and its attempts to
implement big data analytics in its organization. The case
highlights the situation of an original equipment manu-
facturer (OEM) in the automotive industry and the
potentials and challenges the emerging technology big
data analytics may entail for such organizations. The case
tries to help students to grasp the technical characteris-
tics, the value, and organizational implications of big
data analytics as well as the distinct types of analytics
services. The case is presented through the eyes of
Hortensie, an aspiring manager at AUDI, who gained
strong interest in the phenomenon of big data analytics
and received the task to position it within AUDI. To
ramp up the topic big data analytics, AUDI is engaging
with industry and design experts as well as an external
consultancy ITConsult.
Keywords Big data analytics � Organizational adoption �
Organizational change � Organizational transformation �
Organizational benefits � Predictive analytics � Descriptive
analytics � Analytics services � Teaching case
Introduction
On a rainy day in autumn 2014 Hortensie woke up with
only one thought on her mind.
1
Today, was her big day.
She had to present the use cases and value potential of big
data analytics in front of the chief of sales and marketing
(CMO), which she had elaborated with her team over the
last work-intensive months. Nicolas Moreau—the new
CMO at AUDI—not only was known for his positive
attitude toward innovativeness but also for his ability to
find any weakness of potential ideas. He was one of the
persons she did not want to disappoint.
She remembered how everything had started: Soon after
she had become manager at AUDI’s sales and marketing
department, Nicolas joined the company. Coming from one
of the haute écoles of Paris, he had gained experiences as
CMO at Renault and at PSA Peugeot Citroën. He had the
mission to put AUDI ahead in regard to profit, earnings,
and, first and foremost, innovativeness. When one of the
first tasks of Nicolas was the elaboration of the value and
the organizational implications of the emerging technology
big data analytics for AUDI’s sales and marketing
department, Hortensie had willingly accepted the position
as lead of the task force.
& Christian Dremel
[email protected]
Jochen Wulf
Walter Brenner
1
Institute of Information Management, University of St.
Gallen, Mueller-Friedberg-Strasse 8, 9000 St. Gallen,
Switzerland
2
Audi AG, 85045 Ingolstadt, Germany
1
This illustrative case is developed on the basis of a longitudinal
case study with AUDI AG (see Dremel et al. 2017). However, due to
reasons of confidentiality, descriptions of the organization’s inner
processes, organizational hierarchies, names, and roles are anon-
ymized. Any views and statements expressed within this teaching
case do not necessarily reflect the views or policies of any individual
or the organization represented by this case.
J Info Technol Teach Cases (2018) 8:126–138
https://doi.org/10.1057/s41266-018-0036-8
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She first had heard of the potential of big data analytics
in the article ‘‘Data Scientists: The Sexiest Job of the 21st
Century’’ in the October Issue of Harvard Review
in 2012. Since then, she learned about anecdotal evidences
of companies profiting from big data analytics such as
Netflix through their challenge for improving the predic-
tion accuracy of future movie ratings depending of a cus-
tomer’s movie preferences priced with 1,000,000 USD, the
Oakland Athletics as described in the book Moneyball by
Michael Lewis and in the same named movie, or LinkedIn
with the People You May Know feature, which had sug-
gested to her an old friend from her studies at the London
School of Economics just last month.
However, AUDI was a company known for its precision
and quality in building cars for the premium segment as
well as their innovative engineering progress, but not for
their data scientists on-site. So, she never thought of the
possibilities of big data analytics at AUDI. But now, with
the assignment of Nicolas, her thinking changed funda-
mentally. An assignment of the CMO not only meant a
huge responsibility but also a huge commitment of one of
the board members and thus power of persuasion. Hence,
she thought it will be easy to get some data, to try some
analytical scenarios in the context of AUDI, and to put
together a nice slide deck proving the value of big data
analytics. After all, she knew that at least one business unit
at the sales and marketing department was successfully
using analytics, in particular data mining, to improve the
product feature combinations in the car configurator.
AUDI’s slogan is ‘‘Truth in Engineering,’’ which is
well established in the corporate culture and brand image.
Consistent with this slogan, the company aims at further
extending its market leadership by leveraging digital
technology to provide superior products and services to its
customers. Being among the top in its market segment,
AUDI aims not only to differentiate products by innova-
tion from competitors but also to stay competitive
investing in new technologies (see Dremel et al. 2017).
To do so, AUDI AG heavily invests in emerging tech-
nologies to improve its core product, the car, for the profit
of the company. In 2015, AUDI shipped more than 2
million luxury cars to the customers worldwide. Origi-
nally established in 1909 by August Horch, the company
was acquired by Volkswagen in 1966. Headquartered in
Ingolstadt, Germany, it has been operating under the
AUDI name since 1985.
However, the traditional business of AUDI was attacked
by traditional car manufactures such as Daimler, BMW,
Volvo, by innovative market entrants such as Tesla, Fara-
day Future, and by tech-giants like Google or Apple, as
well as Uber and other companies providing innovative
mobility services.
Unraveling big data analytics
The first day of the project, Hortensie had a meeting with
the team members: Matthias, a newly hired employee with
a background in statistics and math and an affinity to data
manipulation, Tobias, an employee with 10 years AUDI
background mainly active in a multitude of projects to
improve AUDI’s retail, and Nadine who worked for
8 years in the marketing strategy department at AUDI.
Before the meeting started, Hortensie remembered one
last explanation of Nicolas, when she was assigned with the
new task (see Exhibit 1): ‘‘I briefly discussed with the other
board members whether they would be willing to invest in
a unified data organization, which could leverage big data
analytics along all departments. You know, though every-
one is thinking of this topic as an interesting one, they want
to minimize the risk to invest budget without any additional
profits or cost reductions. We, as the most innovative
department, will at first elaborate use cases for sales and
marketing alone.’’ She had read the article ‘‘How Smart,
Connected Products are Transforming Companies’’ of
Porter and Heppelmann (2015) last Monday and reflected
since then about a unified data organization. She really
liked the idea to leverage data throughout the whole
company. However, if Nicolas had already made up his
mind and discussed this point with the other board mem-
bers, she could invest her efforts elsewhere.
She abounded her thoughts and started the meeting by
asking one simple question: ‘‘As you all know, Nicolas
gave us the task to elaborate scenarios for big data analytics
at AUDI. But what does big data analytics mean concep-
tually? Is it just the visualization of data? Can we distin-
guish distinct types, supposed there are any?’’ Suddenly,
the whole room was filled with silence. No one in this room
had asked themselves this question before this meeting.
After a brief period of time, however, Matthias started: ‘‘In
my opinion, big data analytics is not just the visualization
of data—if you provide services, which are just visualizing
data, they are reporting services. For analytics, you need at
least an analytical model, which derives causalities within
data, may it be a model examining the present, the past, or
the future.’’ Tobias looked rattled and said: ‘‘And what
about all the tasks I had to coordinate to get a visualization
of our customers configuring their cars in our car config-
urator as well as the technology stack we have to pay every
month to our external provider?’’ Matthias briefly thought
about Tobias’ comment and drew a short image illustrating
the distinct types of analytics on the backboard (see Fig. 1).
Referring to Delen and Demirkan (2013) he explained:
‘‘If you simplify big data analytics services, you can dis-
tinguish descriptive and predictive analytics. Both require a
technological infrastructure, integrated data sources, and of
Understanding the value and organizational implications of big data analytics: the case of… 127
course the visualization of data. This holds also true for
reporting services. However, a descriptive analytics service
possibly explains the past or the presence (i.e., what hap-
pened or what is currently happening and why is it most
probably happening) with the help of an explanatory ana-
lytical model. A predictive analytics service on the other
hand looks into the future using an additional predictive
analytical model. Based on historical data and with the
input of current data it explains or extrapolates what will
happen and why it will happen. However, both require a
certain business acumen since, without any business
questions, no answers can be given through any model. Of
course, reporting services require business acumen as well,
but for the sake of simplicity I neglect it here.’’
Moreover, Tobias added, that you have to consider the
technological characteristics of big data itself as well
because they will pose a challenge regarding the techno-
logical infrastructure (see Table 1).
In particular, initial projects had shown that a car pos-
sibly sends 500 signals per second. Thus, car data will
result in immense high volumes of data requiring appro-
priate big data analytics technologies to enable the appro-
priate analysis. In this context, not every technical system
within every car used the same formats and names for the
same data points resulting in a variety of formats. More-
over, AUDI as recognized brand and through their expen-
sive advertisement videos, for instance, the commercials in
super bowls, generated quite a buzz of data in social media,
which not always had the desired amount of veracity.
Following this meeting, Hortensie looked up the article
‘‘Data, information and analytics as services’’ of Delen and
Demirkan (2013), which Tobias had given her. Soon she
realized, that Tobias had not mentioned a last distinct type
of big data analytics ‘‘prescriptive analytics.’’ Whereas
‘‘predictive analytics’’ used data, text, and media mining as
well as forecasting, ‘‘prescriptive analytics’’ uses either
optimization or simulation or decision modeling to suggest
which action a decision maker should perform based on the
analyzed data. She wondered why Tobias had missed out
this type but realized soon that developing prescriptive
analytics services would be something they could consider
in the long run, but right now the technological infras-
tructure constituting of the technology stack and integrated
data sources did not allow prescriptive analytics. More-
over, Hortensie realized that the future organizational unit
will have to deliver analytics-as-a-service. Thus, the
insights of analytics services will have to be accessible
through a standardized interface such as an analytics
platform.
Bringing big data analytics to AUDI
Soon after, Tobias and Matthias had identified an external
service provider who was one of the leading IT consul-
tancies. After their successful pitch at a meeting with
Hortensie and the other team members, this consultancy
had the mandate not only to help identify potential use
cases for AUDI’s sales and marketing department but
also to identify potential future work models as well as
organizational implications to implement those use cases in
the future in collaboration with the task force. After 2
Fig. 1 The distinct types of
analytics services at AUDI AG
128 C. Dremel et al.
months and several interviews, Stefan, the senior consul-
tant of ITConsult, called Hortensie late on a Thursday
afternoon. After some small talk, he started: ‘‘Currently,
AUDI is contacting the customer through a multitude of
contact points such as the website, the car configurator,
their dealers, and their car. The interactions of AUDI with
its customer produces data that allows to identify customer
desires, to elaborate their preferences, and behavior.
However, you currently miss on leveraging the potentials
of data-driven marketing because of either no integration of
relevant data sources or a lack in the competence to do so.
Please have a look at Fig. 2, you can see how, on a very
generic level, a potential unit could address possible cus-
tomers, such as AUDI’s business units, dealers, and
importers in collaboration with an analytics partner, who is
capable of conducting the entire process of data connec-
tion, exploration, analysis, and visualization.’’
He, further noted that, first, an analytics team or a
loosely coupled analytical competence center must be
implemented to provide analytics-as-a-service. How the
implementation takes place is highly depending on how
strategically and centralized the sales and marketing
department wants to pursue big data analytics. Moreover, it
will most probably demand quite some investment for the
sales and marketing department. In this context, he handed
her Exhibits 2, 3, and 4. The following Monday Hortensie
had a meeting with Nicolas. Hortensie showed him the
exhibits: ‘‘Exhibits 2 and 3 illustrate approaches for an
organizational embedding of big data analytics, or, more
precisely, of the competencies and capacity we need to
succeed in big data analytics through a new subsidiary.
This subsidiary, as the strategy department informed me,
could not only support big data analytics use cases but also
new digital business models and services. Exhibit 4 on the
other hand represents the embedding in our existing sub-
sidiary InnovativeCar.
2
That way we would minimize our
investment, however, this means also that InnovativeCar as
supporter of our engineering departments is taken over
tasks, which were traditionally in the hand of the sales and
marketing. Exhibit 3 assigns the data analytics unit to the
strategy department and thus ensures cross-departmental
responsibility. Exhibit 2, however, clearly prioritizes sales
and marketing related big data analytics topics.’’
2
InnovativeCar was specifically created to support new innovative
technologies and concepts for cars (e.g., autonomous driving and
electric mobility).
Table 1 Technological characteristics of big data
Characteristic Description Exemplary source at AUDI
Volume High volumes of unstructured, volatile and heterogeneous data
enable a company to broadly generate insights, for instance
on customer sentiments. The sheer amount of data exceeds
the ability of traditional business intelligence systems to
process this data
With over 100 sensors, a car produces up to 25 GB of data per
hour
Variety Variety stands for the various formats of data resulting from
the many data sources that are often unstructured and
inconsistent in their nature. The usage of further data sources
increases the variety of data and thus the complexity to
analyze these data points. Big data characterizes the shift
away from predefined categorizations and data schemas
A connected car needs to communicate with multiple external
data sources (such as traffic lights or other cars) as well as
with the driver (for instance via speech recognition)
Velocity Velocity states that big data is produced in high speed
requiring real-time analysis to achieve decisive insights
A connected car needs to respond to external conditions in
real-time (e.g. critical traffic situations)
Veracity Data sources like social media produce data that carries no
single truth and thus requires big data technologies to assess
data accuracy
AUDI needs to interpret social media buzz, to filter out
deceptive data, and to interpret ambiguous statements
Fig. 2 Conceptual work model of an analytical competence center
Understanding the value and organizational implications of big data analytics: the case of… 129
After a while Nicolas explained, that his strategy team
had already planned to create a new, more agile company
that would be able to develop, design, and operate digital
services and their business models. In his opinion, that
might be one solution where synergies could help not only
the development of digital services through big data ana-
lytics but also vice versa. He said: ‘‘I think one point is
crucial. Every future digital service will not only require
data, may it be data from the customer, the car, marketing
agency, weather agency, and so on, but also produce data
on its own. The more we can use big data analytics to our
advantage, the better will our services be and thus our
value proposition. That is why I want to implement ana-
lytics not only in a new company but also in my sales and
marketing department, and of course we should not miss
out our colleagues at the IT department. However, I still
would like to start step-by-step. This is such a new topic. I
am not only talking about a re-organization but also about
required skill sets. We are at the very beginning of
implementing a data-oriented mindset at AUDI. This holds
true for digitization but also for big data analytics.’’
Hortensie, replied: ‘‘Alright, in this case it would make
most sense to go with Exhibit 2 and assign it to the con-
nected retail unit. Here, we are already trying to improve
our retail through data analysis, for instance a targeted
marketing approach for new models, the optimization of
car feature combinations, and feature usage analyses.’’
Nicolas said: ‘‘Sounds great to me. Maybe, you could
already start with a pilot project to support our introduction
of the upcoming e-tron model of the A3 in Germany.’’
Hortensie left partially happy and partially confused. Was
she the one who would be the manager of this analytics
unit? Nicolas had not said it explicitly but implicitly. The
next day, she received the official mail making her the
manager of the data intelligence subunit in the connected
retail unit. The start of a long journey.
The next day, Nadine came to Hortensie’s office after
Hortensie had explained the decision to her team in a
previous 2-h meeting: ‘‘Looks like we will have to build up
some new competencies and skills in analytics. However, I
am not sure whether I like it to engage with external
agencies. This would mean that right from the start we
become dependent on external agencies.’’ Tobias replied:
‘‘Though I think you are absolutely right, we have no
choice but to work with consultancies till we have our
subsidiary Analytics GmbH which will ensure the techno-
logical and analytical competencies through their data
scientists, big data architects, and visualization experts.’’
Hortensie had already thought about bringing in external
expertise. However, she knew that this will be a chal-
lenging task as it meant to implicitly state that right now
the IT department and her team cannot provide the tech-
nological and analytical expertise required to succeed.
However, the creation of the subsidiary was not planned
before 2015. After one discussion with Tobias and having
reflected on Fig. 1, Hortensie was sure that enough busi-
ness acumen was already available at the sales and mar-
keting department. In regard to analytical skills as well as
some technological tasks, however, they fell short in
capacity, skills, and, most of all, experience. Hence, she
and her team had to collaborate with external agencies with
the goal to substitute the agencies with the innovative
subsidiary in the medium term. So, at first Hortensie’s team
and the agencies developed pilot uses cases with Tableau
as well as SPSS Modeler as first tools. Soon, they found
pilot customers with whom they carried out first projects in
the sales and marketing department.
Finally, when the subsidiary was created, Hortensie tried
to sketch how the data analytics unit at sales and market-
ing, the IT department, and most importantly the subsidiary
Analytics GmbH would collaborate and work together:
‘‘Internally, it’s a cross-functional team consisting of our
unit, the Analytics GmbH, and the IT department to ensure
the required capabilities. The data scientists and big data
architects need to work with database engineers, the busi-
ness owner, and people from the sales and marketing
department who are advocating for the end user. It will take
a lot of collaboration.’’
Achieving commitment for big data analytics
The respective parties had to adopt an attitude that values
data along with a data-specific technical infrastructure.
AUDI needed to institutionalize a practice of sharing data
in a standardized way across teams, whether it will be sales
or manufacturing, and integrate it in a central database.
These standards, along with technical systems for ware-
housing and organizing data, had to be established as soon
as possible and facilitated through a defined change man-
agement plan.
To elaborate use cases, industry experts, designers, and
AUDI representatives collaboratively conducted an inno-
vation workshop. Hortensie and her team was excited about
the atmosphere of innovation during these days. However,
most of the potential use cases that were identified would
only be possible in the future when some homework such
as setting up a technology stack and integrating all required
data sources would have been done. Nevertheless, the data
analytics unit started to think about the key steps to derive
insights (see Fig. 3).
In between the discussion rounds, a guest speaker from
General Electric mentioned: ‘‘GE makes large machines
like jet engines and locomotives. GE realized that these big
machines are just commodities and the value to their cus-
tomers lies in telling them more about the machine: What’s
130 C. Dremel et al.
wrong with the machine so they can maintain it more
efficiently. How the machine is performing so its perfor-
mance can be increased. When the machine will need
maintenance so they can schedule the downtime. What
they do is put sensors on the machine and what I do is look
at all this data coming in, look at the people who are going
to use that data, find out what metrics are useful for them,
and present it in a way that makes sense for them.’’
Hortensie reflected on this interesting comment—of course
AUDI was as well producing some kind of machine that
the customers are using to get from point A to point B.
Thus, the similarities to this comment were obvious to her.
Another participant mentioned: ‘‘Oftentimes data means
uncertainty. That’s the biggest source of hesitancy in larger
companies. People ask: Is this is a science experiment?
That is a blanket term, with a little bit more understanding
and empathy, companies could actually differentiate
between a moon shot and what actually makes very clear
sense.’’
Hortensie summarized the workshop: ‘‘Both the creative
teams and industry experts emphasized the importance of
first defining and understanding the end user. Data should
be thought of as adding value for the customer: as some-
thing that might be given back to customers as a mean-
ingful service. In order to engage with users, especially as
concerns over data privacy and ownership grow, human-
centered design methods and a focus on user experience
should drive the development of new products and ser-
vices. Your shared advice included activating emotion,
drawing on both convention and novelty, and empowering
users as active participants.’’
This workshop resulted in a multitude of ideas for new
use cases, for instance the optimization of marketing
approaches based on sociodemographic information and
customer sales data or the optimal planning of sales num-
bers based on historic information. Moreover, the teams
had created a quick proof-of-concept for a reporting service
that describes the usage of AUDI connect (see Table 2).
As a starting point, the business analytics unit developed
a pilot use case for the service MicroTargeting (see
Table 3). To do so, the respective data were gathered in the
form of a data snapshot, because the technological infras-
tructure had not yet been set up. The data included internal
data from AUDI AG and importers (e.g., purchase history
and car specifications of the analyzed car) and were enri-
ched with external data (e.g., sociodemographics, socio-
geographics, behavioral variables, innovation affinity, and
price sensitivity). Afterward, the data were visualized with
the help of a visualization software to elaborate the data
richness and quality. Next, an analytical model was
developed and, in a second step, an analysis of the data
screenshot took place using clustering analysis to group
similar customers. At last, the results were visualized a
second time in a customer-specific dashboard.
Although the first pilot projects led to an initial com-
mitment for big data analytics, the unit did not manage to
create solutions that could be leveraged in a standardized
way across all countries. One major reason was the data-
centered development of services. Based on available data,
use cases were identified with one pilot customer. The
interest of all potential customers was not required. How-
ever, the first pilot cases needed data from the sales and
marketing business units. Although, of course, big data
Fig. 3 Deriving value from
AUDI’s data
Understanding the value and organizational implications of big data analytics: the case of… 131
analytics would be used for the benefit of the whole
company, Hortensie and her team struggled to get access to
relevant data sources due to a lack of understanding of the
benefit of big data analytics, power issues as well as
departmental boundaries. For instance, Nadine had to col-
lect the data of A3 customers to develop in collaboration
with ITConsult the service MicroTargeting. In a meeting,
she received the answer: ‘‘Sure, we have the data of pre-
vious AUDI A3 customers, but not only we do not have
any statement of our management to share the data but also
I do not get the point how your service should help our
customer targeting at all.’’
Equipped with statistics of the pilot use cases, she
replied: ‘‘We improved our targeting by 10% in Spain and
our project in France shows comparable results. Contacting
customers based on big data analytics instead of just
sending any customer advertisement is what Premium car
manufacturing is about. Or do you want to lose any cus-
tomer just because we are not able to collaborate and share
data?’’ This was not the first time she had to talk straight to
get access to data, and she knew it would not be the last.
That moment she remembered how Stefan had explained to
her: ‘‘AUDI is currently too much characterized by parties
that try to improve the business of their single unit instead
of improving the whole company. Big data analytics,
however, as technology innovation requires a mindset of
data-sharing.’’
Hortensie received quite good feedback for this proof of
concept: ‘‘Microtargeting is like a good sales man in my
dealership…if we have a look at the fluctuation it‘s of real
high value to have data-based evidence standardized and
storable.’’ The positive response to MicroTargeting …
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