Managementkompass

Big Data Analytics

:Trends

First Mover Advantage

:Think Tank

Anti Financial

Crime Management

:Practice

OTTO, PAYD, dm drogerie

:Contents

Preface

Executive summary

3

“Leading edge knowledge” 4

Trends

First Mover Advantage

6

Think Tank

On the data treadmill

8

Hype or disruption? 11 Anti Financial Crime Management 16

Tools

Getting started with Big Data

13

Cross-check

14

Practice

_ OTTO: Automated decisions

18

_ A revolution in vehicle insurance 20 _ dm-drogerie: Staff planning 22

Viewpoint

Clarifying legal issues

23

References

24

Glossary

26

Publication data

Disclaimer: All information in this booklet has been carefully researched. The

editor, publisher and distributor accept no liability for the accuracy and

completeness of the content or for any interim changes.

© March 2014

Steria Mummert Consulting GmbH

Hans-Henny-Jahnn-Weg 29, 22085 Hamburg

F.A.Z.-Institut für Management-, Markt- und Medieninformationen GmbH, Frankenallee 68–72, 60327 Frankfurt am Main (also the publisher; general manager: Volker Sach)

All rights reserved, including photographic reproduction and storage in

electronic media.

Editor-in-chief and main author (unless specified otherwise): Eric Czotscher Design and typesetting: Christine Lambert Proofreading: Anna-Luise Knetsch

Printing and processing:

Boschen Offsetdruck GmbH

Alpenroder Straße 14, 65936 Frankfurt am Main

www.boschendruck.de

Printed using eco inks on environmentally-friendly paper.

This booklet has been produced with no adverse impact on the climate. The associated CO2 emissions have been offset through climate protection projects.

ISBN: 978-3-89981-387-6

:Preface

//// For most companies Big Data means having to

deal with spiralling mountains of data. However, Big

Data Analytics methods and tools create value

through their ability to analyse data of different kinds,

to aggregate it and connect it in order to recognise

patterns - whether in customers’ behaviour or risk

factors. Best practices show us that we can attain a

new level of data analysis with Big Data that puts its

current business intelligence application in the shade.

The revelations about the National Security Agency

(NSA) in the US show that there are also risks

involved in doing this. Companies wishing to fully

exploit their data potential should ensure they have

good data governance right from the start. Informing

clients about the advantages of new tools, such as

those used for product design or security, is part of

this governance.

<< Huge opportunities arise as a result

of the intelligent handling of very

large amounts of data. Thus, targeted

customer groups experience a

paradigm change: using Big Data,

companies like ours can make the

right offer to clients at the right time

through the right channels -

independent of the environment or

the hitherto commonly-used online

targeting methods.

Bernhard Brugger, CEO Central

Europe, PAYBACK GmbH

>>

Using Best Practice, the Big Data Analytics

“management compass” reveals the possibilities that

are opened up by new data analysis methods.

However, these are still early days for the subject,

and company decision-makers need to throw their

own ideas into the mix. In principle, every sector and

business segment can use Big Data. You are limited

only by your own imagination.

Big Data Analytics does not necessarily lead to new

insight or new business models, but the current

applications show that, at the very least, the efficiency

and effectiveness of existing processes can be

improved. In an era of non-differentiated products and

services, data-based business processes can make

all the difference. In order to keep costs under control,

a structured approach to Big Data Analytics is

recommended.

<< Big Data has been integrated into

the logistics sector for a long time.

A good example of this is our

“Resilience 360” solution, in which

the analysis of aggregated data

helps to improve supply chains and

protect them against disruption.

This ensures smooth operations

and improves customer

satisfaction.

Dr. Markus Kückelhaus, Head of Trend

research, DHL Customer Solutions &

Innovation

>>

The benchmarks of value-orientated company

management form seven central management

disciplines. Each article in this document contains a

management compass with the relevant disciplines

highlighted. The “cross-check” on page 14 provides a

general overview.

Big Data Analytics provides new insights into your

business, some of which may be counter-intuitive. In

this sense, Big Data can really open our eyes. As

Goethe said, “you only see what you know”. We hope

you find this booklet interesting. II

Steria Mummert Consulting

F.A.Z.-Institut

1 Costs Management

2 Transformation Management

3 Process Management

4 Innovation Management

5 Customer Management

6 Cooperation Management

7 Risk Management

3

:Executive summary

4

“Leading edge knowledge”

Company decision-makers can use Big Data Analytics to learn more about their

business and uncover the hidden success factors. This knowledge enables

better decisions and leads to better company results. However, the pressure to

act on Big Data is currently coming more from data centres than it is from the

strategic side of business: in IT, new technological solutions to manage the

growing data flow are urgently required.

1 : Management recommendation

Hype or revolution: you should use the new methods

and technologies for the analysis of large volumes of

diverse - and polystructured - data. As best practices

from different sectors shows, Big Data has the

potential for both new business models and new

competitors. As a disruptive technology, Big Data

may unbalance existing markets. Therefore, it is

worth taking a closer look.

Big Data delivers specific information with commercial

relevance. Thus banks, for example, can plan which

customers they should contact, when and about what

financial product. If specific information is brought

together, more precise statements about the

creditworthiness of people or companies can be

made. Attempted insurance fraud can be discovered

using specific data patterns, etc.

The Big Data “data scientists” have a different

approach than traditional analysts. Instead of gaining

a causal understanding of relationships and then

testing them against reality, these data scientists

allow the data to speak for itself, while they apply

intelligent algorithms to process large volumes of the

most varied data. New knowledge arises through

creativity and experimentation, and through testing

hypotheses or by chance.

Big Data goes beyond Business Intelligence (BI). It

requires new technologies, as well as new forms of

analysis and presentation, of which many IT

departments have had no experience. Compared to

BI, Big Data has greater tool flexibility. Companies

can assemble their toolbox individually and integrate

existing BI applications into it. Much of this is still in

development.

2

2 : Management recommendation

Big Data supports the control of fast-growing data

volumes. It helps with in-depth analysis of these data

volumes and in the rapid production of

recommendations for your business. Without suitable

analysis tools, this would be like looking for a needle

in a haystack.

In Big Data applications, the processing of datasets is

spread among computer clusters. This also enables

the efficient analysis of large volumes of data. One

consequence of this is that data specialists do not

have to take any statistical samples for their analyses,

but can work with the entire data stock. This makes

prognoses more reliable.

3 : Management recommendation

Use additional data sources to gain a deeper

understanding of your business. As well as analysing

structured data from your operational systems, Big

Data enables you to consider unstructured data like

text, speech, photos and videos from internal and

external sources. For example, an analysis of social

media comments can teach you more about your

customers’ needs.

Executive Summary // Big Data Analytics // Managementkompass

5

The bulk of existing data is unstructured. A content-

based classification through textual analysis

transforms unstructured data into a structured form.

This is how quantitative and qualitative analyses

become possible. For instance, companies already

use unstructured social media data in order to gain

advance warning of market trends and reputational

risks.

4 : Management recommendation

Test the possibilities of Big Data for real-time data

analysis. This is especially important if you offer

digital and mobile services, for which you must react

quickly to customer wishes to gain a competitive

advantage. Big Data Analytics can also be integrated

into business processes in order to make decisions

automatically.

Processes supported by Big Data can provide

customers with timely offers that are suitable for their

situations, based on the location data of their mobile

devices or their digital surfing patterns. Credit card

fraud attempts and the like can be automatically

stopped.

5 : Management recommendation

Data is often so vitally important for service providers

that the transformation into a data-based company

can produce a decisive competitive advantage.

This applies not only to customer and price

management but also to operations such as HR,

Finance or Risk Control. How does your business

model work? Could you optimise, or possibly

automate, your business decisions based on data?

In using Big Data, you should adopt a structured

approach and take into account possible data risks

(see checklist on page 13). Support from top

management is just as important in this as close

cooperation between IT and other departments. You

should ensure solid data governance right from the

start. This includes transparency of the data stock,

data sources and data variety. Only this will allow you

to manage, validate and analyse the data effectively.

Data from external sources contains risks: it may

come from hacked websites and contain sensitive

personal data, false links or viruses, etc. In particular,

social media data contains a lot of “noise” that must

be filtered out.

Managers often make decisions based on gut instinct.

They do consider the available information but this is

usually incomplete and not always up-to-date, so their

intuition must fill the gap. Thanks to Big Data, these

gaps in information could in the future become much

smaller. However, management experience and

intelligence will remain important for strategic

decisions. II

The make-up of Big Data: The three Vs

Volume

Data volume

Since IT-supported business processes continuously produce data, more and more

companies and institutions are holding gigantic, petabyte-sized data mountains.

Alongside internal data, a variety of external data sources, devices and machines are

producing a constant data stream.

Variety

Data variety

Data comes from many sources and is of various kinds. In broad terms, it can be

classified as unstructured, semi-structured and structured. The technological

evaluation of such polystructured data through textual analysis or image recognition

has improved massively.

Velocity

Data speed

Data-supported processes require data collection, integration and analysis to be

carried out increasingly quickly - often in real time - in order to reach relevant

conclusions or induce business actions. Furthermore, data structures, sources and

interfaces are changing very quickly.

6

:Trends

First Mover Advantage

Big Data is trendy and, by virtue of the data espionage and National Security

Agency (NSA) scandal, has even made headline news in the global media. In

practice, however, companies are not fully using the new possibilities presented

by Big Data. Users often limit themselves to the trusted field of Business

Intelligence (BI), but with more data being processed, it becomes commercially

interesting when Big Data brings up new offers and business models.

Eric Czotscher

is Head of

Research/Market research

and editor-in-chief of the

F.A.Z. Institute for

Management, Market and

Media Information.

//// Even when companies talk a lot about Big Data,

only a few of them move forward with innovative

applications – it’s an opportunity for pioneers. With

the new kind of data analysis, companies can learn

more about their customers’ needs and create offers

with added value. Changes in the market can be

identified earlier and companies can then react

quicker to them. But this does not cover the entire

field of application. Based on Big Data Analytics,

intelligent products and services can be developed

which notice changes in the environment and

customer needs and adapt to them. In this case, data

from mobile devices, sensors, social media news,

photos, videos, etc. are used as input.

There is already a host of IT tools for Big Data but

most companies do not know how to use them.

According to a recent study by Saugatuck

Technology, 71% of IT and business decision-makers

regard Big Data Analytics as competitively relevant.

However, 59% lack the resources to use it. Only half

of them have introduced Big Data Analytics into their

businesses. In order to discover the gold in the data

mountain, not only IT skills but also mathematical

knowledge and creativity are required. “Data

scientists”, who hunt out hidden trends using

intelligent algorithms, are much sought-after. They

supply new knowledge and create competitive

advantage for companies. As Chris Anderson, editor

of technology magazine “Wired”, puts it: “The new

availability of huge amounts of data, along with the

statistical tools to crunch these numbers, offers a

whole new way of understanding the world.”

: Pioneers as idea providers

There are some great examples of Big Data users

across diverse sectors. Automotive giant BMW links

production data, vehicle data, maintenance and

customer data to improve quality management.

Telecoms firm China Mobile analyses connection data

in order to slow customer churn and improve service

quality. Retailer dm drogerie uses Big Data in staff

planning (see page 22), while JP Morgan Chase and

many other banks use it in fraud prevention (page

16).

Financial services providers are trailblazers - they

possess extensive data and IT expertise. A survey of

100 top decision-makers carried out for the

“Branchenkompass 2013 Versicherungen” by Steria

Mummert Consulting and F.A.Z.-Institut confirms this.

According to the survey, 24% of insurers in Germany

want to invest in Big Data in the short term and 38%

in the mid to long term. By 2016, every third company

intends to have Big Data solutions in place for sales

management. One of the planned applications is

usage-based vehicle insurance premiums (see page

20).

There is also demand from government and the

public sector. According to the “Branchenkompass

2013 Public Services”, the relevance of reporting will

intensify appreciably in the near future. 69% of those

questioned expect more reporting by 2015. The

growing volume of data means that new analytical

tools will be required in order to improve

administrative control and efficiency. II

Trends // Big Data Analytics // Managementkompass

7

Customers open to intelligent offers

A 2014 survey found that German consumers tend to be

sceptical about passing on data to companies.

Nevertheless, 50% are in principle prepared to disclose

private preferences and activities so that companies can

make them bespoke offers. In addition, about 50% of

social media users accept, at least partially, companies

using information from social media in order to get to

know potential customers better and make appropriate

offers to them. However, 46% of citizens assume that

companies do not implement customers’ suggestions on

product improvements. This gives companies something

to act on. These are the results of Steria Mummert

Consulting’s study, “Potenzialanalyse Big Data”. In

Germany in January 2014, 1000 adults took part in the

survey as part of an online panel. The data is

representative of the German population.

The aim of the survey was to discover customer interest

in offers produced or optimised using Big Data.

Applications were drawn from four fields (see the table

on page 12): efficient processes, mass customisation,

market analysis and intelligent, self-regulating products.

The survey respondents provided information on their

knowledge of the current offers, the expected benefit

and their personal willingness to buy. They were also

asked for their thoughts on data protection. Among the

offers sampled were digitally supported processes that

do not (yet) use Big Data tools. Companies are

increasingly using them as data sources for Big Data

analyses.

These products also include solutions for intelligent

electricity use (smart meters) and for the intelligent

home (smart home). About 60% of respondents knew

about these products. At least 29% of them already use

smart meters and 22% use home products. For smart

meters, 33% of respondents said that they did not use

these products on data protection grounds; for smart

homes, 37% gave the same answer. The rest, on their

own admission, are simply not interested. Typically,

people who do not yet know about these products are

more receptive to using them: 74% can imagine

installing a smart meter and 65% a smart home device.

This suggests that in future the producers of smart

meters and smart home devices should explain their

services better and highlight their data protection

measures.

The survey respondents reacted significantly more

positively when offered actual individual products from

the smart home range. 86% of them rated energy-

saving intelligent thermostats as helpful. The most

popular among useful products that independently

exchange data in order to increase security or efficiency

are systems to locate stolen cars. 82% see a value in

car-to-car communication that warns of hazards or

traffic jams. These types of applications generate large

volumes of varied data that can be evaluated using Big

Data tools to improve processes or adapt services.

Intelligent products with a high level of usefulness

(Usefulness of products which independently exchange data; in percentage of respondents1))

1) In January 2014, 1000 adults took part in an online survey in Germany; multiple answers were possible.

Source: Potenzialanalyse Big Data (Steria Mummert Consulting).

Technology to locate stolen cars

Intelligent thermostats that save energy

Car-to-Car communication that warns of hazards

Safety technology in cars that activates an emergency call in the event of an accident

House technology that lowers blinds when it gets dark outside

Black box in the car to correct driving behaviour (e.g. to prevent rear-end collisions

Vacuum cleaner that adapts suction power depending on how dirty the floor is

Fridge that notices out-of-date goods and writes a shopping list

Smart phone that communicates with your car and indicates fuel level

extremely helpful quite helpful

8

: Think Tank

On the data treadmill

The volumes of data handled worldwide are growing in leaps and bounds. The

new Big Data technologies and methods offer the capability to analyse this data

advantageously. As such, current data warehouses must be enlarged to be

able to use innovative behaviour prediction tools. However, in view of the

constant increase in investment required for IT, companies should analyse the

economic benefit closely.

Prof. Dr. Peter Chamoni holds the Chair in Business IT, esp. Business Intelligence, at the Mercator School of Management, University of Duisburg-Essen.

Jens Kaufmann

is on the staff of the

Department of Business IT,

esp. Business Intelligence,

at the Mercator School of

Management, University of

Duisburg-Essen.

//// If “more” is really better, then Big Data is justifiably

one of the most current and most widely discussed

topics in data processing and analysis. Recent years

and decades have seen IT departments continually

trying to gain control over an ever-increasing

mountain of data. It felt as if requests for more

storage space were being made more frequently than

holiday requests. Suddenly, however, everything has

changed. Big Data is more than just “more”.

The glossary of this management compass (see page

26) neatly encapsulates the current understanding of

the concept: Big Data means “methods and

technologies for the highly scalable capture, storage

and analysis of polystructured data.” This definition

has its origins in the “3Vs” model that Doug Laney

developed back in 2001 (see page 5 and graphic on

this page). At its core, Big Data links large volumes of

data, varied data structures and a high speed of data

production and processing.

: Barely believable volumes of data Regardless of which research you look at, the fact

remains that the raw volume of data is growing

constantly and will continue to do so. People are

talking about zettabytes and yottabytes of new data

being produced each year - volumes so large that

extraordinary comparisons are being used to visualise

them. Whether it is really helpful to know that all the

data produced in 2012 in printed form would create a

stack of paper 600 times bigger than the distance

from Earth to the Sun is dubious - nonetheless, it is a

striking fact.

If all this data is put together with Facebook and

Twitter accounts, with moods and purchasing

opportunities announced almost in real time using

photos and videos, it inevitably attracts the attention

both of commercial and technical decision-makers.

They all agree that something must be done with this

data.

: Cost savings still in the foreground

Interestingly, the most commonly listed expectations

of Big Data are savings in IT department costs (57%)

and business processes (61%), according to a study

by the market research company IDC.

The three Vs of Big Data

Volume

Source: Modelled on TDWI (2011): Big Data Analytics,

TDWI Best Practices Report.

Tera-, Peta-, Exabytes mass transaction data days, months, years

on time in real time streaming

structured semi-structured unstructured

all at the same time

Big Data

Velocity Variety

Think Tank // Big Data Analytics // Managementkompass

9

At the same time, the extensive study,

“biMA®2012/13” by Steria Mummert Consulting (see

page 11), shows that no “killer application” has yet

been identified by users. That means a special use

with a unique selling point is yet to be found. Until

now, the objectives of BI have simply been mapped

onto Big Data. The biMA® study shows that, in

general, users want a better, deeper understanding of

their own companies and the market. This

demonstrates that Big Data is still the new kid on the

block in data analysis terms.

: Data warehouses in flux

It is easier to see the impacts of Big Data on existing

system architectures for data analysis. For several

years, the data warehouse has been established as

the preeminent concept for company-wide, integrated

data storage to support decision-making. The aim of

this was to create a single, homogeneous,

redundancy-free environment into which all the

source systems could deliver data in an allowed and

regular fashion. Analysis and reporting was carried

out on this basis and divergences between plans and

actuality can be established precisely and with

certainty. Exception reports were sent out on time.

Specialised analysts had a body of material that they

could dissect and interpret down to the last comma.

Quite apart from the fact that the specified aims were

seldom perfectly attained in reality, this approach is

barely conceivable today. This is due to the variety of

the data, the ongoing supply of new data and the

incorporation of unconverted data formats (websites,

unverified geodata, scanned forms, photos, videos).

The current data warehouse options must be

extended to include data handling mechanisms that

can also process unstructured data. Hadoop - not

least due to its use by Google and Facebook - is

playing a leading role as a framework for this process.

: New methods of analysis

Since the homogenisation of raw data is no longer

really possible, the focus has shifted to more

comprehensive methods of analysis. Text mining and

natural speech analyses are more frequently than

ever required to extract information from the data.

The linkage of data with one of the best-developed

human abilities, the recognition of patterns in visually

processed data, is also being promoted. People can

interpret the aggregated presentation of many

individual items of information in the form of graphics

more quickly, easily and simply than an automated,

pre-interpreted analysis presented in table form; this

is the case regardless of whether customer churn,

traffic flows or credit card fraud is being analysed.

: Profitable Predictions

Most sectors could benefit from Big Data - the only

issue is at what point. Now it appears that one of the

most exciting and biggest challenges for many

companies is within reach: the ability to look into the

future. Using the catchphrase “Predictive Analytics”,

today’s algorithms can, for instance, not only check

several tens of thousands of fraud cases or loans that

have been carefully prepared and fed into the

database, but also include individual characteristics in

the check in order to make predictions about possible

fraudsters.

In relation specifically to Germany, wherever the strict

data protection laws try to prevent citizen

transparency, there is a wide scope of application for

Predictive Analytics. Thus telecommunications

providers can predict their customers’ general

willingness to change providers on the basis of

millions of items of connection data and entries in

social media; they can then very quickly identify

changes in user behaviour in order to make early

contact with potential leavers. For example, according

to Bitkom (the German association for information

technology, telecommunication and new media),

Telecom Italia is permanently evaluating a pool of 500

million items of connection data for this purpose.

: Recognising

patterns

Graphic

representations of

complicated

situations make

analysis easier

: Data protection

In spite of strict

regulations, Big Data

analyses are possible

Managementkompass // Big Data Analytics // Think Tank

10

Busin

ess/S

pecia

list d

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nt

(da

ta v

olu

me t

o

be a

naly

se

d) pote

ntial o

ptim

um

poin

ts

for

an

aly

sis

/investm

ent

: Many sectors are benefiting

In the health sector, the capture of non-standardised

responses on internet forums is allowing long-term

feedback on the effectiveness of medicines. For

example the Israeli firm Treato is learning from

thousands of sources what the side effects of

medicinal products are - on a global basis.

Specialist departments’ demand constantly rising

Analysis

demand not met

: Credit cards

Big Data is quicker

than the fraudsters

The finance sector is also benefiting from the

possibilities of real-time analysis and the availability of

billions of items of stored transaction data. Credit card

fraud depends on fraudsters’ speed and their skilful,

unobtrusive approach. Using Big Data methods,

institutions can compensate for their current, slower

processing speeds and increase the number of

transactions verified. This can reduce the estimated

annual 10 billion euros in losses caused globally by

credit card fraud. This is not just a matter of

optimising processes but actually a case of reducing

costs.

The question remains of whether all this is the brave

new world of large volumes of data or whether the

technology has reached a point where so much more

data can be processed that no-one notices when a

few million photos, videos and blog entries are stored

that have nothing to do with, for instance, the core

task of analysing transactions in the finance sector.

: Race between the desire for data and IT provision

If a specialist department requires better technology

to be able to analyse more data and the IT

department provides this technology, the speed and

quality of data analysis is improved. Next, the

specialist department will accelerate its demands -

regardless of whether increased data actually

provides an advantage in analysis. If the IT

department follows suit, the game begins all over

again. Analysis options and investments are

continually increasing and an optimal combination is

hard to recognise. Depending on your

Analysis

demand

comfortably met

IT (technology for analysis)

Source: Prof. Dr. Peter Chamoni.

point of view, this either is a vicious circle or it

demonstrates the positive feedback of technology and

information - the classic chicken and egg situation.

: Everyday Big Data - coming soon?

You can think what you like about Big Data - you may

be fascinated or irritated, optimistic or sceptical,

investment-happy or cautious - it will become

ubiquitous. So in five years’ time it is unlikely that Big

Data will still grace every third front page. The same

applies and has applied to smartphones, tablets and

the internet. These technologies have become

commonplace in such a short time that they no longer

require great press attention.

However, it is worth reminding ourselves that

technology is not a fast sell: the hype around video-

telephony and Second Life has taught us that. As the

German comedian Karl Valentin wisely put it: “Where

everybody thinks the same, not much gets thought.”

So do we need Big Data, as far as we understand it to

be? A clear answer: perhaps. Certainly, there is a lot

of potential in the ideas and technologies involved.

We need to enter into a critical debate about it. II

: Think Tank

11

Hype or disruption?

The term “Big Data” is on everyone’s lips. The prospect of one’s business being

able to garner valuable gems of information from a host of varied data makes

Big Data attractive. However, behind the concept, is there really something

completely new, something that is different from current approaches to data

analysis and which opens up new possibilities? Or is this just more marketing

hype?

//// Big Data consists of more than simply

exponentially growing data volumes to be processed

and analysed. Alongside the volumes, there are

challenges in terms of complexity and speed of

operations, integration and analysis of data with a

highly varied structure and origin. In this respect, the

characteristic qualities of Big Data are often called the

3Vs: Volume, Variety and Velocity (see table on page

5 and graphic on page 8).

Accordingly, Big Data consists of methods and

technologies for the highly scalable integration,

storage and analysis of polystructured data. Thus it is

not solely a matter of technical issues. Scalability

refers to data volumes as well as to data currency

and the capacity/efficiency of complex analyses. Big

Data architecture includes all levels of integration,

from storage to data analysis. The term

“polystructured data” refers to the different types of

data: from structured to semi-structured to

unstructured.

The integration, storage and analysis of data in order

to gain a better commercial understanding is hardly

new. For many years, under the heading of Business

Intelligence (BI), businesses have been bringing

together optional data from various IT systems -

usually operational ones - and producing reports and

analyses based on that data. In this sense, Big Data

is not in competition with classical BI but

complements or extends it.

: Data - the oil of the 21st Century

The importance of data is currently being re-

assessed. It is becoming an important production

factor for companies, perhaps even the most

important one. Data changes and improves business

processes and models and companies can improve

their quality, efficiency and effectiveness using better

information. All the signs are pointing towards the

“information-driven company”.

At present, most companies are still in the orientation

phase. Discussions on Big Data centre on the

technology, while the commercial benefits are often

ignored. However, for the overwhelming majority of

companies in Europe, the productive Big Data era has

not yet begun. Only 23% of them rate the topic as

relevant or very relevant to them. Many companies

are still waiting for convincing examples before

engaging with the relevance of Big Data. These

are the results of the study, “Business Intelligence

Maturity Audit (biMA®2012/13)”, carried out by

Steria Mummert Consulting with participants from

20 European countries.

: Four fields of application

The variety of possible applications demonstrates the

broad spectrum of Big Data: from internal analyses

(processes, results of planning/predictions) to

external analyses of the current market (products,

competitors, customers, suppliers). Combining the

two dimensions of issues for analysis and business

model, we can differentiate four classes of Big Data

applications in a matrix.

Dr. Carsten Dittmar

is Senior Manager

Enterprise Information

Management at Steria

Mummert Consulting.

Managementkompass // Big Data Analytics // Think Tank

12

Com

pa

ny

1

2

4

Issue

s fo

r analy

sis

Mark

et

Business model type

existing business model

new business model

3

: Process optimisation

More

exact

predictions for

sales or wear

and tear

The first category contains applications promising

more efficient processes and better management.

The existing business model is unchanged but

internal company procedures are optimised. Thus, in

retail, more precise predictions can be made taking

into account various factors, about when, in what

quantity and which product must be sold and re-

ordered (Forward Demand). Regardless of the sector,

analysis of sensor data can predict the wear and tear

on machines in order to plan timely maintenance

(Predictive Maintenance).

The second category is formed by applications for

mass customisation. If systems take into account all

the relevant information on individual customers

during the processing of a request, customised

services can be created for them. This improves

internal company processes for customer

communication (e.g. recommendation for location-

based services). Mass customisation allows the

process of product production to be customised so

much that new business models may arise, e.g. use-

based insurance products (see page 20).

The third category consists of Big Data applications

that, in the opinion of many different market

participants, are derived from an aggregated overall

estimate. One example of this is the analysis of brand

or product perception on the internet (Sentiment

Analysis). The business model is unchanged by this.

Finally, in the fourth category we find applications that

enable intelligent new products - in concert with new

business models. Hitherto isolated data sources,

which can also be generated and managed by

different market players, are included in this category.

Thus alliances are formed between previously

separate sectors such as telecommunications, the

automotive industry and services. Current examples

of this are intelligent thermostats, self-regulating

houses and driverless vehicles. In the future, more

and more products and machines will be equipped

with Big Data intelligence in order to process and

react to sensor data.

The ability to react quickly and flexibly to new market

situations will in the future be more vital than ever for

commercial success. Successful companies will be

those that know how to extract the gold from their

data mountains. II

New possibilities created by Big Data – four fields of application

3 Market analysis

1 More

efficient processes

and management

4 Intelligent

products

2 Mass

Customisat

ion

More efficient processes and management: Greater predictive security e.g. for sales, energy use, wear and tear; logistics management by GPS.

Mass Customisation: Analysis of customer behaviour (360 degree vision) for more customised customer communications or tailored services (e.g. Next Best Offer, campaign management, use-based billing) and Fraud Management.

Market analysis: Identifying opinion-shapers and trends in companies and products (e.g. advertising impact, brand perception, sentiment analysis).

Intelligent products: Machines or devices that regulate themselves using sensor analysis (e.g.: driverless cars, self-regulating houses).

Source: Steria Mummert Consulting.

13

: Tools

Getting started with Big Data

Investments in Big Data should have a concrete objective. Initially, decision-

makers must clarify departmental requirements and obtain the available data

sources. Sometimes what is in the foreground only allows for a hunch about the

data’s hidden potential. Because of this, Big Data projects are also like

experiments where different hypotheses are tested. The following checklist will

help you to adopt a structured approach.

Setting targets

■ Why does the department concerned want to introduce the data analysis? Which challenges can it overcome with

Big Data that cannot be managed by using existing Business Intelligence solutions (see page 5, the 3Vs of Big

Data)? What Best Practices from other companies could provide ideas for introducing Big Data?

■ Which commercial expectations are associated with a Big Data solution? Will additional revenue be generated or

costs saved? Will risks be reduced? Will product quality or service levels be improved? Precise quantitative or

qualitative targets will enable a subsequent actual vs target comparison (e.g. Cross-/Up-Selling volumes, customer

churn rate, error ratio) and the calculation of the RoI.

Data situation

■ Which data sources are available for the desired analyses? What additional data can be used? Examples: internal

data produced by existing business processes but not used; external data from suppliers and customers; external

data from private and public data providers.

■ Is data from social media (posts, text, photos, videos) used to obtain meaningful results? Marketing and

distribution can benefit from insights into (potential) customers’ tastes and purchasing behaviour, as can product

development and reputation management.

■ What granularity and quality does the available data have? Are there possibilities for improvement here?

■ How is data protection and data security guaranteed in the evaluation of the intended data sources (e.g.

anonymisation)? Are all legal and internal requirements met?

IT infrastructure

■ Data systems should be as simple, robust and error-tolerant as possible and allow for expansion to new functions.

Ad hoc requests should also be possible. When real-time processing is necessary, the latency period must be low.

Systems often used for Big Data are Hadoop (parallel data processing shared across computer clusters using

MapReduce) or in-memory computing.

■ Can an existing BI platform be used as a basis for the first Big Data project? Can new analytical tools be linked

to it? The advantage of this is a uniform solution for BI and Big Data.

■ Are there cost-efficient solutions to close gaps? Which resources can be used in the Public Cloud (e.g. storage

capacity, processor capacities, data analysis tools)?

IT alignment

■ How can the cooperation between the IT and other departments be optimised in order to increase the company’s

agility? The company management should expressly support the Big Data project, since the results may make it

necessary to change the processes and the business model.

■ Complex results should be represented graphically, so that they are easier to grasp and practically useful.

Implementation

■ How is the timely use of new information in the business guaranteed? Which processes must be adapted? Is it

possible to automate processes in connection with Big Data?

14

: Cross-check

Big Data supports managers on many levels: it

increases knowledge of markets, customers and

processes, thereby enabling companies to increase

revenues, reduce risks and costs and promote

innovations. One prerequisite is that your company

must be prepared to change into a data-oriented one.

COST MANAGEMENT

Big Data can not only generate higher revenues but also reduce costs. Examples of this are reductions in insurance claims rates and the streamlining of processes. As soon as a Big Data solution is implemented, costs can be reduced or varied - thus services can be withdrawn from a Public Cloud either by using external storage or computer centres external to the data analysis software. Many applications are open-source software and the infrastructure can be built partly from commodity hardware.

RISK MANAGEMENT

Financial service providers, in particular, but also online shops and other companies are benefiting from new applications for identifying fraud and risk analysis. To prevent fraud, thanks to Big Data, suspicious transactions can be analysed more comprehensively and risk management can be improved through more complex calculations with additional data. However, using external data also involves compliance risks. In order to control these risks, companies should put their faith in transparent

data management.

COOPERATION MANAGEMENT

Big Data does not just provide added value for your own business. Cooperation partners and customers can also benefit from the results of the analysis. For instance, DHL offers a predictive tool for companies that estimates future sales in a particular region. Other data, including data on goods delivered, geodata and company code numbers, is also fed into this process. Conversely, companies should test which external data from suppliers, customers and service providers could be useful for their own analysis.

CUSTOMER MANAGEMENT

Thanks to Big Data, companies can speak to more targeted customer segments and produce better-tailored offers. They can react quicker to market changes and adapt products and services accordingly. The impact of campaigns on turnover and profit can be calculated more accurately and measures can be corrected quicker. In price management, additional data sources and real-time analyses enable more accurate calculation and a better appreciation of

customers’ price expectations.

Cross-check// Big Data Analytics // Managementkompass

Sources: Steria Mummert Consulting; F.A.Z.-Institut.

15

TRANSFORMATION MANAGEMENT

Big Data enables a deep understanding of the business and can trigger transformation processes in an open learning culture. To exploit the potential of Big Data, companies must also acquire expertise. This involves further training of the IT team and other departments and possibly the introduction of “data scientists” - creative statisticians, who use algorithms to identify hidden patterns in the flood of data. IT alignment is critical to the success of Big Data. Principles for data handling (Data Governance) are also important.

PROCESS MANAGEMENT

Data analysis in real time enables automated decision processes. As soon as new signals are identified in transaction data, or even in unstructured data such as sensor readings, text, photos or videos, the business processes adapt to stop any errors in payments or production or in order to adapt an online shop’s products to current customer behaviour. Big Data can also become a source of revenue through new, data-driven value creation processes (see cooperation management).

INNOVATION MANAGEMENT

Among other sources, social media analyses can be drawn upon, as well as analyses of sensor data from products that are already in use, to improve quality and develop new products and services. An example of this is the automotive industry; another is the health sector where Big Data can improve treatments, while clinical trials, medical reports, patient blogs on social media and current telemedical readings on patients

can be correlated and investigated.

16

: Think Tank

Anti Financial Crime Management

Banks have always been both victims and tools of financial crime. Because of

this there are regulatory requirements, for instance to prevent money

laundering, and sanctions monitoring. There are also requirements to protect

customers and banks against fraud. Since the earliest amendment to the

MaRisk (minimum requirements for risk management), integrated risk

management has been part of a bank’s basic specifications.

Dr. Jens-Werner Hinrichs

is Principal Consultant Risk,

Finance & Compliance at

Steria Mummert Consulting.

Benjamin Rische

is Risk Consultant, Finance & Compliance at Steria Mummert Consulting.

Costs Transformation Process Innovation Customers Cooperation Risk

Customers

//// Complex fraud methods require mature detection

and prevention procedures. A current example of this

is the recent attacks against the mTAN procedure in

online banking: not only does the attacker try to obtain

a customer’s personal access to the banking portal

but the customer’s smartphone is also infected with

malware which forwards the authorisation text to the

fraudster. In addition, the trace of the fraudulent

payment is removed from accounts by “finance

agents”. In order to detect this kind of refined

malicious transaction and differentiate it from

legitimate behaviour, the context of the transaction

must be examined. In addition, factual, geographical

and personal features of transactions, accounts and

customers over long periods of time and complex

networks of relationships must be analysed. For

banks, this means examining millions of customers

with a variety of accounts and billions of transactions

every year - a job for Big Data.

Analysis requires a variety of policies, including an

investigation of networks of relationships. This

extends the duration of the analyses and too many

suspicious features are produced that have to be

manually cleaned. The number of genuine hits rises

but the error alarm ratio also worsens initially. To

counteract this, tighter rules are required with a more

complex conditional structure or contra-indicators that

recognise legitimate behaviour. Summing up, the

duration times extend under extreme circumstances

and the interval between criminal action and detection

increases.

This restricts opportunities for damage limitation.

Although more powerful hardware can speed up the

process, this does not solve the problem at its root. In

the medium term, the introduction of modern Big Data

Analytics methods is essential. Only they can balance

out the trade-off between quality/currency and costs.

: Days become minutes

Steria Mummert Consulting has produced a “Proof of

Concept” using SAP HANA Fraud Management (see

infobox). The reduction of the run time from several

days to just minutes gives Fraud Management new

possibilities, based on conventional rules. The gap

between criminal action, detection, investigation and

criminal charge narrows from several weeks to under

two days. There is potential for further efficiency and

quality to be developed through additional rules and

methods.

Substantial time savings

Source: Steria Mummert Consulting.

10 million customers

110 million transactions

>100 hours

Standard system

In-memory system

<12 minutes

12 Minutes

Think Tank // Big Data Analytics // Managementkompass

Proof of Concept with SAP HANA

Together with SAP, in 2013 Steria Mummert

Consulting carried out a “Proof of Concept” based on

the SAP HANA in-memory database. In the HANA

solution a conventional anti-money-laundering policy

was implemented consisting of 150 rules. The current

solution for a month’s processing of 10 million

customers and 110 million transactions requires up to

100 hours in total - the HANA application completed it

in four minutes. Both systems used identical

parameterisations and data, as well as a comparable

hardware configuration. Instead of weekend

processing and long wait times, SAP enabled

calculations to be done in one day or in real time. At

present, policies are being expanded to address

additional fraud risks such as mTAN attacks. The next

step is to optimise the policies used in order to lower

the false positive rate and increase the hit quality. In

the future, the SAP HANA compliance application will

be developed as an integrated compliance solution for

anti-money-laundering, financial sanctions and fraud.

Source: Steria Mummert Consulting.

Pote

ntial be

nefit

Tim

e

savin

gs

Qualit

y

impro

ve

ment

Dam

ag

e

pre

ven

tion

17

A clear benefit at every step

3

Paradigm change

2

Adaptation of model

1 Technology

transformation

Source: Steria Mummert Consulting.

Time

This potential may be obvious to IT experts but for the

department concerned the investment is a big step,

especially if the expected benefit is not precisely

quantifiable in advance. This hurdle can be overcome

through a step-by-step introduction of the system, if

the advantages and costs are transparent for each

step.

1. Step: Technology transformation to make time

savings. The existing conventional infrastructure is

replaced by the new technology. The existing check

logic is correspondingly reproduced 1:1. This has the

advantage that existing processes do not have to be

changed or only have to be changed a little.

Investments and friction remain low, while user

acceptance is high. The result of the transformation is

a dramatic time saving in mass data processing.

Instead of processing being spread over days and

weekends, a full processing run can be achieved

every day. The time between criminal action taking

place and charges being brought is significantly

reduced.

2. Step: Improved quality through advanced check

logic. The conventional rule-set is expanded and

tightened, contra-indicators are introduced. This does

not require any special expertise, simply the skills a

company already has at its disposal. The number of

verified hits increases and the number of false

positives falls. Quality optimisation ensures improved

efficiency and saves costs.

3. Paradigm change through improved

transparency and dynamic loss prevention. New

analytical methods and check methods are introduced

as a complement or as a substitute, e.g. customer

segmentation based on transaction behaviour.

Detection is supplemented with independent pattern

identification mechanisms. Predictive Analytics

methods allow suspicious items to be collected and

fraud to be predicted. Risky transactions can be

subjected to a second checking and authorisation

process before being carried out. This prevents

losses. Expertise is essential, if only at this late point,

but it can also be built into the first two steps.

This staged procedure allows potential benefits of Big

Data technologies to be accessed gradually, thus

streamlining investment and process changes.

Big Data technologies allow detection, investigation

and prevention, as well as case management and

reporting on different subject areas to be integrated

into one application. Under the heading “Financial

Crime” there is now a single solution for

comprehensive MaRisk compliance. II

: Practice

18

OTTO: Automated decisions

The multi-channel company OTTO has mastered the transition to online trading

through the continual adaptation of its business processes. The business’s

current focal point is the online shop www.otto.de, which offers over 2 million

items and represents over 80% of the company’s total annual turnover of more

than 2 billion euros. OTTO controls its decision-making processes throughout

the entire product lifecycle using innovative predictive analytics software.

Michael Sinn

is Director of

Category Support at

OTTO.

//// For some years the competitive environment in the

German mail-order industry has been characterised

by market conditions that are changing at an

increasing rate. The dominant role of online trade and

the simultaneous diminishing importance of classic

catalogue-based business have created new

demands. An increasingly extensive range of

products requires, in the background, intensive

management of each article in order to ensure

commercial success. One of the greatest challenges

in this area is how to make early sales predictions for

each article. After all, in a commercial company, the

profitable purchase of goods is the deciding factor for

success.

Demand planning is based on large volumes of data

and influenced by a host of factors. Conventional

statistical processes for sales predictions no longer

meet these new requirements. For that reason,

several years ago OTTO decided to work with Blue

Yonder, a provider of predictive analytics software.

This application allowed the company to put its huge

volumes of data to good use. Thus OTTO became a

Big Data “first mover” in the field of commerce. Since

then, article sales prediction using predictive analytics

has become securely embedded in the operational

business processes.

Technology-driven product lifecycle overview

Trend identification 1 ■ Early identification

■ New information sources

■ Which product will customers

want in the future?

Sales optimisation 4 ■ Flexible pricing

■ Recommendation engine

■ Returns management

■ Efficient stock control

CLOSED

LOOP

Planning 2 ■ Creation of product range

■ Design of product range

■ Volume estimates

■ Commercial control over internal

management

Prediction 3 ■ Sourcing

■ Sale/return prediction

■ Publication management

Source: OTTO.

Practice// Big Data Analytics // Managementkompass

19

Every day, for each article, an updated prediction by

colour and size is produced based on 200 different

variables (e.g. brand, price, online positioning, stock

situation, season). Every year, over 5 billion individual

predictions are produced. From a commercial

viewpoint, this has been a success: the quality of

prediction has improved compared to conventional

procedures by up to 40% per article and the

remaining stock at the end of the season has dropped

by 20%.

: Cycle-oriented optimisation

Building on the success of the article sales

predictions, we are now identifying throughout the

product chain areas that can benefit from the

introduction of the new technologies. This is how the

idea of the “technology-driven product lifecycle” arose

- an integrated system to manage the product

lifecycle. The management tools put us in a position

to make faster and simpler commercially meaningful

decisions about an article. For this purpose, the

product lifecycle is split into four phases that, taken as

a whole, create a closed loop: trend identification,

planning, prediction and sales optimisation.

Some of the tools are already wholly integrated into

operational business processes. Lifecycle

management is being continually refined to take

account of the changes in the fast-moving market.

There are two cornerstones of this process.

: Finding the optimal price

The requirements for intelligent price management

are incomparably higher in online business than in the

era of the mail-order catalogue. Customers expect a

suitable price and, particularly for branded goods,

price transparency is almost 100%. The optimal price

for a product depends on countless factors which may

vary daily. At every point in the product lifecycle, there

is an optimal price for a product: the challenge is to

set it.

Finding the “ideal price” can now be done

successfully using Big Data. In a six-month pilot

project in the field of men’s fashion, we tested how

pricing can be automated in order to increase sales

and profits. Using our predictive analytics software,

we were in a position to optimise sales, turnover and

profits simultaneously. Based on the results, the

system is being gradually introduced for the entire

range. We expect further notable improvement in

company profits as a result.

: Lowering the rate of returns

Goods returned by customers in online trade have

become a factor critical to success. On the one hand,

returns involve high logistics costs, reprocessing

costs and, in unfortunate cases, write-offs for goods

that can no longer be stored or distributed. On the

other hand, returns are a basic component of the

business model. One advantage of mail-order trade is

that the purchaser can try items out at home at their

leisure, then decide and finally, if necessary, send

some of the goods back. With this in mind, OTTO has

started a project to identify the reasons and drivers

behind returns, to quantify them and hence establish

measures to lower their rate.

In an expert workshop, working hypotheses were

formulated that were tested during the project using

the predictive software. As part of a comprehensive

data model, the development of selection ordering (a

customer orders an article in several sizes or

colours), the connection between delivery times and

returns or the influence of promotional description of

an article were investigated. Based on the results, we

took a number of measures that have led to a clear

fall in the returns rate. In total, through these findings,

around 2 million returns have been avoided. II

: Returns factor Competitive advantage versus costs

: Lowering the returns rate Result of Big Data analysis

: Practice

20

PAYD supports four pillars of mobility

Safety – eCall (emergency), bCall (breakdown);

also management of driving times and work times

Security – geotracking and geofencing

Performance – diagnoses and predictive

analyses

Logistics – fleet management, track & trace, cost

analyses and reporting

Source: Steria Mummert Consulting.

A revolution in vehicle insurance

The car as a data source has caught the attention of everyone involved in the

automotive value creation chain. Insurers are expecting telematics to produce

insight into mass customised products. “Pay as you drive” (PAYD) has created

a new business model. This use-based pricing model enables appropriate risk

classification. The innovation: risk can be verified at all times and from day to

day - something current premium models do not provide.

Janina Röttger

is Senior Insurance

Manager at Steria

Mummert Consulting.

Andreas Behrens-Ziegler

is Senior Insurance

Consultant at Steria

Mummert Consulting.

Dr. Carsten Dittmar

is Senior Manager for

Enterprise Information

Management at Steria

Mummert Consulting.

//// Telematics data can be compared to vehicle

information in Formula 1 where engineers use it to

optimise the performance of cars. Vehicle insurers

can now also work with telematics data, thanks to Big

Data Analytics. Using geotracking data and individual

driving behaviour they can obtain information on

position, journey times, routes and even the

acceleration and braking behaviour of the insured

vehicle. From this they can calculate a risk score. It is

a step towards fairer premiums and a new set of

tariffs.

However, it is just the first step. Further information

from the vehicle’s environment or its inner workings

will also be useful, whether used to analyse the

vehicle’s condition or the driving situation. In this way,

product development and marketing can better

respond to individual customer requirements.

: New, usage-based services

Fear of the “transparent customer” is still holding back

the development of Pay-as-you-Drive (PAYD)

premiums although they offer clear added value, and

thereby market potential, for customers and the after-

sales market. In addition, there is the prospect of

fairer prices and numerous possible product

innovations, together with better claim rates for

insurers.

Individual liability and comprehensive insurance are

only a part of the many possible applications for the

car industry. Thus, using telematics, customers can

be informed about new products in a targeted way.

Data from vehicles provides evidence about current

service and guarantee requirements, as well as wear

and tear and defects. Accidents could be more simply

explained and the data collected could help prevent

theft and fraud.

Using telematics, manufacturers can offer guarantees

and garage management ideas that are tailored to

individual customers. Preventative service measures

and similar offers would improve vehicle use. This

would be attractive for commercial customers, leasing

providers and company car fleet managers.

Moreover, damage prevention models could be

extrapolated from the data. Labour law issues could

also be clarified more easily and, for instance,

compliance with due diligence obligations for

professional drivers could be checked.

: The right strategy is crucial

Competition for customers is increasing. The question

now is: which companies in the vehicle value creation

chain are using Big Data and focusing their entire

service on mobility (see four pillars, below) and

thereby gaining direct customer contact?

Practice// Big Data Analytics // Managementkompass

A market example - discount from a black box

What if someone regularly drives faster than they

are allowed, or always breaks the rules of the road?

A black box attached to a vehicle’s on-board

electronics can record all these things. At regular

intervals it sends data on the vehicle’s location,

speed and acceleration to a computer centre.

Due to data protection, the insurer does not know

the identity of the driver, only the customer

identification number. The data is stored in a similar

way to mobile phone data. Once a month, the data

is aggregated and sent via encrypted transmission

and a score is calculated. It is essentially an

assessment of good driving. If the driving behaviour

is perfect, the customer receives 100 points.

Deductions are made not only for illegal driving,

such as speeding, but also for driving behaviour

that insurance statisticians say create a higher risk

of accidents.

The German Sparkassenversicherung is the first

insurer in Germany to offer this model on a

voluntary basis. Since 1 January 2014, it has been

offering a policy which gives a 5% discount on the

premium if a car owner allows a black box to be

installed in their car. This can prove that the owner

is a careful driver.

There is a lot of interest in this. In a recent survey

51% of respondents said that they would be

interested in installing a black box if it saved them

money. In the short time since its introduction, the

insurance company appears to have concluded

hundreds of contracts. In its first phase, participant

numbers are restricted to 1,000. However, an

extension is planned. Source: F.A.S., 12.01.2014.

21

Telematics strategies for insurers

1) On-board unit.

2) Business Process Management.

Source: Steria Mummert Consulting

They can cooperate or compete with other mobility

providers. They just have to safeguard their original

business area against new competitors.

The technology is now enabling the broad introduction

of PAYD both in the new and used car segment. The

question then arises of how the market will change if

PAYD is introduced not only in small market

segments but in a company’s entire stock, e.g. by a

car manufacturer.

Fixed installations of on-board units (OBUs or “black

boxes”) and corresponding smartphone applications

for telematics could significantly change the

calculation bases for insurance portfolios.

Society’s attitudes towards usage-based services

should change in the short to medium term, since

there is both political and commercial interest in the

application of telematics. For the insurance sector,

this will depend on how the EU regulates customer

access in the motor industry. Will there be free

access? Will automobile manufacturers also control

insurance services at the point of sale?

Customers can expect more attractive and flexible

pricing models. The technology, as well as the

corresponding added value services and the

increasing transparency, should make it easier for

customers to trust telematics even where data

protection risks cannot be completely removed.

An increase in competition from new market

participants like Google or as yet unknown insurance

partners is, in view of the changing business model,

not out of the question. II

: Fear of data

abuse

Greater trust

through

transparency

Segment 1

New cars

Segment 2

Used cars

Mobile technology or fixed installation

Without

retrofitting of OBU1)

With

retrofitting of OBU1)

Customer group, business area

New contracts

for vehicles

Stock

actions

Customer strategy re. insurance and telematics Technology I Technology II

Stock management systems, Big Data tools, etc.

Connection and management of external IT

Services I+II Product and business model

Application Management, Testing, Infrastructure management,

BPO2)

Services

Product design, cost effectiveness, customer relevance

: Practice

22

dm-drogerie: Staff planning

In the financial year 2012/13, dm-drogerie markt had a turnover throughout

Europe of 7.69 billion euros. Over 34,000 people work in the company’s markets

in Germany, in distribution centres and in its headquarters in Karlsruhe. Every

day, using predictive analytics, dm produces reliable predictions of the daily

turnover in its individual markets - it can then plan its staffing requirements

efficiently.

Roman Melcher

is IT Director at dm-

drogerie markt.

//// Few commercial companies in Germany have had

the ranking success of dm-drogerie markt, one

example being its “Most popular German chemist’s

chain” award. This is partly due to the fact that the

company regards and respects its customers and

staff as individuals and is guided by their needs in its

decision-making.

This also includes giving staff enough time to advise

customers, even if there is a lot of activity in the

branch concerned. For this purpose, dm had its own

proprietary software for planning staffing

requirements, but this soon reached its limits.

: Effective predictive tool

dm decided on a predictive analytics solution that

helps branch managers draw up a precise staffing

plan for each day. We can now produce reliable

predictions of the daily turnover for each branch on a

daily basis and plan our staffing accordingly.

The solution even allows us to account for various

exceptional circumstances and thus avoid overstaffing

or understaffing. Four to eight weeks in advance, staff

in each branch enter their personal preferences in the

staffing plan, which they then rely on it once it has

been adjusted. Short-term changes have become

rare.

: External data

Alongside the daily turnover, the distribution centres’

pallet delivery predictions and parameters specific to

individual branches, such as opening times, are also

included in the plan. Both are needed in order to

predict the staff requirement as accurately as

possible. Even incoming goods have a significant

impact on planning.

In addition, expected turnover is essential for capacity

planning. On days when we expect a higher turnover,

more of our colleagues are in place in order to serve

our customers with the least delay possible.

Furthermore, additional data relevant to capacity is

included, even external data. This data includes

market days, holidays in neighbouring countries, or

perhaps a construction site on an access road. Even

the weather forecast could be taken into account in

the future. Predictions are now so close to reality that

we can base our operational processes on them and

plan for the future. In the meantime, dm is putting its

predictive analytics solution into action in another

area: supplier requirements. II

: Viewpoint

Clarifying legal issues

Through Big Data technologies, data is becoming an important business asset.

However, legal issues are also involved in eliciting and using data. To whom

does the data belong, particularly if it comes from different sources? How can

data transactions be designed with legal security? What does data protection

require, if personal references occur through the linkage of (initially) anonymous

data sets from different sources?

//// When companies talk about “their” data and

its commercial value, they often overlook the

fact that there is no ownership of data under

civil law. Even copyright law protects only a

portion of data, for example data sets with

creative content such as music data and text

data. It does not cover geodata, climate data,

sensor data or measurement data. Apart from

trade secrecy and goodwill, this excludes the

proprietary attribution of data - all the more so if

the data comes from different sources.

For example, in the automotive industry, entire

value creation chains are built on the extraction

and exploitation of vehicle movement data. In

this respect, automobile manufacturers,

maintenance services, telematics operators,

insurers, authorities, vehicle owners and drivers

all have different interests, even contrary ones.

To whom does the data belong? Who may use

it or transfer it to others and who must ask

whom for permission to do so? Creative

contractual solutions are required. They are

only partially helpful if the object of the contract

cannot clearly be assigned legally. The problem

is the same for insolvency: what must the

administrator release or how can he use data

with legal security? This question may seem

trivial with regard to individual data sets but is a

central, public legal issue for large volumes of

data.

: Data use and data protection

Database producers have the exclusive right to

the database created by them for 15 years

in order to cover their investment in the

structured system and access to the data (§87a

UrhG, German copyright law). They may transfer

database rights, licence its use or exclude others

from using it. Everything points to an increase in

database licence contracts. However, what about

unstructured data? Can it be used by anyone, or

can contracts ensure legal security? We need to

start by developing contractual best practice and

risk hedging.

In many Big Data cases, there are no personal

references so data protection is not applicable.

Nevertheless, a data protection analysis is

essential and often more relevant than we might

think. On the one hand, this is due to the broad

concept of data with personal references (which

includes IP addresses, among other things). On

the other hand, the linkage of anonymous data

sources may possibly lead to profiles and

personal references. The complexity grows

where data sources are subject to different

national data protection regulations. Those who

have the opportunity to use Big Data must also

bear compliance in mind and carefully address

data origins.

: Open Data and Innovation

Under the recently established Directive on

Public Sector Information (PSI Directive), state

bodies in the EU must provide on-going access

to their data sources. As of 2015, major data

sources will be accessible at low cost and will be

available for Big Data applications. II

Dr. Alexander Duisberg

is Partner and Joint Head

of the International IT

Sector Group at Bird &

Bird LLP.

den

23

: references

24

Links and specialist literature

//www.bitkom.de/big-data

The BITKOM Big Data working group has produced free manuals, “Management von Big-Data-Projekten” and

“Big Data im Praxiseinsatz”. In addition, BITKOM organises a “Big Data Summit” every year.

//www.tdwi.eu

Platform for exchanging views on Business Intelligence (BI) and Data Warehousing.

//medium.com/what-i-learned-building/1b8e3214f96

Big Data checklist by Dimitry Korolev.

//www.kaggle.com

Open competition platform for problem solving using Big Data algorithms.

//www.steria.com/de/bi/bidm-einblicke/europaeische-bima-studie-201213

“Business Intelligence Maturity Audit (biMA®2012/13)” study by Steria Mummert Consulting.

//www.journalofbigdata.com

Open Access journal, “Journal of Big Data” with scientific articles.

Technology and Business

Judith Hurwitz: Big Data for Dummies. Wiley-VCH 2013 (English).

An easily understandable, practice-orientated introduction to Big Data as a management tool. Using the didactic

approach of starting from scratch, readers quickly learn how to implement Big Data in their own business. The

book even enables people who are not IT experts to prepare their first Big Data project. The author explains the

technical requirements and presents the main Big Data Analytics tools. She also shows how these tools can be

linked to existing database structures and Business Intelligence applications.

Pavlo Baron: Big Data for IT decision-makers. Using huge volumes of data and modern technologies to generate

profits. Hanser 2013.

Baron addresses IT experts who have little experience of Big Data. In his view, companies can now no longer

bypass Big Data. The future lies in new, data-based and thereby customer-orientated business models. He

recommends focusing all processes now on obtaining and processing information relevant to business.

However, there is no single Big Data architecture that fits all. Big Data is a growing “ecosystem” containing a

wealth of technologies and components from which every company has to construct appropriate tools.

Ramon Wartala: Hadoop - permissible, distributed and scalable Big Data applications. Open Source

Press 2012.

A book for Big Data practitioners who want to work with Hadoop. Alongside the installation, configuration and

introduction of Hadoop, the author uses practical examples to show how to implement MapReduce-based

applications step by step using Hadoop. He gives tips on error searches and on optimising MapReduce jobs. He

rounds off the book with a chapter on the many applications of Hadoop and a chapter on introducing the

practice in German-speaking countries.

25

References// Big Data Analytics // Management compass

Eric Redmond and Jim R. Wilson: Seven weeks, seven databases - a guide to modern databases and the

NoSQL movement. O’Reilly 2012.

A book for IT developers looking for the right database for their Big Data problem. The authors present seven

open source databases from different fields of application: Redis, Neo4j, CouchDB, MongoDB, HBase, Riak and

PostgreSQL. Readers can test what they learn immediately on their own computers with the help of downloads;

they can also learn how to create platforms from several databases.

Consequences for the economy, society and research

Viktor Mayer-Schönberger and Kenneth Cukier: Big Data - A Revolution That Will Transform How We Live,

Work, and Think. Redline 2013.

Data analysis instead of gut feeling: the authors see Big Data as a new form of creating knowledge. No longer

will the analysis of causal relationships be the goal of researchers but tracking useful correlations using

algorithms. Benefits may apply, for example, in successful marketing, customer retention, innovation or risk

management. The authors show what possibilities Big Data opens up but warn against the loss of the private

sphere.

Heinrich Geiselberger and Tobias Moorstedt (Editors): Big Data - the new promise of omniscience. Suhrkamp

edition unseld 2013.

Exciting articles by IT experts, sociologists and humanities experts on the future of Big Data. Among the authors

are Chris Anderson, Kate Crawford and Cameron Marlow. The reader gains an overview of data mining,

algorithms, textual analysis and predictive analytics. In the light of the NSA affair, critical voices also make their

case. There is an interesting discussion between Michael Hagner and Dirk Helbing on the data-driven “new kind

of science”, which aims to identify correlations rather than develop theories. The extensive glossary is

recommended for Big Data novices.

Rudi Klausnitzer: The end of chance. How Big Data is making us and our lives predictable. Ecowin 2013.

The future belongs to those who know it. Klausnitzer demonstrates the power of predictive analytics and shows

us its social and economic consequences. In almost all areas, according to the author, decisions based on data

analyses are more successful than decisions based on experts. He uses many examples in an informal style to

show the hidden opportunities in Big Data, either in the health or retail sector.

Lateral thinkers

Nate Silver: The Signal and the Noise: Why Most Predictions Fail – but Some Don’t. Heyne 2013.

The statistician Nate Silver shows why predictions by experts often fail and how prediction can be improved

through a more open, self-critical method. He warns against overrating Big Data. The flood of data may disguise

our view of causal relationships. Correlations alone are not enough to make good predictions.

Daniel C. Dennett: Intuition Pumps. Allen Lane 2013 (English).

The cognitive scientist and philosopher Daniel Dennett uses selected “tools for thinking” to show how people

think, decide and act. His “intuition pumps” critically examine received wisdom about human behaviour. The

book is a useful companion for data scientists wanting to generate new knowledge from Big Data.

: Glossary

26

Algorithm

Step-by-step solution to a problem through the

application of precisely defined calculation rules.

Big Data

Methods and technologies for the highly scalable

capture, storage and analysis of polystructured data.

Large volumes of data of highly varied structure and

origin, obtained partly in real time, can be used by Big

Data technologies for complex analyses. Big Data

Analytics includes data mining methods for the

analysis of this data.

Data mining

Systematic use of statistical/mathematical methods in

order to identify causal patterns in data sets.

Data scientist

New profession connected with Big Data. Compared

to conventional data analysts, data scientists have

more mathematical and technological knowledge, as

well as business knowledge and special creativity.

They are tasked with using data mining and intelligent

algorithms to track down business-relevant

information in the data. Data artists visualise this

information by depicting these complicated

relationships graphically.

Data warehouse

Historic, operative data from various data silos is

placed in databases for analysis in order to analyse

the data (data mining) and prepare to make

management decisions. In hybrid models, the data

warehouse sets the context for Big Data analyses.

Geofencing

Linking of geoinformation systems and localisation of

objects in order to act, if a specific object leaves or

enters a pre-defined area.

Hadoop

Open source data system by the Apache Software

Foundation, based on MapReduce. Large analysis

tasks are split into small jobs for distributed computer

clusters to solve in parallel.

MapReduce

Algorithm that shares the processing of large datasets

among several computers working in parallel. Initially,

the jobs are shared among various computer clusters

(“map”), next, the individual results produce an overall

result (“result”). In doing so, different data sources

and formats are used.

MaRisk

Circular 10/2012 (BA) with minimum requirements for

risk management as specified by German regulatory

authority BaFin for risk management by credit

institutions and financial services institutions in

Germany.

mTan

German security standard.

Next Best Offer

Products and services that have a high probability of

interesting customers in their next purchase,

especially in e-commerce.

Not only sQL (NosQL)

Type of database that uses a non-relational approach.

In contrast to SQL databases, NoSQL databases do

not need any specified table schemas.

Predictive Analytics

A type of data mining that introduces statistical

calculations, elements of play theory and operations

research methods in order to make predictions about

future behaviour.

Public Cloud

Offer by a free-to-access provider that makes its

services accessible to anyone via the internet.

Public Sector Information Directive (PSI

Directive)

Directive 2003/98/EC of the European Parliament on

the re-use of public sector information. The

information re-use law, in force since 19 December

2006, implements the PSI Directive in Germany.

Unstructured data

Data with non-formalised content. The data is in

digital form but cannot be processed without further

input from databases and analytical tools. It must first

be modelled and translated into structured data.

Examples: text, video, photos, e-mails, agreements,

guidelines, satellite imagery.

: Current studies

Managementkompass Demographiemanagement

Demographic change is draining the available labour force.

Companies therefore need to orient their staff planning over the

long term. Topics: strategic staff planning, talent management, life-

phase-orientated staff policies, Generation Z. With articles by

Fraport, the City of Munich and Deutsche Bank.

Managementkompass Customer Centricity

Customer focus means more than excellent services. In the

internet, mobile and social media era, the design of customer

relations has reached a new dimension with a host of possibilities

and requirements. With articles by the Generali insurance group,

SCHUFA and Fidor Bank.

Branchenkompass 2013 Versicherungen

Recent survey of 100 top decision-makers in 100 of the largest

insurance firms and brokers on sector trends, as well as growth

strategies and investment goals up to 2016. Core topics:

marketing and customer management, new regulations and M&A.

Also in our range of studies:

_ Managementkompass: incl. sourcing strategies, channel management, earnings protection in the

financial crisis, sustainable value creation, corporate governance, cost-efficient regulation

_ Branchenkompass: incl. public services, telecommunications, credit institutions

_ Kundenkompass: incl. patient requirements, social media in the health sector

_ Themenkompass: incl. health at work

In preparation:

Managementkompass Mobility, Branchenkompass 2014 Energieversorger

Apply to:

F.A.Z.-Institut für Management-, Markt- und Medieninformationen GmbH

P.O. Box 20 01 63, 60605 Frankfurt am Main, Tel.: 0 69 / 75 91 - 21 29, Fax: 0 69 / 75 91 - 19 66

E-Mail: vertrieb@faz-institut.de, Internet: www.branchendienste.de

Contact:

Steria Mummert Consulting GmbH Corporate Communications Birgit Eckmüller Hans-Henny-Jahnn-Weg 29 22085 Hamburg Phone: 0 40 / 2 27 03 - 52 19 Fax: 0 40 / 2 27 03 - 12 19 E-Mail: birgit.eckmueller@steria-mummert.de

F.A.Z.-Institut für Management-, Markt- und Medieninformationen GmbH

Eric Czotscher P.O. Box 20 01 63 60605 Frankfurt am Main

Phone: 0 69 / 75 91 - 32 75 Fax: 0 69 / 75 91 - 19 66 E-Mail: e.czotscher@faz-institut.de

ISBN: 978-3-89981-387-6