Industrial internet big data german market study

Market Evaluation Report about Business
Opportunities in the Field of Data Analytics
Software Applications and Related Services in
Germany
Report Delivery to the FINPRO Project
“Verifying Business Opportunity for
‘Making Most out of Gathered Data”
Focus on Germany
Köln/Dénia February 2015
estupening@estconsulting.de
Tel. 0034 673122136
ESTconsulting Services
Strategische Consultingleistungen
ESTconsulting Services
Strategische Consultingleistungen

PreamblePreamble
• The data, the analysis, interpretation and conclusions are subject to the author‘s
perceptions, opinions, interpretations, and his professional background. The work
was compiled with extraordinary diligence. However, for any further action based
on this report, the author cannot be liable.
• Used data and information sources are manifold: consultants, analysts, vendors,
enduser organisations, inter-trade organisations, reports, statistical institutions,
IT journals, experts.
• For any further publication matter, the originator‘s source of information and its
eventually associated privacy clauses should be considered even if the source is
taken from the Internet.
• Pictures and Icons – if not stated differently - were taken from Pixabay.com and
Clipshrine.com under CCO Public Domain License.
© ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 2

Report StructureReport Structure
1. Scope, Definitions and Background
2. Big Data, IoT, IIoT & Industry 4.0
3. Data Analytics and Big Data
4. Vertical Market Structure and Potentials
5. Customer Readiness
6. Big Data Business
7. Competitive Environment
8. Market Entry
9. Use Cases
10. Conclusions & Recommendations
© ESTconsulting Services 2015 Confidential 3
Customer Focus
Supplier Focus

Report ChapterReport Chapter
8. Market Entry
9. Use Cases
Customer Focus
Supplier Focus

Business
Opportunities
Objectives and Assessment StructureObjectives and Assessment Structure
• ESTconsulting Services shall provide a „Business Opportunity Report“, which discusses the various aspects concerning
market entry, its chances and its challenges. Findings will focus as far as possible on how German SMBs leverage any data,
and what differentiation they gain by making the most out of their data during early stages of IIoT transformation.
• The overall reference base is the so-called “Internet of Things”. It directs with its industrial focus to “Industrial Internet of
Things”. This automatically emphasizes the manufacturing industry. However, other industries will be discussed for
opportunities as well. Central to the “Internet of Things” are processes linked to (Big) data and the various ways to exploit
data for business purposes. Volume, spread sources, ‘real-time’ requirements, and complex contents are characteristics
which compel the use of modern and advanced tools for data gathering, analytics, and presentation.
Source: ESTconsulting Services 2015 Confidential 5© ESTconsulting Services 2015
Big Data
Generation Analytics Delivery
Internet of Things
Industrial IoT
Germany
Finance Utilities Services Manufacturing….
Large
Enterprise
Small and Medium
Sized Business
Public
Sector
Applications
Chapter 1-2
Chapter 3
Chapter 4-5
Chapter 6-7
Chapter 8-10

ScopeScope
• Regional Scope: Germany; if regional view is on a higher level (e.g.
DACH, Western Europe, World), it is assumed that statements are of
generic nature applicable to Germany as well.
• Time Scope: the regular considered timeframe is ‚present‘ and ‚near
future‘ if not differently indicated.
• Object Scope: software tools, solutions and services targeted at
‚smart‘ use of data in terms of generation, management, analysis and
delivery to business processes.
• Industry Scope: there are three levels of differentiation, (1) all
industries and size classes, (2) small and medium sized businesses,
and specific industries like e.g. manufacturing.

DefinitionsDefinitions
• Big Data = large data volumes from any source, which are complex and change rapidly. Thus, they can‘t be
processed, analysed und reported manually through traditional methods and infrastructure. As an extended
view, Big Data adresses also the technologies which are used to gather, structure, analyse and report the
data.
• BI = Business Intelligence = technology based defined process of systematic collection, structuring, analysis,
and presentation of data for the improvement of business decisions and/or operations. Traditionally, BI was
used for controlling, reporting, marketing, and management.
• IoT = Internet of Things = fusion of physical and virtual information by deploying intelligent sensors and
actors to control and to drive processes via Internet or Internet-like computing structures. Partly used
synonyms are „Ubiquitous Computing“.
• IIoT = Industrial Internet of Things = applying the idea of IoT to industrial processes through integration of
physical machinery, sensors, software and network (not necessarily the Internet). IIoT can be regarded as a
sub-layer to the Internet of Things. Synonymous expressions are „Fourth Industrial Revolution“, „Industry
4.0“, „Machine-to-Machine Communications“ (M2M), „Cyber-Physical Systems“.
© ESTconsulting Services 2015 Source: Confidential 7

ConclusionConclusion
• There is an overlap in the practical meaning between ‚Big Data‘ and ‚Business Intelligence‘ if not distinguished by the
amount of data being processed. In both cases, there is a data collection phase, an analytic part, and the reporting or
delivery of results.
• IoT and IIoT relate to processes that produce „Big Data“. Depending on the application which shall be supported, data
generation, analytics and reporting will have a versatile appearance.
• In order to cover both aspects (BI and Big Data), we will refer to the more generic specification (see graph below).
• The contextual framework has changed through IoT and IIoT. The data analytics model still applies whether we look at ‘Big
Data’, ‘IoT’, ‘IIoT’, ‘Digital Factory’ or similar. But, new IT-infrastructure and applications are needed to deploy the data
analytics model in the world of data growth and new emerging challenges.
• Sensors
• RFIDs
• Barcodes
• ERP Systems
• Communications
• Internet
• Surveys
• Processes
• Machines
• Applications SW
Data GenerationData Generation
• Structure
• Analyse
• Visualise
Data AnalyticsData Analytics
• Humans, e.g.
decision making
• Machines, e.g.
calibration
• Processes, e.g.
supply chain
Data DeliveryData Delivery

8. Market Entry
9. Use Cases
Customer Focus
Supplier Focus

What Is Changing the Way IT Is Supporting Business?What Is Changing the Way IT Is Supporting Business?
© ESTconsulting Services 2015 Source: IDC 2013 Confidential 10
3. Platform
2. Platform
1. Platform
Users/Devices Applications/Apps
Billions
Hundreds of Millions
Millions
Millions
Ten Thousands +
Thousands

3rd Platform – Enabler and Source of Changing Environments3rd Platform – Enabler and Source of Changing Environments
© ESTconsulting Services 2015 Source: IDC Predicts the 3rd Platform Will Bring Innovation, Growth, and Disruption Across All Industries in 2015, Dec 2014 Confidential 11
• 3rd Platform – will be the transforming force for every industry, e.g. manufacturing with industry platforms,
public sector with safety, transportation systems, and connected civil services, retail with location based
services.
• 3rd platform accelerates ‘Internet of Things’ related innovations such as embedded systems, platform
solutions and predictive maintenance solutions.
• 3rd platform will fuel strong growth in Big Data spendings on software, hardware and services. As an effect,
‘Data as a Service’ (DaaS) offerings, analytics for rich media (video, audio, image), machine learning
functionality, and IoT analytics will emerge.
• 3rd platform will drive Datacenters to become subject of a transformation process towards new sourcing
models and software-defined infrastructures.
Big Data
Growth
Mobility
Digital
Enterprise
3rd Platform3rd Platform InnovationInnovation SolutionsSolutions
IoT
IIoT
Platforms
Applications
Services
Business
Models

Internet of Things – A Future Consumer/Industrial ScenarioInternet of Things – A Future Consumer/Industrial Scenario
© ESTconsulting Services 2015 Source: http://www.freescale.com/webapp/sps/site/homepage.jsp?code=IOT-INTERNET-OF-THINGS Confidential 12

IoT – It Is a Framework, not a SolutionIoT – It Is a Framework, not a Solution
Applications
Data Analytics
Device Management
Platform
Security
Connectivity
Communications
Control
Business Process
Customers
Intelligent
Assets
Supply Chain
Workers
Employees
ERP & Co.
Services
3rd Party
Services
Intelligent
Products
Strategy

Public
IoT – How to Look at Application MarketsIoT – How to Look at Application Markets
Smart Energy
Smart grid
Fault detection
Power sensors
Consumption meters
Virtual power plants
Smart Buildings
Smart homes
A/C control
Presence sensor
Smart security
Utility metering
Smart Health
Bio sensors
Remote diagnostics
Health monitoring
Ambulances
Smart Transport
Electric mobility
Smart logistics
Infrastructure
High-speed trains
Commuting
Smart Cities
Traffic management
Security
Lighting control
Water management
Smart bins
Manufacturing Utilities Construction Healthcare Automotive
Industry Focus
Area/App Focus
Supplier Focus
Smart Industry
Optim. production
Lighting
Actuators, Robotics
Security
Customer Focus Large
Enterprise
SMB Public
Sector
SW Vendors
Managed Services
Converged Systems Suppliers
Engineering
HW Vendors
DaaS Consulting Solution Vendors
Integration Services Content Providers
Solution Architects

IoT – What Can Be Expected?IoT – What Can Be Expected?
© ESTconsulting Services 2015 Source: Machina Research 2014; BITKOM 2014/2015 Confidential 15
0 1 000 2 000 3 000
Construction
Automotive
Utilities
Smart Cities
Manufacturing
0 50 100 150 200 250
Construction
Automotive
Utilities
Smart Cities
Manufacturing
http://blog.bosch-si.com/categories/internetofthings/2014/05/infographic-
capitalizing-on-the-internet-of-things/
5 Key Markets Worldwide
by 2022 (Total: 596 B€)
Bn€ K Tb
Revenue
Traffic
0
5
10
15
20
25
Chemical
Products
Automotive Mechanical
Engineering
Electric
Equipment
Agriculture ICT
Germany
B€
Revenues by
2025= Manufacturing
Top 5 IT-Management Topics in
Germany 2015
1: Cloud Computing (64%)
2: IT Security (61%)
3: Big Data (48%)
4: Industrial Internet (42%)
5: Mobile Computing (40%)
Generating market growthGenerating market growth
79Bn€
Predictions not
aligned, however, the
trend is obvious.
Predictions not
aligned, however, the
trend is obvious.

Industry 4.0 – Bringing IoT into PracticeIndustry 4.0 – Bringing IoT into Practice
• Industry 4.0 = Industrie 4.0 (German) was incorporated into the German Government’s
high-tech strategy in 2012. The goal is to promote the spread of computerisation in
German industries, particularly in the industrial sector such as ‚Manufacturing‘. It will
bring more intelligence into factories (smart factory) and the associated business
processes with adaptive systems and new resource models as well as the integration of
all stakeholders of the value chain through the connectivity of cyber-physical systems.
Although the foundation for this programme goes back to the year 2006, it can be
regarded as a rather recent initiative as far as the overall perception and the
commercialisation is concerned (see chapter 6).
• The Federal Ministry for Economics and Techonology (BMWi) is actively engaged in
research projects and funding of pilots to enable a greater pervasiveness of IoT.
• Under the scope of «Autonomics programme», in the previous 6 years, 12 pilot projects
were sponsored with focus on SMEs, manufacturing, logistics, and energy. In a further
step the agenda is to support research and pilots for machines, service robots, and other
systems that are able to cope autonomously with complex tasks (www.autonomik.de/en).

• Mobility and Big Data are pillars of the new 3rd platform
triggered IT era. They both are driving the transformation
needs in IT and become substantial parts in concepts of IoT.
• IoT can be regarded as a framework which allows a
magnitude of different definitions, solutions and
classifications. From a generic point of view, the data
analytics model is part of the kernel system in IoT/IIoT-based
processes.
• The way to look at the market of IoT is very complex. Various
focal points (industry, area of application, customer
segment, supplier model) can be used and combined,
resulting in a vast differentiation of application sub-markets.
• As we are still in an early phase of IoT market development
(Hype-Phase), future scenarios as well as market size
projections vary on a broad scale. For Germany, an increase
in perception of ‘Industrie 4.0’ and associated actions can be
stated. The total revenues in the German Manufacturing
sector is estimated to be 79 Billion € in 2025.
• Because of the developments of the 3rd platform and the
governmental actions it can be expected that there will be a
strong market growth in the German Manufacturing markets
fuelling the investments in IT.
Source: ESTconsulting Services 2015, Gartner 2014 Confidential 17© ESTconsulting Services 2015
Gartner‘s Hype-Cycle for Emerging Technologies 2014Gartner‘s Hype-Cycle for Emerging Technologies 2014

8. Market Entry
9. Use Cases
Customer Focus
Supplier Focus

What Is „Data Analytics“?What Is „Data Analytics“?
• Sensors
• RFIDs
• Barcodes
• ERP Systems
• Communications
• Internet
• Surveys
• Processes
• Machines
• Applications SW
Data GenerationData Generation
• Structure
• Analyse
• Visualise
• Humans, e.g.
decision making
• Machines, e.g.
calibration
• Processes, e.g.
supply chain
Data DeliveryData Delivery
• Data Analytics are part of the core value chain to use data for the purpose of IoT applications
and/or Business Intelligence. It is an embedded part of any Big Data approach which uses Big Data
technologies as well as it is core to ‘traditional’ data analytics structures in BI. The difference is
not only a matter of scaling resources according to large or smaller data volumes or to the variety
of data sources. It can be concluded from the newly-created and employed technologies, the
‘real-time’ and automated functionality in capturing and processing data, and the integrated use
of various data sources, sometimes referred to as High Performance Data Analysis (HPDA).
• When the terms Big Data or BI are used, they both include Data Analytics as a core component.
Big Data
Business Intelligence

What Data Sources and Analytic Tools are Actually Used?What Data Sources and Analytic Tools are Actually Used?
© ESTconsulting Services 2015 Source: Analytics: Big Data in der Praxis, IBM Institute for Business Value 2012 Confidential 20
0 20 40 60 80 100
Transactions
Protocols
Event Data
E-Mails
Social Media
Sensors
External Data
RFID/POS Data
Text
Geo Data
Audio
Image/Video
0 20 40 60 80 100
Queries/Reporting
Data Mining
Visualisation
Prediction Models
Optimisation
Simulation
Text to Speech
Geo Analysis
Data Stream Analysis
Image Analysis
Audio Analysis
Data Sources UsedData Sources Used Data Analytics Tools UsedData Analytics Tools Used
Total Sample = 1144, worldwide, all industries, 2012 (24% = no Big Data initiative; 47% have plans; 28% projects running or pilots)
% %
New, Big
Data
Demanding
Sources
Traditional
Sources

German SMBs Still Reluctant towards Big DataGerman SMBs Still Reluctant towards Big Data
© ESTconsulting Services 2015 Source: Potenziale und Einsatz von Big Data, BITKOM 2014 Confidential 21
0 10 20 30 40 50
Not yet considered
Considered, no plans yet
Big Data is planned
Big Data is in use
Big Data Use by Company Size
500+ Employees 50-499 Employees
0 20 40 60 80 100
Others
R&D
Production
Management
HR
IT
Logistics
Controlling
Sales & Marketing
Big Data Use by Business Area and Company Size
500+ Employees 50-499 Employees
%
%
Total Sample = 507, Germany, all industries without public administration, defense,
social insurance, 2014)
But Smaller
SMBs Are
Catching Up
BI ‚Classics‘

Data Analytics and Big Data – Where Is It Going?Data Analytics and Big Data – Where Is It Going?
• More technologies will become combined under the ‘umbrella’ Big Data.
• Advanced analytics will be refined, more use cases emerge.
• Technologies supporting automatic ‘Text-to-Speech’, ‘Speech-to-Text’, and
‘Text-to-Text Translation’ will become more advanced and integral part of
applications.
• Machine learning techniques will become more sophisticated and
integrated into analytic systems to automatically incorporate and utilise
new data sources.
• Integration of content will be functionality of new technologies.
• Business departments are becoming stronger than the IT organisation in
driving demand for analytics applications.

• Continous
process
• Continous
improvements
• Value driven
• Predictive
models
Data Analytics Maturity – What Is Germany‘s Position?Data Analytics Maturity – What Is Germany‘s Position?
Infancy
Ad Hoc
Opportunistic
Repeatable
Managed
Best Practice
• Not aware
• Aware
• No plans
• Experimental
• Pilots
• No processes
• Lack of
resources
• Lack in
infrastructure
• Defining
requirements
• Establish
processes
• Resource
inefficiencies
• Building up
infrastructure
• Strategy
established
• Budgets
• Program
management
• Accepted
• Standard
processes
• Business
adoption
• Measurement of
project
performance
• Investments
• Standards
established
• Security &
compliance
• Directly linked to
business
• Predictive
analytics
Average SMB
Average Large Enterprise
Advanced Large EnterpriseBusiness driven
IT driven
Advanced SMB

How Will Demand Be Evolving?How Will Demand Be Evolving?
• Large enterprises are more agile in looking for competitive advantages.
• SMBs slowly anticipate that Big Data can be a differentiator to competitors.
• Actions, pilots, public attention and the interest of large consulting
companies in Industry 4.0 are promoting the topic, thus, encouraging also
SMBs to consider IT transformation.
• Analytics will go into the cloud, providing advanced techniques and high
processing power to SMB customers.
• Budget restrictions can be overcome with public/hybrid cloud solutions.
• Once, a critical number of SMB use cases is reached, market growth gets to
a new level, encouraging new vendors to provide vertical solutions over the
cloud-platform.

• Data Analytics may be regarded as a substantial part of Big Data and BI applications.
• There are known use cases of Big Data driving business processes, however, still limited
to large and IT-advanced enterprises.
• BI applications can already be found in the SMB sector, however, with focus on
controlling, marketing and sales rather than in production processes or deploying Big
Data analytics.
• As the transformation of IT towards digitisation for the 3rd platform gains more and more
attention, and as technologies and solutions emerge which can cope with the growth of
data, with mobility and social networks, enterprises of all size classes will start
considering Big Data applications.
• Even though IoT has conquered the agenda of IT- and business professionals, many
German companies and the public sector organisations are only in the very beginning of
climbing the maturity ladder of Data Analytics.

8. Market Entry
9. Use Cases
Customer Focus
Supplier Focus

0 100 000 200 000 300 000 400 000 500 000 600 000 700 000
Automotive
Professional Services
Construction
Housing and Real Estate
Manufacturing
Restaurants, Hotels
Other Services
Healthcare
Business Services
Information & Communication
Transport & Logistics
Arts, Recreation, Entertainment
Education
Banking & Insurance
Utilities
Water Supply, Disposal
Mining
250+ 50-249 10-49 0-9
Germany‘s Industrial Structure (Industry & Size Class)Germany‘s Industrial Structure (Industry & Size Class)
© ESTconsulting Services 2015 Source: Statistisches Bundesamt 2015 Confidential 27
# of Employees
3,66 Mill. Companies in
Germany (2012) (without
Agriculture and Public Services)
3,66 Mill. Companies in
Germany (2012) (without
Agriculture and Public Services)
Industry
3 329
246
57 12,9
0
500
1 000
1 500
2 000
2 500
3 000
3 500
0-9 10-49 50-249 250+x1000
Size Class
• 57.000 size 50-249 employees
• 82.000 size 40- 249 (estimate)

0 50 100 150 200 250 300 350 400 450
Vehicles
Machinery
Food
Chemicals
Metal Structure
Manufacturing Revenues by Branch 2013
Germany‘s Manufacturing Industry by Branch (>100 B€ Revenue)Germany‘s Manufacturing Industry by Branch (>100 B€ Revenue)
© ESTconsulting Services 2015 Source: DESTATIS, Statistisches Bundesamt 2015 Confidential 28
Bn€
1.898.072 employed people
1.771 establishments
with

Germany‘s Public ServicesGermany‘s Public Services
No. of communities with
>10.000 registered residents =
1.555 in 2013 (Source: Statista 2015)
No. of communities with
>10.000 registered residents =
1.555 in 2013 (Source: Statista 2015)
No. of courts = 1.044 (in
2012) (Source: Genesis Destatis
2015)
No. of courts = 1.044 (in
2012) (Source: Genesis Destatis
2015)
There is a vast amount of establishments providing Public Services in Germany as part of governmental or
publicly owned entities. Direct public service institutions are organs of the federal government, of regional-level
or of community level like e.g. courts, public authorities, registration offices. Indirect public services are
provided by institutions which are controlled by federal or regional government and/or operate as independent
bodies under public law such as e.g. labour office, Federal Reserve Bank, national insurance. There were 5,73
Mill. employees in Public Services 2013 (Statistisches Bundesamt 2015).
Public service institutions are subject to developments labelled as ‚e-government‘ initiatives which relate to the
digitisation of public service processes. It also reflects the variety of different applications depending on the
type of authority.
Some examples with numbers of associated institutions.

Germany‘s Industries by Big Data Potential ClassificationGermany‘s Industries by Big Data Potential Classification
© ESTconsulting Services 2015 Source: Experton Group 2014 Confidential 30
Data Intensity Data Intensity Data Growth Big Data
2012 2020 per Year Business
(1=low, 10 = high) (1=low, 10 = high) % Potential
Industrial 6 8 20-30 Medium
Mobility and Logistics 4 9 40-50 Very High
Professional Services 5 8 25-35 High
Financial Services 8 10 30-40 High
Healthcare 5 9 40-50 Very High
Government/Education 3 8 10-20 Very High
Utilities 4 6 10-20 Medium
IT, Telco, Media 8 10 50-60 Very High
Retail/Wholesale 2 7 20-30 Very High

• Germany has the largest share of industrial added value in the EU (31%). Within Germany the industrial sector represents
22% of the overall added value (EU average = 15%, Eurostat 2013).
• The German industrial structure is multifarious:
• 3,6 millions of enterprises/establishments
• Thereof 3,3 millions with less than 10 employees
• Small to medium size businesses (SMBs) account for 330.000 if categorised as 10 – 249 employees
• Larger and very large enterprises are about 12.900 (250+ employees)
• 5,7 million people were public servants in 2013 working in institutions on federal, regional or community level
• The largest industrial sectors are Automotive, Construction, and Manufacturing, which sum to more than 1,15 million establishments
(all sizes)
• Within Manufacturing, vehicle and machinery production generate the largest contribution to Manufacturing’s revenues.
• The German Public Services sector with 5,73 million employed people is the largest single group compared to the
industrial breakdown.
• In terms of Big Data potential, classified through the change of data intensity use and data growth, industries such as
Retail/Wholesale, IT/Telco, Government/Education, Healthcare and Mobility/Logistics are leading the rank order.
• Looking just at the volumes by means of revenues, establishments, employees, potentials etc. can be one hint to select
branches or sub-branches. However, each category is big, Germany’s economy is in a very good shape, unemployment rate
is low, and high-level qualified staff is rare – each branch might be a reasonable target.

8. Market Entry
9. Use Cases
Customer Focus
Supplier Focus

Data Analytics in German CompaniesData Analytics in German Companies
© ESTconsulting Services 2015 Source: see text Confidential 33
(1) „73% of German enterprises have already developed a Big-Data-Strategy or are in an early transformation
phase“….
(2) „and it shows how German industry is ahead of other countries with respect to Big Data
transformation“….
(3) „We know, that German businesses are vanguards in process automation. This may be the reason for
being further ahead with Big Data than companies in other countries“ (Absatzwirtschaft.de, 2015, translated).
These statements were based on an analysis of a survey which was conducted by Automic (data collection by
Vanson Bourne) in Germany, UK, France and USA (‘Driving Business Value and Agility’). The sample consisted of
100 decision makers in each of the countries. The surveyed branches were Utilities, Financial Services, Retail
and Telecommunications.
These conclusions cannot be applied to Germany as such. Sample size and industry structure do not allow a
generalisation to the total economy. From our understanding Big Data solution penetration is much lower than
cited above, even in the manufacturing industry. However, considering Data Analytics in the sense of Business
Intelligence, we might support a statement from a different source that 9 out of 10 larger enterprises build its
decision making on IT-based data analysis (Potenziale und Einsatz von Big Data, BITKOM 2014).
(1) „73% of German enterprises have already developed a Big-Data-Strategy or are in an early transformation
phase“….
(2) „and it shows how German industry is ahead of other countries with respect to Big Data
transformation“….
(3) „We know, that German businesses are vanguards in process automation. This may be the reason for
being further ahead with Big Data than companies in other countries“ (Absatzwirtschaft.de, 2015, translated).
These statements were based on an analysis of a survey which was conducted by Automic (data collection by
Vanson Bourne) in Germany, UK, France and USA (‘Driving Business Value and Agility’). The sample consisted of
100 decision makers in each of the countries. The surveyed branches were Utilities, Financial Services, Retail
and Telecommunications.
These conclusions cannot be applied to Germany as such. Sample size and industry structure do not allow a
generalisation to the total economy. From our understanding Big Data solution penetration is much lower than
cited above, even in the manufacturing industry. However, considering Data Analytics in the sense of Business
Intelligence, we might support a statement from a different source that 9 out of 10 larger enterprises build its
decision making on IT-based data analysis (Potenziale und Einsatz von Big Data, BITKOM 2014).

© ESTconsulting Services 2015 Source: Potenziale und Einsatz von Big Data, BITKOM 2014 (N=507, multiple responses) Confidential 34
0 5 10 15 20 25 30 35 40 45 50
Geodata
Emails
Speech, Video, Audio
Social Media
Web Content
Text, Publications
CRM Data
Sensor Data
Log Data
Transactional Data
Master Data
„Which Data Used for Decision Making Is Analysed by Employing IT
Processes“?
Large Enterprises = 500+ Employees
SMBs = 50 – 499 Employees
% of Responses

0 10 20 30 40 50 60 70 80 90
Others
R&D
Production
Management
HR
Logistics
IT
Controlling
Marketing, Sales, PR
„In Which Areas Are Big Data Solutions Used or Planned to Be
Used“?
Large Enterprises = 500+ Employees
SMBs = 50 – 499 Employees
% of Responses
Manufacturing
‚Classic‘ Application
Areas

0 10 20 30 40 50 60 70 80 90
Faster Decision Making by Management
Optimised Ressource Planning
Conduct (more) Competitive Analyses
Establishment of Alerting and Forecast Systems
Preparation of Trend Analyses
Improvement of Customer Knowledge
Enhancement of Previous Decision Data
„How Strong Do You Estimate the Potential for Big Data Solutions
in the Following Application Areas?“
Big Potential
Medium Potential
% of Responses

What Can Be Expected in Manufacturing?What Can Be Expected in Manufacturing?
© ESTconsulting Services 2015 Source: Industrie 4.0 in Deutschland, IDC 2014 Confidential 37
0 10 20 30 40 50
Faster response to changing
requirements
Less energy consumption
Increase of production
capacity
More automated production
processes
Reduction of production costs
Manufacturing: Production
0 10 20 30 40 50
Increase resource capacity
Reduction of engineering
costs
Reduction of time-to-
development
Faster response to changing
requirements
Management of more
complex products
Manufacturing: Engineering
Requirements in Manufacturing
Industry for the Coming 24 Months
Requirements in Manufacturing
Industry for the Coming 24 Months
Machine Data AnalysedMachine Data Analysed
Device Data AnalysedDevice Data Analysed
Transact. Data ConnectedTransact. Data Connected
Operational Data AnalysedOperational Data Analysed
Operational Data AnalysedOperational Data Analysed
Services InnovationServices Innovation
Product InnovationProduct Innovation
Automation of FactoryAutomation of Factory
Geo Data DeploymentGeo Data Deployment
Concurrent EngineeringConcurrent Engineering
Expected Rollout-Sequence of
New BIG Data Functionality in
Manufacturing Industry
Expected Rollout-Sequence of
New BIG Data Functionality in
Manufacturing Industry
Time
%%
Cost Reduction
Productivity
Innovation
Cost Reduction

© ESTconsulting Services 2015 Source:ESTconsulting Services 2015 Confidential 38
• There is a lack of reliable data about the German situation with respect to Big Data solution
penetration. Big Data in other than IT-departments is not a widely used term.
• Using ‘Industrie 4.0’ or ‘IIoT’ as alternative expressions, it similarly can be stated that there is still a lack
in perception and in according actions for German industries. The bigger the enterprise and/or the
faster data is growing, the more can be expected in terms of strategies towards Big Data.
• Data analytics which are carried out still relate more to internal data and do not compile so many
internal and external data sources. The focus is still on the internal master and transactional data
sources.
• The associated applications focus for SMBs on the ‘classical’ control and planning functions in sales,
marketing and management. However, Logistics, HR and Production are catching up.
• Accordingly, decision data improvements, customer knowledge and understanding market trends are
still perceived as big potentials for Big Data applications in the sense of traditional BI.
• The transformation to Big Data usage for more sophisticated decision making and the control of many
other application areas in the enterprise are for the majority of companies still in an early stage.
However, the IIoT and Big Data ‘vehicle’, from a German end-user perspective, has started to move.

8. Market Entry
9. Use Cases
Customer Focus
Supplier Focus

Evolution of BI and Big DataEvolution of BI and Big Data
Data ReportingData Reporting
Visualisation
Dashboard
Visualisation
Dashboard
Data Discovery
Data Analytics
OLAP
Data Discovery
Data Analytics
OLAP
Data Modelling
Predictive
Data Modelling
Predictive
Data MiningData Mining
Embedded
Analytics
Embedded
Analytics
Large Volumes
Large Variety
Large Volumes
Large Variety
High Speed
Realtime
High Speed
Realtime
Complex Data
Unstructured
Complex Data
Unstructured
Automated
Machine-to-
Machine
Automated
Machine-to-
Machine
Technologies
Big Data Technologies
Application
Areas
ControllingMarketing/SalesOperational
Data Analysis Business Process
Time/Complexitiy
Cloud BasedCloud Based
MobileMobile

BI-SW Market Growth in GermanyBI-SW Market Growth in Germany
© ESTconsulting Services 2015 Source: Experton Group 2014, Statistisches Bundesamt 2014 Confidential 41
-10
-5
0
5
10
15
20
25
2007 2008 2009 2010 2011 2012 2013 2014
Standard-SW BI-SW GDP
Forecast
% Growth
of
Preceding
Year

Germany‘s Big Data Market - ForecastGermany‘s Big Data Market - Forecast
© ESTconsulting Services 2015 Source: Experton Group 2014 Confidential 42
0
200
400
600
800
1 000
1 200
1 400
1 600
1 800
2 000
2015 2016 2017 2018 2019
Hardware Software Services
Mill. €
• Consulting
• Integration
• Managed
• Hosting
• Cloud Provisioning
• Customisation
• SaaS
• Consulting
• Integration
• Managed
• Hosting
• Cloud Provisioning
• Customisation
• SaaS
• Standard
Application SW
• Data Warehouse
• Middleware
• Standard
Application SW
• Data Warehouse
• Middleware
• Server
• Storage
• Sensors
• Devices
• Server
• Storage
• Sensors
• Devices

Application Areas by Industry Sector (Samples)Application Areas by Industry Sector (Samples)
• Automotive = Connected Cars, Navigation, Fault Prediction, Traffic Jam Prediction
and Control, Sales Intelligence
• Manufacturing = Preventive Control and Maintenance, Connected Devices,
Supply Chain Management, Market Control, Quality Improvement
• Financial Services = Improved Risk Management, Fraud Detection, Personalised
Services, Customer Experience Management, Up- and Crosselling Improvement
• Healthcare = Improved Diagnostics, Admission Control, Monitoring Public Health
Provisions
• Public Sector = Detection of Social Welfare Fraud, Smart City, Threat Recognition
• Retail/Wholesale = Dynamic Price Tagging, Storage Use Optimisation, Market
Predictions, Personalisation
• Utilities = Short-Term Demand Prediction, Device Customising, Anticipatory
Control and Management
© ESTconsulting Services 2015 Source: ESTconsulting Services Confidential 43

The Role of Big Players in the Manufacturing IIoTThe Role of Big Players in the Manufacturing IIoT
Even if it is a US-based initiative, it needs to be taken into account when thinking of approaches towards IIoT
solutions and engagements: The Industrial Internet Consortium. It is called a „Global Nonprofit Partnership of
Industry, Government and Academia“. It was founded 2014 by AT&T, Cisco, General Electric, Intel, and IBM. All
these global players have an interest to establish their position in this early market phase on a worldwide level
by
• Drivíng innovation through the creation of new industry use cases and testbeds for real-world applications
• Define and develop the reference architecture and frameworks necessary for interoperability
• Influence the global development standards process for internet and industrial systems
• Facilitate open forums to share and exchange real-world ideas, practices, lessons, and insights.
© ESTconsulting Services 2015 Source: http://www.iiconsortium.org/about-us.htm Confidential 44
The goal for these big players is to establish quasi-industry-standards by putting their technologies in place.
The initial founders represent the complete value-chain of basic technologies which are needed for IIoT
applications: Communications, networks, machine communication, computing power and software. Many
other companies have joined the IIC and are establishing testbeds to develop use cases (e.g. Bosch) to become
vanguards in IIoT application technologies.
For any development of M2M based solutions, process-, interface- and protocol-standards as well as enabling
technologies provided by these players need to be considered. However, caution should be exerted when
propriatery systems, devices, and protocols are promoted as long as it is unclear which one will become an
established standard.

• Business with Big Data is twofold. On one hand, those industries employing Big Data solutions are
attributed considerable growth and improvement of market position. Early adopting companies have
understood that they can gain competitive advantages by doing Big Data analytics. On the other hand
there are the technology providers offering products, solutions, and services to the market.
• Industries and technology providers previously were negotiating on BI applications. This still exists, but
is converting to the Big Data approach applicable to more volume, more variety and higher speed. BI
and Big Data technologies converge.
• As far as products and solutions become better and more vertical (branch-application focussed), and
transformation processes become more common to renovate legacy infrastructures, the market will
steadily grow. There are big potentials in Germany.
• BI software - in the traditional sense - is supposed to grow much stronger as the regular software
market due to these reasons. A growth rate between 10-20% per year is being expected.
• A market forecast for Germany predicts more than 3,1 Bn€ for 2019 composed of revenues from
services, software and hardware. The portion for services with about 1,9 Bn€ is larger than the ones for
software and hardware together. This is due to the versatility of evolving services such as cloud
provisioning, SaaS, DaaS etc.

8. Market Entry
9. Use Cases
Customer Focus
Supplier Focus

Gartner‘s Magic Quadrant for BI and Analytics PlatformsGartner‘s Magic Quadrant for BI and Analytics Platforms
© ESTconsulting Services 2015 Source: Gartner 2014, (http://www.gartner.com/technology/reprints.do?id=1-1QLGACN&ct=140210&st=sb) Confidential 47
Vendor SegmentationVendor Segmentation
Large international vendors
with extended portfolio
BI/Big Data focussed vendors with
international presence
BI/Big Data focussed
vendors with some
international presence
BI vendors with
German HQ
e.g. IBM,
Microsoft,
Oracle, SAP, SAS
e.g. GoodData,
Qlik, Tibco,
MicroStrategy
e.g. arcplan,
Information
Builders
e.g. prevero,
evidanza
TechnologyPartners
Open
Source
Solutions
Today
owned by
Tibco

Vendors of BI-Software in Germany 2013 (Selective)Vendors of BI-Software in Germany 2013 (Selective)
© ESTconsulting Services 2015 Source: Business Intelligence als Kernkompetenz, Lünendonk 2014 (Some figures were estimated) Confidential 48
Location Turnover Employees HQ
Mill. € 2013 2013 Germany
SAS Deutschland GmbH Heidelberg 128,8 551
Teradata GmbH Augsburg 63,0 200
MicroStrategy Deutschland GmbH Köln 37,0 110
Informatica GmbH Frankfurt a.M. 28,0 188
QlikTech GmbH Düsseldorf 27,0 120
IDL Beratung für integrierte DV-Lösungen GmbH Schmitten 12,6 115 x
prevero AG München 11,7 60 x
Comma Soft AG Bonn 11,5 110 x
Cubeware GmbH Rosenheim 10,5 114 x
CP Corporate Planning AG Hamburg 10,1 114 x
Information Builders GmbH Eschborn 10,0 29
LucaNet AG Berlin 8,0 120 x
Bissantz & Company GmbH Nürnberg 7,9 85 x
Actuate GmbH Frankfurt a.M. 6,3 18
zetVisions AG Heidelberg 5,8 53 x
Board Deutschland GmbH Bad Homburg 5,2 40
Exasol AG Nürnberg 4,8 59 x
Jedox AG Freiburg 4,3 68 x
MIK GmbH Reichenau 4,0 34 x
evidanza AG Regensburg 2,8 46 x
macs Software GmbH Zimmern 2,5 20 x
Targit Deutschland Hüfingen 1,5 8
RapidMiner GmbH Dortmund 1,2 22 x
mezzodata software solutions Blaubeuren 0,4 3 x
Location Turnover Employees HQ
Mill. € 2013 2013 Germany
SAS Deutschland GmbH Heidelberg 128,8 551
Teradata GmbH Augsburg 63,0 200
MicroStrategy Deutschland GmbH Köln 37,0 110
Informatica GmbH Frankfurt a.M. 28,0 188
QlikTech GmbH Düsseldorf 27,0 120
IDL Beratung für integrierte DV-Lösungen GmbH Schmitten 12,6 115 x
prevero AG München 11,7 60 x
Comma Soft AG Bonn 11,5 110 x
Cubeware GmbH Rosenheim 10,5 114 x
CP Corporate Planning AG Hamburg 10,1 114 x
Information Builders GmbH Eschborn 10,0 29
LucaNet AG Berlin 8,0 120 x
Bissantz & Company GmbH Nürnberg 7,9 85 x
Actuate GmbH Frankfurt a.M. 6,3 18
zetVisions AG Heidelberg 5,8 53 x
Board Deutschland GmbH Bad Homburg 5,2 40
Exasol AG Nürnberg 4,8 59 x
Jedox AG Freiburg 4,3 68 x
MIK GmbH Reichenau 4,0 34 x
evidanza AG Regensburg 2,8 46 x
macs Software GmbH Zimmern 2,5 20 x
Targit Deutschland Hüfingen 1,5 8
RapidMiner GmbH Dortmund 1,2 22 x
mezzodata software solutions Blaubeuren 0,4 3 x
• Top ten listed companies in
Germany employ 1.682 people
with an average of 168.
• Top ten listed companies in
• The listed companies with HQ in
• The listed companies with HQ in
• The turnover/head-ratio varies
from 13.000 € to 195.000 € for
German headquartered
companies.
• The turnover/head-ratio varies
from 13.000 € to 195.000 € for
German headquartered
companies.
Fragmented supplier market
from very small to large, from
strong internationally owned
to local vendors, from poor to
excellent financial results.

Segmentation of German BI-Supplier-MarketSegmentation of German BI-Supplier-Market
Internationally Present BI-Suppliers
Larger SW-Portfolio
No. of EmployeesNo. of Employees Turnover (Typical)Turnover (Typical)
> 10.000
301-1.500
51-300
11-50
2-10
Bns€
100s Mill€
10s Mill€
Couple of Mill€
0,3-1 Mill€
BI/Big Data Vendors and Integrators with some international Presence
Local BI/Big Data, VARs and SIs

BI(G) Data Analytics Vendors in GermanyBI(G) Data Analytics Vendors in Germany
© ESTconsulting Services 2015 Sources: Experton Group 2014; http://www.empolis.com/en/; http://www.isreport.de/epaper/Files%20BI Confidential 50
PortfolioAttractiveness
Competitive Power
• Founded: 1986 as eps (printing systems) Bertelsmann (German
publishing company); in 2000 was renamed to Empolis because of a
merger with 4 software companies. In 2009 it became part of the
Attensity Group (together with living-e and Attensity Corp). Today
no. of employees: 150, revenues: 25 Mio. € (estimated).
• Focus: Empolis Information Management GmbH is a content
management and knowledge management software company with
focus on "smart information management".
• Mission: Empolis Smart Information Management® stands for a
holistic approach towards creation, management, analysis,
intelligent processing, and preparation of information which is
relevant to business processes. Information may be any data,
independent of source, format, user, location and device.
• Offering: Products and solutions for smart content management,
smart knowledge management with predictive analysis (BI), smart
intelligence with Big Data processing focussed on market and
competitive intelligence.
• Business Model: Licensed software run/managed on-premise; Cloud
service as SaaS; Services: Consulting, integration project,
maintenance, support, content outsourcing, user support, IT
application monitoring.
• Technology Partners (selection): Adobe, Google, IBM, Microsoft,
Oracle, SAP
• Customers (selection): Airbus, BMW, Bosch, Busch-Jaeger, Datev,
Felleskatalogen, Hypotheken Management, Kyocera, Norstedts
Juridik, Versatel, Vodafone, Wiley, Wittenstein.
• Founded: 1986 as eps (printing systems) Bertelsmann (German
publishing company); in 2000 was renamed to Empolis because of a
merger with 4 software companies. In 2009 it became part of the
Attensity Group (together with living-e and Attensity Corp). Today
no. of employees: 150, revenues: 25 Mio. € (estimated).
• Focus: Empolis Information Management GmbH is a content
management and knowledge management software company with
focus on "smart information management".
• Mission: Empolis Smart Information Management® stands for a
holistic approach towards creation, management, analysis,
intelligent processing, and preparation of information which is
relevant to business processes. Information may be any data,
independent of source, format, user, location and device.
• Offering: Products and solutions for smart content management,
smart knowledge management with predictive analysis (BI), smart
intelligence with Big Data processing focussed on market and
competitive intelligence.
• Business Model: Licensed software run/managed on-premise; Cloud
service as SaaS; Services: Consulting, integration project,
maintenance, support, content outsourcing, user support, IT
application monitoring.
• Technology Partners (selection): Adobe, Google, IBM, Microsoft,
Oracle, SAP
• Customers (selection): Airbus, BMW, Bosch, Busch-Jaeger, Datev,
Felleskatalogen, Hypotheken Management, Kyocera, Norstedts
Juridik, Versatel, Vodafone, Wiley, Wittenstein.

Vendors of BI-Software in Germany: arcplanVendors of BI-Software in Germany: arcplan
© ESTconsulting Services 2015 Source: http://www.arcplan.com/en/home/ Confidential 51
• Founded: 1993 in Wayne (PA, USA); present in many countries of all
continents; own subsidiaries and/or offices or partners; strong footprint in
Europe, mainly Germany. Also based in the Nordics, Finland, Oikokuja.
Employees worldwide: >100.
• Focus: Analyse multisource data with self-service capabilities and support
planning, budgeting, and forecasting on any end-user device.
• Mission: “arcplan software solutions enable you to deploy business
intelligence, analysis, and planning applications that meet all of your
organizational needs. Our open approach to data connectivity provides
direct access to more than 20 data sources in their native environments”.
• Offering: Enterprise solution platform for analytics, collaboration and
forecasting, application framework, personalisation, workflow engine.
• Business Model: Development, Direct sales, OEM licensing, indirect sales
through channel partners, direct service and support to customers and
partners. Partner business 50%.
• Technology Partners (selection): IBM, Kognitio, LucaNet, Microsoft,
Oracle, SAP, Teradata.
• Customers (selection): 3.200 ADAC, Airbus, Alunorf, Dachser, Datev,
Dekra, Hailo, Hitachi, Leifheit, Outo Kumpu, Nokia, Samsung, Vaillant,
Wacker.
• Founded: 1993 in Wayne (PA, USA); present in many countries of all
continents; own subsidiaries and/or offices or partners; strong footprint in
Europe, mainly Germany. Also based in the Nordics, Finland, Oikokuja.
Employees worldwide: >100.
• Focus: Analyse multisource data with self-service capabilities and support
planning, budgeting, and forecasting on any end-user device.
• Mission: “arcplan software solutions enable you to deploy business
intelligence, analysis, and planning applications that meet all of your
organizational needs. Our open approach to data connectivity provides
direct access to more than 20 data sources in their native environments”.
• Business Model: Development, Direct sales, OEM licensing, indirect sales
through channel partners, direct service and support to customers and
partners. Partner business 50%.
• Technology Partners (selection): IBM, Kognitio, LucaNet, Microsoft,
Oracle, SAP, Teradata.
• Customers (selection): 3.200 ADAC, Airbus, Alunorf, Dachser, Datev,
Dekra, Hailo, Hitachi, Leifheit, Outo Kumpu, Nokia, Samsung, Vaillant,
Wacker.
Arcplan
Platform
Technology
Partners
IBM, MS, Oracle,
SAP…
Channel
Partners
Targeted Markets
(medium to large sized customers)
Airlines, Airports, Banking, Insurance, Utilities,
Healthcare, Logistics, Manufacturing, Pharmaceutical,
Retail/Food, Telco/Media
OEM
Partners
Arcplan‘s EcosystemArcplan‘s Ecosystem

Vendors of BI-Software in Germany: prevero AGVendors of BI-Software in Germany: prevero AG
© ESTconsulting Services 2015 Source: http://www.prevero.com/en/ Confidential 52
• Founded: 1994 in Bamberg, Germany. Present with subsidiaries in Austria,
Switzerland and offices in France, Netherlands, Italy and UK, USA.
Employees worldwide: 100.
• Focus: Controlling, performance management and planning, risk
management and financial planning.
• Mission: ”prevero strives to enable companies of all sizes to use their
numbers in the best possible way for planning and forecasts. prevero has
been known for years for excellent planning and controlling solutions”.
• Business Model: Development, implementation and support; direct sales,
indirect sales through channel partners, direct service and support to
customers and partners.
• Technology Partners: Not a certified sales partner of technology vendors,
but interfacing to Microsoft and SAP.
• Customers (selection): Adler, ADVA, Amprion, Auto Bach, Bizerba, BRP
Powertrain, Endemol, Heidelberger Druckmaschinen, Hirschvogel, Max
Frank, Pfalzwerke, Stadtwerke var., Swisslife, Swisscom, WMF.
• Founded: 1994 in Bamberg, Germany. Present with subsidiaries in Austria,
Switzerland and offices in France, Netherlands, Italy and UK, USA.
Employees worldwide: 100.
• Focus: Controlling, performance management and planning, risk
management and financial planning.
• Mission: ”prevero strives to enable companies of all sizes to use their
numbers in the best possible way for planning and forecasts. prevero has
been known for years for excellent planning and controlling solutions”.
• Business Model: Development, implementation and support; direct sales,
indirect sales through channel partners, direct service and support to
customers and partners.
• Technology Partners: Not a certified sales partner of technology vendors,
but interfacing to Microsoft and SAP.
• Customers (selection): Adler, ADVA, Amprion, Auto Bach, Bizerba, BRP
Powertrain, Endemol, Heidelberger Druckmaschinen, Hirschvogel, Max
Frank, Pfalzwerke, Stadtwerke var., Swisslife, Swisscom, WMF.
Prevero
Platform
Technology
Vendors
MS, SAP…
Channel Partners
Sales, Implementation
Targeted Markets
(small to medium sized customers)
Adler, Auto Bach, Public….
Prevero‘s EcosystemPrevero‘s Ecosystem
Channel Partners
Sales, Implementation
Private Labelling

BI/BigData SW-Ecosystems and Start-ups in GermanyBI/BigData SW-Ecosystems and Start-ups in Germany
© ESTconsulting Services 2015 Source: ESTconsulting Services 2015; various IT journals and newspapers Confidential 53
Venture
Capital
Croud
Funding
Product
Solution
Service
Vendor
Technology
Partners
IBM, MS, Oracle, SAP…
Channel
Partners
OEM
Partners
Technology
Suppliers
e.g. Data Warehouse,
Cloud, Data Mining, ETL,
OLAP, Hadoop
Development
Partners
Near-Shore, Off-Shore
Integration
Support
PartnersSaaS
Provider
Certificate
License
HardwareSuppliersHardwareSuppliers
Supplier Market
Vertical Target Markets
Mapegy: Big Data visualisation tool
Blueyonder: Pattern recognition with predictive analyses
RapidMiner: Predictive analytics
Parstream: Analytics platform for IoT
Mapegy: Big Data visualisation tool
Blueyonder: Pattern recognition with predictive analyses
RapidMiner: Predictive analytics
Parstream: Analytics platform for IoT
Datameer: Hadoop based end-to-end analytics
application
Datameer: Hadoop based end-to-end analytics
application
GPredictive: Data analytics for customer data (SaaS)GPredictive: Data analytics for customer data (SaaS)
Retention Grid: Data analytics for customer data (SaaS)Retention Grid: Data analytics for customer data (SaaS)
Alacris: Patient data analytics (vertical application)Alacris: Patient data analytics (vertical application)
German Start-ups

• The supplier market is structured by various elements and every mix of criteria can be found:
• large players vs. small players
• extended portfolio vs. BI only
• local vs. international
• traditional BI vs. Big Data
• platform providers vs. application designers
• direct sales vs. indirect, OEM sales
• product orientation vs. service orientation
• In Gartner’s radar screen, profiling vendors by ‘Completeness of Vision’ and ‘Ability to Execute’, all large universal vendors
are US companies with the exception of SAP.
• The German supplier landscape consists of the big platform vendors, of internationally present BI/Big Data specialists, and
of German BI/Big Data companies. There is no real start-up company playing a significant role in Germany. All suppliers
have a more or less long history (10-20 years) in the German market with yield in use cases and in footprint.
• Strong players are those ones with international presence which also applies to most of the German suppliers. Typically a
supplier started with BI reporting tools. Modernisation and new developments today allow most of the midsized and small
players to participate in the Big Data arena.
• The typical ecosystem of a solution and service vendor is built on a technology relationship to platform-., ERP- and BI-
technology-vendors, to channel partners in the role of a sales channel and/or OEM partner, and to resource partners. The
majority of suppliers offer also services such as consulting, integration, and customisation.
• German start-up-companies invent solutions ranging from platforms for data analytics up to vertical applications.

8. Market Entry
9. Use Cases
© ESTconsulting Services 2015 : Confidential 55
Customer Focus
Supplier Focus

Barriers for Finnish Entrants? (1)Barriers for Finnish Entrants? (1)
© ESTconsulting Services 2015 Source: see chart Confidential 56
• Mobile connections to the internet for business use (in % of all enterprises, 2012) =
44% (lowest: France, Italy = 20%)
• Employment in technology- and knowledge-intensive sectors (% of total
employment, 2012) = 8% (lowest Portugal = 3 %) (IFR Eurostat, Think Act – Industry 4.0, Roland
Berger Strategy Consultants 2014)
• Low wage workers as percentage of all contracted employees = < 5%, Eurozone
avrg. = 13 %, highest: Estland = 23 %. (BMWF 2012)
• R&D expenses as part of the GDP 2010 = 3,8 %, EU avrg. = 1,9 %, lowest: Romania
= < 0,5% (BMWF 2012)
• High level education individuals as percentage of all individuals in age group 25 –
64 = 39,7 %, EU avrg. = 27,5, lowest: Romania = 15,4 %. (Statistisches Bundesamt 2014)
• Mobile connections to the internet for business use (in % of all enterprises, 2012) =
44% (lowest: France, Italy = 20%)
• Employment in technology- and knowledge-intensive sectors (% of total
employment, 2012) = 8% (lowest Portugal = 3 %) (IFR Eurostat, Think Act – Industry 4.0, Roland
Berger Strategy Consultants 2014)
• Low wage workers as percentage of all contracted employees = < 5%, Eurozone
avrg. = 13 %, highest: Estland = 23 %. (BMWF 2012)
• R&D expenses as part of the GDP 2010 = 3,8 %, EU avrg. = 1,9 %, lowest: Romania
= < 0,5% (BMWF 2012)
• High level education individuals as percentage of all individuals in age group 25 –
64 = 39,7 %, EU avrg. = 27,5, lowest: Romania = 15,4 %. (Statistisches Bundesamt 2014)
Finland is ranked top in …
• High private investments and risk funding for innovation and start-ups. (Web Magazin
2014)
• 70% of all employees work in the services sector.
• High-Tech image component associated with the Nokia brand and with start-up
companies e.g. Jolla, M-Files, SkySQL, ZenRobotics. (cf. Deloitte, Helsinki Business Hub, Red
Herring, Technopolis Online)
• High private investments and risk funding for innovation and start-ups. (Web Magazin
2014)
• 70% of all employees work in the services sector.
• High-Tech image component associated with the Nokia brand and with start-up
companies e.g. Jolla, M-Files, SkySQL, ZenRobotics. (cf. Deloitte, Helsinki Business Hub, Red
Herring, Technopolis Online)
Excellent Position and No Constraints (EU) to Do Work or to Establish Enterprises in Germany!Excellent Position and No Constraints (EU) to Do Work or to Establish Enterprises in Germany!
No. 3 - FINLAND: Ranked 1st
in institutions, health and
primary education, and
innovation
(Business Insider, The 35 Most Competitive Economies
in the World, 2013)

Barriers for Finnish Entrants? (2)Barriers for Finnish Entrants? (2)
© ESTconsulting Services 2015 Source: The Global Competitiveness Report 2014-2015, World Economic Forum 2015 Confidential 57
0
1
2
3
4
5
6
7
Institutions
Infrastructure
Macroeconomic Stability
Healthcare/Elementary
School
Universities and Higher
Education
Efficient Consumer Goods
Markets
Labour Market Efficiency
Finacial Markets Expertise
Technology Readiness
Market Size
Business Expertise
Innovation
CGI Index by Category
Finland Innovation Driven Economies
innovative, highly educated, service orientation, technology orientationinnovative, highly educated, service orientation, technology orientationFinland‘s Characteristics:
Excellent Position to Refer to Innovative, State-of-the Art
Software Solutions and Services!
Excellent Position to Refer to Innovative, State-of-the Art
Software Solutions and Services!
„A culture of young entrepeneurship“
(Tuukka Toivonen, The Guardian 2014)
„A culture of young entrepeneurship“
(Tuukka Toivonen, The Guardian 2014)

Business Intelligence and Big Data – Solution FrameworkBusiness Intelligence and Big Data – Solution Framework
Data SourcesData Sources
ProcessingProcessing
Application
Layer
Application
Layer
Business Intelligence Big DataBusiness Intelligence Big Data
Where to Assign the Finnish Expertise? Which Technology Modules can be targeted?Where to Assign the Finnish Expertise? Which Technology Modules can be targeted?
Reporting
Visualisation
Dashboard
Analysis
OLAP
Data Mining
Predictive
Analytics
Operational
Intelligence
DBMS Enterprise Apps
OLTP ERP, CRM, SCM
ETL
Data
Warehouse
Data Mart
Appliance
NoSQL,
Hadoop, Logs
Cloud
Private, Hybrid, Public
Complex
Event
Processing
Real-
time
Data
Structured,
Unstructured
Streaming
In-Memory
Computing

Vendor/Supplier Classification in Big Data/BI MarketsVendor/Supplier Classification in Big Data/BI Markets
AppliancesAppliances
Storage, ServerStorage, Server
DatabaseDatabase
Data AggregationData Aggregation
Sensors, Tagging devicesSensors, Tagging devices
Visualisation, DashboardsVisualisation, Dashboards
Solution/CloudSolution/Cloud
IT-OperationsIT-Operations
ConsultingConsulting
DataProtection,SecurityDataProtection,Security
Products
HW/SW
SolutionsServices
Infrastructure
Data
Organisation
Data
Management
Data
Analytics
Discovery
Automation
Prediction
?

Preferred Suppliers for IIoT Initiatives in ManufacturingPreferred Suppliers for IIoT Initiatives in Manufacturing
© ESTconsulting Services 2015 Source: Industrie 4.0 in Deutschland, IDC 2014 Confidential 60
0 10 20 30 40 50 60
Machine and Plant Engineering
Telco
Electrical Engineering
IT HW Supplier
IT SW Supplier
IT Services Supplier
Specialist Sensor Technologies
Production Engineering
Supplier
%
Major Sources for General BI/Big
Data Technologies Delivery

Finland‘s Starting Position & The Market‘s ExpectationsFinland‘s Starting Position & The Market‘s Expectations
Weaknesses Strengths
Innovation
Image
Service
Orientation
Proven SW
Development
Language
No Use Cases
Marketing
Expertise
Vertical Knowledge
Local Presence
Certificates
Project/Milestones Planning
Reference
Costs
Criteria
When
Choosing a
Solution
Supplier
Market Position
Vertical Footprint
Local Presence
Ease of Integration
Service & Support
Reference, Show Cases
Total Cost of Ownership
Criteria
When
Choosing a
SW Vendor
+
Viability
+ Functional Scope & Features

Business Models (1): WorkbenchBusiness Models (1): Workbench
Role
Program development as a service to vendors of
Data Analytics products and solutions
Prerequisites
• Top level programming skills
• Knowledge of ERP solutions
• Knowledge of relevant database- and Data
Warehouse-technologies
• Some vertical understanding
• Project management
• Reliability, accuracy
Competition
Big vendors: Off-shore and near-shore
outsourcing markets like India, Poland, Romania
Challenges
• Create awareness
• Establish network
Product
Solution
Services
Vendor
Technology
Partners
Channel
Partners
OEM
Partners
Technology
Suppliers
OLAP, Hadoop
Development
Partners
Integration
Support
PartnersSaaS
Provider
Certificate
License
Supplier Market

Business Models (2): Vertical Solution ApproachBusiness Models (2): Vertical Solution Approach
Role
Developer and vendor of vertical applications for
SMBs
Prerequisites
• Market understanding
• Vertical market and business process
knowledge
• SMB accounting and resource planning
solutions
Competition
All vendors in the German market
Challenges
• Create application with differentiator from
competitors (USP)
• Establishing Ecosystem
• Sales organisation
• Marketing strategy and marketing execution
• Show cases
• Language
Product
Solution
Services
Vendor
Technology
Partners
Channel
Partners
OEM
Partners
Technology
Suppliers
OLAP, Hadoop
Development
Partners
Integration
Support
PartnersSaaS
Provider
Certificate
License
Supplier Market

Business Models (3): Developer of Big Data TechnologiesBusiness Models (3): Developer of Big Data Technologies
Role
Developer and supplier of technology solutions
e.g. platforms, engines for data discovery,
visualisation tool etc.
Prerequisites
• Deep technical knowledge and KnowHow in
Big Data technologies
knowledge
• Familiarity with SMB accounting and resource
planning solutions
Competition
Technology partners, development partners, and
other suppliers of technology products.
Challenges
• Differentiating from other suppliers
• Establish Ecosystem
Product
Solution
Services
Vendor
Technology
Partners
Channel
Partners
OEM
Partners
Technology
Suppliers
OLAP, Hadoop
Development
Partners
Integration
Support
PartnersSaaS
Provider
Certificate
License
Supplier Market

Business Models (4): Customisation of Existing PlatformsBusiness Models (4): Customisation of Existing Platforms
Role
Customising current solutions to vertical market
requirements
Prerequisites
• Market understanding
knowledge
• SMB accounting and resource planning
solutions
Competition
Vendors in the same subsegment of the market
Challenges
• Create application with differentiator from
competitors (USP)
• Establish Ecosystem
• Sales organisation
• Marketing strategy and marketing execution
• Show cases
• Language
Product
Solution
Services
Vendor
Technology
Partners
Channel
Partners
OEM
Partners
Technology
Suppliers
OLAP, Hadoop
Development
Partners
Integration
Support
PartnersSaaS
Provider
Certificate
License
Supplier Market

• Finland has an outstanding reputation regarding the position in the EU-rank order of new
technology adoption, higher-level education, R&D investments and innovation. Together with a
broad service-orientation and excellent software development skills a strong fundamental for
software products and services is compounded. But, do the German industries know about
Finland’s strengths?
• As for EU members, there is no formal restriction to do work or to establish commercial activities
in Germany .
• There is a broad range of possible business cases available along the Big Data Products and
services value chain.
• Criteria which lead to a vendor selection for products and services are diverse and challenging.
• There are several positions in the typical Big Data/BI business ecosystem which could be
conquered by foreign suppliers (such as Finland) as the market anyway consists of many
international players mainly from the US.
• However, for the SMB market local presence (with some exceptions) is necessary.

8. Market Entry
9. Use Cases
Customer Focus
Supplier Focus

Selected Use Cases at a Glance (1)Selected Use Cases at a Glance (1)
© ESTconsulting Services 2015 Source: Big Data – Vorsprung durch Wissen, Innovationspotenzialsanalyse, Fraunhofer-Institut, IAIS, 2012 Confidential 68
Realtime Control of
Complex Facilities
Realtime Control of
Complex Facilities
‚Digital Petroleum Field‘: Increase of productivity through distributed sensors, high-
speed transmission networks and Data Mining resulting in less maintenance, earlier
recognition of dangereous events, and reduction of incidents. (www.chevron.com)
‚Digital Petroleum Field‘: Increase of productivity through distributed sensors, high-
speed transmission networks and Data Mining resulting in less maintenance, earlier
recognition of dangereous events, and reduction of incidents. (www.chevron.com)
Dynamic PricingDynamic Pricing
Consumer Sales: Prices are calculated in dependance of competitors prices and logistic
costs, limitations and sellout periods, allowing a price adjustment every 15 minutes.
(www.mercent.com)
Consumer Sales: Prices are calculated in dependance of competitors prices and logistic
costs, limitations and sellout periods, allowing a price adjustment every 15 minutes.
(www.mercent.com)
High Speed Financial NewsHigh Speed Financial News
Financial Services: Abnormal events in real-time are detected in Twitter and combined
with content of knowledgebase using Data Mining. With statistical data and analytical
add-ons from the own enterprise database, all information is delivered to financial
institutions to foster investment decisions. (www.dataminr.com)
Financial Services: Abnormal events in real-time are detected in Twitter and combined
with content of knowledgebase using Data Mining. With statistical data and analytical
add-ons from the own enterprise database, all information is delivered to financial
institutions to foster investment decisions. (www.dataminr.com)
Games AnalyticsGames Analytics
Online Offerings: Solution to segment online-players, predict their behaviour and
enhance the player‘s experience. Sending appropriate messages at the right point of
time, increases the sales opportunities. (www.gamesanalytics.com)
Online Offerings: Solution to segment online-players, predict their behaviour and
enhance the player‘s experience. Sending appropriate messages at the right point of
time, increases the sales opportunities. (www.gamesanalytics.com)

Selected Use Cases at a Glance (2)Selected Use Cases at a Glance (2)
© ESTconsulting Services 2015 Source: Big Data – Vorsprung durch Wissen, Innovationspotenzialsanalyse, Fraunhofer-Institut, IAIS, 2012 Confidential 69
Connected Intelligent
Products
Connected Intelligent
Products
‚Smart Home‘: Smart temperature control devices measure and predict the individuals
behaviour and the associated preferences for heat and cold. Data from all devices is
collected, sent to the cloud and merged with other information, e.g. weather data, in
order to improve control and reduce costs in the home. (www.nest.com)
‚Smart Home‘: Smart temperature control devices measure and predict the individuals
behaviour and the associated preferences for heat and cold. Data from all devices is
collected, sent to the cloud and merged with other information, e.g. weather data, in
order to improve control and reduce costs in the home. (www.nest.com)
Domain Awareness SystemDomain Awareness System
City Government: Data gathered by street cameras, radiometry, and number plate
readers are collected and combined with other governmental data sources. Real-time
Data Mining Prices allows to detect frauds and to generate alarms at once. (www.nyc.gov)
City Government: Data gathered by street cameras, radiometry, and number plate
readers are collected and combined with other governmental data sources. Real-time
Data Mining Prices allows to detect frauds and to generate alarms at once. (www.nyc.gov)
Risk Dependent PricingRisk Dependent Pricing
Insurance: Telematic-Service to adjust insurance premiums to hours with higher or
lower accident probability. Time, distances and brakes usage are collected from the
onboard diagnostic system and sent to the insurance for analysis. Car holders with less
risky usage pay up 30% less insurance. (www.progressive.com)
Insurance: Telematic-Service to adjust insurance premiums to hours with higher or
lower accident probability. Time, distances and brakes usage are collected from the
onboard diagnostic system and sent to the insurance for analysis. Car holders with less
risky usage pay up 30% less insurance. (www.progressive.com)
There is a vast potential of applications conceivable across all industries and public service institutions. Even if an
application for a niche industry might be adjusted to other industrial or market environments, however at the end,
each is a vertical solution and needs deep vertical knowledge and vertical market understanding accordingly.
There is a vast potential of applications conceivable across all industries and public service institutions. Even if an
application for a niche industry might be adjusted to other industrial or market environments, however at the end,
each is a vertical solution and needs deep vertical knowledge and vertical market understanding accordingly.

Use Cases: AutomotiveUse Cases: Automotive
© ESTconsulting Services 2015
Source: Mieschke Hofmann und Partner Gesellschaft für Management- und IT-Beratung mbH, 2014
Confidential 70
Infra-
structure
Logistics
Services
Gas
Stations
Catering
Social
Media
Retail
Support
OEM
Suppliers
Mobility
Services
Geo Data
Weather
Data
Car manufacturing, distribution, support and associated services represent a manifold composition of
connected applications. It starts with single connections evolving into comprehensive systems and networks.
Car manufacturing, distribution, support and associated services represent a manifold composition of
connected applications. It starts with single connections evolving into comprehensive systems and networks.

Big Data Use Cases along the Value Chain of AutomotiveBig Data Use Cases along the Value Chain of Automotive
© ESTconsulting Services 2015 Source: cf. Mieschke, Hofmann und Partner, Gesellschaft für Management- und IT-Beratung mbH, 2014 Confidential 71
Research &
Developm.
Research &
Developm.
Planning &
Sourcing
Planning &
Sourcing
ProductionProduction
Marketing
& Sales
Marketing
& Sales
Customer
Services
Customer
Services
After SalesAfter Sales
Process driven R&D, complex event processing,
multivariate control systems…
Process driven R&D, complex event processing,
multivariate control systems…
Early warning systems, real-time monitoring,
requirements planning…
Early warning systems, real-time monitoring,
requirements planning…
Predictive maintenance, monitoring, production
planning…
Predictive maintenance, monitoring, production
planning…
Competitive intelligence, sentiment analysis,
customer segmentation…
Competitive intelligence, sentiment analysis,
customer segmentation…
360° customer perspective, churn-rate reduction,
mass customisation…
360° customer perspective, churn-rate reduction,
mass customisation…
Service parts management, predictive material
demand…
Service parts management, predictive material
demand…
Real-time quality assurance…Real-time quality assurance…
Operational monitoring real-time, risk analysis,
fraud detection…
Operational monitoring real-time, risk analysis,
fraud detection…
Clustered data, usage data, geodata…Clustered data, usage data, geodata…
Mobility planning, car sharing, demand-driven
provisioning…
Mobility planning, car sharing, demand-driven
provisioning…
Core Processes Cross-Sectional Tasks Innovation & New Business ModelsCore Processes Cross-Sectional Tasks Innovation & New Business Models

Use Cases: Automotive – One Example – Big Industry, Big DataUse Cases: Automotive – One Example – Big Industry, Big Data
© ESTconsulting Services 2015 Source: cf. Big Data im Praxiseinsatz – Szenarien, Beispiele, Effekte, BITKOM 2012 Confidential 72
• Vertical Industry: Automotive manufacturing, large sized company, HQ in Germany, international presence, key German industrial market.
• The Challenge: No complete insight into the data being generated throughout the whole value chain from R&D, production to after sales
services. Heterogeneous IT environments make data exploration and analyses complicated and time-intensive. The provision and analysis of
data could be used for quality improvement and early fault recognition. Data volumes are continuously growing due to new and more
complex electronic production equipment which raise the difficulty to provide information instantaneously.
• Solution:
• Creation of a common interface for 2.000 internal users who are using data and analytics
• All data sources are collected and aggregated into a central Data Warehouse
• Data is made consistent and responsibilities are attributed
• Analytical support is optimized and early warning procedures are implemented
• Standardisation of analysis and reporting in the areas of Technology and After Sales
• Benefits:
• Decision making is based on all generated and available quality-relevant data in conjunction with the appropriate analytical tools
• Customer satisfaction will be elevated
• Early fault warning increases productivity
• Elimination of error sources creates quality improvement
• Homogeneous data sources and integrated analytical processes reduce the time-cycle for fault management
• ‘Lessons Learned’: ‘Variety’ (multiple heterogenous data sources) composes the biggest challenge because of data structures changes and
expansions. Data governance is essential for a structured and transparent data management. ‘Volume’ and ‘Velocity’ related challenges can
be accomplished through hardware and software enhancements and optimisation.
• Vertical Industry: Automotive manufacturing, large sized company, HQ in Germany, international presence, key German industrial market.
• The Challenge: No complete insight into the data being generated throughout the whole value chain from R&D, production to after sales
services. Heterogeneous IT environments make data exploration and analyses complicated and time-intensive. The provision and analysis of
data could be used for quality improvement and early fault recognition. Data volumes are continuously growing due to new and more
complex electronic production equipment which raise the difficulty to provide information instantaneously.
• Solution:
• Creation of a common interface for 2.000 internal users who are using data and analytics
• All data sources are collected and aggregated into a central Data Warehouse
• Data is made consistent and responsibilities are attributed
• Analytical support is optimized and early warning procedures are implemented
• Standardisation of analysis and reporting in the areas of Technology and After Sales
• Benefits:
• Decision making is based on all generated and available quality-relevant data in conjunction with the appropriate analytical tools
• Customer satisfaction will be elevated
• Early fault warning increases productivity
• Elimination of error sources creates quality improvement
• Homogeneous data sources and integrated analytical processes reduce the time-cycle for fault management
• ‘Lessons Learned’: ‘Variety’ (multiple heterogenous data sources) composes the biggest challenge because of data structures changes and
expansions. Data governance is essential for a structured and transparent data management. ‘Volume’ and ‘Velocity’ related challenges can
be accomplished through hardware and software enhancements and optimisation.
A Real Big Data Challenge

Use Cases: A Real 3rd Platform Application: Carsharing ServiceUse Cases: A Real 3rd Platform Application: Carsharing Service
• Vertical Industry: Automotive, insurance, facilities, fuel/gas/electricity provision, telecom. DriveNow: German example run by a joint
venture of BMW and Sixt.
• Vertical Industry: Automotive, insurance, facilities, fuel/gas/electricity provision, telecom. DriveNow: German example run by a joint
venture of BMW and Sixt.
Service Model
• Peer-to-Peer (e.g. Carzapp)
• Fleet Management
• Car Rental Free Floating (e.g. DriveNow)
Service Model
• Peer-to-Peer (e.g. Carzapp)
• Fleet Management
• Car Rental Free Floating (e.g. DriveNow)
Infrastructure
• Mobile
• High Availability
• High Performance
• Cloud
Infrastructure
• Mobile
• High Availability
• High Performance
• Cloud
Charging Model
• Accounting
• P&L
• ERP
Charging Model
• Accounting
• P&L
• ERP
Mobile NetworkMobile Network
Search
• Localization (Siemens)
Search
• Localization (Siemens)
Connected Car
• Sensors
• Meters
Connected Car
• Sensors
• Meters
Energy
• Fuel Management
• Fuel Provision
Energy
• Fuel Management
• Fuel Provision
Maintenance
• Garages
• Manufacturer
• Mobile Service
Maintenance
• Garages
• Manufacturer
• Mobile Service
SecuritySecurity
InsuranceInsurance EU: 5,5 Mill. registered
members; > 100.000
fleet vehicles in 2016
(Source: Frost & Sullivan 2014)
EU: 5,5 Mill. registered
members; > 100.000
fleet vehicles in 2016
(Source: Frost & Sullivan 2014)
Marketing
• Communications
• Social Networks
• Web Presence
Marketing
• Communications
• Social Networks
• Web Presence

Use Cases: Process Manufacturing – SMB, (medium) DataUse Cases: Process Manufacturing – SMB, (medium) Data
© ESTconsulting Services 2015 Source: http://www.oraylis.de/bilder/upload/dynamicContentFull/Reference-Reports/AWB_ORA_Hessehrddaua0.pdf Confidential 74
• Vertical Industry: Production of wood laquer, medium sized (450 employees), German HQ, family owned since 1910, market leader in
Germany.
• The Challenge: 40.000 different compositions of colours/type have to be targeted to the customer’s need. A new ERP-System was installed
to help to manage these requirements (MS Dynamics). The BI-features already integrated were not sufficient or not of high performance to
really optimise the processes in production, sales and service. This should also include data from the CRM system, the production data, and
from an in-house solution of laboratory information management.
• Solution:
• Establish and integrate a BI System (MS sharepoint based)
• An IT Service company (Oraylis) was chosen for consulting, planning and integration of the solution
• Benefits:
• New reports and analytics were developed: Product finder, P&L, sales forecasts, profitability by product, by customer, supply optimisation
• Analyses and reports at the push of a button
• Customer and employee satisfaction will be elevated
• Less data management efforts because of integrated storing and processing
• ‘Lessons Learned’: Multiple data sources and BI can lead to a solution suited for SMBs based on standard HW/SW components. With a
limited use of external consulting, integration and customisation the customer became prepared for a new era of smart data use for
business processes.
• Vertical Industry: Production of wood laquer, medium sized (450 employees), German HQ, family owned since 1910, market leader in
Germany.
• The Challenge: 40.000 different compositions of colours/type have to be targeted to the customer’s need. A new ERP-System was installed
to help to manage these requirements (MS Dynamics). The BI-features already integrated were not sufficient or not of high performance to
really optimise the processes in production, sales and service. This should also include data from the CRM system, the production data, and
from an in-house solution of laboratory information management.
• Solution:
• Establish and integrate a BI System (MS sharepoint based)
• An IT Service company (Oraylis) was chosen for consulting, planning and integration of the solution
• Benefits:
• New reports and analytics were developed: Product finder, P&L, sales forecasts, profitability by product, by customer, supply optimisation
• Analyses and reports at the push of a button
• Customer and employee satisfaction will be elevated
• Less data management efforts because of integrated storing and processing
• ‘Lessons Learned’: Multiple data sources and BI can lead to a solution suited for SMBs based on standard HW/SW components. With a
limited use of external consulting, integration and customisation the customer became prepared for a new era of smart data use for
business processes.
Not A Real Big Data
Challenge, But a
Typical SMB
Transformation Issue

Use Cases: Manufacturing – Prototype IIoTUse Cases: Manufacturing – Prototype IIoT
© ESTconsulting Services 2015 Source: www.harting.com Confidential 75
• Vertical Industry: Pumping plant as part of a production machinery installation. This is used as a show case under the header: From Sensor
to the Cloud and Back. It may be transferred as a concept to other machinery types.
• The Challenge: Pumps are an essential part of many machinery plants. When there is malfunction, the pump needs to be repaired or
replaced which interrupts the production process, thus, decreasing productivity.
• Solution:
• There was one main pump and one reserve pump installed, both synchronised.
• Pumps were equipped with sensors measuring power consumption, voltage, and input power.
• Two other sensors were taking data about water pressure and water flow-through.
• A protocol converter collected data and sent it via M2M interface to SAP Hana-Cloud every second.
• Data is analysed and compared with historical data and characteristics from other pumps.
• In case of fault prediction an alert is sent out providing proposed actions, service information and spare parts required.
• Benefits:
• Predictive maintenance and support
• Higher availability and productivity
• Enhanced service-levels
• ‘Lessons Learned’: This is a prototype for pump or other machine installations - when sensor-equipped - can communicate with cloud
services being independent of local computer power.
• Vertical Industry: Pumping plant as part of a production machinery installation. This is used as a show case under the header: From Sensor
to the Cloud and Back. It may be transferred as a concept to other machinery types.
• The Challenge: Pumps are an essential part of many machinery plants. When there is malfunction, the pump needs to be repaired or
replaced which interrupts the production process, thus, decreasing productivity.
• Solution:
• There was one main pump and one reserve pump installed, both synchronised.
• Pumps were equipped with sensors measuring power consumption, voltage, and input power.
• Two other sensors were taking data about water pressure and water flow-through.
• A protocol converter collected data and sent it via M2M interface to SAP Hana-Cloud every second.
• Data is analysed and compared with historical data and characteristics from other pumps.
• In case of fault prediction an alert is sent out providing proposed actions, service information and spare parts required.
• Benefits:
• Predictive maintenance and support
• Higher availability and productivity
• Enhanced service-levels
• ‘Lessons Learned’: This is a prototype for pump or other machine installations - when sensor-equipped - can communicate with cloud
services being independent of local computer power.
A Real IIoT
Application
Scenario

Use Case: Manufacturing (Automotive)Use Case: Manufacturing (Automotive)
ChallengeChallenge
Solution for an automated manufacturing process of 8
different car bodies on one assembly line without
process disruption.
Solution for an automated manufacturing process of 8
different car bodies on one assembly line without
process disruption.
SolutionSolution
Based on Windows Embedded and Windows SQL
Server 259 robots were connected by 60.000 sensors
to the ERP system, producing 830 bodies a day
without disruption.
without disruption.
Solution
without disruption.
AdvantagesAdvantages
• Fast modifications possible to produce different
bodies
• Continous production process
• Common user-interface to be deployed with
reduced training support

• There is already a manifold of use cases visible even if we are still at the beginning of the
IIoT wave.
• From use cases one can deduct that there are basic technology solutions which can be
deployed in various branches. However, there is always a specific part of vertical
application to be added. In some cases it might be 20% customisation, in others it might
be 50%.
• There are integrators using basic technologies to compose a complete solution which
needs to be integrated and customised at the front-end (MS Sharepoint, MS Dynamics
connectivity).
• Also industrial applications can be developed as a generic solution, applicable to various
production environments.
• Not all SMB applications require Big Data technologies.

8. Market Entry
9. Use Cases
Customer Focus
Supplier Focus

Conclusions Summary (1)Conclusions Summary (1)
• IoT and IIoT are a manifestation of the new IT era based on the 3rd platform. Big Data is one of the
tremendous forces accelerating the development of new technologies and applications.
• ‘Traditional’ Business Intelligence can be regarded as the foundation for Big Data Analytics. But, BI-‘classics’
cannot cope with volume, variety, velocity, and veracity of today’s growing data streams and associated
analytics.
• Industry 4.0 initiatives from the public as well as from commercial sectors are initialising the transformation
from old (analog) to digitally connected industrial processes. This implies a modernisation of previous
infrastructures to allow new applications for productivity benefits.
• Data Analytics are part of both, Big Data and Business Intelligence. While BI analytics are widely spread in
German industries, Big Data usage is still reluctant for SMBs, better accepted in larger and advanced
enterprises. The application focus is on marketing and controlling. New application areas like production and
the use of Video- and Geodata are still in its infancy but assumed to grow.
• Germany, with its large number of SMBs and Public Services subsidiaries, is a vast potential for all IoT and
IIoT related technologies and applications. There are industry sectors with a larger Big Data or a faster
developing potential for Big Data applications. For Manufacturing, investments are encouraged by gaining
cost reduction, productivity increase and innovation.
• Big Data is better perceived than IIoT as the data growth in businesses and public institutions is obvious and
many initiatives are at least being planned. Again, the focus is on the improvement of traditional planning.

Conclusions Summary (2)Conclusions Summary (2)
• The market in Germany is characterised by growth of BI-revenues much stronger than for the overall
software market. Services are the largest component in the German ‘Big Data’-market.
• There is a variety of players in the international as well as the German market having a track record in
previous BI technologies and applications. Some of them are narrowing both technologies by having
integrated Big Data technologies into their portfolio.
• The supplier segmentation in Germany ranges from large international platform vendors to small niche
application developers. Most of the vendors have a service offering or a service partner associated.
• Besides the typical ecosystem for selling products, solutions and services, there are start-up companies with
Big Data technologies and applications trying to create footprints. However, a significant number focus
primarily on the US market where they think to gain more momentum.
• Finland finds itself in a pole position as far as statistical data about innovation, high education, R&D and
technology penetration are concerned. Probably, this is not so widely known in Germany that it could be
assumed as an easy market opener for Finnish newcomers.
• For a single market approach the selection criteria used by the German customer base are extensive. There
are several prototype models where to find entry points into the German market as part of the ecosystem.
• Use cases are manifold, but there are also vast potentials for new applications if broken down to a smaller
vertical segment.

Bottom LineBottom Line
© ESTconsulting Services 2015 ESTconsulting Services 2015 Confidential 81
A vendor trying to approach the BI/Big Data market in Germany is facing:
• A growing market demand for tools that can manage the information overload, for solutions which
provide decision support, enable business process automation and gain competitive advantages and
productivity wins.
• Technologies which are advanced in using cloud services, scalable computing and storage architectures
and HSDA to exploit all kind of data usages.
• New delivery models and connected devices e.g. cloud based, mobile, mixed platforms.
• New business models through new ecosystems-constructs such as subscription based pricing, ads
supported charging, revenue sharing.
• Presence of large IT vendors ruling submarkets and expand through acquisition, thus making it harder
for small vendors to compete and to differentiate.
• A big potential customer base with a vast number of small and medium size businesses and a
Manufacturing sector which is one of Germany’s most important industrial branches.
• A need for expertise of how to link technologies to business (vertical level).
• Open windows for market entry because of the still early phase in market growth, and a lack of skills
and capacity in the customer IT organisations to handle new technologies.
A vendor trying to approach the BI/Big Data market in Germany is facing:
• A growing market demand for tools that can manage the information overload, for solutions which
provide decision support, enable business process automation and gain competitive advantages and
productivity wins.
• Technologies which are advanced in using cloud services, scalable computing and storage architectures
and HSDA to exploit all kind of data usages.
• New delivery models and connected devices e.g. cloud based, mobile, mixed platforms.
• New business models through new ecosystems-constructs such as subscription based pricing, ads
supported charging, revenue sharing.
• Presence of large IT vendors ruling submarkets and expand through acquisition, thus making it harder
for small vendors to compete and to differentiate.
• A big potential customer base with a vast number of small and medium size businesses and a
Manufacturing sector which is one of Germany’s most important industrial branches.
• A need for expertise of how to link technologies to business (vertical level).
• Open windows for market entry because of the still early phase in market growth, and a lack of skills
and capacity in the customer IT organisations to handle new technologies.

Recommendations: Industry PerspectiveRecommendations: Industry Perspective
• Look for Finnish vertical applications which can be transferred and localised for Germany, which can act as use
cases, and which can demonstrate Finnish expertise in a vertical industrial segment (see Business Model 4).
• Look for Manufacturing samples where engineering was combined with IT technologies (e.g. ship building,
machinery, companies like Kone, Metso) to demonstrate a Finnish track record and to find requirements for Big
Data Analytics on a vertical level.
• Do not focus on Manufacturing only. Any industry or sub-branch which has some affinity to Finland (e.g. paper
industry, utilities (very important for Germany), telecommunications, Media) is worth while to evaluate for
opportunities. Also Public Services are a huge potential in Germany, even if they are harder to approach and
language might be an issue. If there are applications or basic models to support Big Data in public processes and
transferred from Finland, this sector – in the longer run – can be very attractive (see Business Models 2 and 4).
• Enabling technologies (e.g. ETL, Hadoop, search engine) are positioned across industries because they are part of a
complex solution not necessarily requiring vertical knowledge but become an integrated component of complete
solutions to various industry sectors. If there is advanced expertise in one of these fields, it can be engineered to
the Big Data platform (see Business Model 3).
• Look for already developed application technologies that can be used to create vertical applications. For instance,
video relevant technologies in games software might be used in intelligent video analysis to be applied in security,
healthcare, traffic alerts etc. (see Business Model 2).

Recommendations: Supplier PerspectiveRecommendations: Supplier Perspective
• Which supplier model or any hybrid construct to choose depends on the initial position of a potential
supplier with respect to his skills, technology possession and/or vertical industry focus, product or service
orientation and the marketing capabilities.
• As a starting point, if there are no other factors making a special model more appropriate, Business Model 1
could be favoured. If the preconditions can be managed, the major challenge seems to be connecting to the
German market (see Recommendations: Marketing Perspective). Since vendors and suppliers are partner to
external providers according to Business Model 1 no direct interfacing to SMB customers is necessary, the
language problem is of minor importance.
• For a sustainable approach towards foreign countries a platform can be very helpful which assists small
Finnish firms to connect to the German economy. Furthermore, this platform can function as a
communication foundation into the foreign country. We understand the objectives of FinPro to have this
supporting role, to promote the establishment of links from Finland into other economies.
Firm ZFirm Z
Firm AFirm A
Firm BFirm B
Platform
Communication
Alliances
Support
Information
Connections
Platform
Communication
Alliances
Support
Information
Connections
Smooth
Facilitate
Enable
Establish
Smooth
Facilitate
Enable
Establish
Authorities
Ventures Syndicates
Primary PhasePrimary Phase Secondary PhaseSecondary Phase

Recommendations: Marketing PerspectiveRecommendations: Marketing Perspective
• The excellent positioning of Finland in economic and innovation measures is hardly known in the southern and western
parts of Europe. There is a lack in marketing on a national level as well as on a sectoral level which should have created an
image about Finland. This would have a major positive impact on the perception and acceptance of Finnish technologies
and Finnish suppliers and would ease to get footprints into branches and regions. The sale of Nokia’s mobile branch to
Microsoft has lessened Finland’s economic power perception. This needs to be overcome by communication in order to
point to the other important features in Finland. A positive brand recognition – once it is established – has a longer
persistence that single events cannot harm (cf. “Finland Needs to Start Advertising How Great It Is, Ira Kalb, Business Insider, Oct. 2014).
• Awareness is needed to support recognition of any branding strategy: become present, become connected, develop
footprints, show Finland’s expertise. This should be executed on several levels: on a national level, a sectoral (economic)
level, an industry level, and a company level.
Government
Ministries (Labour & Economics, Foreign Affairs, Education & Culture)
Government
Ministries (Labour & Economics, Foreign Affairs, Education & Culture)
SectoralSectoral
ITIT
MetsoMetso
IndustrialIndustrial Telecom.Telecom. RetailRetail
National
Sectoral/Platform
Industry
Company KoneKone
Start-
ups
Start-
ups
Level
• Communications
• Political Events
• Fairs, Exhibitions
• Conferences
• Technology
Summits
• Industrial
Associations
• Road Shows
• PR
• Show Cases
• Relationships
• Communications
• Political Events
• Fairs, Exhibitions
• Conferences
• Technology
Summits
• Industrial
Associations
• Road Shows
• PR
• Show Cases
• Relationships
Export
Connect
Export
Connect
Eco-
systems
Eco-
systems

Appendix: Glossary of AcronymsAppendix: Glossary of Acronyms
Acronym Meaning
DaaS Data as a Service
ERP Enterprise Resource Planning
ETL Extract, Transform, Load
GDP Gross Domestic Product
HPDA High Performance Data Analysis
IIC Industrial Internet Consortium
IIoT Industrial Internet of Things
IoT Internet of Things
M2M Machine to Machine
OLAP Online Analytical Processing
SaaS Software as a Service
SMB Small and Medium Sized Business
USP Unique Selling Position

Das “Internet der Dinge” verstehen und nutzen
33
“”
…und viele Daten!Sie haben Geräte…
Transaktions-
Daten
ERP
Data
CRM
Daten
Dokumente/
Metadaten
Public
Data
Social
Data
Geräte
Tools
Cloud & Netzwerk-
Infrastruktur
Sensoren
Gewinnen Sie
neue Erkennt-
nisse für Ihr
Unternehmen
Führen Sie vor-
handene
Daten
zusammen
Neue Geräte, neue Cloud-Dienste, neue
Daten…
Nutzen Sie
bestehende
Cloud-Dienste
Vernetzen Sie
ihre Geräte

“Internet der Dinge” in der Praxis: Carsharing in
Paris
33
Herausforderung
Lösung
Vorteile
• Reduzierung der CO2-Emissionen um
geschätzte 75 Tonnen bis 2023
• Verringerung der Transportkosten für die
Fahrer um 90 Prozent
• Verbesserung der Benutzererfahrung mit
personalisierten Einstellungen
Der Zweckverband Autolib', ein von der Stadt
Paris und 63 Gemeinden gegründetes
Carsharing-Programm mit Elektrofahrzeugen,
wollte Staus, Lärm- und Luftverschmutzung
verringern und den acht Millionen Einwohnern
mehr Flexibilität bieten.
Basierend auf Windows Embedded wurde eine
Lösung umgesetzt, die 72 Registrierungskiosks,
850 Anmietungskiosks, 4.300 Ladestationen und
2.300 Fahrzeuge mit einem Back-End-System
(Microsoft SQL Server und Windows Server)
verbindet.

Das Zusammenspiel von Cloud und Geräten bietet neue Chancen
Kundenfreundlichkeit durch
personalisierte Oberflächen
und Dienstleistungen
Erschließen neuer
Märkte durch
Innovationen bei
digitalen Produkten
und Dienstleistungen
Steigerung der
Produktivität von
Mitarbeitern durch
standortunabhängigen
Zugriff
Neues Design für
UnternehmensprozesseUnternehmensprozesseUnternehmensprozesseUnternehmensprozesse
Werben neuerneuerneuerneuer
KundenKundenKundenKunden über
neue Kanäle
Stärkung der Reaktionsfähigkeit
von Unternehmen durch sofort
bereitgestellte Kapazität
Umwandlung von
Investitionsausgaben
in BetriebskostenBetriebskostenBetriebskostenBetriebskosten
Umverteilung der
IT-Fähigkeiten
auf strategisch
wichtigere Projekte
Beschleunigte
Anwendungs-
entwicklung
Sofortige
Skalierung bei
steigendem Bedarf
Plattform
Produktivität ErkenntnisseSocial
11
33
22

Industrial internet big data german market study

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