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

[email protected]

Tel. 0034 673122136

ESTconsulting ServicesStrategische Consultingleistungen

ESTconsulting ServicesStrategische Consultingleistungen

PreamblePreamble

• The data, the analysis, interpretation and conclusions are subject to the author‘sperceptions, opinions, interpretations, and his professional background. The workwas compiled with extraordinary diligence. However, for any further action basedon 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 itseventually associated privacy clauses should be considered even if the source istaken from the Internet.

• Pictures and Icons – if not stated differently - were taken from Pixabay.com andClipshrine.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 exploitdata 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 ThingsIndustrial 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 beprocessed, analysed und reported manually through traditional methods and infrastructure. As an extendedview, Big Data adresses also the technologies which are used to gather, structure, analyse and report thedata.

• 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 andactors to control and to drive processes via Internet or Internet-like computing structures. Partly usedsynonyms are „Ubiquitous Computing“.

• IIoT = Industrial Internet of Things = applying the idea of IoT to industrial processes through integration ofphysical 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“, „Industry4.0“, „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

EnterpriseSMB Public

Sector

SW Vendors

Managed Services

Converged Systems Suppliers

Engineering

HW Vendors

DaaS Consulting Solution Vendors

Integration Services Content ProvidersSolution 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’shigh-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 businessprocesses 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 beregarded as a rather recent initiative as far as the overall perception and thecommercialisation 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 projectswere sponsored with focus on SMEs, manufacturing, logistics, and energy. In a furtherstep 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

ManagedBest 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 12,90

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 transformationphase“….

(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 forbeing 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 transformationphase“….

(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 forbeing 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

Co

ntr

ollin

g

Ma

rke

tin

g/S

ale

sO

pe

rati

on

al

Data Analysis Business ProcessTime/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 Predictionand Control, Sales Intelligence

• Manufacturing = Preventive Control and Maintenance, Connected Devices, Supply Chain Management, Market Control, Quality Improvement

• Financial Services = Improved Risk Management, Fraud Detection, PersonalisedServices, Customer Experience Management, Up- and Crosselling Improvement

• Healthcare = Improved Diagnostics, Admission Control, Monitoring Public HealthProvisions

• 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, AnticipatoryControl 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 IIoTsolutions and engagements: The Industrial Internet Consortium. It is called a „Global Nonprofit Partnership ofIndustry, 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 levelby

• 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 IIoTapplications: Communications, networks, machine communication, computing power and software. Manyother companies have joined the IIC and are establishing testbeds to develop use cases (e.g. Bosch) to becomevanguards in IIoT application technologies.

For any development of M2M based solutions, process-, interface- and protocol-standards as well as enablingtechnologies provided by these players need to be considered. However, caution should be exerted whenpropriatery 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

Tech

no

log

y P

art

ne

rs

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

Po

rtfo

lio

Att

ract

ive

ne

ss

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 isrelevant 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, NorstedtsJuridik, 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 isrelevant 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, NorstedtsJuridik, 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

PartnersIBM, 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

VendorsMS, SAP…

Channel PartnersSales, Implementation

Targeted Markets(small to medium sized customers)

Adler, Auto Bach, Public….

Prevero‘s EcosystemPrevero‘s Ecosystem

Channel PartnersSales, 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

PartnersIBM, MS, Oracle, SAP…

Channel

Partners

OEM

Partners

Technology

Supplierse.g. Data Warehouse,

Cloud, Data Mining, ETL,

OLAP, Hadoop

Development

PartnersNear-Shore, Off-Shore

Integration

Support

PartnersSaaS

Provider

Certificate

License

Ha

rdw

are

Su

pp

lie

rsH

ard

wa

re S

up

pli

ers

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

7Institutions

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?

ReportingVisualisation

Dashboard

Analysis

OLAPData Mining

Predictive

Analytics

Operational

Intelligence

DBMS Enterprise Apps

OLTP ERP, CRM, SCM

ETL

Data

Warehouse

Data MartAppliance

NoSQL,

Hadoop, Logs

Cloud

Private, Hybrid, Public

Complex

Event

Processing

Real-

time

Data

Structured,

Unstructured

StreamingIn-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

Da

ta P

rote

ctio

n,

Se

curi

tyD

ata

Pro

tect

ion

, S

ecu

rity 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


Channel

Partners

OEM

Partners

Technology



OLAP, Hadoop

Development


Integration

Support

PartnersSaaS

Provider

Certificate

License

Ha

rdw

are

Su

pp

lie

rsH

ard

wa

re S

up

pli

ers

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


Channel

Partners

OEM

Partners

Technology



OLAP, Hadoop

Development


Integration

Support

PartnersSaaS

Provider

Certificate

License

Ha

rdw

are

Su

pp

lie

rsH

ard

wa

re S

up

pli

ers

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


Channel

Partners

OEM

Partners

Technology



OLAP, Hadoop

Development


Integration

Support

PartnersSaaS

Provider

Certificate

License

Ha

rdw

are

Su

pp

lie

rsH

ard

wa

re S

up

pli

ers

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


Channel

Partners

OEM

Partners

Technology



OLAP, Hadoop

Development


Integration

Support

PartnersSaaS

Provider

Certificate

License

Ha

rdw

are

Su

pp

lie

rsH

ard

wa

re S

up

pli

ers

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 PricingConsumer 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 SystemCity 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 2015Source: Mieschke Hofmann und Partner Gesellschaft für Management- und IT-Beratung mbH, 2014

Confidential 70

Infra-

structure

Logistics

Services

Gas

Stations

Catering

Social

MediaRetail

Support

OEM

Suppliers

Mobility

Services

Geo DataWeather

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 &

SourcingProductionProduction

Marketing

& Sales

Marketing

& Sales

Customer

Services

Customer

ServicesAfter 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 Service Use 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

• ERPMobile 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.

GovernmentMinistries (Labour & Economics, Foreign Affairs, Education & Culture)

GovernmentMinistries (Labour & Economics, Foreign Affairs, Education & Culture)

SectoralSectoral

ITIT

MetsoMetso

IndustrialIndustrial Telecom.Telecom. RetailRetail

National

Sectoral/Platform

Industry

Company KoneKoneStart-

ups

Start-

ups

Leve

l

• 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

Exp

ort

Co

nn

ect

Exp

ort

Co

nn

ect

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 Sieneue Erkennt-nisse für IhrUnternehmen

Führen Sie vor-handeneDatenzusammen

Neue Geräte, neue Cloud-Dienste, neueDaten…

Nutzen SiebestehendeCloud-Dienste

Vernetzen Sieihre 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 durchpersonalisierte Oberflächenund Dienstleistungen

Erschließen neuer Märkte durchInnovationen bei digitalen Produkten und Dienstleistungen

Steigerung der Produktivität von Mitarbeitern durch standortunabhängigen Zugriff

Neues Design für UnternehmensprozesseUnternehmensprozesseUnternehmensprozesseUnternehmensprozesse

Werben neuer neuer neuer neuer Kunden Kunden Kunden Kunden über neue Kanäle

Stärkung der Reaktionsfähigkeit von Unternehmen durch sofort bereitgestellte Kapazität

Umwandlung von Investitionsausgabenin BetriebskostenBetriebskostenBetriebskostenBetriebskosten

Umverteilung der IT-Fähigkeiten auf strategisch wichtigere Projekte

BeschleunigteAnwendungs-entwicklung

Sofortige Skalierung bei steigendem Bedarf

Plattform

Produktivität ErkenntnisseSocial

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Industrial internet big data german market study

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