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Industrial internet big data german market study

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Industrial internet big data german market study

  1. 1. Market Evaluation Report about Business Opportunities in the Field of Data Analytics Software Applications and Related Services in Germany Report Delivery to the FINPRO Project “Verifying Business Opportunity for ‘Making Most out of Gathered Data” Focus on Germany Köln/Dénia February 2015 estupening@estconsulting.de Tel. 0034 673122136 ESTconsulting Services Strategische Consultingleistungen ESTconsulting Services Strategische Consultingleistungen
  2. 2. PreamblePreamble • The data, the analysis, interpretation and conclusions are subject to the author‘s perceptions, opinions, interpretations, and his professional background. The work was compiled with extraordinary diligence. However, for any further action based on this report, the author cannot be liable. • Used data and information sources are manifold: consultants, analysts, vendors, enduser organisations, inter-trade organisations, reports, statistical institutions, IT journals, experts. • For any further publication matter, the originator‘s source of information and its eventually associated privacy clauses should be considered even if the source is taken from the Internet. • Pictures and Icons – if not stated differently - were taken from Pixabay.com and Clipshrine.com under CCO Public Domain License. © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 2
  3. 3. 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
  4. 4. Report ChapterReport Chapter 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 4 Customer Focus Supplier Focus
  5. 5. Business Opportunities Objectives and Assessment StructureObjectives and Assessment Structure • ESTconsulting Services shall provide a „Business Opportunity Report“, which discusses the various aspects concerning market entry, its chances and its challenges. Findings will focus as far as possible on how German SMBs leverage any data, and what differentiation they gain by making the most out of their data during early stages of IIoT transformation. • The overall reference base is the so-called “Internet of Things”. It directs with its industrial focus to “Industrial Internet of Things”. This automatically emphasizes the manufacturing industry. However, other industries will be discussed for opportunities as well. Central to the “Internet of Things” are processes linked to (Big) data and the various ways to exploit data for business purposes. Volume, spread sources, ‘real-time’ requirements, and complex contents are characteristics which compel the use of modern and advanced tools for data gathering, analytics, and presentation. Source: ESTconsulting Services 2015 Confidential 5© ESTconsulting Services 2015 Big Data Generation Analytics Delivery Internet of Things Industrial IoT Germany Finance Utilities Services Manufacturing…. Large Enterprise Small and Medium Sized Business Public Sector Applications Chapter 1-2 Chapter 3 Chapter 4-5 Chapter 6-7 Chapter 8-10
  6. 6. 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. © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 6
  7. 7. DefinitionsDefinitions • Big Data = large data volumes from any source, which are complex and change rapidly. Thus, they can‘t be processed, analysed und reported manually through traditional methods and infrastructure. As an extended view, Big Data adresses also the technologies which are used to gather, structure, analyse and report the data. • BI = Business Intelligence = technology based defined process of systematic collection, structuring, analysis, and presentation of data for the improvement of business decisions and/or operations. Traditionally, BI was used for controlling, reporting, marketing, and management. • IoT = Internet of Things = fusion of physical and virtual information by deploying intelligent sensors and actors to control and to drive processes via Internet or Internet-like computing structures. Partly used synonyms are „Ubiquitous Computing“. • IIoT = Industrial Internet of Things = applying the idea of IoT to industrial processes through integration of physical machinery, sensors, software and network (not necessarily the Internet). IIoT can be regarded as a sub-layer to the Internet of Things. Synonymous expressions are „Fourth Industrial Revolution“, „Industry 4.0“, „Machine-to-Machine Communications“ (M2M), „Cyber-Physical Systems“. © ESTconsulting Services 2015 Source: Confidential 7
  8. 8. 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. Source: ESTconsulting Services 2015 Confidential 8© ESTconsulting Services 2015 • 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
  9. 9. Report ChapterReport Chapter 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 9 Customer Focus Supplier Focus
  10. 10. 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
  11. 11. 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
  12. 12. 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
  13. 13. IoT – It Is a Framework, not a SolutionIoT – It Is a Framework, not a Solution © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 13 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
  14. 14. Public IoT – How to Look at Application MarketsIoT – How to Look at Application Markets © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 14 Smart Energy Smart grid Fault detection Power sensors Consumption meters Virtual power plants Smart Buildings Smart homes A/C control Presence sensor Smart security Utility metering Smart Health Bio sensors Remote diagnostics Health monitoring Ambulances Smart Transport Electric mobility Smart logistics Infrastructure High-speed trains Commuting Smart Cities Traffic management Security Lighting control Water management Smart bins Manufacturing Utilities Construction Healthcare Automotive Industry Focus Area/App Focus Supplier Focus Smart Industry Optim. production Lighting Actuators, Robotics Security Customer Focus Large Enterprise SMB Public Sector SW Vendors Managed Services Converged Systems Suppliers Engineering HW Vendors DaaS Consulting Solution Vendors Integration Services Content Providers Solution Architects
  15. 15. 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.
  16. 16. Industry 4.0 – Bringing IoT into PracticeIndustry 4.0 – Bringing IoT into Practice © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 16 • Industry 4.0 = Industrie 4.0 (German) was incorporated into the German Government’s high-tech strategy in 2012. The goal is to promote the spread of computerisation in German industries, particularly in the industrial sector such as ‚Manufacturing‘. It will bring more intelligence into factories (smart factory) and the associated business processes with adaptive systems and new resource models as well as the integration of all stakeholders of the value chain through the connectivity of cyber-physical systems. Although the foundation for this programme goes back to the year 2006, it can be regarded as a rather recent initiative as far as the overall perception and the commercialisation is concerned (see chapter 6). • The Federal Ministry for Economics and Techonology (BMWi) is actively engaged in research projects and funding of pilots to enable a greater pervasiveness of IoT. • Under the scope of «Autonomics programme», in the previous 6 years, 12 pilot projects were sponsored with focus on SMEs, manufacturing, logistics, and energy. In a further step the agenda is to support research and pilots for machines, service robots, and other systems that are able to cope autonomously with complex tasks (www.autonomik.de/en).
  17. 17. ConclusionConclusion • 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
  18. 18. Report ChapterReport Chapter 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 18 Customer Focus Supplier Focus
  19. 19. What Is „Data Analytics“?What Is „Data Analytics“? © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 19 • 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 • 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
  20. 20. 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
  21. 21. 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‘
  22. 22. Data Analytics and Big Data – Where Is It Going?Data Analytics and Big Data – Where Is It Going? © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 22 • 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.
  23. 23. • Continous process • Continous improvements • Value driven • Predictive models Data Analytics Maturity – What Is Germany‘s Position?Data Analytics Maturity – What Is Germany‘s Position? © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 23 Infancy Ad Hoc Opportunistic Repeatable Managed Best Practice • Not aware • Aware • No plans • Experimental • Pilots • No processes • Lack of resources • Lack in infrastructure • Defining requirements • Establish processes • Resource inefficiencies • Building up infrastructure • Strategy established • Budgets • Program management • Accepted • Standard processes • Business adoption • Measurement of project performance • Investments • Standards established • Security & compliance • Directly linked to business • Predictive analytics Average SMB Average Large Enterprise Advanced Large EnterpriseBusiness driven IT driven Advanced SMB
  24. 24. How Will Demand Be Evolving?How Will Demand Be Evolving? © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 24 • 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.
  25. 25. ConclusionConclusion • 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. Source: ESTconsulting Services 2015 Confidential 25© ESTconsulting Services 2015
  26. 26. Report ChapterReport Chapter 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 26 Customer Focus Supplier Focus
  27. 27. 0 100 000 200 000 300 000 400 000 500 000 600 000 700 000 Automotive Professional Services Construction Housing and Real Estate Manufacturing Restaurants, Hotels Other Services Healthcare Business Services Information & Communication Transport & Logistics Arts, Recreation, Entertainment Education Banking & Insurance Utilities Water Supply, Disposal Mining 250+ 50-249 10-49 0-9 Germany‘s Industrial Structure (Industry & Size Class)Germany‘s Industrial Structure (Industry & Size Class) © ESTconsulting Services 2015 Source: Statistisches Bundesamt 2015 Confidential 27 # of Employees 3,66 Mill. Companies in Germany (2012) (without Agriculture and Public Services) 3,66 Mill. Companies in Germany (2012) (without Agriculture and Public Services) Industry 3 329 246 57 12,9 0 500 1 000 1 500 2 000 2 500 3 000 3 500 0-9 10-49 50-249 250+x1000 Size Class • 57.000 size 50-249 employees • 82.000 size 40- 249 (estimate)
  28. 28. 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
  29. 29. Germany‘s Public ServicesGermany‘s Public Services © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 29 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.
  30. 30. 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
  31. 31. ConclusionConclusion © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 31 • 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.
  32. 32. Report ChapterReport Chapter 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 32 Customer Focus Supplier Focus
  33. 33. Data Analytics in German CompaniesData Analytics in German Companies © ESTconsulting Services 2015 Source: see text Confidential 33 (1) „73% of German enterprises have already developed a Big-Data-Strategy or are in an early transformation phase“…. (2) „and it shows how German industry is ahead of other countries with respect to Big Data transformation“…. (3) „We know, that German businesses are vanguards in process automation. This may be the reason for being further ahead with Big Data than companies in other countries“ (Absatzwirtschaft.de, 2015, translated). These statements were based on an analysis of a survey which was conducted by Automic (data collection by Vanson Bourne) in Germany, UK, France and USA (‘Driving Business Value and Agility’). The sample consisted of 100 decision makers in each of the countries. The surveyed branches were Utilities, Financial Services, Retail and Telecommunications. These conclusions cannot be applied to Germany as such. Sample size and industry structure do not allow a generalisation to the total economy. From our understanding Big Data solution penetration is much lower than cited above, even in the manufacturing industry. However, considering Data Analytics in the sense of Business Intelligence, we might support a statement from a different source that 9 out of 10 larger enterprises build its decision making on IT-based data analysis (Potenziale und Einsatz von Big Data, BITKOM 2014). (1) „73% of German enterprises have already developed a Big-Data-Strategy or are in an early transformation phase“…. (2) „and it shows how German industry is ahead of other countries with respect to Big Data transformation“…. (3) „We know, that German businesses are vanguards in process automation. This may be the reason for being further ahead with Big Data than companies in other countries“ (Absatzwirtschaft.de, 2015, translated). These statements were based on an analysis of a survey which was conducted by Automic (data collection by Vanson Bourne) in Germany, UK, France and USA (‘Driving Business Value and Agility’). The sample consisted of 100 decision makers in each of the countries. The surveyed branches were Utilities, Financial Services, Retail and Telecommunications. These conclusions cannot be applied to Germany as such. Sample size and industry structure do not allow a generalisation to the total economy. From our understanding Big Data solution penetration is much lower than cited above, even in the manufacturing industry. However, considering Data Analytics in the sense of Business Intelligence, we might support a statement from a different source that 9 out of 10 larger enterprises build its decision making on IT-based data analysis (Potenziale und Einsatz von Big Data, BITKOM 2014).
  34. 34. Data Analytics in German CompaniesData Analytics in German Companies © 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
  35. 35. Data Analytics in German CompaniesData Analytics in German Companies © ESTconsulting Services 2015 Source: Potenziale und Einsatz von Big Data, BITKOM 2014 (N=507, multiple responses) Confidential 35 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
  36. 36. Data Analytics in German CompaniesData Analytics in German Companies © ESTconsulting Services 2015 Source: Potenziale und Einsatz von Big Data, BITKOM 2014 (N=507, multiple responses) Confidential 36 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
  37. 37. 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
  38. 38. ConclusionConclusion © 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.
  39. 39. Report ChapterReport Chapter 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 39 Customer Focus Supplier Focus
  40. 40. Evolution of BI and Big DataEvolution of BI and Big Data © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 40 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 Business Intelligence Technologies Big Data Technologies Application Areas ControllingMarketing/SalesOperational Data Analysis Business Process Time/Complexitiy Cloud BasedCloud Based MobileMobile
  41. 41. 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
  42. 42. 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
  43. 43. Application Areas by Industry Sector (Samples)Application Areas by Industry Sector (Samples) • Automotive = Connected Cars, Navigation, Fault Prediction, Traffic Jam Prediction and Control, Sales Intelligence • Manufacturing = Preventive Control and Maintenance, Connected Devices, Supply Chain Management, Market Control, Quality Improvement • Financial Services = Improved Risk Management, Fraud Detection, Personalised Services, Customer Experience Management, Up- and Crosselling Improvement • Healthcare = Improved Diagnostics, Admission Control, Monitoring Public Health Provisions • Public Sector = Detection of Social Welfare Fraud, Smart City, Threat Recognition • Retail/Wholesale = Dynamic Price Tagging, Storage Use Optimisation, Market Predictions, Personalisation • Utilities = Short-Term Demand Prediction, Device Customising, Anticipatory Control and Management © ESTconsulting Services 2015 Source: ESTconsulting Services Confidential 43
  44. 44. The Role of Big Players in the Manufacturing IIoTThe Role of Big Players in the Manufacturing IIoT Even if it is a US-based initiative, it needs to be taken into account when thinking of approaches towards IIoT solutions and engagements: The Industrial Internet Consortium. It is called a „Global Nonprofit Partnership of Industry, Government and Academia“. It was founded 2014 by AT&T, Cisco, General Electric, Intel, and IBM. All these global players have an interest to establish their position in this early market phase on a worldwide level by • Drivíng innovation through the creation of new industry use cases and testbeds for real-world applications • Define and develop the reference architecture and frameworks necessary for interoperability • Influence the global development standards process for internet and industrial systems • Facilitate open forums to share and exchange real-world ideas, practices, lessons, and insights. © ESTconsulting Services 2015 Source: http://www.iiconsortium.org/about-us.htm Confidential 44 The goal for these big players is to establish quasi-industry-standards by putting their technologies in place. The initial founders represent the complete value-chain of basic technologies which are needed for IIoT applications: Communications, networks, machine communication, computing power and software. Many other companies have joined the IIC and are establishing testbeds to develop use cases (e.g. Bosch) to become vanguards in IIoT application technologies. For any development of M2M based solutions, process-, interface- and protocol-standards as well as enabling technologies provided by these players need to be considered. However, caution should be exerted when propriatery systems, devices, and protocols are promoted as long as it is unclear which one will become an established standard.
  45. 45. ConclusionConclusion © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 45 • 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.
  46. 46. Report ChapterReport Chapter 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 46 Customer Focus Supplier Focus
  47. 47. Gartner‘s Magic Quadrant for BI and Analytics PlatformsGartner‘s Magic Quadrant for BI and Analytics Platforms © ESTconsulting Services 2015 Source: Gartner 2014, (http://www.gartner.com/technology/reprints.do?id=1-1QLGACN&ct=140210&st=sb) Confidential 47 Vendor SegmentationVendor Segmentation Large international vendors with extended portfolio BI/Big Data focussed vendors with international presence BI/Big Data focussed vendors with some international presence BI vendors with German HQ e.g. IBM, Microsoft, Oracle, SAP, SAS e.g. GoodData, Qlik, Tibco, MicroStrategy e.g. arcplan, Information Builders e.g. prevero, evidanza TechnologyPartners Open Source Solutions Today owned by Tibco
  48. 48. 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 Germany employ 1.682 people with an average of 168. • The listed companies with HQ in Germany employ 1.118 people with an average of 74. • The listed companies with HQ in Germany employ 1.118 people with an average of 74. • 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.
  49. 49. Segmentation of German BI-Supplier-MarketSegmentation of German BI-Supplier-Market © ESTconsulting Services 2015 Source: ESTconsulting Services Confidential 49 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
  50. 50. BI(G) Data Analytics Vendors in GermanyBI(G) Data Analytics Vendors in Germany © ESTconsulting Services 2015 Sources: Experton Group 2014; http://www.empolis.com/en/; http://www.isreport.de/epaper/Files%20BI Confidential 50 PortfolioAttractiveness Competitive Power • Founded: 1986 as eps (printing systems) Bertelsmann (German publishing company); in 2000 was renamed to Empolis because of a merger with 4 software companies. In 2009 it became part of the Attensity Group (together with living-e and Attensity Corp). Today no. of employees: 150, revenues: 25 Mio. € (estimated). • Focus: Empolis Information Management GmbH is a content management and knowledge management software company with focus on "smart information management". • Mission: Empolis Smart Information Management® stands for a holistic approach towards creation, management, analysis, intelligent processing, and preparation of information which is relevant to business processes. Information may be any data, independent of source, format, user, location and device. • Offering: Products and solutions for smart content management, smart knowledge management with predictive analysis (BI), smart intelligence with Big Data processing focussed on market and competitive intelligence. • Business Model: Licensed software run/managed on-premise; Cloud service as SaaS; Services: Consulting, integration project, maintenance, support, content outsourcing, user support, IT application monitoring. • Technology Partners (selection): Adobe, Google, IBM, Microsoft, Oracle, SAP • Customers (selection): Airbus, BMW, Bosch, Busch-Jaeger, Datev, Felleskatalogen, Hypotheken Management, Kyocera, Norstedts Juridik, Versatel, Vodafone, Wiley, Wittenstein. • Founded: 1986 as eps (printing systems) Bertelsmann (German publishing company); in 2000 was renamed to Empolis because of a merger with 4 software companies. In 2009 it became part of the Attensity Group (together with living-e and Attensity Corp). Today no. of employees: 150, revenues: 25 Mio. € (estimated). • Focus: Empolis Information Management GmbH is a content management and knowledge management software company with focus on "smart information management". • Mission: Empolis Smart Information Management® stands for a holistic approach towards creation, management, analysis, intelligent processing, and preparation of information which is relevant to business processes. Information may be any data, independent of source, format, user, location and device. • Offering: Products and solutions for smart content management, smart knowledge management with predictive analysis (BI), smart intelligence with Big Data processing focussed on market and competitive intelligence. • Business Model: Licensed software run/managed on-premise; Cloud service as SaaS; Services: Consulting, integration project, maintenance, support, content outsourcing, user support, IT application monitoring. • Technology Partners (selection): Adobe, Google, IBM, Microsoft, Oracle, SAP • Customers (selection): Airbus, BMW, Bosch, Busch-Jaeger, Datev, Felleskatalogen, Hypotheken Management, Kyocera, Norstedts Juridik, Versatel, Vodafone, Wiley, Wittenstein.
  51. 51. 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”. • 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. Arcplan Platform Technology Partners IBM, MS, Oracle, SAP… Channel Partners Targeted Markets (medium to large sized customers) Airlines, Airports, Banking, Insurance, Utilities, Healthcare, Logistics, Manufacturing, Pharmaceutical, Retail/Food, Telco/Media OEM Partners Arcplan‘s EcosystemArcplan‘s Ecosystem
  52. 52. 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”. • Offering: Enterprise solution platform for analytics, collaboration and forecasting, application framework, personalisation, workflow engine. • 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”. • Offering: Enterprise solution platform for analytics, collaboration and forecasting, application framework, personalisation, workflow engine. • Business Model: Development, implementation and support; direct sales, indirect sales through channel partners, direct service and support to customers and partners. • Technology Partners: Not a certified sales partner of technology vendors, but interfacing to Microsoft and SAP. • Customers (selection): Adler, ADVA, Amprion, Auto Bach, Bizerba, BRP Powertrain, Endemol, Heidelberger Druckmaschinen, Hirschvogel, Max Frank, Pfalzwerke, Stadtwerke var., Swisslife, Swisscom, WMF. Prevero Platform Technology Vendors MS, SAP… Channel Partners Sales, Implementation Targeted Markets (small to medium sized customers) Adler, Auto Bach, Public…. Prevero‘s EcosystemPrevero‘s Ecosystem Channel Partners Sales, Implementation Private Labelling
  53. 53. BI/BigData SW-Ecosystems and Start-ups in GermanyBI/BigData SW-Ecosystems and Start-ups in Germany © ESTconsulting Services 2015 Source: ESTconsulting Services 2015; various IT journals and newspapers Confidential 53 Venture Capital Croud Funding Product Solution Service Vendor Technology Partners IBM, MS, Oracle, SAP… Channel Partners OEM Partners Technology Suppliers e.g. Data Warehouse, Cloud, Data Mining, ETL, OLAP, Hadoop Development Partners Near-Shore, Off-Shore Integration Support PartnersSaaS Provider Certificate License HardwareSuppliersHardwareSuppliers Supplier Market Vertical Target Markets Mapegy: Big Data visualisation tool Blueyonder: Pattern recognition with predictive analyses RapidMiner: Predictive analytics Parstream: Analytics platform for IoT Mapegy: Big Data visualisation tool Blueyonder: Pattern recognition with predictive analyses RapidMiner: Predictive analytics Parstream: Analytics platform for IoT Datameer: Hadoop based end-to-end analytics application Datameer: Hadoop based end-to-end analytics application GPredictive: Data analytics for customer data (SaaS)GPredictive: Data analytics for customer data (SaaS) Retention Grid: Data analytics for customer data (SaaS)Retention Grid: Data analytics for customer data (SaaS) Alacris: Patient data analytics (vertical application)Alacris: Patient data analytics (vertical application) German Start-ups
  54. 54. ConclusionConclusion © ESTconsulting Services 2015 Confidential 54 • 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.
  55. 55. Report ChapterReport Chapter 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 55 Customer Focus Supplier Focus
  56. 56. 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)
  57. 57. Barriers for Finnish Entrants? (2)Barriers for Finnish Entrants? (2) © ESTconsulting Services 2015 Source: The Global Competitiveness Report 2014-2015, World Economic Forum 2015 Confidential 57 0 1 2 3 4 5 6 7 Institutions Infrastructure Macroeconomic Stability Healthcare/Elementary School Universities and Higher Education Efficient Consumer Goods Markets Labour Market Efficiency Finacial Markets Expertise Technology Readiness Market Size Business Expertise Innovation CGI Index by Category Finland Innovation Driven Economies innovative, highly educated, service orientation, technology orientationinnovative, highly educated, service orientation, technology orientationFinland‘s Characteristics: Excellent Position to Refer to Innovative, State-of-the Art Software Solutions and Services! Excellent Position to Refer to Innovative, State-of-the Art Software Solutions and Services! „A culture of young entrepeneurship“ (Tuukka Toivonen, The Guardian 2014) „A culture of young entrepeneurship“ (Tuukka Toivonen, The Guardian 2014)
  58. 58. Business Intelligence and Big Data – Solution FrameworkBusiness Intelligence and Big Data – Solution Framework © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 58 Data SourcesData Sources ProcessingProcessing Application Layer Application Layer Business Intelligence Big DataBusiness Intelligence Big Data Where to Assign the Finnish Expertise? Which Technology Modules can be targeted?Where to Assign the Finnish Expertise? Which Technology Modules can be targeted? Reporting Visualisation Dashboard Analysis OLAP Data Mining Predictive Analytics Operational Intelligence DBMS Enterprise Apps OLTP ERP, CRM, SCM ETL Data Warehouse Data Mart Appliance NoSQL, Hadoop, Logs Cloud Private, Hybrid, Public Complex Event Processing Real- time Data Structured, Unstructured Streaming In-Memory Computing
  59. 59. Vendor/Supplier Classification in Big Data/BI MarketsVendor/Supplier Classification in Big Data/BI Markets © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 59 AppliancesAppliances Storage, ServerStorage, Server DatabaseDatabase Data AggregationData Aggregation Sensors, Tagging devicesSensors, Tagging devices Visualisation, DashboardsVisualisation, Dashboards Data AnalyticsData Analytics Solution/CloudSolution/Cloud IT-OperationsIT-Operations ConsultingConsulting DataProtection,SecurityDataProtection,Security Products HW/SW SolutionsServices Infrastructure Data Organisation Data Management Data Analytics Discovery Automation Prediction ?
  60. 60. 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
  61. 61. Finland‘s Starting Position & The Market‘s ExpectationsFinland‘s Starting Position & The Market‘s Expectations © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 61 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
  62. 62. Business Models (1): WorkbenchBusiness Models (1): Workbench © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 62 Role Program development as a service to vendors of Data Analytics products and solutions Prerequisites • Top level programming skills • Knowledge of ERP solutions • Knowledge of relevant database- and Data Warehouse-technologies • Some vertical understanding • Project management • Reliability, accuracy Competition Big vendors: Off-shore and near-shore outsourcing markets like India, Poland, Romania Challenges • Create awareness • Establish network Product Solution Services Vendor Technology Partners IBM, MS, Oracle, SAP… Channel Partners OEM Partners Technology Suppliers e.g. Data Warehouse, Cloud, Data Mining, ETL, OLAP, Hadoop Development Partners Near-Shore, Off-Shore Integration Support PartnersSaaS Provider Certificate License HardwareSuppliersHardwareSuppliers Supplier Market Vertical Target Markets
  63. 63. Business Models (2): Vertical Solution ApproachBusiness Models (2): Vertical Solution Approach © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 63 Role Developer and vendor of vertical applications for SMBs Prerequisites • Market understanding • Vertical market and business process knowledge • SMB accounting and resource planning solutions Competition All vendors in the German market Challenges • Create application with differentiator from competitors (USP) • Establishing Ecosystem • Sales organisation • Marketing strategy and marketing execution • Show cases • Language Product Solution Services Vendor Technology Partners IBM, MS, Oracle, SAP… Channel Partners OEM Partners Technology Suppliers e.g. Data Warehouse, Cloud, Data Mining, ETL, OLAP, Hadoop Development Partners Near-Shore, Off-Shore Integration Support PartnersSaaS Provider Certificate License HardwareSuppliersHardwareSuppliers Supplier Market Vertical Target Markets
  64. 64. Business Models (3): Developer of Big Data TechnologiesBusiness Models (3): Developer of Big Data Technologies © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 64 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 • Vertical market and business process knowledge • Familiarity with SMB accounting and resource planning solutions Competition Technology partners, development partners, and other suppliers of technology products. Challenges • Differentiating from other suppliers • Establish Ecosystem Product Solution Services Vendor Technology Partners IBM, MS, Oracle, SAP… Channel Partners OEM Partners Technology Suppliers e.g. Data Warehouse, Cloud, Data Mining, ETL, OLAP, Hadoop Development Partners Near-Shore, Off-Shore Integration Support PartnersSaaS Provider Certificate License HardwareSuppliersHardwareSuppliers Supplier Market Vertical Target Markets
  65. 65. Business Models (4): Customisation of Existing PlatformsBusiness Models (4): Customisation of Existing Platforms © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 65 Role Customising current solutions to vertical market requirements Prerequisites • Market understanding • Vertical market and business process knowledge • SMB accounting and resource planning solutions Competition Vendors in the same subsegment of the market Challenges • Create application with differentiator from competitors (USP) • Establish Ecosystem • Sales organisation • Marketing strategy and marketing execution • Show cases • Language Product Solution Services Vendor Technology Partners IBM, MS, Oracle, SAP… Channel Partners OEM Partners Technology Suppliers e.g. Data Warehouse, Cloud, Data Mining, ETL, OLAP, Hadoop Development Partners Near-Shore, Off-Shore Integration Support PartnersSaaS Provider Certificate License HardwareSuppliersHardwareSuppliers Supplier Market Vertical Target Markets
  66. 66. ConclusionConclusion © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 66 • 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.
  67. 67. Report ChapterReport Chapter 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 67 Customer Focus Supplier Focus
  68. 68. Selected Use Cases at a Glance (1)Selected Use Cases at a Glance (1) © ESTconsulting Services 2015 Source: Big Data – Vorsprung durch Wissen, Innovationspotenzialsanalyse, Fraunhofer-Institut, IAIS, 2012 Confidential 68 Realtime Control of Complex Facilities Realtime Control of Complex Facilities ‚Digital Petroleum Field‘: Increase of productivity through distributed sensors, high- speed transmission networks and Data Mining resulting in less maintenance, earlier recognition of dangereous events, and reduction of incidents. (www.chevron.com) ‚Digital Petroleum Field‘: Increase of productivity through distributed sensors, high- speed transmission networks and Data Mining resulting in less maintenance, earlier recognition of dangereous events, and reduction of incidents. (www.chevron.com) Dynamic PricingDynamic Pricing Consumer Sales: Prices are calculated in dependance of competitors prices and logistic costs, limitations and sellout periods, allowing a price adjustment every 15 minutes. (www.mercent.com) Consumer Sales: Prices are calculated in dependance of competitors prices and logistic costs, limitations and sellout periods, allowing a price adjustment every 15 minutes. (www.mercent.com) High Speed Financial NewsHigh Speed Financial News Financial Services: Abnormal events in real-time are detected in Twitter and combined with content of knowledgebase using Data Mining. With statistical data and analytical add-ons from the own enterprise database, all information is delivered to financial institutions to foster investment decisions. (www.dataminr.com) Financial Services: Abnormal events in real-time are detected in Twitter and combined with content of knowledgebase using Data Mining. With statistical data and analytical add-ons from the own enterprise database, all information is delivered to financial institutions to foster investment decisions. (www.dataminr.com) Games AnalyticsGames Analytics Online Offerings: Solution to segment online-players, predict their behaviour and enhance the player‘s experience. Sending appropriate messages at the right point of time, increases the sales opportunities. (www.gamesanalytics.com) Online Offerings: Solution to segment online-players, predict their behaviour and enhance the player‘s experience. Sending appropriate messages at the right point of time, increases the sales opportunities. (www.gamesanalytics.com)
  69. 69. Selected Use Cases at a Glance (2)Selected Use Cases at a Glance (2) © ESTconsulting Services 2015 Source: Big Data – Vorsprung durch Wissen, Innovationspotenzialsanalyse, Fraunhofer-Institut, IAIS, 2012 Confidential 69 Connected Intelligent Products Connected Intelligent Products ‚Smart Home‘: Smart temperature control devices measure and predict the individuals behaviour and the associated preferences for heat and cold. Data from all devices is collected, sent to the cloud and merged with other information, e.g. weather data, in order to improve control and reduce costs in the home. (www.nest.com) ‚Smart Home‘: Smart temperature control devices measure and predict the individuals behaviour and the associated preferences for heat and cold. Data from all devices is collected, sent to the cloud and merged with other information, e.g. weather data, in order to improve control and reduce costs in the home. (www.nest.com) Domain Awareness SystemDomain Awareness System City Government: Data gathered by street cameras, radiometry, and number plate readers are collected and combined with other governmental data sources. Real-time Data Mining Prices allows to detect frauds and to generate alarms at once. (www.nyc.gov) City Government: Data gathered by street cameras, radiometry, and number plate readers are collected and combined with other governmental data sources. Real-time Data Mining Prices allows to detect frauds and to generate alarms at once. (www.nyc.gov) Risk Dependent PricingRisk Dependent Pricing Insurance: Telematic-Service to adjust insurance premiums to hours with higher or lower accident probability. Time, distances and brakes usage are collected from the onboard diagnostic system and sent to the insurance for analysis. Car holders with less risky usage pay up 30% less insurance. (www.progressive.com) Insurance: Telematic-Service to adjust insurance premiums to hours with higher or lower accident probability. Time, distances and brakes usage are collected from the onboard diagnostic system and sent to the insurance for analysis. Car holders with less risky usage pay up 30% less insurance. (www.progressive.com) There is a vast potential of applications conceivable across all industries and public service institutions. Even if an application for a niche industry might be adjusted to other industrial or market environments, however at the end, each is a vertical solution and needs deep vertical knowledge and vertical market understanding accordingly. There is a vast potential of applications conceivable across all industries and public service institutions. Even if an application for a niche industry might be adjusted to other industrial or market environments, however at the end, each is a vertical solution and needs deep vertical knowledge and vertical market understanding accordingly.
  70. 70. Use Cases: AutomotiveUse Cases: Automotive © ESTconsulting Services 2015 Source: Mieschke Hofmann und Partner Gesellschaft für Management- und IT-Beratung mbH, 2014 Confidential 70 Infra- structure Logistics Services Gas Stations Catering Social Media Retail Support OEM Suppliers Mobility Services Geo Data Weather Data Car manufacturing, distribution, support and associated services represent a manifold composition of connected applications. It starts with single connections evolving into comprehensive systems and networks. Car manufacturing, distribution, support and associated services represent a manifold composition of connected applications. It starts with single connections evolving into comprehensive systems and networks.
  71. 71. Big Data Use Cases along the Value Chain of AutomotiveBig Data Use Cases along the Value Chain of Automotive © ESTconsulting Services 2015 Source: cf. Mieschke, Hofmann und Partner, Gesellschaft für Management- und IT-Beratung mbH, 2014 Confidential 71 Research & Developm. Research & Developm. Planning & Sourcing Planning & Sourcing ProductionProduction Marketing & Sales Marketing & Sales Customer Services Customer Services After SalesAfter Sales Process driven R&D, complex event processing, multivariate control systems… Process driven R&D, complex event processing, multivariate control systems… Early warning systems, real-time monitoring, requirements planning… Early warning systems, real-time monitoring, requirements planning… Predictive maintenance, monitoring, production planning… Predictive maintenance, monitoring, production planning… Competitive intelligence, sentiment analysis, customer segmentation… Competitive intelligence, sentiment analysis, customer segmentation… 360° customer perspective, churn-rate reduction, mass customisation… 360° customer perspective, churn-rate reduction, mass customisation… Service parts management, predictive material demand… Service parts management, predictive material demand… Real-time quality assurance…Real-time quality assurance… Operational monitoring real-time, risk analysis, fraud detection… Operational monitoring real-time, risk analysis, fraud detection… Clustered data, usage data, geodata…Clustered data, usage data, geodata… Mobility planning, car sharing, demand-driven provisioning… Mobility planning, car sharing, demand-driven provisioning… Core Processes Cross-Sectional Tasks Innovation & New Business ModelsCore Processes Cross-Sectional Tasks Innovation & New Business Models
  72. 72. 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
  73. 73. Use Cases: A Real 3rd Platform Application: Carsharing ServiceUse Cases: A Real 3rd Platform Application: Carsharing Service © ESTconsulting Services 2015 Source: ESTconsulting Services Confidential 73 • Vertical Industry: Automotive, insurance, facilities, fuel/gas/electricity provision, telecom. DriveNow: German example run by a joint venture of BMW and Sixt. • Vertical Industry: Automotive, insurance, facilities, fuel/gas/electricity provision, telecom. DriveNow: German example run by a joint venture of BMW and Sixt. Service Model • Peer-to-Peer (e.g. Carzapp) • Fleet Management • Car Rental Free Floating (e.g. DriveNow) Service Model • Peer-to-Peer (e.g. Carzapp) • Fleet Management • Car Rental Free Floating (e.g. DriveNow) Infrastructure • Mobile • High Availability • High Performance • Cloud Infrastructure • Mobile • High Availability • High Performance • Cloud Charging Model • Accounting • P&L • ERP Charging Model • Accounting • P&L • ERP Mobile NetworkMobile Network Search • Localization (Siemens) Search • Localization (Siemens) Connected Car • Sensors • Meters Connected Car • Sensors • Meters Energy • Fuel Management • Fuel Provision Energy • Fuel Management • Fuel Provision Maintenance • Garages • Manufacturer • Mobile Service Maintenance • Garages • Manufacturer • Mobile Service SecuritySecurity InsuranceInsurance EU: 5,5 Mill. registered members; > 100.000 fleet vehicles in 2016 (Source: Frost & Sullivan 2014) EU: 5,5 Mill. registered members; > 100.000 fleet vehicles in 2016 (Source: Frost & Sullivan 2014) Marketing • Communications • Social Networks • Web Presence Marketing • Communications • Social Networks • Web Presence
  74. 74. 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
  75. 75. 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
  76. 76. Use Case: Manufacturing (Automotive)Use Case: Manufacturing (Automotive) © ESTconsulting Services 2015 Confidential 76 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. 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. Solution 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. AdvantagesAdvantages • Fast modifications possible to produce different bodies • Continous production process • Common user-interface to be deployed with reduced training support
  77. 77. ConclusionConclusion © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 77 • 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.
  78. 78. Report ChapterReport Chapter 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 78 Customer Focus Supplier Focus
  79. 79. Conclusions Summary (1)Conclusions Summary (1) © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 79 • 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.
  80. 80. Conclusions Summary (2)Conclusions Summary (2) © ESTconsulting Services 2015 Source: ESTconsulting Services 2015 Confidential 80 • 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.
  81. 81. 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.

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