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Download Research Results D2.7: Annual Report on Opportunities Workpackage WP2 – IMPACT: Framing the European Data Economy and maximize Impact Editor(s): Sonja Zillner (Siemens AG) Responsible Partner: Siemens AG Contributors Sonja Zillner, Ingra Marangoni (Siemens AG) Internal Reviewer Nuria de Lama, ATOS Andreas Metzger, UDE Status-Version: V1.0 for submission Due to 31/3/2020 Submission Date: 31/3/2020 EC Distribution: Public Abstract: To stimulate and foster the growth of the European data economy, the continuous identification of data-driven opportunities is a critical task. In this deliverable we introduce the design and outcome of the Data-driven Innovation (DDI) Framework including empirical data and findings derived from a complementary research study. We present statistical findings as well as findings related to the success patterns of data-driven innovation opportunities. Those findings are enhanced by the detailed description of successful start-ups of each success cluster as well as by an analysis of the progression of data- driven start-ups in the past years. Finally, the evaluation of the DDI Framework in classroom and workshop settings is summarized. This is the third version of a series of This document is issued within the frame and for the purpose of the BDVE project. This project has received funding from the European Union’s Horizon 2020 Programme (H2020-ICT-2016-2017) under Grant Agreement No. 732630 D2.7: Annual Report on Opportunities documents. In this report we have been able to present findings derived from the percentage-frequency analysis, the methodology and findings of the success pattern analysis, a more detailed representation of success data- driven start-ups, a reflections of how our sample changed over time as well as the ongoing evaluation of our workshops. 2 D2.7: Annual Report on Opportunities Table of Contents EXECUTIVE SUMMARY ............................................................................ 10 1 INTRODUCTION ............................................................................... 14 2 DATA-DRIVEN INNOVATION (DDI) FRAMEWORK ............................. 16 2.1 OVERVIEW OF THE DDI FRAMEWORK ....................................................... 16 2.2 RELATION TO DELIVERABLE 2.5 AND 2.6 .................................................. 17 3 DDI RESEARCH STUDY ..................................................................... 19 3.1 METHODOLOGY ................................................................................ 19 3.1.1 Percentage-Frequency Analysis ............................................................. 19 3.1.2 Pattern Analysis ..................................................................................... 20 3.1.3 Description of Clusters ........................................................................... 27 4 FINDINGS FROM PERCENTAGE-FREQUENCY ANALYSIS .................... 30 4.1 TARGET CUSTOMER ........................................................................... 30 4.1.1 Finding: Majority of start-ups address B2B markets ............................ 30 4.1.2 Finding: Majority of start-ups have clear sector focus .......................... 32 4.2 VALUE PROPOSITION / DATA VALUE ........................................................ 33 4.2.1 Finding: 66% of start-ups use data analytics to generate value ............ 34 4.2.2 Finding: 40% of start-ups are using combinations of data analytics ..... 35 4.2.3 Finding: Process automation and Match-making are less frequently used 36 4.3 DATA ............................................................................................ 37 4.3.1 Finding: Personal Data is most frequently used data source ................ 38 4.3.2 Finding: 43% of start-ups using unstructured data in their offering ..... 43 4.3.3 Finding: Every 2nd company using unstructured data also rely on semantic technology............................................................................................. 44 4.4 TECHNOLOGY .................................................................................. 47 4.4.1 Finding: Data Analytics is the most frequent ......................................... 47 4.4.2 Finding: 59% of startups combine two or more SRIA Technologies ...... 48 4.4.3 Finding: Only every fourth start-up uses two or more complementary technologies ......................................................................................................... 49 4.5 NETWORK STRATEGY .......................................................................... 50 4.5.1 Finding: 57% of start-ups rely on network effects................................. 50 4.6 REVENUE MODEL .............................................................................. 55 4.6.1 Finding: Data-driven innovation relies on multiple revenue models ... 55 4.7 TYPE OF BUSINESS ............................................................................ 58 4.7.1 Finding: Data-Driven Services are the most frequent Type-of Business 59 4.7.2 Finding: Data-driven Marketplaces are complex to build and less frequent 60 3 D2.7: Annual Report on Opportunities 5 FINDINGS FROM SUCCESS PATTERN ANALYSIS ............................... 63 5.1 START-UPS IN CLUSTER A (DATA PRE-PROCESSING) … ................................ 65 5.1.1 … address technical challenging pre-processing tasks .......................... 65 5.1.2 … are likely to rely on unstructured data .............................................. 69 5.1.3 … heavily rely on technologies for data management .......................... 70 5.1.4 … include own hardware components in their offerings more often ... 70 5.1.5 … are not likely to benefit from network effects ................................... 71 5.1.6 … are not characterized by a dominant revenue model ....................... 72 5.2 START-UPS IN CLUSTER B: INTERNET OF THINGS (IOT) APPLICATION … ............. 76 5.2.1 … focus on sector specific solutions ...................................................... 76 5.2.2 … rarely use unstructured data .............................................................. 76 5.2.3 … but frequently use industrial data...................................................... 78 5.2.4 … offer hardware solutions more frequently ........................................ 79 5.2.5 … rely on the seamless combination of many different technologies .. 80 5.2.6 … use the freemium revenue model more frequently .......................... 83 5.2.7 … rely on network effects on data level ................................................ 84 5.2.8 … are twice as often niche player in a functioning ecosystem .............. 84 5.3 START-UPS IN CLUSTER C: INDUSTRIAL SERVICES …. .................................. 85 5.3.1 … do only address business customer ................................................... 85 5.3.2 … tend to focus on insight generation and process automation ........... 86 5.3.3 … rely on industrial data and personal data .......................................... 86 5.3.4 … process IoT data but do not include IoT technology in their offering88 5.3.5 … provide little public information related to their revenue model ..... 89 5.3.6 … use network effects on infrastructure level above average .............. 90 5.3.7 … rely mainly on data-driven services ................................................... 91 5.3.8 … are less likely to receive funding ........................................................ 92 5.4 START-UPS IN CLUSTER D: DESCRIPTIVE VALUE … ...................................... 93 5.4.1 … focus on descriptive analytics for non-industrial data ....................... 93 5.4.2 … rely on extensive use of different data types .................................... 93 5.4.3 … use mainly the subscription model as source of income ................... 96 5.4.4 … have networks effect on data and infrastructure level m, but no on marketplace level ................................................................................................. 97 5.4.5 … are likely to receive funding ............................................................... 98 5.5 START-UPS IN CLUSTER E: PREDICTIVE VALUE …. ..................................... 100 5.5.1 … offer all predictive analytics ............................................................. 100 5.5.2 … rely mainly on personal and unstructured but no industrial data ... 100 5.5.3 … rely on small number of different types of technologies ................ 102 4 D2.7: Annual Report on Opportunities 5.5.4 … rely 50% more often on asset sale and selling of services ............... 104 5.5.5 …observe lower network effects when compared to others .............. 107 5.5.6 … mainly focus on data-driven services ............................................... 108 5.6 START-UPS IN CLUSTER F: CONNECTING PEERS … ..................................... 109 5.6.1 … are relying on match-making as central functionality ..................... 109 5.6.2 … wide range of data sets are used for match-making ....................... 110 5.6.3 … are very often relying on commission fee ........................................ 112 5.6.4 … have strong network effects on data and marketplace level .......... 113 5.6.5 … are mainly as data-driven marketplace ........................................... 115 6 EXAMPLE OF CLUSTER REPRESENTATIVES ..................................... 116 6.1 CLUSTER A: PRE-PROCESSING TECHNOLOGIES ......................................... 116 6.2 CLUSTER B: INTERNET OF THINGS APPLICATIONS ...................................... 120 6.3 CLUSTER C: INDUSTRIAL SERVICES .....................................................
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