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BDV Webinars - How to monetize your data in an open data Marketplace

Tue, Jun 23, 2020 3:00 PM - 4:00 PM CEST

CPP data ecosystem gives cross-sectorial industries access to a huge amount of sensor data coming from high volume products such as vehicles or smart buildings. The new information sources create new value, improving existing services, and fostering the creation of new data-driven services. The project overcomes data monetization and exchange barriers by defining a unique ecosystem integrating data streams coming from various cyber-physical systems, especially in the automotive and smart building sectors. It pays particular attention to cross-sectorial data and services. The objective of this session is to perform a Live Demo of the integrated Cross-CPP data marketplace to understand the benefits and impact of this kind of solution for your data-driven business.

Cross-CPP Ecosystem and data-monetization opportunities
Cross-CPP data marketplace live-demo including Integrated Analytics Tool Box, Context Services, and Security and Privacy Policies
Promotion of Cross-CPP Beta Testing Campaign published in ReachOut




Presenters: Christian Wolff (ATB), Víctor Corral (ATOS), Ernestisna Mensalvas (UPM).

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BDV Webinars - How to monetize your data in an open data Marketplace

  1. 1. Horizon 2020 European Union Funding for Research & Innovation Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources * This project has received funding from the European Union’s Horizon 2020 research and innovation programm under grant agreement No. 780167. *
  2. 2. Horizon 2020 European Union Funding for Research & Innovation Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources BDVe Webinar: How to monetize your data in an open data marketplace
  3. 3. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 3 What is Cross-CPP about Giving Access to CPP Data The puzzle of observations from CPP creates a digital model of “the world” in the cloud. This is a NEW prime data resource for new business opportunities.
  4. 4. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 4 CPP Data Ecosystem History -> Chain of data platform projects • Giving data customers access to personal and industrial data streams to build sectorial and cross-sectorial services • Empower data owners to exploit their most valuable asset (IoT data) Data Diversity & Volume SystemComplexity • Brand independent vehicle data MP • Agreed data model (CVIM) • CPP data from divers sectors • Analytic toolbox Further projects: smashHit …
  5. 5. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 5 CPP Data Ecosystem Driven by the needs of its key stakeholders • Data Owner: control over data (trustful, secure, traceable), fair compensations • Data Customer/Consumer: Brand independent data market, standardized interface data access point, easy manageable solution with just one interface, data quality/integrity Make Data Markets more attractive for its key stakeholders:
  6. 6. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources Contract Data Discovery Offer The Marketplace enables digital trade with CPP data between interested parties and data providers. It covers data discovery and provisioning, accounting and billing, offer and contract negotiation, SDKs and development support. Provisioning Transaction CPP Data Ecosystem Marketplace for CPP data Is this really so easy? Needed by Data Customers:  Brand independent concept  Single CPP data access point with just one interface  Controlled access to data streams of diverse CPP  Win-Win value chain for all ecosystem partners Current Situation:  No or limited access to CPP data  Limited possibilities to use cross-sectorial CPP big data streams  Missing preconditions to establish such cross-sectorial data market  Non-economical brand-specific service platform solutions  Wasted Innovation Potentials 6
  7. 7. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources CPP Data Ecosystem Brand independent Ecosystem open for integration of any CPP Data 7  Standardized Cross Industrial Data Model Flexible to incorporate CPP data coming from various industrial sectors.  CPP Big Data Marketplace with Analytics Toolbox “One-Stop-Shop” will provide Service Providers a single point of access to data streams from multiple mass products.  Cross Industrial Services The consortium partners have developed several innovative cross- sectorial services.
  8. 8. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources Success factors • Ecosystem  Driven by the needs of Data Owners, Data Providers and Data Customers  Brand independent, Open platform with standardized interface -> High attractiveness for SP  Linking CPP data from different sectors enables higher quality content and NEW services world  Economical solution for all value chain partners, due to a greater amount of data customers  Data Providers can profit from Innovation Potentials by thousands of external experts • User Engagement  Increased willing to provide IoT data by data providers and data owners  The owner can fully control which data he provides to which Service Provider 8
  9. 9. Horizon 2020 European Union Funding for Research & Innovation Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources Cross-CPP data Marketplace Live Demo AGORA MVP
  10. 10. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources The story behind Cross-CPP... 10 2015 2016 2017 2018 AutoMat Kickoff H2020 aims to develop an Open Big Data Marketplace from 3 leading EU OEMs AutoMat Early Prototype Initial steps on the first Early Prototype of the solution AutoMat Final Prototype Industry validation by VW, Renault and Fiat. 4M data-packages indexed. 3 BCs supported Cross-CPP New EU project aiming to extend marketplace to cross-sectorial sectors. 2020 2019 Cross-CPP and AGORA MVP go-to-market Define the strategy to land into data-exchange markets. Approach to real customers Cross-CPP Full Prototype From R&D to MVP. Officially launch of the data-marketplace Atos solution
  11. 11. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources What we offer? 11 Brand-independent data Marketplace to trade-off and monetize your Data Lakes New products and services Offer Contract ExchangeCatalogue API-based: unlock new data-driven services based on harmonized industrial datasets
  12. 12. Horizon 2020 European Union Funding for Research & Innovation Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources Type of Sources and Data; The Common Industrial Data Model (CIDM)
  13. 13. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources Data Sources and Type of Data 13 Connected Vehicles Smart Buildings • Datapackage: Set of measurements • TimeSeries • Histograms • Geo-Histograms • Basic-Information • Event-based • Format: JSON • How to collect?: • Demand • Subscription
  14. 14. Horizon 2020 European Union Funding for Research & Innovation Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources How to Monetize your data: B2B and B2C Monetization process
  15. 15. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources Key Concepts 23 Data Consumers - Create Request- Data Owners - Accept / Terminate Offers- Monetization active between Data Owner and Consumer through Contracts • AGORA is governed by a suscription-based mechanism; • Data Request and Offers • Every interested Data Consumer creates a - Data Request (s)- which includes all the parameters, filters and configuration within the Data Discovery process. It defines the needed conditions to receive concrete data. • Once the Data Request is published by Data Consumers in the marketplace, Data Owners can accept Offer (s) and get the consent to deliver their data through that request and this specific Data Consumer (s). This acceptance process is formalized in an Contract.
  16. 16. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources Steps flow 24
  17. 17. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources Horizon 2020 European Union Funding for Research & Innovation Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources Data Analytics Toolbox
  18. 18. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 26 Data analytical model Data
  19. 19. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 27 Data Analytics Toolbox
  20. 20. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 28 Data Analytics Toolbox
  21. 21. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources Streaming: Time series library Prediction Drift Detection • Core functionalities: 1. Prediction: forecasting of the future behavior of a given signal. 2. Drift Detection: metric representing the severity of a signal’s fluctuation. 3. Correlation metrics between two or more signals.
  22. 22. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 30 Use Cases – Time series’ module • Identify malfunctioning sensors • Drift estimation functions to measure a signal’s fluctuations. • Can also help the detection of actual drift on a car’s maneuvers. Drift metric • Any subsequent analysis (e.g. prediction, correlation) benefits from having outliers detected and filtered out.
  23. 23. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 31 Data Analytics Toolbox
  24. 24. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 32 Batch: Trajectory library • There are 4 main functionalities available in the module: 1. Interpolation: It refers to the method to estimate new data within the range of known data. 2. Statistics: Provides the average velocity, the duration and distance of a trajectory. 3. Clustering: Enables to group trajectories based on their similarities. 4. Anomalies detection: It allows to detect uncommon patterns in the data. Statistics ClusteringInterpolation Anomalies detection
  25. 25. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 33 Use Cases – Trajectory Library • Focus on specific routes • Similar trajectories are grouped together. • Aids further inspection of each group (e.g. sensors’ behaviour).
  26. 26. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 34 Marketplace integration – Trajectory Library
  27. 27. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 35 Use Cases – Trajectories’ module • Remove anomalous coordinates • Atypical positions can be better identified when a centroid trajectory is defined. • Linear interpolation can then be used to obtain an estimation of how they should look in reality.
  28. 28. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 36 Marketplace integration – Trajectory Library
  29. 29. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 37 Data Analytics Toolbox
  30. 30. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 38 Batch: Networks library • The functionalities available in the module are: 1. Create a network: Create a network from nodes and links. 2. Update the networks: Enables to represent time or other evolutions in the system. – The networks could be live updated. 3. Compute basic metrics of the networks. Network Creation Update Networks Metrics Networks
  31. 31. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 39 Use Cases – Networks’ module • Weather’s influence on sensors • Signals with similar patterns are neighbors in a network. • Separated groups can be related to distinct behaviors of a sensor. • Aids inspection of the correlations and causality of each different group.
  32. 32. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 40 Use Cases – Networks’ module • Investigate outliers • Non-neighboring or significantly distance groups may be worth inspecting. • Helps discover weird occurrences of a signal.
  33. 33. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 41 Introduction to the platform 1 Obtaining access to the data: 1- Selecting the type of data ( vehicles/buildings, temperature o GPS…)
  34. 34. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 42 Introduction to the platform • In the Data Discovery the user can add channels to have access to its data. • It is possible to set geographical an time filters inside a channel. 1 Obtaining access to the data: 2- Request the data:
  35. 35. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 43 Introduction to the platform • Once the Data provider has accepted the user request, it is possible to carry out analytics. • In the Toolbox the user may create analytics. • Also it displays every analytic performed in the past in each library. (Time Series, Trajectories, Networks and Machine Learning) 2 Perform Analytics: 1- Carrying out a model:
  36. 36. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 44 Introduction to the platform • In the menu of a new analytic the user shall choose among the type of analysis that suit the best its purpose: By instance, “Permutation Entropy” is a good feature for detecting Malfunctioning sensors. • A brief description of each model is included to assist in the model decision making. • Follow the steps to complete the analytic. 2 Perform Analytics: 1- Carrying out a model:
  37. 37. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 45 Introduction to the platform • The new analytic appears in the Analytics view. 2 Perform Analytics: 1- Carrying out a model:
  38. 38. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources 46 Introduction to the platform • The new analytic appears in the Analytics view. • For streaming data, the analytic output consist in an Output AEON subscription URL to receive the results in streaming. 2 Perform Analytics: 2- Inspecting the new analytic:
  39. 39. Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources Thank you for your attention ! Horizon 2020 European Union Funding for Research & Innovation 47

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