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A Taxonomy of the Data Resource in the Networked Industry

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This presentation reports on the design of a taxonomy of the data resource in the networked industry. It was held on the 7th International Scientific Symposium on Logistics on June 6, 2014, in Cologne, Germany. The presentation motivates the topic, analyzes four networking industry cases and discusses a first version of the taxonomy. The presentation argues that for companies aiming at designing a future-proof data architecture leveraging the potentials of the industrial internet, collaborative forms of organizations etc. transparency about data sources, data ownership, criticality, compliance of standards of data, data quality are key for success. In addition, the presentation introduces a first sketch of a method supporting businesses in applying the taxonomy.

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A Taxonomy of the Data Resource in the Networked Industry

  1. 1. © Fraunhofer TOWARD A TAXONOMY OF THE DATA RESOURCE IN THE NETWORKED INDUSTRY Boris Otto, Rene Abraham, Simon Schlosser Cologne, June 5, 2014
  2. 2. © Fraunhofer AGENDA  Data in the Networked Industry  Research Approach  Case Studies on Data in the Networked Industry  Data Morphology Design  Method Support  Outlook
  3. 3. © Fraunhofer A set of current developments foster the adoption of networked forms of organization in many industries Globalization Internet of Things Consumer-Centricity Product Complexity Networked Forms of Organization
  4. 4. © Fraunhofer The role of data has evolved from a by-product to a product in its own right traded on data markets Factual InfoChimps Windows Azure Data Market Data.com Year of Foundation 2007 2009 2010 2010 (formerly Jigsaw, 2004) Owner Venture capital firms CSC Microsoft Salesforce.com Offering Open data platform, API use for free or at a charge. 15,000 data sets, open data platform, four different pricing models, web service. Wide range of data, including open data platform. Buying and selling data via Azure marketplace. Data sets for increasing master data quality, maintained by community of 2.000.000 users. Services Data mining, data retrieval, data acquisition from external parties. Data collection, infrastructure development, hosting and distribution. Software as a Service (SaaS) applications and data sets, partially real-time access. Different service and pricing models. Access to contact information, real- time updated data sets.
  5. 5. © Fraunhofer Companies in the networked industry struggle with finding an appropriate data architecture Data in the outer circles is of higher “fuzziness”, volume, change frequency… Data in the outer circles is of less control, criticality, unambiguity… “Nucleus Data” (Customer master data, product master data etc.) “Community Data” (Geo-information, GTIN, addresses, ISO codes, GS1 data etc.) “Open Big Data” (Tweets, social media streams, sensor data etc.) Megabytes Gigabytes Terabytes Petabytes
  6. 6. © Fraunhofer The scientific knowledge base falls short in explaining the role of data in the networked industry Networked Industry Perspective Selected Contributions with Data Focus Summary of Knowledge Base Enterprise (Addo-Tenkorang, Helo, Shamsuzzoha, Ehrs & Phuong, 2012), (Bettoni, Alge, Rovere, Pedrazzoli & Canetta, 2012), (Legner & Schemm, 2008) Data modeling in supply chains Supply chain data management Network (Howard, Vidgen, Powell & Graves, 2001), (Lampathaki, Mouzakitis, Gionis, Charalabidis & Askounis, 2009), (Legner & Schemm, 2008), (Nelson, Shaw & Qualls, 2005) Data and information sharing Data standards Interoperability Technology (Chalasani & Boppana, 2007), (D'Amours, Lefrançois & Montreuil, 1996), (Derakshan et al., 2007), (Dreibelbis et al., 2008), (Parlanti, Paganelli & Giuli, 2011) (Wang & Jin, 2008) Data as a service (SOA) Information systems design RFID data architecture design
  7. 7. © Fraunhofer The goal to increase understanding of data in the networked industry translates in two research questions Research Question 1  How does a morphology of the data resource in the networked industry look like? Research Question 2  How should a methodology be designed that helps companies in the networked industry to apply the morphology for data architecture design?
  8. 8. © Fraunhofer The explorative and design-oriented approach follows a two-phased research process Phase IIPhase I Literature Review: DRM/DAM Case Analysis Morphology Analysis and Design Literature Review: DRM Method Engineering Method for Morphology Application Legend: DRM - Data Resource Management; DAM - Data Architecture Management.
  9. 9. © Fraunhofer Four cases were analyzed for morphology analysis and design Case A B C D Perspective Consumer-Centricity Supply Chain Excellence, IoT Purchasing Electronic commerce Industry Consumer goods and retail Consumer goods and retail Pharmaceutical, chemical, food Online retailing Data objects in focus Suppliers, retailers, products, consumers Suppliers, retailers, load carrier Suppliers Customers, products Case study partners Beiersdorf, Migros Mars, Rewe, Chep Bayer, Nestlé, Novartis, Syngenta Amazon Data collection and analysis Interviews Participatory case study Expert interviews Case study Interviews, focus groups, data overlap analysis Participatory case study Archival records, public documentation Case Study Project context Competence Center Corporate Data Quality SmaRTI Corporate Data League -
  10. 10. © Fraunhofer In Case A, Beiersdorf analyzed the betweenness of product data flows in its network Agency Consumer information provider Brand owner Consumer Retailer Consumer Agency Consumer information provider Consumer technology provider GDSN Social network Online retailer Brand owner Retailer web shop Forum & Blogs 2007 2012 Legend: GDSN - Global Data Synchronisation Network. Media
  11. 11. © Fraunhofer Analysis of Case A revealed shortcomings when it comes to managing data in a networked industry  Today, the label drives product data management  Carbon foot print information or allergen implications not considered  Product data quality differs  High quality in supply chain data, low quality with regard to product information  Data sources are not transparent when controlled by the consumer (ratings, blogs, posts about products etc.).  Variety of data formats increases (videos, streams, images etc.)
  12. 12. © Fraunhofer Case B analyzes the consumer goods supply chain in the context of the SmaRTI project Cloud-based data service for data aggregation and provisioning etc.  Cloud-based  Service-oriented  Standardized Intelligent load carriers such as  Retail pallets  Air cargo pallets Process modeling following Internet of Things design principles  Self-controlled  Decentralized Internet of Service  Data marketplace  Business intelligence  Apps
  13. 13. © Fraunhofer Analysis of Case B revealed shortcomings when it comes to managing data in a networked industry  Collaborative environment needed to collect, aggregate, analyze data from EPCIS events  Value network-wide standardization of data formats and semantics needed  Traditional design principles for application systems becoming obsolete  Maintaining pallets as stock items  Real-time data availability on item level conflicts with standard document flow  Ownership of collaborative data unclear  Integration of structured ECPIS data and value-added PoS and multimedia data not clear Legend: EPCIS - Electronic Product Code Information Services; PoS - Point-of-Sale.
  14. 14. © Fraunhofer The data morphology for the networked industry covers various dimensions Dimension Characteristics Business criticality Competitive advantage Compliance relevant Operations relevant Data classification Private Public Purpose-related Data domain type Account Party Thing Other Data format ASCII Audio JPEG Video Numeric XML Data management level Class Instantiation Data occurrence Batch Stream Data ownership Owned by one legal entity “Club” good Public good Data quality Authoritative Within tolerance, fuzzy Below thresholds Data source Internal External Data standardization Semantics Syntax Values Data trustworthiness Not trusted Trusted Data sharing Open Free Proprietary Data maintenance costs Low Medium High
  15. 15. © Fraunhofer Phase I: Identify domain and scope A method provides methodological support for applying the morphology in practice  Design data architecture  Create transparency  Managing risks  Find data management patterns Activities Results Roles I.1 Define scope I.2 Identify data objects and items Phase III: Design Phase II: Analyze II.1 Create transparency II.2 Analyze and assess III.1 Derive design requirements III.1 Design data architecture Identified data domain and analysis objective List of data objects and items to be analyzed Data steward Data steward, data architect, data owner Data steward, data owner, data scientist, (business partners) Data scientist, data architect Data (heat) map Risks and opportunities Requirements list Data architecture Data architect, data steward Data architect
  16. 16. © Fraunhofer The morphology identifies data resource patterns as the example of business partner data from Case C shows Dimension Characteristics Business criticality Competitive advantage Compliance relevant Operations relevant Data classification Private Public Purpose-related Data domain type Account Party Thing Other Data format ASCII Audio JPEG Video Numeric XML Data management level Class Instantiation Data occurrence Batch Stream Data ownership Owned by one legal entity “Club” good Public good Data quality Authoritative Within tolerance, fuzzy Below thresholds Data source Internal External Data standardization Semantics Syntax Values Data trustworthiness Not trusted Trusted Data sharing Open Free Proprietary Data maintenance costs Low Medium High Legend: The darker the more apprproiate.
  17. 17. © Fraunhofer The research has limitations and points the ways to some further research opportunities  Limitations  Qualitative data  First design cycle only  Morphology needs refinement  No large scale evaluation  For pattern detection  Outlook  Data architecture patterns for verticals  Elaboration of methodological support  Networked data management systems
  18. 18. © Fraunhofer Please get in touch for further information Univ.-Prof. Dr. Ing. habil. Boris Otto TU Dortmund University Audi-Endowed Chair of Supply Net Order Management LogistikCampus Joseph-v.-Fraunhofer-Straße 2-4 D-44227 Dortmund Tel.: +49-231-755-5959 Boris.Otto@tu-dortmund.de Fraunhofer Institute for Material Flow and Logistics Director Information Management & Engineering Joseph-v.-Fraunhofer-Straße 2-4 D-44227 Dortmund Tel.: +49-231-9743-655 Boris.Otto@iml.fraunhofer.de

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