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TOWARD A TAXONOMY OF THE DATA RESOURCE IN THE NETWORKED INDUSTRY
Boris Otto, Rene Abraham, Simon SchlosserCologne, June 5, 2014
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AGENDA
Data in the Networked Industry
Research Approach
Case Studies on Data in the Networked Industry
Data Morphology Design
Method Support
Outlook
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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
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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 usefor free or at acharge.
15,000 data sets, open data platform, fourdifferent pricingmodels, 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.
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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
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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 chainsSupply 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 sharingData standardsInteroperability
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 designRFID data architecture design
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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?
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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.
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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
InterviewsParticipatory case study
Expert interviewsCase study
Interviews, focus groups, data overlap analysisParticipatory case study
Archival records, public documentationCase Study
Project context Competence Center Corporate Data Quality
SmaRTI Corporate Data League
-
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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
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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.)
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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
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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.
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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
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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
Activ ities Results Roles
I.1 Define scope
I.2 Identify dataobjects and items
Phase III: Design
Phase II: Analyze
II.1 Createtransparency
II.2 Analyzeand assess
III.1 Derive designrequirements
III.1 Design dataarchitecture
Identified data domainand analysis objective
List of data objects anditems 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
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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.
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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
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Please get in touch for further information
Univ.-Prof. Dr. Ing. habil. Boris Otto
TU Dortmund Univers ity
Audi-Endowed Chair ofSupply Net Order Management
LogistikCampus
Joseph-v.-Fraunhofer-Straße 2-4
D-44227 Dortmund
Tel.: +49-231-755-5959
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