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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

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