Management of the data resource in the industrial enterprise becomes a strategic capability in the digital age. The talk motivates data resource management, presents proven practices and outlines principles of modern data management approaches.

1. © Fraunhofer ISST
DATA RESOURCE MANAGEMENT
GOOD PRACTICES TO MAKE THE MOST OF A HIDDEN TREASURE
Prof. Dr.-Ing. Boris Otto  Dortmund  12 July 2018
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2. © Fraunhofer ISST
TABLE OF CONTENT
 Data as an Enterprise Resource
 Good Practices for Data Resource Management
 Data Ecosystems
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3. © Fraunhofer ISST
Data is a driver for innovation
Image source: youtube.com (2018), urban-hub.com (2018); sildeshare.com (2018); SmartFace project (2016).
Mobility
 Autonomous driving
 Mobility services
 Smart traffic
management
Service Innovation
Manufacturing
 Smart factory
 Adaptive manufacturing
 Industry 4.0
Organizational Innovation
Healthcare
 Personalized medicine
 Translational medicine
 Smart healthcare devices
Product Innovation
Retail
 Supply chain visibility
 Goods and data
traceability
 Sustainability
Process Innovation
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 Data Intelligence Hub
 Data sharing platform
 Data sovereignty and security
The data economy is here
Sources: Deutsche Telekom (2018); HERE (2018); SAP (2018).
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 HERE Tracking Cloud
 SAP Asset Intelligence Network
 Asset Management Ecosystem
Deutsche Telekom HERE SAP
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Data has become a strategic enterprise resource
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Data as a Process Result Data as a Process Enabler Data as a Product Enabler Data
Information systems have been used
since the 1960s and 1970s to support
enterprise functions, but data wasn‘t
shared between functions, let alone
enterprises.
With the proliferation of
Manufacturing Resource Planning
(MRP) and Enterprise Resource
Planning (ERP) in the 1980s and
1990s data enabled end-to-end
business processes such as order-to-
cash, procure-to-pay, make-to-stock
etc.
Since the millennium change, data
has increasingly become an enabler
of innovative product-service-
systems and integrated solutions.
Recently, data marketplaces
emerged offering data APIs at a
volume or frequency based fee.
Data has become a product in its
own right.
Mainframe Computing Enterprise Systems Electronic Business Data Economy
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6. © Fraunhofer ISST
A strategic resource is a source of competitive advantage
Strategic
Resource
V Value
R Rarity
I Inimitability
N/O
Non-substitutability
Organization
Source: Barney (1991); Makadok (2001).
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VRIN/VRIO Framework
 Resources
 »all assets, capabilities, organizational processes,
firm attributes, information, knowledge, etc.
controlled by a firm that enable the firm to conceive
of and implement strategies that improve its
efficiency and effectiveness«
 Capabilities
 »special type of resource, specifically an
organizationally embedded non-transferable firm-
specific resource whose purpose is to improve the
productivity of the other resources possessed by the
firm«
Resource-Based View of the Firm
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7. © Fraunhofer ISST
TABLE OF CONTENT
 Data as an Enterprise Resource
 Good Practices for Data Resource Management
 Data Ecosystems
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8. © Fraunhofer ISST
Data Resource Management is purely reactive often – as Materials
Management has been 100 years ago…
Source: Otto & Österle (2015).
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Data management today… FIRE-FIGHTING…
 Enormous costs for reactive, follow-up measures
 No target values, no controlling, no management
 Budgets and staff not planned
 Digitalization impossible
… causing high follow-up costs …
 Investments in CDQM pay off as complexity costs and costs for
poor data quality are reduced
 Logic analogous to tangible goods (materials, products etc.)
Data Quality
Time
Legend: »Data Quality Issues« (e.g. migrations, process
disruptions, reporting errors).
Project 1 Project 2 …
As-is
Overall Costs
Costs
Data Quality
Follow-up Costs
of Poor Data
Quality
CDQM Costs
To-be
Legend: CDQM – Corporate Data Quality Management.
Saving
Potential
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9. © Fraunhofer ISST
Data resource management has to be planned, monitored, executed as
materials and asset management
Source: Womack et al. (1991).
»Our findings were eye-opening. The Japanese plant requires
(less effort the American and European plants). At the same
time, the Japanese plant greatly exceeds the quality level of all
plants execpt one in Europe - and this plant requires four times
the effort […]«
»When we visited the high-quality but low productivity
European plant […] we didn‘t have to go far to find the basic
problem […]. At the end of the assembly line was an enormous
rework and rectification area where armies of technicians in
white laboratory jackets labored to bring the finished vehicles
up to the company‘s fabled quality standard.«
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If you refer to data as a resource, treat it as such!
Source: Volkswagen (2018).
Manufacturing Resource Management Data Resource Management
?
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Drivers for data resource management typically are of enterprise-wide nature
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Group Level
Division 2Division 1 Division 3
Business units
Business processes
Locations
Business units
Business processes
Locations
Business units
Business processes
Locations
Compliance to regulations
360 degree view of the customer
Integrated and automated business processes
»Single Source of the Truth«
Industry 4.0
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12. © Fraunhofer ISST
Data Governance and Data Quality Management are closely interrelated
Source: Otto (2011).
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Legend: Goal Function Data.
Data
Governance
Data Quality
Management
Maximize
Data Quality
Maximize
Data Value
Data Resource
Data Resource
Management
is sub-goal of
supports supports
is led by is sub-function
of
are object of is object of
are object of
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13. © Fraunhofer ISST
Reasons for poor data quality are manifold – as the example of Bayer
CropScience shows
NB: For background on the case study see Ebner et al. (2011).
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Data Quality
Issues
Employees Data Maintenance
DQ Management Standards Organization
Training and education
inadequate
Data quality not integrated in
performance management systems
Various software
solutions in place
Master data can be edited
in target systems
No integrated software
support
Data maintenance not
harmonized on global level
No data quality
metrics
No continuous data
quality monitoring
No binding rules,
standards, operating
procedures
Too many local rules,
exceptions
No
“Data Governance”
Missing business
responsibilities
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14. © Fraunhofer ISST
Developed by the Competence Center Corporate Data Quality, the Data Excellence
Model (DXM) defines building blocks for data management
Source: Competence Center Corporate Data Quality (2017).
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GOALS ENABLERS RES ULTS
D A T A
S T R A T E G Y
P E O P L E , R O L E S &
R E S P O N S I B I L I T I E S
P R O C E S S E S &
M E T H O D S
D A T A
L I F E C Y C L E
D A T A
A P P L I C A T I O N S
D A T A
A R C H I T E C T U R E
P E R F O R M A N C E
M A N A G E M E N T
B U S I N E S S
C A P A B I L I T I E S
D A T A
M A N A G E M E N T
C A P A B I L I T I E S
B U S I N E S S
V A L U E
D A T A
E X C E L L E N C E
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Smart Data Engineering is model-based, method-oriented approach for
building up an effective Data Resource Management capability
 Defining the data strategy
 Assigning roles and responsibilities for
core data domains
 Managing data as an economic good
 Designing a consistent data
architecture for the digitalized
enterprise
 Controlling the business benefit
contribution of the data resource
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Smart Data Engineering follows
a set of 8 key principles
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In line with business objectives
A data strategy defines the principles for
identifying and managing corporate data
assets to generate business value.
DATA STRATEGY
Representative digital use cases
Take a look at representative use cases
and understand status quo and required
digital capabilities of the companies value
creation.
USE CASES
1 2 3
5 6 7
4
8
Data needs responsibility
The definition of data roles and
responsibilities including the necessary
interaction between these roles creates
the basic element for data-driven
organizational structures.
DATA ROLES
Leading systems and data objects
The enterprise architecture identifies
relevant business processes, leading
systems, data objects and data streams
at the conceptual level.
DIGITAL ENTERPRISE
ARCHITECTURE
Data monetarization
Achieving data excellence for turning
data into money by improving existing
products, create innovation, and new
digital business models.
BUSINESS VALUE
Management of data flow
The implementation of standards,
guidelines and processes promotes data
interoperability, reuse and traceability.
DATA OPERATIONS
Quality assurance ensures high
product standards
The implementation of quality standards
and processes for data parameters
requires continuous monitoring and
improvement in order to increase the
value of existing data.
DATA QUALITY
Turning theory into practice
Development of a suitable data
architecture model for the
implementation of a target state in the
company.
RESULT
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17. © Fraunhofer ISST
Data Governance is typically established as an enterprise-wide virtual
organization – as the example of BOSCH shows
Source: Bosch (2008).
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Master Data
Owner n
Executive Management
Master Data Management
Steering Committee
…
Group Division/
Central Function
Accountability on
Business Unit Level
(Data Maintenance)
IT Projects
IT Platforms, IT Target Systems
Overall Accountability
(organizational level) Master Data
Owner A
Master Data
Domain 1
Master Data
Domain n
Report
Governance
Working Group
Team of Experts
ConceptsConcepts
Governance
… …
e.g. Vendor Master Data Chart of Accounts
Interdisciplinarily
staffed
Master Data
Officer
Master Data
Officer
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Establishing effective data governance regimes is not a one-off issue – as the
example of Johnson & Johnson shows
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The ideal lifecycle of data governance capabilities follows an S curve
Founding Phase »First Time Right« Cleansing
Legend: E  Effectiveness; A  Amount of Activity.
E
A
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20. © Fraunhofer ISST
Johnson & Johnson has reached a six sigma data quality level
99,503
94,586
95,506
96,102
95,778
96,312
95,656
89,855
91,629
96,324 96,383
97,433
95,417
99,135
99,885 99,971 99,993 99,999
84
86
88
90
92
94
96
98
100
02.15.11 04.15.11 06.15.11 08.15.11 10.15.11 12.15.11 02.15.12 04.15.12 06.15.12
Evolution of Data Quality Index at Johnson & Johnson
Data Quality Index
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21. © Fraunhofer ISST
Data is a valuable Resource
Source: Disparte & Wagner (2016).
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»Accurately measuring enterprise value (EV)
has never been more important or challenging.
Even more so because firms are confronted by
growing volumes of data, and the stakes
implied in misinterpreting the value of that
data have risen to new heights.«
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22. © Fraunhofer ISST
Despite its intangible nature, industrial data has a value which can be
quantified
Source: Moody & Walsh (1999).
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Number of users
Share of value
100%
Data
Tangible
Goods
Tangible
Goods
Value
Data
Usage Time
Potential value
Data
Data quality
Value
100%
Data
Integration
Value
Data
Volume
Value
Data
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23. © Fraunhofer ISST
Many examples exist demonstrating the applicability of valuation procedures
in the data domain
Source: Otto (2012); Otto (2015).
Company Industry Country Data domain
Valuation
approach
Value per record
Retail US
Customer data
including shopping
profile
Market value 1.6 EUR
Social Network US User data Market value 225 USD
Automation and
drives
DE Master data on parts
Production
costs
500 to 5.000 EUR
Agrochemical CH Material master data Use value 184 CHF
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24. © Fraunhofer ISST
D!VA is a software for supporting the key task of the Chief Data Officer,
namely managing the value of the data resource
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Smart data management enables digital twins of the real word
Source: VDI (2015).
Reference Architecture Model Industry 4.0 Administrative Shell Concept
The Administrative Shell stores all data of a hardware or software component in production scenarios
It makes data and services related to that component available for Industry 4.0 scenarios in a standardized way
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26. © Fraunhofer ISST
Data Owner:
Process Owner
Data User:
Enterprise-wide Business Units
Declarative representations of the data resource are prerequisite for
innovative use cases – as the predictive maintenance scenario at VW shows
Source: Volkswagen (2017).
Assets
Data
Organization

27. © Fraunhofer ISST
The flow of the data resource through a company’s ecosystem has to be
managed in an integrated way
Legend: Information flow; Material flow.
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Data
Value Chain Data
Commercial
Services
Industrial
Services
Lot-Size 1
End-to-End Customer
Process
Business Ecosystem
Hybrid Offerings
Smart Data
Management
Interoperability
Human-Machine-
Collaboration
Autonomous Systems
Internet of Things
Customer
Production
Networks
Logistics
Networks
Digitized Value PropositionDataDigitized Value Creation

28. © Fraunhofer ISST
A comprehensive data service architecture is needed for effective and
efficient management of the data resource
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Industrial Data Sources
ERP  MES  SCADA  Installed Base etc.
Commercial Data Sources
CRM  Loyalty Programs etc.
Social Data Sources
Facebook  Twitter etc.
Cloud-based Data Storage
Data Source Connectors  Data Space Infrastructure  Shared Information Model
Industrial Data Service Architecture
Data Quality Assurance Mapping/Transformation Integration/Aggregation Data Provenance …
Data Analysis Data Mining Visualization Data Delivery …
Industrial Use-Cases
Preventive Maintenance  Digital
Farming  Supply Chain Visibility
Commercial Use-Cases
Smart Home  Mobility  HealthCare
etc.
Internal Use-Cases
Data as a Process Enabler
Context-free Use
Data as a Product
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29. © Fraunhofer ISST
RIOTANA® is a flexible, easy-to-use IoT Analytics framework
Legend: IoT – Internet of Things.
IDS Sensor Connector
IDS Connector
RIOTANA App
Sensor Data Analytics
Data Visualization
IDS Message
(Payload in SenML)
MQTT Message
(SenML)
MQTT
Broker
Bosch XDK
Brightness
Temperature
ESP 8266
(incl. WLAN 802.11)
Sensor module
GY-87 10DOF
Temperature
Acceleration
Direction
Backend Computer (PC, VM)
(incl. WLAN 802.11)Sensor Data
Acquisition
Sensor Data
Processing
MQTT
Client
IDS
Messaging
IDS
Messaging

30. © Fraunhofer ISST
A set of design principles guides the transformation to modern Data Resource
Management
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Design Perspective Design Principles Implementation Examples
Strategic principles Productizing of data Data products with clearly defined data elements or
configuration, service levels …
Managing data as an asset Data valuation and pricing, data lifecycle management …
Data co-creating and sharing Collaboration in communities of interest and eco-systems
Organizational principles Governing data in participative ways Transparent responsibilities, digital sovereignty, data owners in
control …
Managing data supply chains and life-cycles end-to-end Data acquisition, pre-processing, processing, distribution, use,
retirement…
Recognizing data quality as probabilistic Dealing with fuzzy and volatile data
with limited traceability
Systems and architecture
principles
Deploying federated architectures Open platforms, linked data…
Decentralizing information security and data sovereignty Data tagging, blockchain technologies…
Sharing data processing resources Cloud platforms, intelligent devices, edge computing

31. © Fraunhofer ISST
TABLE OF CONTENT
 Data as an Enterprise Resource
 Good Practices for Data Resource Management
 Data Ecosystems
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32. © Fraunhofer ISST
The Industrial Data Space addresses the most important issues in Data
Sharing
Source: PwC (2017).
57%
worry about revealing
valuable data and
business secrets.
59%
fear the loss of control
over their data.
55%
feel inconsistent
processes and systems
as a (very) big obstacle.
32%
fear that platforms do
not reach the critical
mass, so that data
exchange will be
interesting.
InteroperabilityData SovereigntyTrust and Security Join us!
Today
Industrial
Data Space
Approach

33. © Fraunhofer ISST
The Industrial Data Space provides an architecture for the sovereign exchange
of data
Legend: IDS Connector; Usage Constraints; Non-IDS Communication.
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Industrial
Data Cloud
IoT Cloud
Enterprise
Cloud
Data
Marketplace
Company 1 Company 2 Company n + 2Company n + 1Company n
Open Data
Source
IDS
IDS IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS

34. © Fraunhofer ISST
The Industrial Data Space forms an ecosystem around the sovereign exchange
of data
Source: Otto et al. (2017); extended representation of the reference architecture model content.
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Runtime EnvironmentRuntime Environment
authorize
publish app
transfer data
data flow
metadata flow
software flow
identification
useIDSsoftware
useIDSsoftware
useIDSsoftware
identify
Data
Owner
App
Provider
Vocabulary
Provider
Clearing
House
App Store
Provider
Identity
Provider
Data
Consumer
Broker
Service
Provider
Service
Provider
Software
Provider
Data
Provider
Certification mandatory
Membership in the IDSA mandatory
Certification
Authority

35. © Fraunhofer ISST
Central Architectures
(e.g. Data Lakes)
Federated Architectures
(e.g. Industrial Data Space)
Distributed Architectures
(e.g. pure Blockchain)
Data Ownership Central or distributed Distributed Distributed
Data Stewardship Central or distributed Distributed Distributed
Data Capture and
Creation
Distributed Distributed Distributed
Data Storage Central Distributed Distributed, redundant
Data Enrichment and
Data Preprocessing
Central Distributed Distributed
Data Integration and
Fusion
Central Central (e.g. through Linked Data and
Data Space approaches)
Distributed
Data Sovereignty Central (if any) Distributed Distributed
Data Provenance Central (if any) Central Distributed
Data Brokering,
Clearing, Billing
Central Central Distributed
Different architecture patterns exist when it comes to data Exchange and Sharing

36. © Fraunhofer ISST
Business data use cases drive the selection of a specific architecture pattern
Business Data
Use Case
Architecture
Pattern
Knowledge
Generation
Data Integration
Trusted Data
Exchange
Data Sovereignty
Data Consistency
Data Transparency
Data Lake Industrial Data Space Blockchain

37. © Fraunhofer ISST
The Corporate Data League is a community-driven data sharing platform for
business partner data
Source: CDQ AG; Corporate Data League (2018). NB: For details see https://www.corporate-data-league.ch/.
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38. © Fraunhofer ISST
Data ecosystems are emerging in practice, but are still a relatively unexplored
phenomenon from a scientific perspective
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Dagstuhl Seminar
Data Ecosystems: Sovereign Data Exchange
among Organizations
22-27 September 2019
BISE Special Issue
Data Sovereignty and Data Space Ecosystems
1 October 2019

39. © Fraunhofer ISST
Prof. Dr.-Ing. Boris Otto
Fraunhofer ISST · Executive Director
TU Dortmund · Faculty of Mechanical Engineering
Boris.Otto@isst.fraunhofer.de · Boris.Otto@tu-dortmund.de
https://de.linkedin.com/pub/boris-otto/1/1b5/570
https://twitter.com/drborisotto
https://www.xing.com/profile/Boris_Otto
http://www.researchgate.net/profile/Boris_Otto
http://de.slideshare.net/borisotto
Please get in touch!
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40. © Fraunhofer ISST
DATA RESOURCE MANAGEMENT
GOOD PRACTICES TO MAKE THE MOST OF A HIDDEN TREASURE
Prof. Dr.-Ing. Boris Otto  Dortmund  12 July 2018
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