data governance · 2019-08-26 · data governance council data owner data stewards (business and...
TRANSCRIPT
© Fraunhofer ISST
DATA GOVERNANCE
Prof. Dr.-Ing. Boris Otto 28 September 2018 Dortmund
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Bildquelle: guinnessworldrecords.com (2017).
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CONTENT
A Brief History of Data Governance
Data Governance in Business Ecosystems
The IDS Approach to Data Governance
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Around the millennium change Data Governance increasingly received attention as a response to compliance risks
Image sources: infrapark-baselland.com (2018), bruecken.deutschebahn.com (2018). Logos from company websites and Wikipedia (2018).
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Financial Regulations
Bankruptcy of energy giant Enron due to fictional financial reporting
In the course of this process, Arthur Andersen found guilty of obstruction of justice for shredding thousands of documents
The company surrendered its CPA license on August 31, 2002, and 85,000 employees lost their jobs
Governmental Regulations
»Leistungs- und Finanzierungsvereinbarung(LuFV)« links funding of Deutsche Bahn to quality of infrastructure inventory
Direct relationship between quality of data and financial situation
Environmental Regulations
Chemical spill into the river Rhine in 1986 at Sandoz plant in Basel-Schweizerhalle
No data about nature and implications of chemical substances spilled
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Business drivers for Data Governance were – and still are – multifold and affect the company as a whole
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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« for business reporting
Smooth business integrations
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Data quality evolves over time according to a »jigsaw« pattern
Legend: Data quality issues.
Data Quality
TimeProject 1 Project 2 Project 3
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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 qualitymetrics
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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Corporate life is hard without Data Governance
Image source: Strassmann (1995).
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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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A strategic resource is a source of competitive advantage
Strategic Resource
V Value
R Rarity
I Inimitability
N/ONon-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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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
ValueData
Usage Time
Potential value
Data
Data quality
Value
100%
Data
Integration
Value
Data
Volume
Value
Data
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Many examples exist demonstrating the applicability of valuation procedures in the data domain
Source: Otto (2012); Otto (2015), Zechmann (2017).
Company Industry Country Data domainValuationapproach
Value per record
Retail USCustomer 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 partsProduction costs
500 to 5.000 EUR
Agrochemical CH Material master dataUse/income value
184 CHF
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Data Governance aims at allocating decision rights for the management and use of data within an organization
Source: Otto (2011).
Data Governance Organization
Data Governance Goals Data Governance Structure
Formal Goals
Business Goals
Ensure compliance Enable decision-making Improve customer satisfaction Increase operational efficiency Support business integration
IS/IT-related Goals
Increase data quality Support IS integration (e.g. migrations)
Functional Goals
Create data strategy and policies Establish data quality controlling Establish data stewardship Implement data standards and metadata management Establish data life-cycle management Establish data architecture management
Locus of Control
Functional Positioning
Business department IS/IT department
Executive management Middle management
Hierarchical Positioning
Organizational Form
Centralized Decentralized/local Project organization Virtual organization Shared service
Roles and Committees
Sponsor Data governance council Data owner Data stewards (business and technical)
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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 ManagementSteering Committee
…
Group Division/Central Function
Accountability onBusiness Unit Level(Data Maintenance)
IT Projects
IT Platforms, IT Target Systems
Overall Accountability(organizational level) Master Data
Owner A
Master DataDomain 1
Master DataDomain n
Report
Governance
Working GroupTeam of Experts
ConceptsConcepts
Governance
… …
e.g. Vendor Master Data Chart of Accounts
Inte
rdiscip
lina
rilysta
ffed
Master Data Officer
Master Data Officer
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A data quality index is an effective performance management tool at Bayer CropScience
Source: Ebner & Brauer (2011).
84
86
88
90
92
94
96
98
100
11/2009 01/2010 03/2010 05/2010 07/2010 09/2010 11/2010 01/2011
Material Master Data Quality Index
Asia Pacific
Europe
Latin America
North America
[%]
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Johnson & Johnson has reached a six sigma data quality level
Source: Otto (2013).
99,503
94,586
95,50696,102
95,77896,312
95,656
89,855
91,629
96,324 96,383
97,433
95,417
99,135
99,885 99,971 99,993 99,999
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94
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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
Data Quality Index
Data Quality Index
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Five key principles lead to excellence in master data governance
Source: Otto & Österle (2015).
Capture Data at the Source
Enter Data »First Time Right«
Measure to Manage
Build up a Data Governance Capability
Scale Capabilities Globally
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Life’s good with Data Governance
Image source: Strassmann (1995).
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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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CONTENT
A Brief History of Data Governance
Data Governance in Business Ecosystems
The IDS Approach to Data Governance
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Data has become a strategic enterprise resource
Legend: MRP – Manufacturing Resource Planning; ERP – Enterprise Resource Planning.
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Data as a Process Result Data as a Process Enabler Data as a Product Enabler Data as a Product
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 MRP and ERP systems 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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In the era of digitalization, companies must develop their Data Management from »Defense« to »Offense«
Source: DalleMulle & Davenport (2017).
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Defense Offense
Key ObjectivesEnsure data security, privacy, integrity, quality, regulatory compliance, and governance
Improve competitive position andprofitability
Core ActivitiesOptimize data extraction, standardization,storage, and access
Optimize data analytics, modeling,visualization, transformation, andenrichment
Data Management Orientation
Control Flexibility
Enabling Architecture Single Source of Truth Multiple Versions of the Truth
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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); CDQ (2018).
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HERE Tracking Cloud
Community approach to data management
Using the power of many
Deutsche Telekom HERE Corporate Data League
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Sharing data is a prerequisite for ecosystems
Image sources: Johns Hopkins University (2016), Umweltbundesamt (2016), Smellgard, Schneider & Farkas (2016), urbanmanagement.nl (2017).
Data Sharing
Energy
Health Care
Material Sciences
Manufacturing and Logistics
»Smart Cities«
Sharing of material information along the entire product life cycle
Shared use of process data for predictive asset maintenance
Exchange of master and event data along the entire supply chain
Anonymized, shared data pool for better drug development
Shared use of data for end-to-end consumer services
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Data sovereignty is a prerequisite for innovative business models in variousdomains
Image sources: perm4.com (2017); hccs.edu (2017); dvz.de (2017).
Health Care Patient Data
Use purpose
Anonymization
System constraints
Personalized medicine
Better healthcareservices
Domain Data Usage Conditions Value Potential
ProductionProduct Data
Process Data
Usage frequency
Usage types
Use purpose
Innovative productionnetworks
»Production as a Service«
Automotive Planning and Risk Data
Use purpose
Expiration date
System constraints
Better risk management
Less production bottlenecks
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The role of Data Governance differs between Offense and Defense Data Management…
Image source: ebay (2018).
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Defense Offense
Scope Enterprise-internal Ecosystem, Customer
Ownership Setting data standards Executing property rights
Stewardship Quality Curation
Organization Hierarchy Market, Community
Data Flows Internal between application systems Data value chains in networks
Usage Access Rights Usage Rights
Economics Cost and Use Value Market value
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CONTENT
A Brief History of Data Governance
Data Governance in Business Ecosystems
The IDS Approach to Data Governance
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The IDS Reference Architecture Model responds to the most important issues in data sharing
Source: PwC (2017). The International Data Spaces (IDS) Association publishes the IDS Reference Architecture Model (IDS-RAM). The Industrial Data Space is a vertical application of the IDS-RAM.
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
IDS Approach
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Data sovereignty is needed for effective Supply Chain Risk Management
OEM»Tier 1« Supplier
Risk Management
Supplier Management
• Contact person
• Risk type
• Risk location
• Affected parts
• Affected sub-suppliers
• Capacities and inventory levels
• Contact person
• Parts demand
• Inventory levels
Use contextRisk management
ConditionDeletion after 3 days
Use contextSupplier management
ConditionDeletion after 14 days
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Data sovereignty is needed for innovation in the pharmaceutical industry
Pharma Company
Usage context
Clinical research
Anonymization
Data record must
consists of at least
150 individual
anonymized data
sets
University Hospital
Patient Management
Smart Drug Development
• Health data
• Medication plan
• Electronic case records
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Data sovereignty is a prerequisite for flexible and dynamic production networks
“Production as a Service” Provider
OEM
ProductionPlanning and
Control
• CAD data
• Configuration parameters
• Production volume
• Usage time
• Temperature data
• Certificates
Usage contextMaintenance, no forwarding
ConditionOperator anonymous
Maintenance
Usage contextMachine type
ConditionDelete CAD data after first use
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Usage conditions for data are multifold
Dimension Specification Example
Geo-information
Coordinates 51.493773, 7.407025, radius 1km
Geo polygon
ZIP code 44227
Country code DE
Expiration date Absolute date December 24, 2017
Anonymization
Role, function
Usage purposePositive list Use for machine configuration
Negative list Not for marketing use
PropagationAllow, deny
Allow on a fee Yes, with 20 percent surplus charge
Number of uses Absolute figure Once
Deletion
System constraints
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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
IDSIDS
IDS
IDSIDS
IDS
IDS
IDS
IDS
IDS
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The Industrial Data Space forms an ecosystem around the sovereign exchange of data
Quelle: IDS Reference Architecture Model Version 2.0 (2018).
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Data Governance activities are distributed to the different roles in the IDS ecosystem
NB: Activities in brackets are to be discussed.
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IDS Role Data Governance Activity IDS Software Component
Data Owner/Provider
Define usage constraints for data resources Publish metadata (incl. usage constraints) to broker Transfer data with usage constraints linked to data Receive information about data transaction from Clearing House Bill data (if required) (Monitor policy enforcement)
IDS Connector
DataConsumer/User
Use data in compliance with use constraints IDS Connector
Broker Match data demand and supply Broker Software
Clearing House Monitor and log data transactions and data value chains (Monitor policy enforcement) (Perform data accounting)
Clearing House Software
App StoreProvider
Offer data governance and data quality services App Store Software
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Prof. Dr.-Ing. Boris Otto
Fraunhofer ISST · Executive DirectorTU Dortmund · Faculty of Mechanical Engineering
[email protected] · [email protected]
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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DATA GOVERNANCE
Prof. Dr.-Ing. Boris Otto 28 September 2018 Dortmund
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Bildquelle: guinnessworldrecords.com (2017).
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