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Data Governance


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This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.

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Data Governance

  1. 1. © Fraunhofer ISST DATA GOVERNANCE Prof. Dr.-Ing. Boris Otto  28 September 2018  Dortmund public Bildquelle: (2017). · 1
  2. 2. © Fraunhofer ISST CONTENT  A Brief History of Data Governance  Data Governance in Business Ecosystems  The IDS Approach to Data Governance public· 2
  3. 3. © Fraunhofer ISST Around the millennium change Data Governance increasingly received attention as a response to compliance risks Image sources: (2018), (2018). Logos from company websites and Wikipedia (2018). public 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 · 3
  4. 4. © Fraunhofer ISST Business drivers for Data Governance were – and still are – multifold and affect the company as a whole public 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 · 4
  5. 5. © Fraunhofer ISST Data quality evolves over time according to a »jigsaw« pattern Legend: Data quality issues. Data Quality Time Project 1 Project 2 Project 3 public· 5
  6. 6. © 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). public 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 · 6
  7. 7. © Fraunhofer ISST Corporate life is hard without Data Governance Image source: Strassmann (1995). public· 7
  8. 8. © Fraunhofer ISST Data Governance and Data Quality Management are closely interrelated Source: Otto (2011). public 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 · 8
  9. 9. © 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). public 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 · 9
  10. 10. © Fraunhofer ISST Despite its intangible nature, industrial data has a value which can be quantified Source: Moody & Walsh (1999). public 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 · 10
  11. 11. © Fraunhofer ISST Many examples exist demonstrating the applicability of valuation procedures in the data domain Source: Otto (2012); Otto (2015), Zechmann (2017). 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/income value 184 CHF public· 11
  12. 12. © Fraunhofer ISST 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) public· 12
  13. 13. © Fraunhofer ISST Data Governance is typically established as an enterprise-wide virtual organization – as the example of BOSCH shows Source: Bosch (2008). public 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 · 13
  14. 14. © Fraunhofer ISST 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 [%] public· 14
  15. 15. © Fraunhofer ISST Johnson & Johnson has reached a six sigma data quality level Source: Otto (2013). 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 Data Quality Index Data Quality Index public· 15
  16. 16. © Fraunhofer ISST 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 public· 16
  17. 17. © Fraunhofer ISST Life’s good with Data Governance Image source: Strassmann (1995). public· 17
  18. 18. © 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). public 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 & ME 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 MA N C E MA N A G E ME 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 MA N A G E ME 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 · 18
  19. 19. © Fraunhofer ISST 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 public· 19
  20. 20. © Fraunhofer ISST CONTENT  A Brief History of Data Governance  Data Governance in Business Ecosystems  The IDS Approach to Data Governance public· 20
  21. 21. © Fraunhofer ISST Data has become a strategic enterprise resource Legend: MRP – Manufacturing Resource Planning; ERP – Enterprise Resource Planning. public 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 · 21
  22. 22. © Fraunhofer ISST In the era of digitalization, companies must develop their Data Management from »Defense« to »Offense« Source: DalleMulle & Davenport (2017). public Defense Offense Key Objectives Ensure data security, privacy, integrity, quality, regulatory compliance, and governance Improve competitive position and profitability Core Activities Optimize data extraction, standardization, storage, and access Optimize data analytics, modeling, visualization, transformation, and enrichment Data Management Orientation Control Flexibility Enabling Architecture Single Source of Truth Multiple Versions of the Truth · 22
  23. 23. © Fraunhofer ISST  Data Intelligence Hub  Data sharing platform  Data sovereignty and security The data economy is here Sources: Deutsche Telekom (2018); HERE (2018); CDQ (2018). public  HERE Tracking Cloud  Community approach to data management  Using the power of many Deutsche Telekom HERE Corporate Data League · 23
  24. 24. © Fraunhofer ISST Sharing data is a prerequisite for ecosystems Image sources: Johns Hopkins University (2016), Umweltbundesamt (2016), Smellgard, Schneider & Farkas (2016), (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 public· 24
  25. 25. © Fraunhofer ISST Data sovereignty is a prerequisite for innovative business models in various domains Image sources: (2017); (2017); (2017). Health Care Patient Data  Use purpose  Anonymization  System constraints  Personalized medicine  Better healthcare services Domain Data Usage Conditions Value Potential Production Product Data Process Data  Usage frequency  Usage types  Use purpose  Innovative production networks  »Production as a Service« Automotive Planning and Risk Data  Use purpose  Expiration date  System constraints  Better risk management  Less production bottle necks public· 25
  26. 26. © Fraunhofer ISST The role of Data Governance differs between Offense and Defense Data Management… Image source: ebay (2018). public 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 · 26
  27. 27. © Fraunhofer ISST CONTENT  A Brief History of Data Governance  Data Governance in Business Ecosystems  The IDS Approach to Data Governance public· 27
  28. 28. © Fraunhofer ISST 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 public· 28
  29. 29. © Fraunhofer ISST 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 context Risk management Condition Deletion after 3 days Use context Supplier management Condition Deletion after 14 days public· 29
  30. 30. © Fraunhofer ISST 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 public· 30
  31. 31. © Fraunhofer ISST Data sovereignty is a prerequisite for flexible and dynamic production networks “Production as a Service” Provider OEM Production Planning and Control • CAD data • Configuration parameters • Production volume • Usage time • Temperature data • Certificates Usage context Maintenance, no forwarding Condition Operator anonymous Maintenance Usage context Machine type Condition Delete CAD data after first use public· 31
  32. 32. © Fraunhofer ISST 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 purpose Positive list Use for machine configuration Negative list Not for marketing use Propagation Allow, deny Allow on a fee Yes, with 20 percent surplus charge Number of uses Absolute figure Once Deletion System constraints public· 32
  33. 33. © Fraunhofer ISST The Industrial Data Space provides an architecture for the sovereign exchange of data Legend: IDS Connector; Usage Constraints; Non-IDS Communication. public 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 · 33
  34. 34. © Fraunhofer ISST The Industrial Data Space forms an ecosystem around the sovereign exchange of data Quelle: IDS Reference Architecture Model Version 2.0 (2018). public· 34
  35. 35. © Fraunhofer ISST Data Governance activities are distributed to the different roles in the IDS ecosystem NB: Activities in brackets are to be discussed. public 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 Data Consumer/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 Store Provider  Offer data governance and data quality services App Store Software · 35
  36. 36. © Fraunhofer ISST Prof. Dr.-Ing. Boris Otto Fraunhofer ISST · Executive Director TU Dortmund · Faculty of Mechanical Engineering · Please get in touch! public· 36
  37. 37. © Fraunhofer ISST DATA GOVERNANCE Prof. Dr.-Ing. Boris Otto  28 September 2018  Dortmund public Bildquelle: (2017). · 37