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I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PA...
I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PA...
I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PA...
I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PA...
I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PA...
I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PA...
I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PA...
8
DQ & MDM
Workflow
Modelling
(Data &
Process)
9
“Organisations that do not
understand the overwhelming
importance of managing
information as tangible assets in
the new ...
I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PA...
11
WHAT IS INFORMATION MANAGEMENT?
“The management of information”
• No prizes here
“A set of principles to derive maximum...
12
KEY INFORMATION MANAGEMENT DIMENSIONS
Data Governance
Data Architecture
& Design
Data Integration
Business
Intelligence...
13
WHAT IS DATA GOVERNANCE?
Where did
this figure
come from?
Data model?
What data
model?
Don't believe
everything
you rea...
14
DATA GOVERNANCE
DAMA –DMBOK Functional Framework v3 (Source: DAMA)
Data Quality
Management
DWH and BI
Management
Refere...
15
DATA GOVERNANCE
• DRIVERS FOR &
BENEFITS OF
DATA GOVERNANCE
16
WHY IS EFFECTIVE IM SO CRUCIAL TODAY?
Higher volumes of data generated by organisations
• Information is all pervasive ...
17
3 DRIVERS FOR DATA GOVERNANCE
1. Reactive Governance
2. Pre-emptive Governance
3. Proactive Governance
18
REACTIVE GOVERNANCE
• Tactical exercise
• Efforts designed to respond to current pains
• Organization has suffered a re...
19
PRE-EMPTIVE GOVERNANCE
• Organization is facing a major change or threats.
• Designed to ward off significant issues th...
20
BUT BEWARE ….
If your main motivation for
Data Governance is
Regulation & Compliance, the
best you can ever hope to
ach...
21
PROACTIVE DATA GOVERNANCE
• Efforts designed to improve capabilities to
resolve risk and data issues.
• Build on reacti...
22
BENEFITS OF DATA GOVERNANCE
Assurance and evidence that data is managed effectively reduces
regulatory compliance risk ...
23
Now – That should clear up a few things around here!
“Ultimately, poor data quality is like dirt on
the windshield. You...
24
dave.huxford@ipl.com
Data Governance Framework
• A Data Governance
Framework
25
DG CONTEXT IN INFORMATION ARCHITECTURE
FRAMEWORK
Master Data MI/BI Data
Transaction
Data
Structured
Technical
Data
Unst...
26
A DATA
GOVERNANCE
FRAMEWORK
IPL DG
Framework
Council &
Organisation
Council Terms
of Reference
Working Groups
Alignment...
27
Data Governance
Framework
• Council & Organisation
28
A DATA
GOVERNANCE
FRAMEWORK
IPL DG
Framework
Council &
Organisation
Council Terms
of Reference
Working Groups
Alignment...
29
DG ORGANISATION
Roles
Teams
Management
Governance
Direction Board
DG Council
(Owners)
Data Quality
Working
Groups
Stewa...
30
TYPICAL GOVERNANCE STRUCTURE
Data Working
Group
Lead Data
Steward
Data Working
Group
Lead Data
Steward
Data Working
Gro...
31
Board
Security Management
Committee
Compliance
Committee
Data Governance Council
Data Quality
Management
Master & Refer...
32
INFORMATION GOVERNANCE
Ongoing data maintenance
and quality
Compliance with policy
and procedures
Three tiered governan...
33
DATA GOVERNANCE
FRAMEWORK
• ROLES &
RESPONSIBILITIES
34
A DATA
GOVERNANCE
FRAMEWORK
IPL DG
Framework
Council &
Organisation
Council Terms
of Reference
Working Groups
Alignment...
35
ROLES
CIO
Lead Data Steward
Data Steward
Data Management Exec
Data Custodian
STEWARDSHIP (LEGISLATIVE & JUDICIAL) DATA ...
36
INFORMATION
Quality
Reporting
Location
Modelling
Analysis
TECHNOLOGY
Architecture
Processing
Integration
Access
Develop...
37
DATA GOVERNANCE
FRAMEWORK
• POLICIES,
PRINCIPLES,
PROCESSES
38
A DATA
GOVERNANCE
FRAMEWORK
IPL DG
Framework
Council &
Organisation
Council Terms
of Reference
Working Groups
Alignment...
39
POLICIES
A set of measurable rules for a set of data elements, in the context of an
organizational scope, for the benef...
40
TAXONOMY OF PRINCIPLES
A principle is a rule or belief that governs behaviour and consists of:
– Statement
• A descript...
41
The Enterprise, rather than any individual or business unit, owns all data.
Every data source must have a defined custo...
42
DATA GOVERNANCE
FRAMEWORK
• PROGRAMME &
MATURITY
43
A DATA
GOVERNANCE
FRAMEWORK
IPL DG
Framework
Council &
Organisation
Council Terms
of Reference
Working Groups
Alignment...
44
Maturity
45
Overall Data Governance Maturity
Level 1 - Initial
Level 2 -
Repeatable
Level 3 -
Defined
Level 4 -
Managed
Level 5 -
O...
46
DATA GOVERNANCE MATURITY BY COMPONENT
Level 1 Initial Level 2
Repeatable
Level 3 Defined Level 4 Managed Level 5
Optimi...
47
Maturity: Data Governance Council & Organisation
Level 1 Initial Level 2
Repeatable
Level 3
Defined
Level 4
Managed
Lev...
48
Maturity: Data Ownership & Stewardship Roles +
Responsibilities
Level 1 Initial Level 2
Repeatable
Level 3
Defined
Leve...
49
Maturity: Principles, Policies & Standards
Level 1 Initial Level 2
Repeatable
Level 3
Defined
Level 4
Managed
Level 5
O...
50
Maturity: Data Governance Programme
Level 1 Initial Level 2
Repeatable
Level 3
Defined
Level 4
Managed
Level 5
Optimise...
51
Maturity: Data Governance Reporting & Assurance
Level 1 Initial Level 2
Repeatable
Level 3
Defined
Level 4
Managed
Leve...
52
DG MATURITY
BY COMPONENT
0
1
2
3
4
5
Data Governance
Council &
Organisation
Data Ownership &
Stewardship Roles
+ Respon...
53
DATA GOVERNANCE IMPLEMENTATION
54
A DATA GOVERNANCE METHODOLOGY
Conceptual Models
55
ENABLERS FOR DATA GOVERNANCE
• High Level Sponsorship
• Data Management Strategy
• Data Management Plan
• Data Architec...
56
MATURITY – MODELS & TAXONOMY
57
EXAMPLE GOVERNANCE WORKFLOW
Responsible (R)
Accountable
(A)
Consulted (C) Informed (I)
Gordon Banks
Chief Steward (Fina...
58
DATA GOVERNANCE
FRAMEWORK
• REPORTING &
ASSURANCE
59
A DATA
GOVERNANCE
FRAMEWORK
IPL DG
Framework
Council &
Organisation
Council Terms
of Reference
Working Groups
Alignment...
60
Dimensions Measures
Data Governance
Organisation &
Structures
Roles &
Responsibilities
Assigned
Standards &
Guidelines
...
61
Dimensions Measures Indicators
Data Quality
Accuracy
Validity
Percentage of Fields
Deemed to be Valid
Integrity
Credibi...
62
SUMMARY
• DATA GOVERNANCE
63
LESSONS FROM THE FIELD ….
One size does NOT fit all
Need to have a flexible approach to Data Governance that delivers
m...
64
THE BOTTOM LINE
This is only important if
Information is REALLY treated as
a valuable corporate asset in
YOUR Business
65
Examples
66
PRODUCTS CONCEPTUAL DATA MODEL
67
REQUEST LOAN PROCESS
STATOIL ENTERPRISE MODELS
Business partner
Statoil Enterprise Data Model
Exploration ( DG1) & Petroleum technology (DG1-D...
STATOIL ENTERPRISE MASTER DATA MODEL
CATALOG CURRENT INITIATIVES
USING THE PROJECT PORTFOLIO
Decision gate: Where is the
initiative in the life project process...
Prioritise by multiple criteria (willingness to engage, feasibility, timescales, importance)
Forget: Timescales, level of ...
HARMONISE & XREF WITH DATA MODEL
PRIORITISE BY INTEREST
74
COLLECTIONS EXAMPLE ILLUSTRATIVE PURPOSES
ONLY
75
AS-IS: UNMANAGED SUBJECT & COLLECTIONS
Business Party
Customer
Supplier
Counter Party
- DUNS #
- Counterparty Name
R&M ...
76
TO-BE: MANAGED SUBJECT & COLLECTIONS
Business Party
Customer
Supplier
Counterparty
- DUNS #
- Counterparty Name
R&M
IST...
77
HOW DOES THIS HELP THE BUSINESS COMMUNICATE
WITH IT&S?
Governed by the Business;
modeled by IT&S
Governed by IT&S
Commu...
78
BUSINESS DATA GOVERNANCE ROLES
1. Organizational Delegation of Authority (DOA); Examples:
• Backbone Governance Board
•...
79
BUSINESS
SPECIFICA
TION AND
CONTENT
GOVERNA
NCE
Local Information
Director
Local Specification
Owners
[local data]
Data...
80
INFORMATION GOVERNANCE
Ongoing data maintenance
and quality
Compliance with policy
and procedures
Three tiered governan...
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Implementing Effective Data Governance

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Introduction to Data Governance
Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance

Published in: Business, Technology
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  • Very interesting slides. Do you have any reference if the data governance regarding geographic information differs or work done on that subject?
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  • Hi there, interesting deck. I'm wondering if you might provide the original power point presentation, as there is some text running off the slides that is also not caputred in the transcript. Thanks!
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  • For the previous post slide 52, refer to http://tdwi.org/Articles/2012/01/17/10-Elements-Data-Strategy.aspx?Page=2
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  • I have a few thoughts on this deck. first of all, I love the flow and layout of the presentation. Maybe it is an "English" thing, but I do not agree on a few of the items listed. His subsequent presentation regarding dmbok 2.0 is more spot on. 1. Slide 29. The governance structure is confusing. - Does the Council report to the board or is the Board made up of the council and the committees? Also, Does the council report to all the other Committees? And below the council, is there a team or strucuture? Why is the role of Enterprise Data Architect in the council? I presume the job of the Enterprse Data Architect is to provide input into the decision making process for the Council, but not be a part of the council itself. Or maybe the council is the middle tier - Steering committee 2. Slide 52. Like the layout, but diagree a lot especially the deliverables. If DG is responsible for some of the deliverales like Data Strategy, Data Management Roadmap we are not talkig DG anymore. Since when does governing mean creating strategy for data. DG's role is to identify the integration with any process, strategy and provide policies, escalation strategy for issue resolution.
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  1. 1. I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PAGE 1 IMPLEMENTING EFFECTIVE DATA GOVERNANCE IMPLEMENTING EFFECTIVE DATA GOVERNANCE Seminar October 2013 Christopher Bradley
  2. 2. I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PAGE 2 INTRODUCTION: WHO AM I? My blog: Information Management, Life & Petrol http://infomanagementlifeandpetrol.blogspot.com @InfoRacer uk.linkedin.com/in/christophermichaelbradley/ CHRISTOPHER BRADLEY Information Strategist chris@chrismb.co.uk
  3. 3. I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PAGE 3 RECENT PRESENTATIONS DAMA UK Webinar: June 2015; “Data Modelling” Disciplines of the DAMA DMBoK” PRISME Pharmaceutical Congress: May 2015, Basel, CH; “Building & exploiting a Pharmaceutical Industry consensus data model” MDM DG Europe (IRM): May 2015, London; “CDMP Examination Preparation” & “Data Governance By Stealth?, Can you ‘sell’ Data Governance if the stakeholders don’t get it?” DAMA UK Webinar: April 2015; “Master & Reference Data Management” Disciplines of the DMBoK” Enterprise Data World: April 2015, Washington DC USA; “Data Modelling For The Business” and “Evaluating Information Management Tools” DAMA UK Webinar: February 2015; “An Introduction to the Information Disciplines of the DMBoK” Dataversity Webinar: February 2015; “How to successfully introduce Master & Reference data management” Petroleum Information Management Summit 2015: February 2015, Berlin DE, “How to succeed with MDM and Data Governance” Enterprise Data & Business Intelligence 2014: (IRM), November 2014, London, UK “Data Modelling 101 Workshop” Enterprise Data World: (DataVersity), May 2014, Austin, Texas, “MDM Architectures & How to identify the right Subject Area & tooling for your MDM strategy” E&P Information Management Dubai: (DMBoard),17-19 March 2014, Dubai, UAE “Master Data Management Fundamentals, Architectures & Identify the starting Data Subject Areas” DAMA Australia: (DAMA-A),18-21 November 2013, Melbourne, Australia “DAMA DMBoK 2.0”, “Information Management Fundamentals” 1 day workshop” Data Management & Information Quality Europe: (IRM Conferences), 4-6 November 2013, London, UK “Data Modelling Fundamentals” ½ day workshop: “Myths, Fairy Tales & The Single View” Seminar “Imaginative Innovation - A Look to the Future” DAMA Panel Discussion IPL / Embarcadero series: June 2013, London, UK, “Implementing Effective Data Governance” Riyadh Information Exchange: May 2013, Riyadh, Saudi Arabia, “Big Data – What’s the big fuss?” Enterprise Data World: (Wilshire Conferences), May 2013, San Diego, USA, “Data and Process Blueprinting – A practical approach for rapidly optimising Information Assets” Data Governance & MDM Europe: (IRM Conferences), April 2013, London, “Selecting the Optimum Business approach for MDM success…. Case study with Statoil” E&P Information Management: (SMI Conference), February 2013, London, “Case Study, Using Data Virtualisation for Real Time BI & Analytics” E&P Data Governance: (DMBoard / DG Events), January 2013, Marrakech, Morocco, “Establishing a successful Data Governance program” Big Data 2: (Whitehall), December 2012, London, “The Pillars of successful knowledge management” Financial Information Management Association (FIMA): (WBR), November 2012, London; “Data Strategy as a Business Enabler” Data Modeling Zone: (Technics), November 2012, Baltimore USA “Data Modelling for the business” Data Management & Information Quality Europe: (IRM), November 2012, London; “All you need to know to prepare for DAMA CDMP professional certification” ECIM Exploration & Production: September 2012, Haugesund, Norway: “Enhancing communication through the use of industry standard models; case study in E&P using WITSML” Preparing the Business for MDM success: Threadneedles Executive breakfast briefing series, July 2012, London Big Data – What’s the big fuss?: (Whitehall), Big Data & Analytics, June 2012, London, Enterprise Data World International: (DAMA / Wilshire), May 2012, Atlanta GA, “A Model Driven Data Governance Framework For MDM - Statoil Case Study” “When Two Worlds Collide – Data and Process Architecture Synergies” (rated best workshop in conference); “Petrochemical Information Management utilising PPDM in an Enterprise Information Architecture” Data Governance & MDM Europe: (DAMA / IRM), April 2012, London, “A Model Driven Data Governance Framework For MDM - Statoil Case Study” AAPG Exploration & Production Data Management: April 2012, Dead Sea Jordan; “A Process For Introducing Data Governance into Large Enterprises” PWC & Iron Mountain Corporate Information Management: March 2012, Madrid; “Information Management & Regulatory Compliance” DAMA Scandinavia: March 2012, Stockholm, “Reducing Complexity in Information Management” (rated best presentation in conference) Ovum IT Governance & Planning: March 2012, London; “Data Governance – An Essential Part of IT Governance” American Express Global Technology Conference: November 2011, UK, “All An Enterprise Architect Needs To Know About Information Management” FIMA Europe (Financial Information Management):, November 2011, London; “Confronting The Complexities Of Financial Regulation With A Customer Centric Approach; Applying a Master Data Management And Data Governance Process In Clydesdale Bank “ Data Management & Information Quality Europe: (DAMA / IRM), November 2011, London, “Assessing & Improving Information Management Effectiveness – Cambridge University Press Case Study”; “Too Good To Be True? – The Truth About Open Source BI” ECIM Exploration & Production: September 12th 14th 2011, Haugesund, Norway: “The Role Of Data Virtualisation In Your EIM Strategy” Enterprise Data World International: (DAMA / Wilshire), April 2011, Chicago IL; “How Do You Want Yours Served? – The Role Of Data Virtualisation And Open Source BI” Data Governance & MDM Europe: (DAMA / IRM), March 2011, London, “Clinical Information Data Governance” Data Management & Information Management Europe: (DAMA / IRM), November 2010, London, “How Do You Get A Business Person To Read A Data Model? DAMA Scandinavia: October 26th-27th 2010, Stockholm, “Incorporating ERP Systems Into Your Overall Models & Information Architecture” (rated best presentation in conference) BPM Europe: (IRM), September 27th – 29th 2010, London, “Learning to Love BPMN 2.0” IPL / Composite Information Management in Pharmaceuticals: September 15th 2010, London, “Clinical Information Management – Are We The Cobblers Children?” ECIM Exploration & Production: September 13th 15th 2010, Haugesund, Norway: “Information Challenges and Solutions” (rated best presentation in conference) Enterprise Architecture Europe: (IRM), June 16th – 18th 2010, London: ½ day workshop; “The Evolution of Enterprise Data Modelling”
  4. 4. I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PAGE 4 RECENT PUBLICATIONS Book: “Data Modelling For The Business – A Handbook for aligning the business with IT using high-level data models”; Technics Publishing; ISBN 978-0-9771400-7-7; http://www.amazon.com/Data-Modeling-Business-Handbook-High-Level White Paper: “Information is at the heart of ALL Architecture disciplines”,; March 2014 Article: The Bookbinder, the Librarian & a Data Governance story ; July 2013 Article: Data Governance is about Hearts and Minds, not Technology January 2013 White Paper: “The fundamentals of Information Management”, January 2013 White Paper: “Knowledge Management – From justification to delivery”, December 2012 Article: “Chief INFORMATION Officer? Not really” Article, November 2012 White Paper: “Running a successful Knowledge Management Practice” November 2012 White Paper: “Big Data Projects are not one man shows” June 2012 Article: “IPL & Statoil’s innovative approach to Master Data Management in Statoil”, Oil IT Journal, May 2012 White Paper: “Data Modelling is NOT just for DBMS’s” April 2012 Article: “Data Governance in the Financial Services Sector” FSTech Magazine, April 2012 Article: “Data Governance, an essential component of IT Governance" March 2012 Article: “Leveraging a Model Driven approach to Master Data Management in Statoil”, Oil IT Journal, February 2012 Article: “How Data Virtualization Helps Data Integration Strategies” BeyeNETWORK (December 2011) Article: “Approaches & Selection Criteria For organizations approaching data integration programmes” TechTarget (November 2011) Article: Big Data – Same Problems? BeyeNETWORK and TechTarget. (July 2011) Article “10 easy steps to evaluate Data Modelling tools” Information Management, (March 2010) Article “How Do You Want Your Data Served?” Conspectus Magazine (February 2010) Article “How do you want yours served (data that is)” (BeyeNETWORK January 2010) Article “Seven deadly sins of data modelling” (BeyeNETWORK October 2009) Article “Data Modelling is NOT just for DBMS’s” Part 1 BeyeNETWORK July 2009 and Part 2 BeyeNETWORK August 2009 Web Channel: BeyeNETWORK “Chris Bradley Expert Channel” Information Asset Management http://www.b-eye-network.co.uk/channels/1554/ Article: “Preventing a Data Disaster” February 2009, Database Marketing Magazine
  5. 5. I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PAGE 5 WHAT IS DATA GOVERNANCE?
  6. 6. I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PAGE 6 CONTENTS ›Introduction to Data Governance ›Drivers for Data Governance & Benefits ›A Data Governance Framework »Organization & Structures »Roles & responsibilities »Policies & Processes »Programme & Implementation »Reporting & Assurance ›Summary ›Case Studies
  7. 7. I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PAGE 7 DATA GOVERNANCE ACTIVITIES ›Data Governance (DMBoK)
  8. 8. 8 DQ & MDM Workflow Modelling (Data & Process)
  9. 9. 9 “Organisations that do not understand the overwhelming importance of managing information as tangible assets in the new economy will not survive.” Tom Peters Data and information are the lifeblood of the 21st century economy. In the Information Age, data is recognized as a vital enterprise asset. The Data Management Association (DAMA International) is the Premiere organization for data professionals worldwide. DAMA International is an international not-for-profit membership organization, with over 10,000 members in 40 chapters around the globe. Its purpose is to promote the understanding, development, and practice of managing data and information to support business strategies. Data Architecture Management Database Operations Management Reference & Master Data Management DW & BI Management Document & Content Management Meta-data Management Data Quality Management Data Governance Data Modelling & Data Development Data Security & Risk Management
  10. 10. I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PAGE 10 INTRODUCTION ›Data Governance Terms & Definitions
  11. 11. 11 WHAT IS INFORMATION MANAGEMENT? “The management of information” • No prizes here “A set of principles to derive maximum value from an organisation’s information” • It’s about deriving real value from information, not just storing data for data’s sake “A set of principles to derive maximum value from an organisation’s information, whilst protecting it as a key corporate asset” • If the information is valuable it needs to be treated as such “The execution of a set of principles and processes to derive maximum value from an organisation’s information, whilst protecting it as a key corporate asset” • There’s no point in the theory, if it’s not put into practice!!!
  12. 12. 12 KEY INFORMATION MANAGEMENT DIMENSIONS Data Governance Data Architecture & Design Data Integration Business Intelligence Master Data Management Data Quality Management The key to ensuring information is exploited to its full potential The key to managing and maintaining the “critical entities” of an organisation The key to enterprise- wide quality assurance of data The key to combining information from disparate systems The key to developing effective information systems The key to exercising positive control over the management of information
  13. 13. 13 WHAT IS DATA GOVERNANCE? Where did this figure come from? Data model? What data model? Don't believe everything you read Multiple personality disorder Spreadsheets, spreadsheets everywhere Where's that darned report? Data Governance Data Architecture and Design Data Quality Management Master Data Management Data Warehousing and ETL Business Intelligence Includes standards/policies covering … Design and operation of a management system to assure that data delivers value and is not a cost Who can do what to the organisation’s data and how. Ensuring standards are set and met A strategic & high level view across the organisation To ensure … Key principles/processes of effective Information Management are put into practice Continual improvement through the evolution of an Information Management strategy Data Governance is NOT … Tactical management Technology and IT department alone The exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets. (DAMA International)
  14. 14. 14 DATA GOVERNANCE DAMA –DMBOK Functional Framework v3 (Source: DAMA) Data Quality Management DWH and BI Management Reference & Master Data Management Data Architecture & Modelling Management Data Governance Key Data Management Functions for Governance At the heart of Information Management
  15. 15. 15 DATA GOVERNANCE • DRIVERS FOR & BENEFITS OF DATA GOVERNANCE
  16. 16. 16 WHY IS EFFECTIVE IM SO CRUCIAL TODAY? Higher volumes of data generated by organisations • Information is all pervasive – if you don’t have a strategy to manage it, you will certainly drown in it Proliferation of data-centric systems • ERP, CRM, ECM… Greater demand for reliable information • Accurate business intelligence is vital to gain competitive advantage, support planning/resourcing and monitor key business functions Tighter regulatory compliance • Far more responsibility now placed on organisations to ensure they store, manage, audit and protect their data Business change is no longer optional – it’s inevitable • Mergers/acquisitions, market forces, technological advances… • Data Governance is essential for managing Information in “The Cloud”
  17. 17. 17 3 DRIVERS FOR DATA GOVERNANCE 1. Reactive Governance 2. Pre-emptive Governance 3. Proactive Governance
  18. 18. 18 REACTIVE GOVERNANCE • Tactical exercise • Efforts designed to respond to current pains • Organization has suffered a regulatory breach or a data disaster
  19. 19. 19 PRE-EMPTIVE GOVERNANCE • Organization is facing a major change or threats. • Designed to ward off significant issues that could affect success of the company • Probably driven by impending regulatory & compliance needs
  20. 20. 20 BUT BEWARE …. If your main motivation for Data Governance is Regulation & Compliance, the best you can ever hope to achieve is just to be compliant Chris Bradley
  21. 21. 21 PROACTIVE DATA GOVERNANCE • Efforts designed to improve capabilities to resolve risk and data issues. • Build on reactive governance to create an ever- increasing body of validated rules, standards, and tested processes. • Part of a wider Information Management strategy
  22. 22. 22 BENEFITS OF DATA GOVERNANCE Assurance and evidence that data is managed effectively reduces regulatory compliance risk and improves confidence in operational and management decisions Known individuals, their responsibilities and escalation route reduces the time and effort to resolve data issues Increased capability to respond to change and events faster through joint understanding across users and IT Reduced system design and integration effort Reduced risk of departmental silos and duplication leading to reconciliation effort and argument
  23. 23. 23 Now – That should clear up a few things around here! “Ultimately, poor data quality is like dirt on the windshield. You may be able to drive for a long time with slowly degrading vision, but at some point you either have to stop and clear the windshield or risk everything.” Ken Orr, The Cutter Consortium Businesses NEED a common vocabulary for communication
  24. 24. 24 dave.huxford@ipl.com Data Governance Framework • A Data Governance Framework
  25. 25. 25 DG CONTEXT IN INFORMATION ARCHITECTURE FRAMEWORK Master Data MI/BI Data Transaction Data Structured Technical Data Unstructured Data Models / Taxonomy Catalog / Meta data Distribution & Infrastructure Services Quality Lifecycle Management Governance Information Planning Goals Principles 1 2 3 4 5 6 7 8 9 10 11 12 13 0 1 2 3 4 5 IM Principles Data Governance IM Planning Data Quality IM Lifecycle Management Integration & Access Models & Taxonomy Catalog & Metadata Master Data Management Business Intelligence To-Be As-Is 13 components containing ... • Principles & rationale • Maturity model • Detailed methodology • Tools & templates • Example business cases
  26. 26. 26 A DATA GOVERNANCE FRAMEWORK IPL DG Framework Council & Organisation Council Terms of Reference Working Groups Alignment Liaison Roles & Responsibilities Owners Stewards Custodians Data Governance Office Data Management Policies & Processes Principles Policies Standards Processes Programme Maturity Matrix Strategy Scope Business Case Implementation Reporting & Assurance Perform Measur Contin Improve Evide Repos Commun
  27. 27. 27 Data Governance Framework • Council & Organisation
  28. 28. 28 A DATA GOVERNANCE FRAMEWORK IPL DG Framework Council & Organisation Council Terms of Reference Working Groups Alignment Liaison Roles & Responsibilities Owners Stewards Custodians Data Governance Office Data Management Policies & Processes Principles Policies Standards Processes Programme Maturity Matrix Strategy Scope Business Case Implementation Reporting & Assurance Perform Measur Contin Improve Evide Repos Commun
  29. 29. 29 DG ORGANISATION Roles Teams Management Governance Direction Board DG Council (Owners) Data Quality Working Groups Stewards Quality Analysts Master & Reference Data Domain Working Group Stewards Custodians Data Warehousing & BI BICC Business Analysts Providers Change Programme Enterprise Architecture Data Architecture Repository / ETL Architects Models & Metadata Enterprise / Application Modellers Analysts Other functions such as security, lifecycle, compliance & risk management also need to be covered as applied to same enterprise data
  30. 30. 30 TYPICAL GOVERNANCE STRUCTURE Data Working Group Lead Data Steward Data Working Group Lead Data Steward Data Working Group Lead Data Steward Data Working Group Lead Data Steward Data Governance Council Lead Data Stewards Key Business Unit Heads Chief Information Officer (CIO) Initiatives Guidance Issues Measures Data Mgt Exec Data Steward Data Custodian Data Steward Data Custodian Data Steward Data Custodian Data Steward Data Custodian Working Groups aligned to Subject Area
  31. 31. 31 Board Security Management Committee Compliance Committee Data Governance Council Data Quality Management Master & Reference Data Management Data Warehouse & BI Management Data Security & Privacy Data Architecture Management Value or Risk Initiatives & Projects Change Programme Committee Chief Information Officer Head of Data Management Head of Marketing Head of Compliance Head of Finance Head of Operations Enterprise Data Architect Data Quality Manager IT Security Manager Lead Data Steward (s)
  32. 32. 32 INFORMATION GOVERNANCE Ongoing data maintenance and quality Compliance with policy and procedures Three tiered governance with individual accountability: By SUBJECT AREA Information Owners: Information Stewards: Information Director: Maintain high-level corporate data model Define the overall process and framework Allocate accountability for individual data entities Determine business process to manage data Mandate stewardship and quality activity Primacy over entire data entity, including data quality metrics
  33. 33. 33 DATA GOVERNANCE FRAMEWORK • ROLES & RESPONSIBILITIES
  34. 34. 34 A DATA GOVERNANCE FRAMEWORK IPL DG Framework Council & Organisation Council Terms of Reference Working Groups Alignment Liaison Roles & Responsibilities Owners Stewards Custodians Data Governance Office Data Management Policies & Processes Principles Policies Standards Processes Programme Maturity Matrix Strategy Scope Business Case Implementation Reporting & Assurance Perform Measur Contin Improve Evide Repos Commun
  35. 35. 35 ROLES CIO Lead Data Steward Data Steward Data Management Exec Data Custodian STEWARDSHIP (LEGISLATIVE & JUDICIAL) DATA MANAGEMENT SERVICES (EXECUTIVE)
  36. 36. 36 INFORMATION Quality Reporting Location Modelling Analysis TECHNOLOGY Architecture Processing Integration Access Development Operations BUSINESS Risk Finance Actuarial Underwriting Marcoms HR Data Owners & Data Stewards Data Management Data Custodians GOVERNANCE
  37. 37. 37 DATA GOVERNANCE FRAMEWORK • POLICIES, PRINCIPLES, PROCESSES
  38. 38. 38 A DATA GOVERNANCE FRAMEWORK IPL DG Framework Council & Organisation Council Terms of Reference Working Groups Alignment Liaison Roles & Responsibilities Owners Stewards Custodians Data Governance Office Data Management Policies & Processes Principles Policies Standards Processes Programme Maturity Matrix Strategy Scope Business Case Implementation Reporting & Assurance Perform Measur Contin Improve Evide Repos Commun
  39. 39. 39 POLICIES A set of measurable rules for a set of data elements, in the context of an organizational scope, for the benefit of a business process, irrespective of where the data is stored and the party that provides the data 1. Data Model 2. Data Definitions 3. Data Quality 4. Data Security 5. Data Lifecycle Management 6. Reference Data 7. Master Data
  40. 40. 40 TAXONOMY OF PRINCIPLES A principle is a rule or belief that governs behaviour and consists of: – Statement • A description of the principle to be adopted – Rationale • The reason(s) for adopting the principle – Implications: • The conclusions drawn from the principle – Key actions • The key actions required by BICC and other functions to ensure the principles are adopted within Riyad Bank – References • Supporting artefacts/tools that support or relate to the principle (initially many of these will not exist and will form a key part of the next steps)
  41. 41. 41 The Enterprise, rather than any individual or business unit, owns all data. Every data source must have a defined custodian (a business role) responsible for the accuracy, integrity, and security of those data. Wherever possible, data must be simple to enter and must accurately reflect the situation; they must also be in a useful, usable form for both input and output. Data should be collected only if they have known and documented uses and value. Data must be readily available to those with a legitimate business need. Processes for data capture, validation, and processing should be automated wherever possible. Data must be entered only once. Processes that update a given data element must be standard across the information system. Data must be recorded as accurately and completely as possible, by the most informed source, as close as possible to their point of creation, and in an electronic form at the earliest opportunity. Where practical, data should be recorded in an auditable and traceable manner. The cost of data collection and sharing must be minimised. Data must be protected from unauthorised access and modification. Data must not be duplicated unless duplication is absolutely essential and has the approval of the relevant data steward. In such cases, one source must be clearly identified as the master, there must be a robust process to keep the copies in step, and copies must not be modified (i.e., ensuring that the data in the source system is the same as that in other databases). Data structures must be under strict change control, so that the various business and system implications of any change can be properly managed. Whenever possible, international, national, or industry standards for common data models must be adopted. When this is not possible, organisational standards must be developed instead. Data should be defined consistently across the Enterprise. Users must accurately present the data in any use that is made of them.
  42. 42. 42 DATA GOVERNANCE FRAMEWORK • PROGRAMME & MATURITY
  43. 43. 43 A DATA GOVERNANCE FRAMEWORK IPL DG Framework Council & Organisation Council Terms of Reference Working Groups Alignment Liaison Roles & Responsibilities Owners Stewards Custodians Data Governance Office Data Management Policies & Processes Principles Policies Standards Processes Programme Maturity Matrix Strategy Scope Business Case Implementation Reporting & Assurance Perform Measur Contin Improve Evide Repos Commun
  44. 44. 44 Maturity
  45. 45. 45 Overall Data Governance Maturity Level 1 - Initial Level 2 - Repeatable Level 3 - Defined Level 4 - Managed Level 5 - Optimised There is no clear data ownership assigned. Data Owners, (if any), evolve on their own approach during project rollouts (i.e. self appointed data owners). No standard tools nor documentation is available for use across the whole enterprise. A Data Ownership Stewardship & Governance Model does not exist. Owners are commissioned in the short- term for specific projects & initiatives. This is often department or silo focused leading to ownership by A defined Enterprise wide Data Ownership, Stewardship & Governance Model exists. Conceptual Enterprise wide Data model in place & ownership model is loosely applied to major data entities. Limited collaboration. Organisation not Enterprise Data Ownership, Stewardship & Governance Model is implemented for the major data entities. Collaboration between stakeholders is in place. Governance process regularly reviews this model and its Enterprise wide Data Ownership, Stewardship & Governance Model has been extended such that the majority of data assets are now under active stewardship. Effective data governance processes are employed by stakeholders & stewards. Well
  46. 46. 46 DATA GOVERNANCE MATURITY BY COMPONENT Level 1 Initial Level 2 Repeatable Level 3 Defined Level 4 Managed Level 5 Optimised Data Governance Council & Organisation Individual project boards and functional areas reacting to data issues when raised. Informal group of data champions / subject matter experts without budget advising functional areas and projects Vision for Data Governance defined but not fully bought into . Data issues addressed by programme management or Enterprise Architecture Executive level sponsorship and council full terms of reference and sub groups in place. Accountabilities for all aspects of data defined and regularly reviewed Recognised by C level executives with regular meetings and decisions communicated DG Council part of business internal controls Ownership / Stewardship Roles & Responsibilit ies No clear ownership assigned. Individual system and analysts assumed responsible for data or self appointed Data champions or super users in business functions but limited collaboration for shared data. Ownership and stewardship defined and loosely applied to a Master Data subject. Responsibilities part of role descriptions Key data subjects have owners / stewards appointed with responsibilities measured and rewarded Majority of data subjects are actively stewarded in accordance with polices and standards and are accepted across organisation Principles, Policies & Standards No policies or standards specifically covering relevant component subjects. Limited number of formal policies but ways of working in hand or projects initiated. Principles and Policies for all subjects agreed and published Standards adopted or being rolled out Processes in place to assure policies and standards are being adopted and achieved. Dispensations and issues resolved Policies and standards regularly reviewed and approved by DG Council. Changes readily adopted in operations and projects Data Governance Programme Data issues raised and considered as part of requirements for projects. No cross business area mandate Individual data projects cover local initiatives with some interaction Data Governance and Management Strategy across organisation developed and communicated. Programme kicked off to establish DG processes Major components of DG covered. 2nd iteration to refine processes and management taking place. Constant communication and DG part of induction training Programme completed and continuous improvement of Governance components through review and refine cycle Communication and updating training ongoing Reporting & Limited, ad-hoc and varied levels of reporting Standards for projects and Shared repository for data related documents and Documents and measures regularly reviewed and DG Council working on exception reporting basis. As-Is To-BeTransition Plan
  47. 47. 47 Maturity: Data Governance Council & Organisation Level 1 Initial Level 2 Repeatable Level 3 Defined Level 4 Managed Level 5 Optimised Individual project boards (where they exist) and Business functional areas reacting to data issues when they are raised . No proactive data planning. An informal group of data champions or data subject matter experts without budget or a central function advising functional areas and projects. Need for Data Governance recognised & pushed by 1 or 2 visionaries but A vision for Enterprise Data Governance is defined but not fully bought into across the business. Data issues are addressed by Programme Management or Enterprise Architecture. Executive level sponsorship established and full terms of reference for a DG council is established. Sub groups start to be put in place. RACI / accountabilities for all aspects of data are defined, workflows established and DG fully recognised by C level executives with regular meetings and decisions communicated DG Council part of business internal controls
  48. 48. 48 Maturity: Data Ownership & Stewardship Roles + Responsibilities Level 1 Initial Level 2 Repeatable Level 3 Defined Level 4 Managed Level 5 Optimised No clear Data ownership has been assigned. Individual system owners and/or technicians or analysts assumed to be responsible Data champions or super users with passion for data emerge in business functions. Limited collaboration for shared data, common data policies & Data ownership and stewardship is defined and loosely applied to a Master Data subject area. Responsibilitie s for Data now become part of role Corporate Data model developed, Data Subject areas defined. Major data subjects have data owners / stewards appointed with their responsibilitie s measured All data subject areas have Data owners. The majority of data subjects areas are actively stewarded in accordance with polices and standards and are
  49. 49. 49 Maturity: Principles, Policies & Standards Level 1 Initial Level 2 Repeatable Level 3 Defined Level 4 Managed Level 5 Optimised No published principles, policies or standards specifically covering relevant component data subjects. A limited number of formal policies emerge. Limited traction in turning policies / principles into actions. Principles, Policies and Standards for most Data subjects agreed and published. Standards adopted and being rolled out Processes put in place to assure the principles, policies and standards are being adopted and achieved. Dispensations and issues resolved via agreed workflow involving Data owners. Data Principles, Policies and standards are regularly reviewed and approved by the Data Governance Council. Changes readily adopted in operations and projects
  50. 50. 50 Maturity: Data Governance Programme Level 1 Initial Level 2 Repeatable Level 3 Defined Level 4 Managed Level 5 Optimised Data issues (if identified) are raised and considered as part of requirements for projects. Shared data subject areas not considered. No cross business area mandate for data. Individual data projects within one business area cover local initiatives. Interaction regarding shared data & ownership is primarily within one business unit. Limited interaction outside of business unit. Data Governance and Information Management Strategy across the organisation developed and communicated. Formal programme is kicked off to establish DG processes. Major components of DG now covered. Communities of interest established. 2nd iteration to refine processes and management taking place. Constant communication regarding DG forms part of DG Programme completed with continuous improvement of Governance components through review and refine cycle. Regular communication and updated training is on- going.
  51. 51. 51 Maturity: Data Governance Reporting & Assurance Level 1 Initial Level 2 Repeatable Level 3 Defined Level 4 Managed Level 5 Optimised Limited, ad- hoc and varied levels of Data Governance & Quality reporting. Where it exists is aligned to local initiatives of functional areas, business processes or Standards being defined and enacted for projects relating to Data Governance, Quality and operational reporting of data issues and architecture. A shared widely accessible repository exists for data related documents and data models. Detailed requirements for data quality measures and metrics are developed. Models, data related documents and Data Quality measures are regularly reviewed and approved. Processes put in place to deliver assurance and to audit documentation . Data Governance Council now working on an exception reporting basis. Few assurance and audit issues are apparent but where they exist are resolved quickly.
  52. 52. 52 DG MATURITY BY COMPONENT 0 1 2 3 4 5 Data Governance Council & Organisation Data Ownership & Stewardship Roles + Responsibilities Information Principles, Policies & Standards Data Governance Programme Data Governance Reporting & Assurance Vision DG Maturity Target DG Maturity Baseline DG Maturity
  53. 53. 53 DATA GOVERNANCE IMPLEMENTATION
  54. 54. 54 A DATA GOVERNANCE METHODOLOGY Conceptual Models
  55. 55. 55 ENABLERS FOR DATA GOVERNANCE • High Level Sponsorship • Data Management Strategy • Data Management Plan • Data Architecture & Models … rich metadata • Data Principles, Policies and Standards • Organisation Structures, Roles & Responsibilities, Terms of Reference • Governance Processes • Performance Measurement and Reporting • Tools / Supporting IT
  56. 56. 56 MATURITY – MODELS & TAXONOMY
  57. 57. 57 EXAMPLE GOVERNANCE WORKFLOW Responsible (R) Accountable (A) Consulted (C) Informed (I) Gordon Banks Chief Steward (Finance) Bobby Moore Chief Steward (Sales) Geoff Hurst Data Steward (Finance) Nobby Stiles Business Steward (Finance) 1 2 3 4 Review Approve Notify Example: New (or revised) data definition, quality criteria, security (eg access control) are required for data items in a data subject area. In this example we’ll use some financial data such as Credit Limit, Debt amount, Current Credit Amount The request is received and the business data steward in Finance Nobby (2) is consulted and reminds Geoff (1) that it’s not just finance who use this data, although its only finance who should be permitted to update Credit Limit. Gordon (3) makes a great save and approves the changes which are then made. The changes (or additions) are notified to the chief data steward in Sales Bobby (4) because Sales are also stakeholders for this data.
  58. 58. 58 DATA GOVERNANCE FRAMEWORK • REPORTING & ASSURANCE
  59. 59. 59 A DATA GOVERNANCE FRAMEWORK IPL DG Framework Council & Organisation Council Terms of Reference Working Groups Alignment Liaison Roles & Responsibilities Owners Stewards Custodians Data Governance Office Data Management Policies & Processes Principles Policies Standards Processes Programme Maturity Matrix Strategy Scope Business Case Implementation Reporting & Assurance Perform Measur Contin Improve Evide Repos Commun
  60. 60. 60 Dimensions Measures Data Governance Organisation & Structures Roles & Responsibilities Assigned Standards & Guidelines Training & Mentoring Data Definitions Accuracy Integrity Consistency Completeness Validity Workflow & Decisions Decision workflow queues Decisions resolved & outstanding EXAMPLE DATA GOVERNANCE METRICS
  61. 61. 61 Dimensions Measures Indicators Data Quality Accuracy Validity Percentage of Fields Deemed to be Valid Integrity Credibility Percentage of Numerical Aggregations within Tolerance Currency Timeliness Punctuality Percentage of Records Received On Time Coverage Completeness Percentage of Mandatory Fields Supplied Uniqueness Percentage of Records Deemed to be Unique Percentage of Records Deemed to be Valid Percentage of Optional Fields Supplied Percentage of Expected Records Received EXAMPLE DATA QUALITY METRICS
  62. 62. 62 SUMMARY • DATA GOVERNANCE
  63. 63. 63 LESSONS FROM THE FIELD …. One size does NOT fit all Need to have a flexible approach to Data Governance that delivers maximum business value from its data asset Data Governance can drive massive benefit Needs reuse of data, common models, consistent understanding, data quality, and shared master and reference data A matrix approach is needed … Different parts of the organisation and data types will need to be driven from different directions … And central organization is required To drive Data Governance adoption, implement corporate repositories and establish corporate standards
  64. 64. 64 THE BOTTOM LINE This is only important if Information is REALLY treated as a valuable corporate asset in YOUR Business
  65. 65. 65 Examples
  66. 66. 66 PRODUCTS CONCEPTUAL DATA MODEL
  67. 67. 67 REQUEST LOAN PROCESS
  68. 68. STATOIL ENTERPRISE MODELS Business partner Statoil Enterprise Data Model Exploration ( DG1) & Petroleum technology (DG1-DG4) Seismic Wellbore data Geological & reservoir models Production volumes ReservesTechnical info (G&G reports) License Contractors Supply chain Inventory Requisitions Agreements IT Administrative info Operation and Maintenance Petroleum technical data Corporate Executive Committee Operations Government Marketing & Supply Contract Price Email Operation assurance Delivery Finance & Control Perform reporting Production, License split (SDFI), Invoice Management system Governing doc. SDFI Customer Drilling & well technology ( DG4) Drilling data Monitoring data IT inventory Geography IT project portfolio LogisticsProject portfolio (Business case) Global ranking Redeterminations Reservoir mgmt plans Maintenance program Material master Technical information (LCI) Risk information Archived info Mgmt info (MI) Vendor Vendor Authorities Partners Directional data Process area Equipment monitoring Contract Deal Market info Profit structure Invoice Volume Commodity Invoice Position and risk result Delivery Monitoring plan Operating model Human Resources Health, Safety & Environment Health info Safety info HSE Risk Incidents Attraction information Security info Env. info Emergency info Plant Project portfolio Drilling candidates Master drilling plan Drilling plans Well construction Project development Technical concepts Facility def. package Technology qualifications Quality planProject framing Project work planWBS Manpower projection planProject portfolio CD&E: Management system Values Variation orders Project documentation GSS O&P Financial transactions Financial reports Fin planning Calendar Investment analysis Fin authorities Operation profit IM/IT strategies Estimates Risk register Document plan Credit info Supply plan Refining plan Lab analysis Contact portfolio Financial results Legal Company register Service Management Service catalogue Ethics & anti-corruption Corp. social resp. Social risks and impacts Governing body doc Integrity Due Diligence reportsSustain. rep CSR plans Enquiries Agreements Technology dev. R&D portfolio IPR register Communication Brand Authority information Facilities Real Estate Access info Country analysis Risk Corp risk Business continuity plans Insurance Organisational info Capital Value Process Business planning DG0 Feasibility DG1 Concept DG2 Definition DG3 Execution DG4 Operation Post Investment ReviewBenchmarkingDecision Gate Support Package Decision memo Project infoBusiness Case Leadership Team infoBusiness case Functional location (tag) Volume monitoring Version 21-Jan-2011 Investment project structure: PETEC, D&W, FM, OM Perf. and reward info A yellow background indicates that the information subject area contains Enterprise Master Data Maintenance projects
  69. 69. STATOIL ENTERPRISE MASTER DATA MODEL
  70. 70. CATALOG CURRENT INITIATIVES USING THE PROJECT PORTFOLIO Decision gate: Where is the initiative in the life project process right now? Owner: Which Business area owns this initiative? Item Name: What’s the internal name of the project / program / initiative? Business Data Objects: What (in their own terms) are the Business Data “things” affected by this program? Interest: How interested / willing is this project to engage with the MDM initiative? Importance: How important to the Data Area is the MDM initiative?
  71. 71. Prioritise by multiple criteria (willingness to engage, feasibility, timescales, importance) Forget: Timescales, level of engagement, strategic importance wrong. “Train has left the station” Improbable: Timescales for Business initiative too tight to successfully introduce MDM without adversely affecting Business programme. Stretch: Good engagement, good strategic fit, tight timescales. Spiking in resources immediately can make these data areas fly. Prime Candidates: Great engagement, good strategic fit, ok timescales & widely usable Data subject areas.
  72. 72. HARMONISE & XREF WITH DATA MODEL
  73. 73. PRIORITISE BY INTEREST
  74. 74. 74 COLLECTIONS EXAMPLE ILLUSTRATIVE PURPOSES ONLY
  75. 75. 75 AS-IS: UNMANAGED SUBJECT & COLLECTIONS Business Party Customer Supplier Counter Party - DUNS # - Counterparty Name R&M IST Subject Hierarchy Subject Attribute Self Appointed Data Collection Multiple Processes need the same data! Delegation of Data Subject Authority not resolved. Results: duplication, inconsistency and re-work Subject Self Appointed Data Collection
  76. 76. 76 TO-BE: MANAGED SUBJECT & COLLECTIONS Business Party Customer Supplier Counterparty - DUNS # - Counterparty Name R&M IST Subject Hierarchy Subject Subject Attribute Governed Data Collection Governed Data Collection
  77. 77. 77 HOW DOES THIS HELP THE BUSINESS COMMUNICATE WITH IT&S? Governed by the Business; modeled by IT&S Governed by IT&S Communication Bridge Collaboration between the business & IT&S, and modeled by IT&S High level Subjects and Subject hierarchies, grouped into collections Collections, Subjects, Subject Hierarchies & Attributes = IT&S “Logical Data Model” Physical Model
  78. 78. 78 BUSINESS DATA GOVERNANCE ROLES 1. Organizational Delegation of Authority (DOA); Examples: • Backbone Governance Board • Function Leader, Segment Leader • SPU leader • BU Leader • Etc. 2. Implementation & Improvements • Information Director 3. Specification Owners (Makes the rules) • Subject Owner – hierarchy and other specifications • Attribute Owner – detailed specifications • Collections Owner – sets subject hierarchy boundaries 4. Content • Data Steward (Follows the rules) • Quality Control Data Steward (enforces the rules)
  79. 79. 79 BUSINESS SPECIFICA TION AND CONTENT GOVERNA NCE Local Information Director Local Specification Owners [local data] Data Steward(s) Data Quality Steward(s) Collaborating Specification Owners [Data common across many localities] + Collaborating Information Director(s)+ IT&S & Business Implementation re-using common data
  80. 80. 80 INFORMATION GOVERNANCE Ongoing data maintenance and quality Compliance with policy and procedures Three tiered governance with individual accountability: By SUBJECT AREA Information Owners: Information Stewards: Information Director: Maintain high-level corporate data model Define the overall process and framework Allocate accountability for individual data entities Determine business process to manage data Mandate stewardship and quality activity Primacy over entire data entity, including data quality metrics
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