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Introduction to Data Governance Drivers for Data Governance & Benefits Data Governance Framework Organization & Structures Roles & responsibilities Policies & Processes Programme & Implementation Reporting & Assurance
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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 - E N T E R P R I S E A R C H I T E C T S © 2 0 1 4 | PAGE 1
IMPLEMENTING EFFECTIVE
DATA GOVERNANCE
SEMINAR
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 - E N T E R P R I S E A R C H I T E C T S © 2 0 1 4 | PAGE 2
INTRODUCTION: WHO AM I?
My blog: Information Management, Life & Petrol
http://infomanagementlifeandpetrol.blogspot.co
m
@InfoRacer
uk.linkedin.com/in/christophermichaelbradley
/
CHRISTOPHER BRADLEY
Chief Information Architect &
Enterprise Services Director
ENTERPRISE ARCHITECTS
3
What is Data Governance
4
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
5
Data Governance Activities
• Data Governance (DMBoK)
6
DQ & MDM
Workflow Modelling (Data & Process)
7
“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
8
Introduction
• Data Governance Terms & Definitions
9
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!!!
10
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
11
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)
12
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
13
Data Governance
• Drivers for & Benefits of Data Governance
14
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”
15
3 drivers for Data Governance
1. Reactive Governance
2. Pre-emptive Governance
3. Proactive Governance
16
Reactive Governance
• Tactical exercise
• Efforts designed to respond to current pains
• Organization has suffered a regulatory breach or a data disaster
17
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
18
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
19
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
20
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
21
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
22
dave.huxford@ipl.com
Data Governance Framework
• A Data Governance Framework
23
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
5IM Principles
DataGovernance
IM Planning
Data Quality
IM LifecycleManagement
Integration &Access
Models &Taxonomy
Catalog &Metadata
Master DataManagement
BusinessIntelligence
To-Be
As-Is
13 components containing ...
• Principles & rationale
• Maturity model
• Detailed methodology
• Tools & templates
• Example business cases
24
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
Performance Measurement
Continuous Improvement
Evidence Repository
Communication
25
Data Governance Framework
• Council & Organisation
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
Performance Measurement
Continuous Improvement
Evidence Repository
Communication
27
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
28
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
Gu
idan
ce
Issu
es
Mea
sure
s
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
29
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)
30
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
31
Data Governance Framework
• Roles & Responsibilities
32
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
Performance Measurement
Continuous Improvement
Evidence Repository
Communication
33
Roles
CIO
Lead Data Steward
Data Steward
Data Management Exec
Data Custodian
STEWARDSHIP (LEGISLATIVE & JUDICIAL) DATA MANAGEMENT SERVICES (EXECUTIVE)
34
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
35
Data Governance Framework
• Policies, Principles, Processes
36
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
Performance Measurement
Continuous Improvement
Evidence Repository
Communication
37
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
38
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)
39
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.
40
Data Governance Framework
• Programme & Maturity
41
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
Performance Measurement
Continuous Improvement
Evidence Repository
Communication
42
Maturity
43
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. Evidence of data “mine”ing widespread.
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 “Data Teams” or “Super Users” that manage “all” data.
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 yet fully 'bought in' to data ownership & governance at an Enterprise level.
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 application, updating and improving as needed. Benefits begin to be realised.
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 defined standards are adopted.
44
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 & Responsibilities
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 & Assurance
Limited, ad-hoc and varied levels of reporting aligned to local initiatives of functional areas, business processes or projects
Standards for projects and operational reporting of data issues and architecture
Shared repository for data related documents and models exists. Requirements for data quality measures developed
Documents and measures regularly reviewed and approved. Processes in place to deliver assurance and to audit documentation.
DG Council working on exception reporting basis. Few assurance and audit issues apparent but resolved quickly
As-Is To-Be Transition Plan
45
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 with no corporate traction.
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 regularly reviewed.
DG fully recognised by C level executives with regular meetings and decisions communicated DG Council part of business internal controls
46
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 for data or self appointed
Data champions or super users with passion for data emerge in business functions. Limited collaboration for shared data, common data policies & approaches.
Data ownership and stewardship is defined and loosely applied to a Master Data subject area. Responsibilities for Data now become part of role descriptions.
Corporate Data model developed, Data Subject areas defined. Major data subjects have data owners / stewards appointed with their responsibilities measured and rewarded
All data subject areas have Data owners. The majority of data subjects areas are actively stewarded in accordance with polices and standards and are accepted across organisation.
47
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
48
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 induction training.
DG Programme completed with continuous improvement of Governance components through review and refine cycle. Regular communication and updated training is on-going.
49
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 projects
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.
50
DG Maturity by component
50
0
1
2
3
4
5
Data GovernanceCouncil &
Organisation
Data Ownership &Stewardship Roles+ Responsibilities
InformationPrinciples, Policies
& Standards
Data GovernanceProgramme
Data GovernanceReporting &Assurance
Vision DG Maturity
Target DG Maturity
Baseline DG Maturity
51
Data Governance Implementation
52
A Data Governance Methodology
Conceptual Models
53
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
54
Maturity – Models & Taxonomy
55
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.
56
Data Governance Framework
• Reporting & Assurance
57
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
Performance Measurement
Continuous Improvement
Evidence Repository
Communication
58
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
59
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
60
Summary
• Data Governance
61
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
62
The bottom line
This is only important if Information is REALLY treated as
a valuable corporate asset in YOUR Business
63
Examples
64
Products conceptual data model
65
Request loan process
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
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 &
EnvironmentHealth 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
Statoil Enterprise Master Data Model
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?
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.
Harmonise & Xref with Data Model
Prioritise by interest
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Collections Example Illustrative Purposes Only
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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
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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
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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
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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)
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Business Specification and Content Governance
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
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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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