implementing effective data governance

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

http://infomanagementlifeandpetrol.blogspot.com/

http://infomanagementlifeandpetrol.blogspot.com/

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


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

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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

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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

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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

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[email protected]

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

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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


• Council & Organisation

26


IPL DG Framework



Working Groups

Alignment Liaison


Owners

Stewards

Custodians

Data Governance

Office

Data Management


Principles

Policies

Standards

Processes

Programme

Maturity Matrix

Strategy

Scope

Business Case

Implementation




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

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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

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Board

Security Management Committee

Compliance Committee

Data Governance Council


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)

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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

31


• Roles & Responsibilities

32


IPL DG Framework



Working Groups

Alignment Liaison


Owners

Stewards

Custodians

Data Governance

Office

Data Management


Principles

Policies

Standards

Processes

Programme

Maturity Matrix

Strategy

Scope

Business Case

Implementation




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


• Policies, Principles, Processes

36


IPL DG Framework



Working Groups

Alignment Liaison


Owners

Stewards

Custodians

Data Governance

Office

Data Management


Principles

Policies

Standards

Processes

Programme

Maturity Matrix

Strategy

Scope

Business Case

Implementation




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)

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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.

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• Programme & Maturity

41


IPL DG Framework



Working Groups

Alignment Liaison


Owners

Stewards

Custodians

Data Governance

Office

Data Management


Principles

Policies

Standards

Processes

Programme

Maturity Matrix

Strategy

Scope

Business Case

Implementation




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


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


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


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


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.

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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


• Reporting & Assurance

57


IPL DG Framework



Working Groups

Alignment Liaison


Owners

Stewards

Custodians

Data Governance

Office

Data Management


Principles

Policies

Standards

Processes

Programme

Maturity Matrix

Strategy

Scope

Business Case

Implementation




Evidence Repository

Communication

58

Dimensions Measures

Data Governance

Organisation & Structures


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

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 &

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

72

Collections Example Illustrative Purposes Only

73

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

74

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

75

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

76

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)

77

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

78

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

implementing effective data governance

Business

organisations data

data governancedrivers

data governance1

itproliferation of data

data professionals

management of data assets

benefits of data governance

higher volumes of data