Defines a data audit approach to creating an enterprise current data state view as part of defining an enterprise data strategy

1. Alan McSweeney
Data Audit Approach To
Developing An Enterprise
Data Strategy
1

2. Objective
• Define a data audit approach to creating an enterprise
current data state view as part of defining an enterprise
data strategy
February 18, 2015 2

3. Developing And Implementing An Enterprise Data
Strategy
• Any enterprise data strategy of an existing and mature
organisation with a substantial portfolio of applications
and associated data should start with a data audit that
establishes a baseline that will be one input to a data
strategy
• Any new strategy needs to take into account this (possibly)
substantial applications and data legacy
• Any strategy has to be implementable and operable
• There will be a current state and a future state where the
future state represents the fully actualised strategy
February 18, 2015 3

4. Current
State
Desired
Long-Term
Steady
State
Need to Move From
Current State To Future
State In A Series Of
Steps
Developing And Implementing An Enterprise Data
Strategy
February 18, 2015 4

5. Business
Objectives
Business
Operational
Model
Enterprise
Architecture
Solution
Implementation
and
Delivery
Management
And
Operations
Business
Processes
Required
Operational
Business
Systems
Business
Strategy
Systems
Design/
Selection
Business IT
Strategy
IT Function
Strategy
Enterprise
Data
Strategy
Required
Operational
Processes
Required
Infrastructure
Business
Systems
Systems
Design/
Selection
Information
and Data
Architecture
Enterprise Data Strategy In Business And IT Context
February 18, 2015 5

6. Enterprise Data Strategy In Context
• An enterprise data strategy exists in a wider organisation and IT
context
− The organisation will have an overall IT strategy to accomplish the
organisation strategy and associated objectives
− The IT function will then need its own internal IT strategy that will
structure the function in order to ensure that it can deliver on the wider
organisation strategy
− The enterprise data strategy is connected to the overall IT strategy, the
enterprise architecture and the internal IT strategy
− The enterprise data strategy will be implemented and operated through
an information and data architecture that is part of the overall
enterprise architecture
− This context is important in ensuring that the enterprise data strategy
fits into the overall IT and wider organisational structure
− The enterprise data strategy exists to ultimately deliver a business
benefit and contribute to the achievement of the business strategy
− The strategy must be translated into an operational framework to
enable the strategy to be actualised
February 18, 2015 6

7. Traditional View Of Information And Data
Architecture In An Enterprise Architecture Context
February 18, 2015 7
Enterprise Architecture
Information Systems
Architecture
Data
Architecture
Solutions and
Application
Architecture
Business
Architecture
Technology
Architecture

8. Data-Oriented View Of Information And Data
Architecture In An Enterprise Architecture Context
February 18, 2015 8
Enterprise Architecture
Information and Data Architecture
Information
Systems
Architecture
Solutions
and
Application
Architecture
Business
Architecture
Technology
Architecture

9. Traditional View Of Information And Data
Architecture In An Enterprise Architecture Context
• Data and Information Architecture - the structure of an
organisation's logical and physical data assets and data
management resources – is defined as a subset of
Information Systems Architecture which key applications
and data that form the core of mission-critical business
processes
• Data and Information Architecture manages the
information of the enterprise by clarifying business
relationships and enhancing the understanding of the
business processes and rules implemented by the
enterprise
• Data and Information Architecture links Business Processes
to the Information Systems that support the processes
February 18, 2015 9

10. It’s All About The Data (And The Processes)
• Data needs to be organised by business process, not by
application
− The enterprise is the sum of its processes
• An effective data architecture is a principal driver of
successful business models and therefore competitive
advantage
• Providing business experts timely access to accurate data
is the key factor in improving the ability of the enterprises
to make effective and informed business decisions
February 18, 2015 10

11. Components Of An Information And Data
Architecture And Associated Strategy
February 18, 2015 11
Information and Data
Architecture
Data Governance Data Architecture Management
Data Development Data Operations Management
Data Security Management Data Quality Management
Reference and Master Data
Management
Data Warehousing and Business
Intelligence Management
Document and Content
Management
Metadata Management

12. Components Of An Information And Data
Architecture And Associated Strategy
• Data Governance - planning, supervision and control over data management and use
• Data Architecture Management - defining the blueprint for managing data assets
• Data Development - analysis, design, implementation, testing, deployment, maintenance
• Data Operations Management - providing support from data acquisition to purging
• Data Security Management - Ensuring privacy, confidentiality and appropriate access
• Data Quality Management - defining, monitoring and improving data quality
• Reference and Master Data Management - managing master versions and replicas
• Data Warehousing and Business Intelligence Management - enabling reporting and
analysis
• Document and Content Management - managing data found outside of databases,
including digital strategy and social media
• Document and Content Management - integrating, controlling and providing metadata
February 18, 2015 12

13. Information And Data Architecture Components And
Their Functional Elements
• There are a number of
functional elements
associated with each of
these components
February 18, 2015 13
Data Management
Functional Elements
Goals and Principles Activities
Primary Deliverables
Roles and
Responsibilities
Practices and
Techniques
Technology
Organisation and
Culture

14. Information And Data Architecture Components And
Their Functional Elements
• Goals and Principles - directional business goals of each function and the fundamental
principles that guide performance of each function
• Activities - each function is composed of lower level activities, sub-activities, tasks and
steps that are function-specific
• Primary Deliverables - information and physical databases and documents created as
interim and final outputs of each function. Some deliverables are essential, some are
generally recommended, and others are optional depending on circumstances
• Roles and Responsibilities - business and IT roles involved in performing and supervising
the function, and the specific responsibilities of each role in that function. Many roles will
participate in multiple functions
• Practices and Techniques - common and popular methods and procedures used to perform
the processes and produce the deliverables and may also include common conventions,
best practice recommendations, and alternative approaches without elaboration
• Technology - categories of supporting technology such as software tools, standards and
protocols, product selection criteria and learning curves
• Organisation and Culture – this can include issues such as management metrics, critical
success factors, reporting structures, budgeting, resource allocation issues, expectations
and attitudes, style, cultural, approach to change management
February 18, 2015 14

15. Why It Happened?
Why Is Likely To
Happen In The Future?
What Is Currently
Happening?
What Happened?
Every Organisation Aspires To ...
February 18, 2015 15
Reporting
Insight/
Forecast
Monitoring Analysis

16. Trailing And Leading Indicators
Reporting
• Report on Gathered Information On What Happened
To Understand Pinch Points, Quantify Effectiveness,
Measure Resource Usage And Success
Monitoring
• Gather Information In Realtime To Understand
Activities, Respond And Make Reallocation Decisions
Analysis
• Understand Reasons For Outcomes and Modify
Operation To Embed Improvements
Insight and Forecast
• Quantify Propensities, Forecast Likely Outcomes,
Identify Leading Indicators, Create Actionable
Intelligence
February 18, 2015 16
Trailing
Indicators
Leading
Indicators

17. Every Organisation Needs An Effective Enterprise
Data Strategy
February 18, 2015 17
Data Operations Management
Data Quality Management
Data Development
Metadata Management
Document and Content Management
Reference and Master Data Management
Data Security Management
Data Warehousing and Business Intelligence
Management
Data Governance
Data Architecture
Management
Reporting
Insight/
Forecast
Monitoring Analysis
Solid
Data
Management
Foundation
and
Framework
} You Cannot
Have This ...
... Without
This

18. Measurement Framework Iceberg
February 18, 2015 18
To Do This ...
... You Need To Do
This ...
... Which Requires
This ...
... Which In Turn
Needs This ...
... And So On ...
...
...
...
Be Able To Take Action
Based on Reliable
Information
Measure What is
Important
Know What Is
Important In Order To
Measure It
Define Measurements
Define Consistent
Units of
Measurements
Define Measurement
Processes
Define Operational
Framework
Define Collection
Process
Define Data Storage
Model
Define Transformation
And Standardisation
Install Data Collection
Facilities
Collect Data
Monitor Data
Collection
Manage Data
Collection
Validate And Store
Data
Report And Analyse
Stored Data
Define Reports
Run And Distribute
Reports
Define Analyses
Run And Distribute
Analyses
Provide Realtime
Access To Collected
Data
Define Data Tools And
Infrastructure

19. Processes Define How The Organisation Delivers Its
Products And Services
February 18, 2015 19
Business
Function
Business
Function
Business
Function
Business
Function
Business
Function
Partners
Regulators
Customers
Service
Providers
Suppliers
Collaborators

20. Core And Extended Organisation Landscape
February 18, 2015 20
Business
Function
Business
Function
Business
Function
Business
Function
Business
Function
Partners
Regulators
Customers
Service
Providers
Suppliers
Collaborators
Core Landscape
Extended Landscape

21. Processes Define How The Organisation Delivers Its
Products And Services
• Work – products and services - moves throughout the
extended organisation landscape as it is delivered to the
customer
• Data accompanies – supports, describes, enables,
measures – work
February 18, 2015 21

22. February 18, 2015 22
Cross Functional Processes Crossing “Vertical”
Operational Organisational Units To Deliver Work

23. February 18, 2015 23
Core Cross Functional Processes
• Three cross-functional processes that are common to all
organisations
− Product/service delivery
• From order/specification/design/selection to
delivery/installation/implementation/provision and billing
− Customer management
• From customer acquisition to management to repeat business to up-sell/cross-sell
− New product/service provision
• From research to product/service design to implementation and commercialisation
• These processes cross multiple internal organisation boundaries and
have multiple handoffs but they are what concern customers
• Cross-functional processes deliver value
− Value to the customer
− Value to the enterprise
• Integrated cross-functional processes means better customer service
and more satisfied and more customers

24. February 18, 2015 24
Core Cross Functional Processes and Customer View
Product/Service
Delivery: from order
to completion
Customer
Relationship
Management
New Product/
Service
Provision
The organisation sees the structure vertically and in a compartmentalised
view and all to frequently does not see the customer viewpoint
The customer sees across the structure and is not concerned with but is all too
often aware of the operational elements, their complexity and lack of
interoperability

25. Organisation Data
• Data flows within the organisation between business
functions, supporting the key processes of:
− Delivery of products and services
− Customer acquisition, management and retention
− Product and service development
• Enterprise data model needs to be structured to define
process interactions and associated data
− Feed data into processes to enable their efficient operation
− Take data from processes to allow their operation to be
monitored
February 18, 2015 25

26. Organisation Information And Data Landscape
• Information and data landscape defines the operational
data environment for the organisation
− Operational Use
• Storage
• Manage
• Share
• Exchange
− Analytic Use
• Monitoring
• Reporting
• Analysis
• Forecast
February 18, 2015 26

27. Enterprise Data Model Needs To Encapsulate Data
Landscape
February 18, 2015 27
Enterprise Data
Model
Subject Area
Model
Conceptual
Data Model
Enterprise
Logical Data
Models
Enterprise Data
Model
Elements
Data Steward
Responsibility
Assignments
Valid Reference
Data Values
Data Quality
Specifications
Entity Life
Cycles

28. February 18, 2015 28
Generalised Enterprise Business Process Model
Business
Controlling
Process
Processes That
Direct and Tune
Other Processes
Core Processes
Processes That Create Value for the Customer
Customer
Acquisition
Product
Delivery
Order
Fulfilment
Customer
Support
Enabling Processes
Processes That Supply Resources to Other Processes
Channel
Management
Supply
Management
Human
Resources
Information
Technology
Business
Acquisition
Business
Measurement
Process
Processes That
Monitor and
Report the
Results of Other
Processes
Customer’s Process Needs
Supplier’s Processes
Business Environment
Competitors, Governments Regulations and Requirements, Standards, Economics

29. Generic Enterprise Business Process Model
• Representation of the key processes within and across an
enterprise
− The enterprise is the sum of its processes
• Key processes require and generate data
• Data model needs represent data to and from processes
February 18, 2015 29

30. February 18, 2015 30
Data Collection And Measures Need To Be Linked To
Key Enterprise Processes
Business
Controlling
Process
Processes That
Direct and Tune
Other Processes
Core Processes
Processes That Create Value for the Customer
Customer
Acquisition
Product
Delivery
Order
Fulfilment
Customer
Support
Enabling Processes
Processes That Supply Resources to Other Processes
Channel
Management
Supply
Management
Human
Resources
Information
Technology
Business
Acquisition
Business
Measurement
Process
Processes That
Monitor and
Report the
Results of Other
Processes
Customer’s Process Needs
Supplier’s Processes
Business Environment
Competitors, Governments Regulations and Requirements, Standards, Economics
Number of
New
Customers
Customer
Turnover
Profitability
Per Customer
Customer
Acquisition
Cost
Number of
Customers
Complaints
Time to
Resolve
Complaints
Delivery
Time
Accuracy
Number of
Returns
Payment
Times
Inventory
Time to Fulfil
Order
Invoice
Accuracy
Forecast
Accuracy

31. Enterprise Data Model Needs To Encapsulate Data
Landscape
February 18, 2015 31
Business
Function
Business
Function
Business
Function
Business
Function
Business
Function
Partners
Regulators
Customers
Service
Providers
Suppliers
Collaborators
Enterprise
Data Model

32. February 18, 2015 32
Enterprise Data Model
• Build an enterprise data model in layers
• Focus on the most critical business subject areas
− Subject Area Model
− Conceptual Data Model
− Enterprise Logical Data Models

33. February 18, 2015 33
Subject Area Model
• List of major subject areas that collectively express the
essential scope of the enterprise
• Important to the success of the entire enterprise data
model
• List of enterprise subject areas becomes one of the most
significant organisation classifications
• Acceptable to organisation stakeholders
• Useful as the organising framework for data governance,
data stewardship, and further enterprise data modeling

34. February 18, 2015 34
Conceptual Data Model
• Conceptual data model defines business entities and their
relationships
• Business entities are the primary organisational structures in a
conceptual data model
• Business needs data about business entities
• Include a glossary containing the business definitions and other
metadata associated with business entities and their relationships
• Assists improved business understanding and reconciliation of terms
and their meanings
• Provide the framework for developing integrated information
systems to support both transactional processing and business
intelligence.
• Depicts how the enterprise sees information

35. February 18, 2015 35
Enterprise Logical Data Models
• Logical data model contain a level of detail below the
conceptual data model
• Contain the essential data attributes for each entity
• Essential data attributes are those data attributes without
which the enterprise cannot function – can be a subjective
decision

36. February 18, 2015 36
Enterprise Data Model Components
• Data Steward Responsibility Assignments- for subject
areas, entities, attributes, and/or reference data value sets
• Valid Reference Data Values - controlled value sets for
codes and/or labels and their business meaning
• Data Quality Specifications - rules for essential data
attributes, such as accuracy / precision requirements,
currency (timeliness), integrity rules, nullability,
formatting, match/merge rules, and/or audit requirements
• Entity Life Cycles - show the different lifecycle states of
the most important entities and the trigger events that
change an entity from one state to another

37. February 18, 2015 37
Data Strategy
• High-level course of action to achieve high-level goals
• Data strategy is a data management program strategy a
plan for maintaining and improving data quality, integrity,
security and access
• Address all data management functions relevant to the
organisation

38. February 18, 2015 38
Elements Of Information And Data Strategy
• Vision for data management
• Summary business case for data management
• Guiding principles, values, and management perspectives
• Mission and long-term directional goals of data management
• Management measures of data management success
• Short-term data management programme objectives
• Descriptions of data management roles and business units along
with a summary of their responsibilities and decision rights
• Descriptions of data management programme components and
initiatives
• Outline of the data management implementation roadmap
• Scope boundaries

39. February 18, 2015 39
Data Strategy
Data Management
Scope Statement
Goals and objectives for a
defined planning horizon and
the roles, organisations, and
individual leaders accountable
for achieving these objectives
Data Management
Programme Charter
Overall vision, business case,
goals, guiding principles,
measures of success, critical
success factors, recognised risks
Data Management
Implementation
Roadmap
Identifying specific programs,
projects, task assignments, and
delivery milestones

40. Data Audit And Information And Data Strategy
• The objectives of the audit are to understand the current
data management systems, structures and processes
• This will then feed into the development of the strategy
and the identification of gaps
• Data audit views
1. Data landscape view
2. Data supply chain view
3. Data model view
4. Data lifecycle view
5. Current information and data architecture and data strategy
view
6. Current data management view
February 18, 2015 40

41. Data Landscape View
February 18, 2015 41

42. Data Landscape View
• The purpose of the Data Landscape View is to describe the entities
and functional units within and outside the organisation with which
the organisation interacts and to describe the interactions in terms
of data flows
• This will show the participants in data flows
• These can be business units, partners, service providers, regulators
and other entities
• The data landscape view can be created at different levels of details:
− Level 1 – Main Interactions - Main interactions and functions associated with
the Enterprise Level
− Level 2 – Business Function - Specific data exchanges of the function
− Level 3 – Function - What is done within each function as a series of activities
− Level 4 – Procedure - How each activity is carried out through a series of tasks
− Level 5 - Sub Procedure - Detailed steps which are carried out to complete a
task
February 18, 2015 42

43. Data Supply Chain View
• The data supply chain view looks at in-bound and out-
bound data paths within and outside the organisations in
terms of the applications and the data that flows along the
data paths
• It can be a subset or an extension of the Data Landscape
View
February 18, 2015 43

44. Data Model View
• Enterprise data model is a set of data specifications that
reflect data requirements and designs and defines the
critical data produced and consumed across the
organisation
• Data model view quantifies the status of the development
and specification of the enterprise data model
February 18, 2015 44

45. Enterprise Data Model Needs To Encapsulate Data
Landscape
February 18, 2015 45
Enterprise Data
Model
Subject Area
Model
Conceptual
Data Model
Enterprise
Logical Data
Models
Enterprise Data
Model
Elements
Data Steward
Responsibility
Assignments
Valid Reference
Data Values
Data Quality
Specifications
Entity Life
Cycles

46. Data Lifecycle View
• When analysing data, what you are really analysing is the
state of the processes around its lifecycle: how well
defined those processes are, how automated, how risks
and controls are defined and managed
February 18, 2015 46

47. Data Lifecycle View
February 18, 2015 47

48. Data Lifecycle View
• The stages in this generalised lifecycle are:
− Architect, Budget, Plan, Design and Specify - This relates to the design and specification of the data
storage and management and their supporting processes. This establishes the data management
framework
− Implement Underlying Technology- This is concerned with implementing the data-related hardware and
software technology components. This relates to database components, data storage hardware, backup
and recovery software, monitoring and control software and other items
− Enter, Create, Acquire, Derive, Update, Integrate, Capture- This stage is where data originated, such as
data entry or data capture and acquired from other systems or sources
− Secure, Store, Replicate and Distribute - In this stage, data is stored with appropriate security and access
controls including data access and update audit. It may be replicated to other applications and distributed
− Present, Report, Analyse, Model - This stage is concerned with the presentation of information, the
generation of reports and analysis and the created of derived information
− Preserve, Protect and Recover- This stage relates to the management of data in terms of backup,
recovery and retention/preservation
− Archive and Recall - This stage is where information that is no longer active but still required in archived
to secondary data storage platforms and from which the information can be recovered if required
− Delete/Remove - The stage is concerned with the deletion of data that cannot or does not need to be
retained any longer
− Define, Design, Implement, Measure, Manage, Monitor, Control, Staff, Train and Administer, Standards,
Governance, Fund - This is not a single stage but a set of processes and procedures that cross all stages
and is concerned with ensuring that the processes associated with each of the lifestyle stages are
operated correctly and that data assurance, quality and governance procedures exist and are operated
February 18, 2015 48

49. Data Audit Approach
1. Build an application landscape view, including internal and external systems and
third-parties from which data may be obtained and to which data may be supplied
− The application view can be supplement with a system and infrastructure view that shows the hardware and
software components behind an application
2. Layer onto this information capture, storage and flows: where and what types of
information is maintained by applications and that is passed between applications
− An application is a collection of systems and infrastructure that delivers an integrated set of functions
− It may or may not be necessary to document the underlying infrastructure associated with applications
− This may be further complicated because the underlying infrastructure may not be isolated but may itself be part
of an application - this would be the case where the server infrastructure is virtualised and managed by
virtualisation manager
3. Categorise information by a classification such as: Operational Data, Master and
Reference Data, Analytic Data and Unstructured Data
4. Define the business units/functions and their use of applications
5. View the information capture, storage and flows identified above across the stages
of their lifecycle
6. Identify how well the processes and their controls associated with the lifecycle
stages are defined, documented and operated. This will identify gaps to be
remediated
− This will then form the basis of a work plan to resolve any data-related process gaps
February 18, 2015 49

50. Data Audit Approach – Application Landscape
February 18, 2015 50
Application
1
Application
2
Application
3
Application
4
Application
5
Application
6
Application
7
Application
8
Application
9

51. Data Audit Approach – Data Capture, Storage And
Transfer
February 18, 2015 51
Application
1
Application
2
Application
3
Application
4
Application
5
Application
6
Application
7
Application
8
Application
9

52. Data Audit Approach – Infrastructure And System
View
February 18, 2015 52
Application
Web Server
Database
Web Server
Application
Server
Application
Server
Database Server Database Server
Load Balancer Load Balancer Authentication
Server
User DirectoryFirewall Firewall
Consists
of

53. Classification Information By Operational Data, Master
and Reference Data, Analytic Data and Unstructured Data
February 18, 2015 53
Architect, Budget, Plan, Design and Specify
Enter, Create, Acquire, Derive, Update, Integrate,
Capture
Secure, Store, Replicate and Distribute
Preserve, Protect and Recover
Archive and Recall
Delete/Remove
Implement Underlying Technology
Present, Report, Analyse, Model
Define, Design, Implement, Measure, Manage,
Monitor, Control, Staff, Train and Administer,
Standards, Governance, Fund
Operational
Data
Analytic and
Derived Data
Unstructured
Data
Master and
Reference
Data

54. Business Functions And Application Use
February 18, 2015 54
Application 1 Application 2 Application 3
Application 4 Application 5 Application 6
Application 7 Application 8 Application 9
Business
Function 1
Business
Function 2
Business
Function 3
Business
Function 4

55. Information Capture, Storage And Flows Identified
Above Across The Stages Of Their Lifecycle
February 18, 2015 55
Architect, Budget, Plan, Design and Specify
Enter, Create, Acquire, Derive, Update, Integrate,
Capture
Secure, Store, Replicate and Distribute
Preserve, Protect and Recover
Archive and Recall
Delete/Remove
Implement Underlying Technology
Present, Report, Analyse, Model
Define, Design, Implement, Measure, Manage,
Monitor, Control, Staff, Train and Administer,
Standards, Governance, Fund
Data Type
1
Data Type
3
Data Type
4
Data Type
2

56. Identify How Well The Processes And Their Controls
Associated With The Lifecycle Stages Are Defined
February 18, 2015 56
Architect, Budget, Plan, Design and Specify
Enter, Create, Acquire, Derive, Update, Integrate,
Capture
Secure, Store, Replicate and Distribute
Preserve, Protect and Recover
Archive and Recall
Delete/Remove
Implement Underlying Technology
Present, Report, Analyse, Model
Define, Design, Implement, Measure, Manage,
Monitor, Control, Staff, Train and Administer,
Standards, Governance, Fund
Data Type
1
Data Type
3
Data Type
4
Data Type
2

57. Identify How Well The Processes And Their Controls
Associated With The Lifecycle Stages Are Defined
• Provides a baseline of the status of data processes in the
organisation
• Identify gaps to be remediated
• This will then form the basis of a workplan to resolve any
data-related process gaps
February 18, 2015 57

58. Current Information and Data Architecture And Data
Strategy and View
• Review current
information and
data architecture
and
implementation
and operational
under the key
component areas
February 18, 2015 58
Information and Data
Architecture
Data Governance
Data Architecture
Management
Data Development
Data Operations
Management
Data Security Management Data Quality Management
Reference and Master Data
Management
Data Warehousing and
Business Intelligence
Management
Document and Content
Management
Metadata Management

59. Current Data Management View
• The data strategy components and the functional
elements are be combined to create a view of all the
potential elements of an operational data strategy
implementation and operational framework
• Not all of these facets will have the same importance
• Each of these facets will also be in a different state of
effective operation
• You can create a high-level representation of the state of
data management strategy and its implementation
February 18, 2015 59

60. Data Management View – Components And
Functional Elements
Goals and
Principles
Activities Primary
Deliverables
Roles and
Responsibilities
Practices and
Techniques
Technology Organisation
and Culture
Data
Governance
Data
Architecture
Management
Data
Development
Data
Operations
Management
 Scope of Each Data Management Function 
Data Security
Management
Data Quality
Management
Reference and
Master Data
Management
Data
Warehousing
and Business
Intelligence
Management
Document and
Content
Management
Metadata
Management
February 18, 2015 60

61. Goals and
Principles
Activities Primary
Deliverables
Roles and
Responsibilities
Practices and
Techniques
Technology Organisation
and Culture
Importance
Current
State
Importance
Current
State
Importance
Current
State
Importance
Current
State
Importance
Current
State
Importance
Current
State
Importance
Current
State
Data Governance
Data Architecture
Management
Data Development
Data Operations
Management
Data Security
Management
Data Quality
Management
Reference and Master
Data Management
Data Warehousing and
Business Intelligence
Management
Document and
Content Management
Metadata
Management
Data Management View – Importance and State
February 18, 2015 61

62. = High Importance
= Medium Importance
= Low Importance
= Good State
= Medium State
= Poor State
Data Management View – Importance and Status
• Coding of data management components and functional
elements
• Understand their importance and current state of
implementation and operation
February 18, 2015 62

63. Data Audit Views And Results
• Data Landscape View – quantify and understand where data exists
• Data Supply Chain View – quantify and understand data exchanges
and interfaces
• Data Model View – quantify and understand the development and
specification of the enterprise data model
• Data Lifecycle View – identify how well the processes and the
controls associated with the lifecycle stages are defined
• Current Information And Data Architecture And Data Strategy View
– identify current information and data architecture and
implementation and operational under the key component areas
• Current Data Management View – quantify the relative importance
and current state of implementation and operation of data
management components and functional elements
February 18, 2015 63

64. Data Audit Views And Results
• Gives a comprehensive view of the current state, desired
future state and gaps/deficiencies
• Provides a current state view within the context of a future
state
• Ensures that any information and data architecture and
strategy is based on evidence
• Enables a realistic workplan to be developed and worked
through to achieve the desired results
• Approach can be applied to the entire enterprise or
functional component
February 18, 2015 64

65. Now All That Is Left Is The Implementation And
Operation
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66. February 18, 2015 66
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Alan McSweeney
http://ie.linkedin.com/in/alanmcsweeney