FOCUS AREA: - Identify data requirements and goals. - IT solution (data) design. - Focus on data development and configuration (solutions/projects). - Develop data standards. - Ensure data integration. - Ensure correct data testing. - Maintain and optimize data solutions. RELATION TO STRATEGY: - Develop data solutions based on business/IT requirements Develop data solutions and goals based on operational objectives. - Link business KPI’s to system KPI’s. - Ensure correct data reporting in terms of system reports, cockpits, dashboards and scorecards.

1. Presenter: Prof. Mark von Rosing
Using the LEAD Data Reference Content
Data Reference Content

2. 2
Focus Area
• Identify data requirements and goals
• IT solution (data) design
• Focus on data development and configuration (solutions/projects)
• Develop data standards
• Ensure data integration
• Ensure correct data testing
• Maintain and optimize data solutions
Relation to Strategy
• Develop data solutions based on business/IT requirements
• Develop data solutions and goals based on operational objectives
• Link business KPI’s to system KPI’s
• Ensure correct data reporting in terms of system reports, cockpits,
dashboards and scorecards
Way of Thinking around Data: Strategic Aspect
Tasks

3. 3
The Way of Thinking about Data: Conceptual Level

4. 4
The Way of Thinking about Data: Data Artifacts
(example maps)

5. 5
The Way of Working with Data: Logical Level

6. 6
The Way of Working with Data: Data Specification
(example matrices)

7. 7
The Way of Modelling with Data: Physical Level

8. 8
The Way of Modelling with Data: Data Concrete
(example models)

9. 9
Data Context

10. 10
Data Execution

11. 11
Data Impact: Strategic Outcomes vs. Operational
Outcomes

12. 12
Data Value Aspects: Value Identification, Value
Creation and Value Realization

13. 13
Layered Data Architecture

14. 14
Data Design through working with Data Objects and
Data Notations

15. 15
The Way of Data Governance

16. 16
Data Maps
Description: The Data Map provides an overview of the data components,
objects, entities, tables and services as well as the business, service and
process roles etc. of the enterprise.
Usage: Identify and capture the values attributed to the main meta objects
(columns) of the Data Map such as the data components, objects, entities,
tables and services as well as the business, service and process roles etc.
of the enterprise.

17. 17
Data #
What Where Who
Data
Compon
ent
Data
Object
Data
Entity
Data
Table
Data
Service
Data
Channel
Data
Media
Busines
s Role
Service
Role
Process
Role
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
Example of a Data Map

18. 18
Data Maps
The Data Map should capture the main meta objects (columns) such as the
data components, objects, entities, tables and services as well as the
business, service and process roles etc. of the enterprise.
The Interrogative Specification for each main meta object (columns) is to be
identified according to the following modelling rules:
• Who (person or persons involved): Business Role, Service Role,
Process Role.
• What (identity, nature, or value of a object or matter): Data
Component, Data Object, Data Entity, Data Table, Data Service.
• Where (location, area or place): Data Channel, Data Media.

19. 19
Tasks & Services
• Define data standardization and integration
• Develop and define data components, services and rules
• Define information and data objects and system flow
• Ensure data compliance to governance, business, process, application
and service rules
• Develop data flow
• Enable data channels and media
• Enable devices to work with data
• Set up data measures and monitoring
• Benchmark data maturity
Way of Working with Data: Tactical Aspect Tasks

20. 20
Description: The Data Matrix provides an overview of the data components,
objects, entities, tables and services as well as the business, service and
process roles etc. of the enterprise as well as the location of where they
impact the organization. This information is taken directly from the map, and
then related individually to other meta objects (rows).
Usage: Identify, capture and relate the meta objects (rows) such as
requirements, goals, timing, quality, risks, security, contracts and products
etc. to the data components, objects, entities, tables and services as well as
the business, service and process roles etc. of the enterprise.
Data Matrices

21. 21
Interro
gative
Specifi
cation
Data #
What Where Who
Data
Compo
nent
Data
Object
Data
Entity
Data
Table
Data
Service
Data
Channe
l
Data
Media
Busine
ss Role
Service
Role
Proces
s Role
What
(busine
ss,
applica
tion or
technol
ogy
goal)
Goal 1 #
Goal 2 #
Goal N #
Example of Data Matrices (Data Goals and Data
Security)
Interro
gative
Specifi
cation
Data #
What Where Who
Data
Compo
nent
Data
Object
Data
Entity
Data
Table
Data
Service
Data
Channe
l
Data
Media
Busine
ss Role
Service
Role
Proces
s Role
What
Securit
y 1
#
Securit
y 2
#
Securit
y N
#

22. 22
The Data Matrix should capture the related meta objects (rows) such as
requirements, goals, timing, quality, risks, security, contracts and products
etc. and be aligned with the main meta objects (columns) such as the data
components, objects, entities, tables and services as well as the business,
service and process roles etc. of the enterprise.
The Interrogative Specification for each related meta object (rows) is to be
identified according to the following modelling rules:
• Who (person or persons involved): Application Role, Data Owner.
• What (identity, nature, or value of a object or matter): Requirement,
Goal, Quality, Risk, Security, Contract, Product, Business Service,
Application Service, Application Task, Information Object, Data
Compliance.
• Where (location, area or place): Location.
• How (manner, method or way): Data Rule.
• When (time or timing): Timing.
Data Matrices

23. 23
Tasks & Services
• Collect data goals and requirements
• Analyze meta data
• Identify and define data components, entities, roles, flows, services
• Identify system measurements, application components, platform services
and components, reporting requirements
• Map data entities to application tasks
• Compose data map and data service matrix and model
• Define data distribution scenarios
• Define level of data service standardization and integration
• Define interface map
• Align data service flows to application service flows
• Identify application to application communication and data dissemination
Way of Modelling with Data: Operational Aspect
Tasks

24. 24
Define, develop and create the following maps, matrices and/or models:
• Data Component and Entity
• Data Type and Service
• Data Flow
• Data Owner
• Data Rules and Compliance (including security)
• Object (information and data)
• Data Channel and Media
Decisions
• IT solution options
• Data management possibilities
• Data options e.g. standardization, integration, flows, etc.
Way of Modelling with Data: Operational Aspect
Tasks

25. 25
Scope
• Enterprise-wide data modelling
• Data area-specific
• Data projects e.g. software projects, business intelligence projects,
etc.
• Data solutions
The needed skill for abstraction level for an Information eXpert and/or
Architect:
• Concrete
• Descriptive and specification
• Execution
Way of Modelling with Data: Operational Aspect
Tasks

26. 26
Data Decomposition & Composition

27. 27
Data Ontology: Objects

28. 28
Data Ontology: Models

29. 29
Data Ontology: Meta Models

30. 30
Data Ontology: Meta Meta Models

31. 31
Data Tagging

32. 32
Data Tagging

33. 33
Data Tagging

34. 34
Data Tagging

35. 35
Data Tagging

36. 36
Architecture Overview Diagram

37. 37
Blueprinting Roadmap [Project]

38. 38
Enterprise Architecture [Application]

39. 39
Layered Information & Data Architecture

40. 40
System Services & Component Interactions

41. 41
Analytical Applications Component

42. 42
Component Relationship Model

43. 43
Data Management Component

44. 44
Enterprise Content Management Component

45. 45
Enterprise Information Integration

46. 46
Master Data Management Component

47. 47
Metadata Management Component

48. 48
Architectural Patterns From Generalized
Operational Models

49. 49
Logical View of the LEAD EIA Reference
Architecture

50. 50
Multi-Tier High Availability for Critical Data Pattern

51. 51
Near-Real-Time Business Intelligence Operational
Pattern

52. 52
System Services & Component Interactions

53. 53
Retention Management Operational Pattern

54. 54
The Five Pillars of Enterprise Information

55. 55
Information Reference Model

56. 56
Standard Node Types Categorization

57. 57
Information Services Layer

58. 58
Operational Patterns by Scope of Integration

59. 59
Business Model Underperformers vs.
Outperformers

60. 60
Business Unit Analytics

61. 61
Change Matters

62. 62
Emphasis For Measurement

63. 63
Greatest Value From Measurement Data

64. 64
LEADing Practice versus Best Practice

65. 65
Outperformers Tend Toward Data Transformation

66. 66
Sophisticates Know They Need To Be Even Better

67. 67
Three Primary Business Challenges

68. 68
Data Blueprinting & Implementation Roadmap
[Deliverables]

69. 69
Data Engineering, Data Modelling & Data
Architecture

70. 70
Data Management Lifecycle: Analyze Phase

71. 71
Data Management Lifecycle: Analyze Phase

72. 72
Data Management Lifecycle: Design Phase

73. 73
Data Management Lifecycle: Design Phase

74. 74
Data Management Lifecycle: Build Phase

75. 75
Data Management Lifecycle: Build Phase

76. 76
Data Management Lifecycle: Deploy/Implement
Phase

77. 77
Data Management Lifecycle: Deploy/Implement
Phase

78. 78
Data Management Lifecycle: Run/Maintain Phase

79. 79
Data Management Lifecycle: Run/Maintain Phase

80. 80
Data Management Lifecycle: Continuous
Improvement Phase

81. 81
Data Management Lifecycle: Continuous
Improvement Phase

82. 82
Data Management Lifecycle: Change Management

83. 83
Data Management Lifecycle: Change Management

84. 84
Data Model

85. 85
Enterprise Architecture [Application]

86. 86
Hosting Services & Data Delivery

87. 87
Information Engineering, Process Modelling &
Information Architecture

88. 88
Information Flow Defined By Business Context

89. 89
Layered Information & Data Architecture

90. 90
Questions?
Global University Alliance
Professor Mark von Rosing
LEAD Enterprise Architect
HEAD of Global University Alliance
Mobile +45 2888 8901
E-Mail: MvR@GlobalUniversityAlliance.net
For more information:
www.globaluniversityalliance.net

92. 92
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