Enterprise architecture is about business outcomes, value assessment, definition and realization through effective delivery and deployment. It covers many aspects overlapping with ITSM practices in terms of services management and operations, though not limited to technology management.

Per the ITIL framework, ITSM best practices are grouped in 5 core perspectives:

  • Service strategy
  • Service design
  • Service transition
  • Service operation
  • Continuous service improvement

First and foremost, enterprise architecture is about forward-looking and continuous business alignment, towards effective data governance and business transformation.

Enterprise architecture: driving how data contributes to value creation


Nightingale (2009) introduced the 7 principles of enterprise architecture thinking to guide organizations towards achieving sustainable transformation:

  1. Adopt a holistic approach to enterprise transformation
  2. Identify relevant stakeholders and determine their value propositions
  3. Focus on enterprise effectiveness before efficiency
  4. Address internal and external enterprise interdependencies
  5. Ensure stability and flow within and across the enterprise
  6. Cultivate leadership to support and drive enterprise behaviors
  7. Emphasize organizational learning

Enterprise architecture principle: focus on effectiveness before efficiency.

Deborah Nightingale (2009)

Data governance fosters effective and efficient data usage and management


Xlifecycle

Enterprise Architecture

Data Continuity

Performing product development and operational maturity assessment, identifying opportunities for improvement, critical success factors and associated metrics

Data Ownership

Building capability and enterprise data alignment with stakeholder map, defining and driving data governance, managing capability implications across business functions

Change Strategy

Defining value statements based on expected change, alining digitalization and integration roadmaps, from data migration to archiving, cleansing and decommissioning strategies

Change drivers

Building the business value realization strategy, linking to the capability implementation roadmap to address current and future imperatives, pain point and risk mitigations

Master Data Strategy

Driving single source and version of truth clarity, identifying configuration items, building data continuity and linkages across enterprise platforms and system interfaces

Operational Model

Assessing how the business operates, internally across teams and with the supply chain, defining how the change will affect current operations and how value will be realized

Portfolio Synergies

Defining product modularity, driving platformization, mapping business processes and capabilities, improving knowledge capitalization and standardization and talent development

Business Scnearios

Understanding capability and improvement requirements, mapping user stories and business processes across functions, defining change roadmaps and deployment strategies

Decision-Making

Assessing change assumptions and interdependencies, mapping prioritization decisions, building awareness, initiating transformation governance and tracking effectiveness

Strategic Alignment


  • How does the business currently operates?
  • What is the current application and process landscape?
  • What are the core strategic enterprise capabilities?
  • Who are the process and data owners, and the key interdependencies across these capabilities?
  • How are external parties interacting with enterprise platforms?
  • What gaps or overlaps exist across capabilities, internally, and when collaborating with the supply chain?
  • What is the cost of ownership and how does it align to the level of support service is provided by vendors and partners?
  • Is there an improvement and evolution roadmap in place to account for industry best practices and technology advances?

Process Analysis


  • Are existing processes documented and purposely managed with current key user groups?

  • How effective is the current operating governance?
  • Is process maturity and adherence effectively governed across business functions and projects?

  • What education programs and onboarding trainings are supporting the user communities?

  • How is data quality governed, within each programs or project, and across projects? 

  • How are standards and data usage governed across business processes and supportive platforms?

  • What is regulated / mandated vs open for user-led adaptation?

Business Analytics


  • What are the success factors and key performance metrics to drive delivery results?

  • How are these indicators interrelated across the relevant  balanced scorecard?

  • What are essential KPI metrics currently measured, and how are they informing ongoing projects?

  • How is delivery health currently assessed?

  • What additional or new metrics should also be measured?

  • Is the relevant data available to measure operational health?

  • What data feeds should be enhanced to drive quality assurance?

  • What metrics relate to resource and process efficiency, and how do they compare with those used by other projects, what constitute best practice?

Building and continuously aligning the operating model is a mandatory exercise to maximise effectiveness and embed continuous learning and improvement, considering the required adjustments to design, build, test, and deploy the solution and associated change:

  1. Delivery model: what is the solution and associated service, how they will be packaged and delivered?
  2. Engagement model: how will the team engage with the business and IT stakeholders to deliver the solution?
  3. Commercial model: how much is the solution proposed for and will be the total cost of ownership, and how products and services are commercially bundled?
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Key roles driving effective data governance: data owners, data stewards, and data custodians