Enterprise architecture focuses on achieving business outcomes and value realization through effective solution definition and deployment. It encompasses various aspects, including services management and operations, but is not limited to technology management.

In alignment with the ITIL framework, ITSM best practices are classified into five core perspectives:

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

At its core, enterprise architecture is about continuous business alignment and forward-looking planning towards effective data governance and business transformation.

Enterprise architecture: driving how data create value



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)

Effective data governance fosters better ways of working


Xlifecycle

Enterprise architecture: 9-block value grid

Data Continuity

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

Stakeholders

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, aligning digitalisation and integration roadmaps, from education to data migration, archiving and decommissioning strategies

Value drivers

Building the business value realisation 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 single version of truth clarity, identifying configuration items, building data continuity and linkages across enterprise platforms and system interfaces

Operating 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 realised

Portfolio Synergies

Defining product modularity, driving platformisation, mapping business processes and capabilities, improving knowledge capitalisation and standardisation and talent development

Business Capabilities

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?

To maximize effectiveness and embed continuous learning and improvement, it is essential to build and continuously align the operating model. This involves considering the following questions in relation to the solution design, build, test, and deployment, as well as associated changes:

  1. Delivery model: What is the proposed solution and associated service? How will they be packaged and delivered?
  2. Engagement model: How will the team engage with business and IT stakeholders to deliver the solution?
  3. Commercial model: How much will the solution cost, and what will be the total cost of ownership? How are products and services commercially bundled?
Read the blog

Data governance roles: from process owners to data stewards and system custodians