We intuitively understand that services allow us to have or do things that we don’t make or perform for ourselves. In theory, the best case scenario is that available services are tightly aligned to both our desires and our expectations. That brings in a variety of stakeholders who can create, use, and assure things heading in the right direction. But expressing what is needed, and how, to achieve the best scenario is often too imprecise and confusing to adequately work out, complete, and sustain or remodel against current and future circumstances.

The Archestra framework of service design parameters is an evolving artifact. Shown here is the framework as of May 2015.

The framework is an instrument for consistently identifying variable factors by which a service is obtainable and evident across its varying states of relevance (what service is the “right” one) and worth (what is the “upside” of the service). A single service may be “identified” through a variety of descriptive points of view; the framework contains them within a logical system of cross-references. Note (again) that the whole framework is used to refer to any single given service.

Service Design Parameters 2015

Goals of the framework:

  • Simple, explicit overall perspective
  • Uniformly global and pragmatic
  • Equally synthetic and diagnostic
  • Based on observable practices and events

 

  • No logical redundancies or contradictions
  • No reliance on a specific industry, business, process or technology vocabulary
  • No need for synonyms

 

  • Completely neutral: devoid of marketing, sales and philosophy clutter
  • Entirely compatible with governance, risk and asset management
  • Agnostic to physical vs. virtual, and actual versus logical, service formats
  • The semantics work the same way across Blueprints, Inventories, CMDBs, Portfolios and Catalogs, which intentionally select and assert distinctive views

 

  • Recognizes any manageable variable that can change the service structure or service status
  • Assures that any instantiated variables (realized actuals) are fully contextualized
  • Assures that different variables are never mistaken for each other

 

  • Supports classic modeling by exposing the identification, selection, integration and coordination of instantiated variables.
  • Shows that construction and deconstruction follow the same pathway
  • Shows that absence-or-presence of service is a logical threshold in a defined context, thus enabling knowledge-driven systematic problem management, policy determination, and renovation/innovation