Semantic IT Practices

As noted in a previous post, the current set of methodologies employed in the day to day IT operations of a typical enterprise is poised for perhaps its most significant paradigm shift in several decades. This evolutionary shift is not the introduction of Semantic technology or standards per se, but rather the complete re-visioning of how IT works in the context of Semantic Interoperability. Semantic IT provides us with two crucial capabilities that we simply never had before:

1 – The ability to abstract the governance and maintenance of the systems under our control in an automated fashion.

2 – The ability to link all the various disparate IT-related processes through that same abstracted Semantic governance layer.

What’s really missing at this stage are the semantically enabled methodologies and/or practices that will allow us to exploit these capabilities effectively.

So what exactly is a semantically enabled practice or methodology? The difference that Semantic Enablement brings to enterprise operations is the deliberate focus on operational fusion or merger of processes in an effort to achieve a holistic management paradigm. This means that there are no longer any “stove-piped” approaches or technologies, all efforts must have the capability to inter-relate with all other capabilities and share the same foundation.

This holistic approach will require several new or at least modified IT practices with updated methodologies designed to take advantage of both the philosophical and technical shift in thinking. It is likely that this should lead to a certain level of consolidation in the number or diversity of existing IT practices as the central tenet involved is essentially holistic awareness and interoperability. Once it is realized that all aspects of IT architecture and operation are in fact related, the need for diversification and specialization will be reduced. There should not for example, need to be separate disciplines for Master Data Management and Semantic Integration, as MDM represents a specific application of Semantic Integration across multiple data sources. In fact, many or most aspects of data architecture or integration could at some point be considered within the umbrella of Semantic Integration.

One of the real benefits of viewing IT methodologies or practices this way is the deliberate attempt to keep them technology agnostic. These practices are not driven by vendor solutions. Previous practice approaches based upon individual or categories of vendor solutions have often proved short-sided and counter-productive, leaving the enterprises which adopted them vulnerable in many ways. The application of operational IT capability (i.e. business services) doesn’t require specialization of support infrastructures, instead it should have a flexible common framework that can be redefined as needed. Our goal is not to be able to predict all possible permutations of our solutions and build that into some rigid roadmap – the true goal is to give control of that map to those who need to adapt their solution in their own context, according to their needs.

That foundation then is an abstracted governance or management layer which mediates between all others aspects of IT architecture. It is accessible to and modifiable by stakeholders, which allows for much more economical and efficient governance processes. One of the most exciting parts of this is the opportunity we now have to merge design, architecture and governance using semantic modeling. This will likely manifest itself in a blending of ontology management and EA framework or even agile modeling techniques.

I’ve taken an initial stab at defining a set of Semantic IT practices, these have all been developed to replace existing counterparts and designed to be interoperable with one another. These practices encompass most if not all current IT processes and include:

  • Semantic Integration (most generic in nature, next generation solution for enterprise systems integration).
  • Service Oriented Integration (service & application logic).
  • Program Lifecycle Management (program, product, project, portfolio, process synergy).
  • Dynamic Learning Orchestration (organizational and personal learning and knowledge management).

In my next blog entry, I will describe Program Lifecycle Management, the new PLM, in more detail.

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