Preamble
The recent paralysis of British Airways world operations (due to a power failure, if officials are to be believed), following the crash of Delta Airlines’ reservation system and a number of similar incidents, once again points to the reliability of large and critical IT systems.
Particularly at risk are airlines or banking systems, whose seasoned infrastructures, at the cutting edge when introduced half a century ago, have been strained to their limit by waves of extensive networked new functionalities. Confronted to the magnitude and complexity of overall modernization, most enterprises have preferred piecemeal updates to architectural leaps. Such policies may bring some respite, but they may also turn into aggravating factors, increasing stakes and urgency as well as shortening odds.
Assuming some consensus about stakes, hazards, and options, the priority should be to overcome jumping fears by charting a reassuring perspective in continuity with current situation. For that purpose models may provide heartening parachutes.
Models: Intents & Doubts
Models can serve two kinds of purposes:
- Describe business contexts according to enterprise objectives, foretell evolution, and simulate policies.
- Prescribe the architecture of supporting systems and the design of software components.
Frameworks were supposed to combine the two perspectives, providing a comprehensive and robust basis to systems governance. But if prescriptive models do play a significant role in engineering processes, in particular for code generation, they are seldom fed by their descriptive counterpart.
Broadly speaking, the noncommittal attitudes toward descriptive models comes from a rooted mistrust in non executable models: as far as business analysts and software engineers are concerned, such models can only serve as documentary evidence. And since prescriptive models are by nature grounded to systems’ inner making, there is no secure conceptual apparatus linking systemic changes with their technical consequences. Hence the jumping frights.
Overcoming those frights could be achieved by showing the benefits of secure and soft landings.
Models for Secure Landings
As any tools, models must be assessed with regard to their purpose: prescriptive ones with regard to feasibility and reliability of architectures and design, descriptive ones with regard to correctness and consistency. As already noted, compared to what has been achieved for the former, nothing much has been done about the validity of the latter.
Yet, and contrary to customary beliefs, the rigorous verification of descriptive (aka extensional) models is not a dead-end. Of course these models can never be proven true because there is no finite scope against which they could be checked; but it doesn’t mean that nothing can be done to improve their reliability:
- Correctness: How to verify that all the relevant individuals and features are taken into account. That can only be achieved empirically by building models open to falsification.
- Consistency: How to verify that the symbolic descriptions (categories and connectors) are complete, coherent and non redundant across models and abstraction levels. That can be formally verified.
- Alignment: How to verify that current and required business processes are to be seamlessly and effectively supported by systems architectures. That can be managed by introducing a level of indirection, as illustrated by MDA with platform independent models (PIMs) set between computation independent (CIMs) and platform specific (PSMs) ones.
Once established on secure grounds, models can be used to ensure soft landings.
Models for Soft Landings
Set within model based system engineering frameworks, models will help to replace piecemeal applications updates by seamless architectures modernization:
- Systems: using models shift the focus of change from hardware to software.
- Enterprise: models help to factor out the role of organization and regulations.
- Project management: models provide the necessary hinge between agile and phased projects, the former for business driven applications, the latter for architecture oriented ones. Combining both approaches will ensure than lean and just-in-time processes will not be sacrificed to system modernization.
More generally, and more importantly, models are the option of choice (if not the only one) for enterprise knowledge management:
- Business: Computation independent models (CIMs), employed to trace, justify and rationalize business strategies and processes portfolios.
- Systems: Platform specific models (PSMs), employed to trace, justify and rationalize technical alternatives and decisions.
- Decision-making and learning: Platform independent models (PIMs), employed to align business and systems and support enterprise architecture governance.
And knowledge management is arguably the primary factor for successful comprehensive modernization.
Strategic Decision-making: Cash or Crash
Governance is all about risks and decision-making, but investing on truly fail-safe systems for airlines or air traffic control can be likened to a short bet on the Armageddon, and that cannot be easily framed in a neat cost-benefit analysis. But that may be the very nature of strategic decision-making: not amenable to ROI but aiming at risks assessment and the development of the policies apt to contain and manage them. That would be impossible without models.