This post is part of my collaborative research with Shinsei Bank on highly-evolvable enterprise software.  It is licensed under the Creative Commons Attribution-ShareAlike 3.0 license.  I am indebted to Jay Dvivedi and his team at Shinsei Bank for supporting this research.  All errors are my own.

When I adopted the information factory metaphor, I found that Caltech professor K. Mani Chandy had been there first.  Chandy and his colleagues have written a number of papers and a book¹ about system architectures, and I’d like to introduce his perspective here and draw some connections to the theory that I’m developing.  Chandy and his colleagues propose a typology of system architectures based on the way that subsystems interact.  They distinguish three modes of interaction²:

• Time-driven or schedule: “Groups of components interact at scheduled times.”
• Request-driven or pull: “A component requests information from other components, which then reply to the requests.”
• Event-driven or push: “A component sends information to other components when it discovers state changes relevant to its listeners.”

According to Chandy, time-driven and request-driven architectures are relatively common, while event-driven architectures are relatively new to the enterprise computing landscape.  In their book, Chandy and Schulte give two reasons for the rising interest in event-driven systems: first, “an explosion of event ‘streams’ flowing over corporate networks” facilitates programmatic access to events, and, second, “Companies today are operating at a faster pace, so early notification of emerging business threats and opportunities is increasingly important” (xii).

On a practical level, event-driven systems encounter several difficulties.  First, what constitutes an event?  Chandy et al. posit that “An event is a significant change in the state of the universe. A significant state change is one for which an optimal response by the system is to take an action.”  This definition makes events sound objective, but as students of Simon we know that an organization (or any other complex system operating in the real world) can never determine an optimal response to any situation.  Significant changes are therefore in the eye of the beholder: an event is a change in the state of the universe in response to which a system believes that action should be taken.

This leads to a second difficulty.  If our systems are to be decentralized, how can we ensure consistent interpretation of changes in the universe?  One component’s event might be another’s noise.  This, I think, is why Chandy et al. note the importance of shared models:

An agent that is responsible for initiating a response takes action based on its estimates of the state of the universe. Its estimates are based on a model of the universe which, in turn, is based on models that it shares with agents that provide it with information.

If there were objective criteria for defining events, shared models would not be necessary: all (properly functioning) system components would agree on which changes in the state of the universe represent events (to which the system should respond).  Since no such objective criteria exist, components must possess models sufficiently similar to yield complementary behavior.  As enterprise systems become increasingly complex, I suspect that the problem of inconsistent models will pose ever-larger challenges, since this is essentially the old organizational problem of differentiation and integration playing out in a new domain³.

So are Shinsei’s information assembly lines event processing systems?  I think that they may be, but they differ in an important way from the systems described by Chandy and Schulte.  Many of Shinsei’s systems are event driven, in the sense that events (e.g., customer requests for banking services) trigger the production of an information product (e.g., a credit decision or a funds transfer).  In the language of the factory metaphor, Shinsei manufactures custom products on demand rather than standard products in advance of demand.

Shinsei does not, however, seem to have a role for abstract events-as-messages that distribute information to listeners.  Instead, events propagate along the assembly line rather than through meta-level communication channels.  The manufacturing metaphor may help clarify the distinction.  Consider the management of parts inventory on an assembly line.

The event-driven architecture described by Chandy and Schulte would use a sensor to detect when the supply of parts at a workstation declines below a certain level.  The sensor would respond by generating an “almost out of parts” event, which would be sent to the factory’s order management department, which would respond to the event by placing an order with the appropriate supplier.  Two distinct interaction channels can be distinguished: a physical channel composed of trucks and parcels, and a communication channel composed of sensors and computer networks.

By contrast, Shinsei’s approach resembles the Japanese kanban system.  The physical channel for movement of parts is designed to trigger the replenishment of inventory, so no “out-of-band” communication is necessary.  Events are detected and handled implicitly through the design of the process.  In this approach, there is only one interaction channel.

This distinction, between systems that internalize event handling and those that handle events explicitly using dedicated communication networks external to the underlying process, may be a useful dimension for classifying enterprise system architectures.

The relative effectiveness of the two approaches may be an empirical question, and it could depend on the goals of the system.  For straightforward business processes, explicitly modeling events and creating a communication network to detect and process them seems likely to add unnecessary complexity, which could render systems more difficult to modify and ultimately decrease agility.  However, if businesses want to run data fusion algorithms over event streams (Chandy and Schulte use the term “complex event processing”), then it may be necessary to have explicit event representations and dedicated event processing systems.  Hybrids of the two models may also be possible.

¹ An abridged edition is available free of charge from Progress Software.  All page number references are to the abridged edition.

² Definitions from Chandy et al., “Towards a Theory of Events“, 2007.

³ When organizations develop differentiated subunits, the decision-makers in these subunits tend to focus on achieving subunit goals.  This reduces complexity, but often results in suboptimization.  I can see no reason why enterprise systems should not suffer from similar problems.